CN114254473A

CN114254473A - Vehicle energy consumption scoring method and device

Info

Publication number: CN114254473A
Application number: CN202011014717.3A
Authority: CN
Inventors: 何召华; 刘真通; 彭帅华
Original assignee: Huawei Technologies Co Ltd
Current assignee: Shenzhen Yinwang Intelligent Technology Co ltd
Priority date: 2020-09-24
Filing date: 2020-09-24
Publication date: 2022-03-29
Also published as: WO2022062569A1

Abstract

The application provides a vehicle energy consumption scoring method and device, which aim to improve the accuracy of scoring of energy consumption of vehicles in a preset journey. The method for evaluating the energy consumption of the vehicle comprises the following steps: acquiring an energy consumption influence factor set of the vehicle, wherein the energy consumption influence factor set comprises a first type of energy consumption influence factor and/or a second type of energy consumption influence factor, the first type of energy consumption influence factor comprises a windowing influence parameter, the windowing influence parameter is used for indicating the influence degree of the wind resistance caused by the opening of the window of the vehicle in a preset journey on the energy consumption of the vehicle, and the second type of energy consumption influence factor indicates the influence degree of the driving environment where the vehicle is in the preset journey on the energy consumption of the vehicle; and combining the energy consumption influence factors to input the energy consumption scoring model so as to determine scores corresponding to one or more types of energy consumption influence factors in the energy consumption influence factor set, wherein the scores corresponding to the one or more types of energy consumption influence factors are used for indicating the influence degree of the one or more types of energy consumption influence factors on the energy consumption of the vehicle in a preset journey.

Description

Vehicle energy consumption scoring method and device

Technical Field

The present application relates to the field of vehicles, and more particularly, to a method and apparatus for scoring vehicle energy consumption.

Background

Energy consumption of a vehicle is one of important indexes for evaluating new energy of the vehicle. Particularly for electric vehicles, the energy consumption index of the vehicle not only relates to the economical efficiency of the vehicle, but also reflects the comprehensive level of one automobile. At present, reducing vehicle energy consumption and improving vehicle driving economy are a hot problem in the field of vehicle energy consumption.

The traditional vehicle energy consumption scoring method is to input driving behavior data of a driver in a vehicle and road data of a road on which the vehicle runs into an energy consumption scoring model so as to calculate the score of the vehicle energy consumption in a preset journey. The driving behavior data comprises the frequency of acceleration and deceleration of the driver in a preset time period, the intensity of acceleration and deceleration of the driver in the preset time period and the like. The road data includes road grade, road unevenness, and the like.

However, as the degree of vehicle electronization is higher and higher, the factors influencing vehicle energy consumption are more and more, and the above-mentioned conventional vehicle energy consumption scoring method is only performed based on driving behavior data and road data, so that the calculated score of vehicle energy consumption is not accurate.

Disclosure of Invention

The application provides a vehicle energy consumption scoring method and device, which aim to improve the accuracy of scoring of energy consumption of vehicles in a preset journey.

In a first aspect, a method for evaluating vehicle energy consumption is provided, including: acquiring an energy consumption influence factor set of a vehicle, wherein the energy consumption influence factor set comprises a first type of energy consumption influence factor and/or a second type of energy consumption influence factor, the first type of energy consumption influence factor comprises a windowing influence parameter, the windowing influence parameter is used for indicating the influence degree of wind resistance caused by the opening of a window of the vehicle in a preset journey on the energy consumption of the vehicle, and the second type of energy consumption influence factor indicates the influence degree of the driving environment where the vehicle is located in the preset journey on the energy consumption of the vehicle; and inputting the energy consumption influence factors into an energy consumption scoring model in a combined mode to determine scores corresponding to one or more types of energy consumption influence factors in the energy consumption influence factor set, wherein the scores corresponding to the one or more types of energy consumption influence factors are used for indicating the influence degree of the one or more types of energy consumption influence factors on the energy consumption of the vehicle in the preset journey.

In the embodiment of the application, the scores corresponding to the first type energy consumption influence factors and the second type energy consumption influence factors are determined by inputting the first type energy consumption influence factors and/or the second type energy consumption influence factors into the energy consumption scoring model, so that the influence of neglecting the first type energy consumption influence factors and the second type energy consumption influence factors in the existing scoring calculation process is avoided, and the accuracy of scoring of the energy consumption of the vehicle in the preset journey is improved.

In a possible implementation manner, the second type of energy consumption influencing factor includes one or more of an external temperature non-suitability degree influencing parameter, a weather influencing parameter, a day and night influencing parameter or a wind speed influencing parameter, wherein the external temperature non-suitability degree influencing parameter is used for indicating the degree of influence of the environmental temperature outside a preset suitable temperature range in the preset journey on the working state of the vehicle, the weather influencing parameter is used for indicating the degree of influence of the weather in the preset journey on the working state of the vehicle, the day and night influencing parameter is used for indicating the degree of influence of the day and night environment in the preset journey on the working state of the vehicle, and the wind speed influencing parameter is used for indicating the degree of influence of the wind speed in the preset journey on the working state of the vehicle.

In the embodiment of the application, the second type of energy consumption influence factors comprise one or more factors of an external temperature non-suitability degree influence parameter, a weather influence parameter, a day and night influence parameter or a wind speed influence parameter, and the accuracy of calculating the score of the energy consumption of the vehicle in the preset journey is improved.

In one possible implementation, the external temperature non-suitability degree influence parameter is obtained by performing linear normalization processing on external temperature non-suitability degree data, and the external temperature non-suitability degree data includes an external environment temperature of the vehicle.

In the embodiment of the application, the external environment temperature of the vehicle is normalized to obtain the external temperature non-suitability degree influence parameter, namely, the dimensional external environment temperature is converted into a dimensionless score, so that a driver can determine the influence of the external temperature non-suitability degree influence parameter on the energy consumption of the vehicle in a preset stroke through the score.

In one possible implementation, the external temperature non-suitability degree influences parameter x₁₁By the formula

Is determined, and

wherein, T₁Represents a preset lower limit, T, of a suitable operating temperature range of said component₂Represents the upper limit of the preset proper working temperature interval of the component, n represents the total number of sampling points in the preset stroke, i represents the ith sampling point in the preset stroke, i is 1,2, …, n, delta t_maxIs a preset value, t_iRepresenting the ambient temperature outside the vehicle collected at the ith sampling point.

In the embodiment of the application, the influence parameter of the improper degree of the external temperature is determined based on the proper working temperature range of the part in the vehicle and the external environment temperature of the vehicle, so that the rationality of calculating the influence parameter of the improper degree of the external temperature is improved.

In a possible implementation manner, the weather influence parameters are obtained by performing linear normalization processing on weather influence data, where the weather influence data include one or more gears where the wiper of the vehicle operates in the preset stroke and the operating time of the wiper in each gear in the preset stroke.

In the embodiment of the application, the weather influence parameters are obtained by performing linear normalization processing on the working gear of the windscreen wiper and the working time of the windscreen wiper in each gear, namely dimensional weather influence data are converted into dimensionless scores, so that a driver can determine the influence of the weather influence parameters on vehicle energy consumption in a preset journey through the scores.

In one possible implementation, the weather-affecting parameter x₁₂By the formula

Determining wherein M_YDenotes the total number of operating positions of a wiper of the vehicle, l denotes the l-th operating position in which the wiper is operated, and l is 1,2, …, M_Y，P_lRepresents the power consumption, T, required by the windscreen wiper to work at the I-th working gear in unit time_lRepresents the time length of the windscreen wiper working at the l-th gear position, P_maxRepresenting the power consumption corresponding to a first working gear of the windscreen wiper in unit time, wherein the first working gear is the working gear with the maximum energy consumption of the vehicle in all working gears of the windscreen wiper, and T is the power consumption of the vehicle_maxRepresenting the total time required for the vehicle to travel the preset trip.

In the embodiment of the application, the weather influence parameters are calculated through the formula, so that the rationality of calculating the weather influence parameters is improved.

In a possible implementation manner, the diurnal influence parameter is obtained by performing linear normalization processing on diurnal influence data, and the diurnal influence data includes one or more gears in which the lamps of the vehicle operate in the preset stroke and the operating time length of the lamps in each gear in the preset stroke.

In the embodiment of the application, day and night influence parameters are obtained by normalizing day and night influence data, namely, dimensional day and night influence data are converted into dimensionless scores, so that a driver can determine the influence of the day and night influence parameters on vehicle energy consumption in a preset journey through the scores.

In a possible implementation, the circadian influence parameter x₁₃By the formula

Determining wherein M_DRepresenting the total number of working gears of the lamps of the vehicle, l 'representing the l' working gear, l 'of the lamps'＝1,2,…,M_D，D_l'Represents the power consumption, T, required by the vehicle lamp to work in the l' th working gear in unit time_l'Indicating the duration of operation of the vehicle lamp in the i' th operating position, D_maxRepresenting the power consumption required by the vehicle lamp to work at a second working gear in the unit time, wherein the second working gear is a working gear with the maximum energy consumption of the vehicle in all working gears of the vehicle lamp, and T is the maximum energy consumption of the vehicle_maxRepresenting the total time required for the vehicle to travel the preset trip.

In the embodiment of the application, the day and night influence parameters are calculated through the formula, so that the rationality of calculating the day and night influence parameters is improved.

In a possible implementation manner, the wind speed influence parameter is obtained by performing nonlinear normalization processing on wind speed influence data, where the wind speed influence data includes a speed component obtained by projecting the wind speed in the preset journey on the heading of the vehicle.

In the embodiment of the application, the wind speed influence parameters are obtained by carrying out nonlinear normalization processing on the wind speed influence data, namely, dimensional wind speed influence data are converted into dimensionless scores, so that a driver can determine the influence of the wind speed influence parameters on vehicle energy consumption in a preset journey through the scores.

In one possible implementation, the wind speed influencing parameter x₁₄By the formula

Determining, wherein,

n represents the total number of sampling points in the preset run, i represents the ith sampling point in the preset run, i is 1,2, …, n, vector

Representing the wind direction, vector, collected at the ith sample point

Represents the heading, V, of the vehicle collected at the ith sample point_iRepresenting the wind speed, v, collected at the ith sample point_iAnd theta and e are preset constants and represent the driving speed of the vehicle collected at the ith sampling point.

In the embodiment of the application, the wind speed influence parameters are calculated through the formula, so that the rationality of calculating the wind speed influence parameters is improved.

In a possible implementation manner, the windowing influence parameter is obtained by performing linear normalization processing on windowing influence data, the windowing influence data includes one or more window opening and closing state combinations of the vehicle, which are acquired within the preset mileage, and the window opening and closing state combinations of the vehicle include opening and closing states of each window in the vehicle.

In the embodiment of the application, the windowing influence parameters are obtained by carrying out linear normalization processing on the windowing influence data, namely, dimensional windowing influence data are converted into dimensionless scores, so that a driver can determine the influence of the windowing influence parameters on the energy consumption of the vehicle in a preset stroke through the scores.

In one possible implementation, the windowing affecting parameter x₂₁By the formula

Determining wherein M_WRepresents the total number of window open-close state combinations of the vehicle, l ' represents the l ' th open-close state combination of the window, l ' is 1,2, …, M_W，R_iRepresents the power consumption of the vehicle consumed by the combination of the I' th opening and closing state of the vehicle window in unit time, R_maxRepresents the power consumption, T, of the vehicle consumed by the combination of the first opening and closing state of the vehicle window in the unit time_maxAnd the first opening and closing state combination is the opening and closing state combination with the maximum energy consumption in all the opening and closing state combinations of the windows in the vehicle.

In the embodiment of the application, the windowing influence parameters are calculated through the formula, so that the rationality of calculating the windowing influence parameters is improved.

In a second aspect, a method for training an energy consumption scoring model is provided, including: acquiring a training data set, wherein the training data set comprises first type energy consumption influence factors and/or second type energy consumption influence factors, the first type energy consumption influence factors comprise windowing influence parameters, the windowing influence parameters are used for indicating the influence degree of the wind resistance caused by the opening of the windows of the vehicles in a preset journey on the energy consumption of the vehicles, and the second type energy consumption influence factors indicate the influence degree of the driving environment where the vehicles in the preset journey are located on the energy consumption of the vehicles; and inputting the training data set into a raw energy consumption scoring model to obtain an energy consumption scoring model, wherein the energy consumption scoring model is used for calculating scores corresponding to the first type of energy consumption influence factors and/or the second type of energy consumption influence factors, and the scores corresponding to the first type of energy consumption influence factors and/or the second type of energy consumption influence factors are used for indicating the influence degree of the first type of energy consumption influence factors and/or the second type of energy consumption influence factors on the energy consumption of the vehicle in the preset journey.

In the embodiment of the application, the energy consumption scoring model is obtained by inputting the first type of energy consumption number influence factor and/or the second type of energy consumption number influence factor into the original energy consumption scoring model for training. The method is favorable for improving the accuracy of calculating the grade of the energy consumption of the vehicle in the preset journey.

Optionally, the method further comprises: and if the error of the energy consumption model is higher than a preset value, updating the energy consumption scoring model.

In the embodiment of the application, if the error of the energy consumption model is higher than the preset value, the energy consumption scoring model is updated, so that the accuracy of the energy consumption scoring model is improved.

Alternatively, the energy consumption model error α may be expressed by the formula

Determining, wherein N represents the total number of samples in the training set, C_i-honstIndicating the history of the vehicleTrue value of energy consumption in the run, C_i-predictAnd a predicted value representing the energy consumption of the vehicle in the historical trip.

In a possible implementation manner, the second type of energy consumption influence factor includes one or more of an external temperature non-suitability degree influence parameter, a weather influence parameter, a day and night influence parameter or a wind speed influence parameter, wherein the external temperature non-suitability degree influence parameter is used for indicating the influence degree of the environmental temperature outside a preset suitable temperature interval in a historical trip on the working state of the vehicle, the weather influence parameter is used for indicating the influence degree of weather in the historical trip on the working state of the vehicle, the day and night influence parameter is used for indicating the influence degree of the day and night environment in the historical trip on the working state of the vehicle, and the wind speed influence parameter is used for indicating the influence degree of the wind speed in the historical trip on the working state of the vehicle.

It should be noted that the historical trip is a trip before the preset trip.

In the embodiment of the application, the second type of energy consumption influence factors comprise one or more factors of an external temperature non-suitability degree influence parameter, a weather influence parameter, a day and night influence parameter or a wind speed influence parameter, and the accuracy of calculating the score of the energy consumption of the vehicle in the historical journey is improved.

In the embodiment of the application, the external environment temperature of the vehicle is normalized to obtain the external temperature non-suitability degree influence parameter, namely, the dimensional external environment temperature is converted into a dimensionless score, so that a driver can determine the influence of the external temperature non-suitability degree influence parameter on the vehicle energy consumption in a historical travel through the score.

Is determined, and

wherein, T₁Represents a preset lower limit, T, of a suitable operating temperature range of said component₂Represents the upper limit of the preset proper working temperature interval of the component, n represents the total number of sampling points in the historical travel, i represents the ith sampling point in the historical travel, i is 1,2, …, n, delta t_maxIs a preset value, t_iRepresenting the ambient temperature outside the vehicle collected at the ith sampling point.

In a possible implementation manner, the weather influence parameters are obtained by performing linear normalization processing on weather influence data, where the weather influence data includes one or more gears where the wiper of the vehicle operates in the historical trip and the operating time of the wiper in each gear in the historical trip.

In the embodiment of the application, the weather influence parameters are obtained by performing linear normalization processing on the working gear of the windscreen wiper and the working time of the windscreen wiper in each gear, namely dimensional weather influence data are converted into dimensionless scores, so that a driver can determine the influence of the weather influence parameters on vehicle energy consumption in a historical travel through the scores.

Determining wherein M_YDenotes the total number of operating positions of a wiper of the vehicle, l denotes the l-th operating position in which the wiper is operated, and l is 1,2, …, M_Y，P_lRepresents the power consumption, T, required by the windscreen wiper to work at the I-th working gear in unit time_lRepresents the time length of the windscreen wiper working at the l-th gear position, P_maxRepresenting the power consumption corresponding to a first working gear of the windscreen wiper in unit time, wherein the first working gear is the working gear with the maximum energy consumption of the vehicle in all working gears of the windscreen wiper, and T is the power consumption of the vehicle_maxRepresents a total time period required for the vehicle to travel the historical trip.

In one possible implementation, the diurnal influence parameter is obtained by performing a linear normalization process on diurnal influence data, the diurnal influence data including one or more gears in which the vehicle's lamps are operated in the historical travel, and an operating time period in which the lamps are in each gear in the historical travel.

In the embodiment of the application, the day and night influence parameters are obtained by normalizing the day and night influence data, namely, the dimensional day and night influence data are converted into dimensionless scores, so that a driver can determine the influence of the day and night influence parameters on the energy consumption of a vehicle in a historical journey through the scores.

Determining wherein M_DIndicating the total number of operating positions of the lamps of the vehicle, l ' indicating the l ' th operating position of the lamps, l ' being 1,2, …, M_D，D_l'Represents the power consumption, T, required by the vehicle lamp to work in the l' th working gear in unit time_l'Indicating the duration of operation of the vehicle lamp in the i' th operating position, D_maxThe power consumption required by the vehicle lamp to work in a second working gear in the unit time is represented, and the second working gear is the work gear which consumes the largest energy consumption of the vehicle in all working gears of the vehicle lampAs gear position, T_maxRepresents a total time period required for the vehicle to travel the historical trip.

In a possible implementation manner, the wind speed influence parameter is obtained by performing nonlinear normalization processing on wind speed influence data, and the wind speed influence data includes a speed component obtained by projecting the wind speed on the heading of the vehicle in the historical travel.

In the embodiment of the application, the wind speed influence parameters are obtained by carrying out nonlinear normalization processing on the wind speed influence data, namely, dimensional wind speed influence data are converted into dimensionless scores, so that a driver can determine the influence of the wind speed influence parameters on vehicle energy consumption in a historical travel through the scores.

Determining, wherein,

n represents the total number of sample points within the historical run, i represents the ith sample point within the historical run, i is 1,2, …, n, vector

Representing the wind direction, vector, collected at the ith sample point

In the embodiment of the application, the windowing influence parameters are obtained by carrying out linear normalization processing on the windowing influence data, namely, dimensional windowing influence data are converted into dimensionless scores, so that a driver can determine the influence of the windowing influence parameters on the energy consumption of the vehicle in a historical travel through the scores.

Determining wherein M_WRepresents the total number of window open-close state combinations of the vehicle, l ' represents the l ' th open-close state combination of the window, l ' is 1,2, …, M_W，R_iRepresents the power consumption of the vehicle consumed by the combination of the I' th opening and closing state of the vehicle window in unit time, R_maxRepresents the power consumption, T, of the vehicle consumed by the combination of the first opening and closing state of the vehicle window in the unit time_maxAnd the first opening and closing state combination is the opening and closing state combination with the maximum energy consumption in all opening and closing state combinations of windows in the vehicle.

In a third aspect, a vehicle energy consumption scoring apparatus is provided, which includes means for performing the first aspect or any one of the possible implementations of the first aspect.

In a fourth aspect, a vehicle energy consumption scoring apparatus is provided, the apparatus comprising means for performing the second aspect or any one of the possible implementations of the second aspect.

In a fifth aspect, a computing device is provided, the apparatus having the functionality of the apparatus in the method design implementing the first aspect. These functions may be implemented by hardware, or by hardware executing corresponding software. The hardware or software includes one or more units corresponding to the above functions.

In a sixth aspect, a computing device is provided, which has the function of implementing the apparatus in the method design of the second aspect. These functions may be implemented by hardware, or by hardware executing corresponding software. The hardware or software includes one or more units corresponding to the above functions.

In a seventh aspect, a computing device is provided that includes an input-output interface, a processor, and a memory. The processor is configured to control the input/output interface to send and receive signals or information, the memory is configured to store a computer program, and the processor is configured to call and run the computer program from the memory, so that the computing device executes the method of the first aspect.

In an eighth aspect, a computing device is provided that includes an input-output interface, a processor, and a memory. The processor is configured to control the input/output interface to send and receive signals or information, the memory is configured to store a computer program, and the processor is configured to call and run the computer program from the memory, so that the computing device executes the method of the second aspect.

In a ninth aspect, there is provided a computer program product, the computer program product comprising: computer program code which, when run on a computer, causes the computer to perform the method of the above-mentioned aspects.

In a tenth aspect, a computer-readable medium is provided, having program code stored thereon, which, when run on a computer, causes the computer to perform the method of the above aspects.

In an eleventh aspect, a chip system is provided, the chip system comprising a processor for a computing device to perform the functions recited in the above aspects, e.g. to generate, receive, transmit, or process data and/or information recited in the above methods. In one possible design, the system-on-chip further includes a memory for storing program instructions and data necessary for the computing device. The chip system may be formed by a chip, or may include a chip and other discrete devices.

Drawings

Fig. 1 is a schematic diagram of a system architecture to which an energy consumption scoring model according to an embodiment of the present application is applied.

FIG. 2 is a schematic flow chart of a vehicle energy consumption scoring method according to an embodiment of the application.

Fig. 3 is a schematic flow chart of calculating scores for 5 energy consumption impact factors according to an embodiment of the present application.

FIG. 4 is a schematic diagram of a user interface of an embodiment of the application.

FIG. 5 is a schematic view of a user interface of another embodiment of the present application.

Fig. 6 is a schematic diagram of a system architecture to which an embodiment of the present application is applicable.

Fig. 7 is a schematic flow chart of a training method of an energy consumption scoring model according to an embodiment of the present application.

Fig. 8 is a schematic diagram of a vehicle energy consumption scoring device according to an embodiment of the present application.

FIG. 9 is a schematic block diagram of a computing device of another embodiment of the present application.

Detailed Description

The technical solution in the present application will be described below with reference to the accompanying drawings.

For ease of understanding, a schematic diagram of a system architecture to which the energy consumption scoring model of the embodiment of the present application is applicable is described below with reference to fig. 1. The system 100 shown in FIG. 1 includes an execution device 210, a training device 220, a database 230, a client device 240, a data storage system 250, and a data collection device 260.

The data collection device 260 is configured to collect vehicle energy consumption impact data and store the data in the database 230, and the training device 220 generates a target model/rule 201 (i.e., an energy consumption scoring model) based on training data maintained in the database 230.

It should be noted that, for the training process of the energy consumption scoring model, please refer to the method shown in fig. 7 below, and details are not described herein again for brevity.

The target models/rules obtained by the training device 220 may be applied in different systems or devices. In the embodiment of the present application, the target model/rule described above may be applied to an in-vehicle device.

The execution device 210 may call data, code, etc. from the data storage system 250 and may store data, instructions, etc. in the data storage system 250. Optionally, the executing device 210 may include a computing module 211 and an input/output (I/O) interface 212.

I/O interface 212 for data interaction with external devices, and a "user" may input data to I/O interface 212 via client device 240. In the embodiment of the present application, the client device 240 may be a data collection device in the vehicle 260.

The calculation module 211 processes the energy consumption impact factors of the vehicle using the target model/rule 201 to determine a score corresponding to each energy consumption impact factor, wherein the score corresponding to each energy consumption impact factor is used to indicate the impact of the energy consumption impact factor on the energy consumption of the vehicle.

Finally, the I/O interface 212 returns the results of the processing to the client device 240 for presentation to the user. Alternatively, the client device 240 may be a display device in a vehicle, so as to remind the user of the score corresponding to each energy consumption impact factor through the display device. The client device 240 may also be a display device of a terminal device of a driver bound to the vehicle, which is not limited in this embodiment of the application.

It should be noted that the terminal device may be a mobile phone, a tablet computer, a notebook computer, or the like.

In FIG. 1, a user may manually specify data to be input into the execution device 210, for example, to operate in an interface provided by the I/O interface 212. Alternatively, the client device 240 may automatically enter data into the I/O interface 212 and obtain the results, and if the client device 240 automatically enters data to obtain authorization from the user, the user may set the corresponding permissions in the client device 240. The user can view the result output by the execution device 210 at the client device 240, and the specific presentation form can be display, sound, action, and the like. The client device 240 may also act as a data collector to store the characteristic data of the collected messages in the database 230.

It should be noted that fig. 1 is only a schematic diagram of a system architecture provided by an embodiment of the present invention, and the position relationship between the devices, modules, and the like shown in fig. 1 does not constitute any limitation, for example, in fig. 1, the data storage system 250 is an external memory with respect to the execution device 210, and in other cases, the data storage system 250 may also place the data storage system 250 in the execution device 210.

In one possible implementation, the execution device 210, the data storage system 250, and the client device 240 may be devices configured in a vehicle 260.

The traditional vehicle energy consumption scoring method is that driving behavior data of a driver in a vehicle and road data of a road on which the vehicle runs are input into an energy consumption scoring model to calculate the score of vehicle energy consumption in a preset journey. The driving behavior data comprises the frequency of acceleration and deceleration of the driver in a preset time period, the intensity of acceleration and deceleration of the driver in the preset time period and the like. The road data includes road grade, road unevenness, and the like.

In order to avoid the above problem, the embodiment of the present application provides a new vehicle energy consumption scoring method, that is, based on factors affecting vehicle energy consumption in a current vehicle, energy consumption influencing factors are readjusted. In the embodiment of the present application, the energy consumption influencing factors are mainly classified into five types, and the five types of energy consumption influencing factors are described below.

Energy consumption influencing factor x of the first kind₁: and the environment influence factor is used for indicating the influence degree of the running environment where the vehicle is located in the preset journey on the energy consumption of the vehicle. The environmental influence factors mainly comprise one or more factors of external temperature non-suitability degree influence parameters, weather influence parameters, day and night influence parameters or wind speed influence parameters. The following description is made for the above 4 environmental impact parameters, respectively.

1) And the external temperature non-suitability degree influence parameter is used for indicating the influence degree of the environmental temperature outside the preset suitable temperature range in the preset stroke on the working state of the part in the vehicle.

Generally, the external temperature directly affects whether components in the vehicle are cooled or heated. For example, when the external temperature is too low, the vehicle needs to consume a certain amount of energy to warm the components in the vehicle in order to provide a suitable operating temperature for the components in the vehicle. For another example, when the external temperature is too high, the vehicle needs to consume a certain energy source to cool the components in the vehicle in order to provide the proper operating temperature for the components in the vehicle. Therefore, the influence of the external temperature nonconformity degree influence parameter on the energy consumption of the vehicle is important.

The applicant finds that the influence of the external temperature non-suitability degree influence parameter on the energy consumption of the vehicle can be regarded as linear, and the external temperature non-suitability degree influence parameter can be obtained by performing linear normalization processing on external temperature non-suitability degree data, wherein the external temperature non-suitability degree data comprises the external environment temperature of the vehicle in the preset journey.

Let T be₁Represents the lower limit, T, of the optimum operating temperature range of a component in a vehicle₂Represents the upper limit of the proper working temperature interval of the component, so that the proper working temperature interval corresponding to the component is [ T₁,T₂]. The degree of out-of-comfort of the external temperature affects the parameter x₁₁Can be represented by formula

Is determined, and

wherein, Δ t_maxN represents the total number of sampling points in the preset journey, i represents the ith sampling point in the preset journey, t_iRepresents the vehicle external ambient temperature, i ═ 1,2, …, n, collected at the ith sample point.

Note that Δ t_maxThe vehicle can be configured by a manufacturer when the automobile leaves a factory. Further, the manufacturer may set based on the vehicle's regular driving environment. For example, the normal running environment of the vehicle is China, the above-mentioned Δ t_maxMay be set at 50 degrees celsius.

It should be noted that the above-mentioned vehicle component may refer to a relatively important component in a vehicle, such as an engine and the like. The above-mentioned vehicle component may also refer to a plurality of or all of components in the vehicle, and the embodiment of the present application is not limited thereto.

If the components in the vehicle are a plurality of or all of the components in the vehicle, the suitable external temperature ranges corresponding to different components may be different in order to improve the accuracy of calculating the energy consumption score. Of course, if the suitable external temperature range is configured in order to simplify the configuration process of the suitable external temperature range, the same suitable external temperature range may be configured for a plurality of or all of the components, and the embodiment of the present application is not limited thereto.

2) And the weather influence parameter is used for indicating the influence degree of the weather in the preset journey on the working state of the vehicle.

On the one hand, in rainy and snowy weather, the number of parts of the vehicle which need to be started is large, such as a windscreen wiper, a rear window heating device and the like. The more components that are activated, the more energy consumption of the vehicle increases. On the other hand, in rainy and snowy weather, the road adhesion is reduced due to wet and slippery road surface, the running speed of the vehicle is slow, the vehicle needs large driving force, and the energy consumption of the vehicle is increased. Therefore, the influence of the weather-affecting parameter on the energy consumption of the vehicle is important.

The applicant finds that the influence of the weather influence parameters on the energy consumption of the vehicle can be regarded as linear, and the weather influence parameters can be obtained by performing linear normalization processing on weather data of the vehicle, wherein the weather influence data comprises one or more gears on which a wiper of the vehicle works in a preset stroke and the working time of the wiper in each gear in the preset stroke.

It should be noted that the weather data may be collected based on a sensor in the vehicle, however, a sensor capable of sensing weather is not installed in all vehicles, and therefore, in order to expand the application scenarios of the present application, the operating range of the wiper and the operating duration corresponding to each range may be used as the weather data.

Optionally, the weather affecting parameter x₁₂Can be represented by formula

Determining wherein M_YDenotes the total number of operating positions of a wiper of the vehicle, l denotes the l-th operating position in which the wiper is operated, and l is 1,2, …, M_Y，P_lRepresents the power consumption, T, required by the windscreen wiper to work at the I-th working gear in unit time_lRepresents the time length of the windscreen wiper working at the l-th gear position, P_maxThe power consumption T corresponding to the first working gear of the windscreen wiper in unit time is represented_maxAnd the total time required by the vehicle to travel the preset journey is represented, and the first working gear is the working gear with the maximum energy consumption of the vehicle in all working gears of the windscreen wiper.

It should be noted that, in the embodiment of the present application, the operating state data of other components related to weather in the vehicle may be used as the weather data, for example, the operating state data of the rear window heating device may be used as the weather data, and for example, the operating state data of the rear window heating device and the wiper blade may be integrated as the weather data. The embodiments of the present application do not limit this.

3) And the day and night influence parameter is used for indicating the influence degree of day and night environment in the preset journey on the working state of the vehicle.

In general, many components are required to be activated in a vehicle traveling at night, and examples thereof include lighting devices such as automobile headlamps and tail lamps. The more components that are activated, the more energy consumption of the vehicle increases. The influence of the diurnal influencing parameters on the energy consumption of the vehicle is therefore of importance.

The applicant has found that the impact of the circadian influence parameters on the energy consumption of the vehicle can be considered linear, and the circadian influence parameters can be obtained by linear normalization of the circadian data of the vehicle. The day and night influence data comprise one or more gears in which the lamps of the vehicle work in a preset stroke and the working time of the lamps in each gear in the preset stroke.

It should be noted that the day and night data may be collected based on sensors in the vehicle, however, sensors capable of sensing day and night are not installed in all vehicles, and therefore, in order to expand the application scenarios of the present application, the operating gear of the vehicle headlamp and the operating time corresponding to each gear may be used as the day and night data.

Optionally, the circadian influence parameter x₁₃By the formula

Determining wherein M_DIndicating the total number of operating positions of the lamps of the vehicle, l ' indicating the l ' th operating position of the lamps, l ' being 1,2, …, M_D，D_l'Represents the power consumption, T, required by the vehicle lamp to work in the l' th working gear in unit time_l'Indicating the duration of operation of the vehicle lamp in the i' th operating position, D_maxRepresents the power consumption, T, required by the vehicle lamp to work in the second working gear in unit time_maxAnd the total time required by the vehicle for running the preset journey is represented, and the second working gear is the working gear with the maximum energy consumption of the vehicle in all the working gears of the vehicle lamp.

It should be noted that, in the embodiment of the present application, the operating state data of other components related to the day and night in the vehicle may also be used as the day and night data, for example, the operating state data of the tail lamp may also be used as the day and night data, and for example, the operating state data of the tail lamp and the automobile headlamp may also be integrated as the day and night data. The embodiments of the present application do not limit this.

4) And the wind speed influence parameter is used for indicating the influence degree of the wind speed in the preset stroke on the working state of the vehicle.

Generally, vehicles traveling against the wind encounter a large resistance and require a large driving force. The driving force encountered by the vehicle running downwind is larger, and the driving force required by the vehicle is smaller. The influence of the wind speed influencing parameter on the energy consumption of the vehicle is therefore of great importance.

The applicant finds that the influence of the wind speed influencing parameter on the energy consumption of the vehicle can be regarded as non-linear, and the wind speed influencing parameter can be obtained by carrying out non-linear normalization processing on the wind speed data of the vehicle. Alternatively, the wind speed may be projected onto the heading of the vehicle to obtain a speed component as wind speed data to determine the wind speed influencing parameter.

Optionally, the above wind speed influencing parameter x₁₄By the formula

Determining, wherein,

Representing the wind direction, vector, collected at the ith sample point

Represents the heading, V, of the vehicle collected at the ith sample point_iRepresenting the wind speed, v, corresponding to the ith sample point_iAnd theta is a preset constant and can be generally 0.05, and e is a constant and can be generally 2.718281828.

Class II energy consumption influencing factor x₂: comfort habit impact factor for indicating the driver's need for driving environment comfort within a preset journeyThe degree of influence on the energy consumption of the vehicle is determined. In general, a driver adjusts an operating state of a component in a vehicle in order to improve driving comfort. For example, the driver can control the opening and closing of the window, the driver can control the working state of the air conditioner, the driver can adjust the temperature of the seat, and the like. The influence of the opening and closing of the vehicle windows controlled by the driver on the energy consumption of the vehicle and the influence of the working state of the air conditioner in the vehicle on the energy consumption of the vehicle are mainly described below, wherein the influence of the working state of the air conditioner on the energy consumption of the vehicle can be detailed into three aspects of an air conditioner dependence influence parameter, an air conditioner temperature preference degree influence parameter and an air conditioner windshield preference degree influence parameter.

1) And when the driver controls the window to be in a closed state, the resistance encountered in the vehicle running process is smaller. Therefore, the windowing influencing parameter has a large influence on the energy consumption of the vehicle.

The applicant finds that the influence of the windowing influence parameters on the energy consumption of the vehicle can be regarded as linear, the windowing influence parameters can be obtained by performing linear normalization processing on windowing data of the vehicle, the windowing data of the vehicle comprises one or more window opening and closing state combinations of the vehicle, which are acquired within the preset mileage, and the window opening and closing state combinations of the vehicle comprise the opening and closing state of each window in the vehicle. For example, the window opening and closing state combination of the vehicle, which is acquired at a certain acquisition time within the preset mileage, includes that the vehicle door and window 1 is in a closed state, the vehicle door and window 2 is in an open state, the vehicle door and window 3 is in a closed state, and the vehicle door and window 4 is in a closed state.

The window of the vehicle may include not only a window of the vehicle, but also a sunroof of the vehicle, and the like, which is not limited in the embodiment of the present application.

Optionally, the windowing affecting parameter x₂₁Can be represented by formula

Determination of the above M_WShowing the opening and closing of the window of the vehicleThe total number of state combinations, l "represents the l" opening and closing state combination of the window, l "is 1,2, …, M_W，R_iRepresents the power consumption of the vehicle consumed by the combination of the I' th opening and closing state of the vehicle window in unit time, R_maxRepresents the power consumption, T, of the vehicle consumed by the combination of the windows of the vehicle in the first open/close state in unit time_maxAnd the first opening and closing state combination is the opening and closing state combination with the maximum energy consumption in all the opening and closing state combinations of the windows in the vehicle.

2) And the air conditioner dependence influence parameter is used for indicating the influence degree of the working time of the air conditioner on the energy consumption of the vehicle in the preset journey. The longer the operation time period of the air conditioner within the preset trip is, the greater the amount of energy of the vehicle is consumed. Therefore, the air-conditioning dependent factor has a large influence on the energy consumption of the vehicle.

The applicant finds that the influence of the air-conditioning-dependent influence parameter on the energy consumption of the vehicle can be regarded as linear, and the air-conditioning-dependent influence parameter can be obtained by performing linear normalization processing on the air-conditioning-dependent data of the vehicle.

Optionally, the above-mentioned on-air-conditioning dependent influence parameter x₂₂Can be represented by formula

Determining, wherein M represents the total driving time required for the vehicle to travel the preset mileage, t_cAnd the total time length of the air conditioner in the working state in the preset mileage is represented.

3) And the air conditioner temperature preference degree influence parameter is used for indicating the influence degree of the working temperature of the air conditioner on the energy consumption of the vehicle in the preset journey. The greater the difference between the working temperature of the air conditioner and the external temperature difference of the vehicle in the preset journey is, the greater the energy amount of the vehicle is consumed. Therefore, the influence of the air conditioner temperature preference degree influence parameter on the energy consumption of the vehicle is large.

The applicant finds that the influence of the air conditioner temperature preference degree influence parameter on the energy consumption of the vehicle can be regarded as linear, and the air conditioner temperature preference degree influence parameter can be obtained by performing linear normalization processing on the temperature data of the air conditioner.

Optionally, the air conditioner temperature preference degree influence parameter x₂₃Can be represented by formula

Determining, wherein n represents the total number of sample points within the preset run, T_ciIndicating the operating temperature, T, of the air conditioner collected at the ith sampling point_cmaxIndicating the maximum temperature limit, T, of the air conditioner_cminIndicating the minimum temperature limit, C, of the air conditioner_iIndicating the cooling mode of the air conditioner at the ith sampling point to be on or off, H_iThe heating mode of the air conditioner at the ith sampling point is turned on or off, and i is 1,2, …, n.

In addition, the above-mentioned C_iAnd H_iThere can be two values of 0 or 1. Wherein, when C_iWhen the value is 0, the cooling mode of the air conditioner is off at the ith sampling point. When C is present_iWhen the sampling point is 1, the refrigeration mode of the air conditioner is in an opening state at the ith sampling point. When H is present_iWhen the sampling point is 0, the heating mode of the air conditioner is off at the ith sampling point. When H is present_iWhen the sampling point is 1, the heating mode of the air conditioner is in an opening state at the ith sampling point.

4) And the air conditioner windshield preference degree influence parameter is used for indicating the influence of the working gear of the air conditioner windshield on the energy consumption of the vehicle in a preset stroke. The higher the working gear of the air-conditioning windshield in the preset journey is, the larger the energy amount of the vehicle is consumed. Therefore, the influence of the air conditioner windshield preference degree influence parameter on the energy consumption of the vehicle is large.

The applicant finds that the influence of the air conditioner windshield preference degree influence parameter on the energy consumption of the vehicle can be regarded as linear, and the air conditioner windshield preference degree influence parameter can be obtained by performing linear normalization processing on the windshield data of the air conditioner.

Optionally, the air conditioner windshield preference degree influence parameter x₂₄By the formula

Determining wherein M_cRepresents the total number of windshields of the air conditioner of the vehicle, c represents that the windshields of the air conditioner are in a c-th gear position, c is 1,2, …, M_c，S_cRepresents the power consumption T required by the air conditioner when the wind gear of the air conditioner is the c-th gear in unit time_cThe working duration when the wind gear of the air conditioner is the c gear in the preset stroke is represented by S_CmaxRepresents the power consumption T required when the wind gear of the air conditioner is the third working gear in unit time_maxAnd the third working gear is a gear which consumes the largest energy consumption of the vehicle in all gears of the wind gear of the air conditioner.

Class III energy consumption factor x₃: and the driving behavior influence factor is used for indicating the influence degree of the driving behavior of the driver on the energy consumption of the vehicle in the preset journey. The driving behavior influence factors mainly comprise one or more of acceleration and deceleration frequency influence parameters, acceleration and deceleration intensity influence parameters, non-economic vehicle speed duration influence parameters or high-speed influence parameters. The following description is made for the above 4 environmental impact parameters, respectively.

1) And the acceleration and deceleration frequency influence parameter is used for indicating the influence of the acceleration and deceleration frequency of the driver in the preset journey on the energy consumption of the vehicle. The higher the acceleration and deceleration frequency within the preset trip, the greater the amount of energy of the vehicle consumed. Therefore, the influence of the acceleration/deceleration frequency influence parameter on the energy consumption of the vehicle is large.

The applicant finds that the influence of the acceleration and deceleration frequency degree influence parameter on the energy consumption of the vehicle can be regarded as linear, and the acceleration and deceleration frequency degree influence parameter can be obtained by performing linear normalization processing on the acceleration and deceleration frequency degree data. The acceleration and deceleration frequency degree data can comprise the time length of the vehicle exceeding the first slowdown speed interval in the preset travel and the driving time length required by the preset mileage during the vehicle driving.

Optionally, the acceleration/deceleration frequency degree affects parameter x₃₁The speed-increasing and speed-reducing duration exceeding the first speed-reducing gentle section can be determined based on the ratio of the speed-increasing and speed-reducing duration exceeding the first speed-reducing gentle section within the preset travel to the driving duration required by the preset mileage of the vehicle.

The first deceleration flattening section may be preset by a manufacturer, and this is not particularly limited in the embodiments of the present application.

2) And the acceleration and deceleration intensity influence parameter is used for indicating the influence of the acceleration and deceleration variation of the driver in the preset journey on the energy consumption of the vehicle. The higher the amount of change in acceleration and deceleration within the preset trip, the greater the amount of energy of the vehicle consumed. Therefore, the influence of the acceleration/deceleration severity influence parameter on the energy consumption of the vehicle is large.

The applicant finds that the influence of the acceleration and deceleration intensity influence parameter on the energy consumption of the vehicle can be regarded as linear, and the acceleration and deceleration intensity influence parameter can be obtained by performing linear normalization processing on the acceleration and deceleration intensity data. The acceleration and deceleration intensity data may include a duration that the speed variation of the vehicle is located outside the second acceleration and deceleration gentle section in the preset trip, and a distribution probability of the speed variation of the vehicle in the preset trip.

Optionally, the acceleration/deceleration intensity influence parameter x₃₂The proportion of the sum of the absolute values of acceleration and deceleration of the vehicle exceeding the gentle speed section in the preset travel to the whole body can be determined.

For example, the second acceleration/deceleration gentle section may be represented by [ A ]₁，A₂]Indicating that the acceleration/deceleration intensity affects the parameter x₃₂Can be represented by formula

And is

Determining, where n represents the total number of sampling points within the preset run, i represents the ith sampling point within the preset run, and i is 1,2, …, n, a_iRepresenting the acceleration of the vehicle collected at the ith sample point within the preset trip, b_iRepresents the acceleration of the vehicle collected at the ith sampling point within the preset trip, and b_iIs positioned in a second gradual acceleration and deceleration section (A)₁，A₂]And (c) out.

The first deceleration flattening section and the second deceleration flattening section may be the same section or different sections, and the present embodiment is not limited to this.

It should also be understood that the second deceleration flattening section may be configured by a manufacturer, or based on a relationship between a historical speed and energy consumption, which is not specifically limited in this embodiment of the application.

3) And the non-economic vehicle speed duration influence parameter is used for indicating the influence degree of the vehicle running duration on the vehicle energy consumption in the preset travel according to the vehicle speed in the first non-economic speed interval. Generally, the longer the vehicle travels at a vehicle speed in the non-economical speed section within the preset trip, the greater the amount of energy of the vehicle is consumed. Therefore, the non-economical vehicle speed duration influencing parameter has a large influence on the energy consumption of the vehicle.

The applicant finds that the influence of the non-economic vehicle speed duration influence parameters on the energy consumption of the vehicle can be regarded as linear, and the non-economic vehicle speed duration influence parameters can be obtained by performing linear normalization processing on the non-economic vehicle speed duration data. The non-economic vehicle speed duration data can comprise vehicle speed running duration of the vehicle in a first non-economic speed interval in a preset journey.

Optionally, the non-economic vehicle speed duration influencing parameter x₃₃The determination may be based on a travel time period ratio in which the vehicle exceeds the economical speed section within a time period required for the vehicle to travel the preset trip.

The energy of the vehicle consumed by the vehicle speed in the first economy speed interval is smaller than the energy of the vehicle consumed by the vehicle speed in the first non-economy speed interval, and the embodiment of the present application is not limited to this.

It should also be understood that the first economic speed interval may be configured by a manufacturer or based on a relationship between historical speed and energy consumption, which is not specifically limited in the embodiments of the present application.

4) And the non-economic vehicle speed mileage influence parameter is used for indicating the influence degree of the vehicle energy consumption caused by the vehicle speed mileage in the second non-economic speed interval in the preset journey. Generally, the greater the number of miles traveled by the vehicle at the speed in the non-economical speed section within the preset trip, the greater the amount of energy consumed by the vehicle. Therefore, the influence of the non-economic vehicle speed mileage influencing parameter on the energy consumption of the vehicle is large.

The applicant finds that the influence of the non-economic vehicle speed mileage influence parameter on the energy consumption of the vehicle can be regarded as linear, and the non-economic vehicle speed mileage influence parameter can be obtained by performing linear normalization processing on the non-economic vehicle speed mileage data. The non-economic vehicle speed mileage data may include the vehicle speed mileage within a second non-economic speed interval within a preset trip.

Optionally, the non-economic vehicle speed mileage influencing parameter x₃₄The determination may be based on a mileage occupying ratio of the vehicle exceeding the economic speed interval within the preset mileage traveled by the vehicle.

The energy of the vehicle consumed by the vehicle speed in the second economy speed section is smaller than the energy of the vehicle consumed by the vehicle speed in the second non-economy speed section, and the embodiment of the present application is not limited to this.

It should also be understood that the economic speed interval may be configured by a manufacturer, or based on a relationship between historical speed and energy consumption, which is not specifically limited in the embodiments of the present application.

Class IV energy consumption Effect factor x₄: and the vehicle working condition influence factor is used for indicating the influence degree of the working condition of the vehicle in the preset stroke on the energy consumption of the vehicle. The vehicle working condition influence factor mainly comprises a vehicle load influence parameter or a vehicle aging degree influence parameter.

1) And the vehicle load influence parameter is used for indicating the influence of the vehicle load on the energy consumption of the vehicle in the preset travel. The heavier the vehicle load is within the preset range, the greater the amount of energy of the vehicle is consumed. Therefore, the influence of the vehicle load influence parameter on the energy consumption of the vehicle is large.

The applicant finds that the influence of the vehicle load influencing parameter on the energy consumption of the vehicle can be regarded as linear, and the vehicle load influencing parameter can be obtained by performing linear normalization processing on the vehicle load data. Wherein the vehicle load data may include a load of the vehicle within a preset trip.

Optionally, the vehicle load influencing parameter x₄₁Can be represented by formula

Determining, wherein G represents the weight of the vehicle in the preset journey, G_cAnd the factory rated weight of the whole vehicle in the preset stroke is shown.

2) And the vehicle aging degree influence parameter is used for indicating the influence of the vehicle aging degree on the energy consumption of the vehicle in the preset journey. The more the vehicle ages within the preset trip, the greater the amount of energy consumed by the vehicle. Specifically, vehicle aging can result in a decrease in the efficiency of the components in the vehicle to transmit power, some of which is lost during transmission. Therefore, the influence of the vehicle load influence parameter on the energy consumption of the vehicle is large.

The applicant finds that the influence of the vehicle aging degree influence parameter on the energy consumption of the vehicle can be regarded as non-linear, and the vehicle aging degree influence parameter can be obtained by carrying out non-linear normalization processing on the vehicle aging data. The vehicle aging data may include, among other things, total miles traveled (O) of the vehicle, age (m) of the vehicle.

Optionally, the vehicle aging degree influence parameter x₄₂Can be represented by formula

Wherein A is 2, B is-1, slope is 0.1, h is 0,

It should be noted that, the above formula only provides one possible implementation manner of the parameter value in the formula, and the parameter value may also be adjusted based on an actual situation, which is not specifically limited in this embodiment of the present application.

Class V energy consumption Effect factor x₅: and the road condition influence factor is used for indicating the influence degree of the road condition in the preset journey on the energy consumption of the vehicle. The road condition influence factor mainly comprises a road grade influence parameter or a road relief degree influence parameter.

1) And the road grade influence parameter is used for indicating the influence of the road grade in the preset journey on the energy consumption of the vehicle. The higher the road grade is within the preset trip, the greater the amount of energy of the vehicle is consumed. The higher the road rank is, the worse the running condition of the corresponding road is. Therefore, the road grade impact parameter has a large impact on the energy consumption of the vehicle.

The applicant finds that the influence of the road grade influence parameter on the energy consumption of the vehicle can be regarded as linear, and the road grade influence parameter can be obtained by performing linear normalization processing on the road grade data. The road grade data may include a length of time that the vehicle travels on each grade road within a preset trip.

Optionally, the road grade influence parameter x₅₁Can be represented by formula

Determining wherein M_RIndicating the total number of road grades, r indicating the road of the r-th grade, r being 1,2, …, M_r，P_rRepresents the power consumption, T, required by the vehicle to travel on the road of the r-th level per unit time_rIndicating a period of time, P, that the vehicle is traveling on the road of the r-th level_rmaxRepresents the power consumption required by the vehicle to run on the road of the first road grade in unit time, wherein the first road grade is the road with the maximum power consumption required by the vehicle to run in all road grades, T_maxAnd the total running time required by the vehicle to run the preset journey is represented.

2) And the road fluctuation degree influence parameter is used for indicating the influence of the road fluctuation degree of the road on which the vehicle runs in the preset journey on the energy consumption of the vehicle. The longer the section of road on which the vehicle is required to climb the slope in the road on which the vehicle travels within the preset trip, the greater the amount of energy of the vehicle consumed. Therefore, the road undulation degree influence parameter has a large influence on the energy consumption of the vehicle.

The applicant finds that the influence of the road undulation degree influence parameter on the energy consumption of the vehicle can be regarded as linear, and the road undulation degree influence parameter can be obtained by performing linear normalization processing on the road undulation data. The road fluctuation data may include a predetermined in-travel gradient interval probability and a gradient interval average value.

Alternatively, the above-mentioned road undulation degree influence parameter x₅₂Can be represented by formula

Determining, wherein, [ slope_min，slope_max]For a predetermined smooth section of the road, slope_minRepresents the lower limit, slope, of the gradient interval within the preset stroke_maxRepresenting the upper limit, P, of the range of slopes within a predetermined stroke_slopeiRepresents the gradient section probability of the i-th section, S_iRepresents the average value of the gradient interval.

It should be noted that the road undulation plateau region may be configured by a manufacturer, or configured based on a relationship between historical speed and energy consumption, which is not specifically limited in this embodiment of the present application.

The energy consumption influence parameters provided by the embodiment of the present application are described above from 5 dimensions, and the vehicle energy consumption scoring method according to the embodiment of the present application is described below with reference to fig. 2.

FIG. 2 is a schematic flow chart of a vehicle energy consumption scoring method according to an embodiment of the application. The method shown in fig. 2 may be performed by the performing device 210 shown in fig. 1. The method shown in fig. 2 includes step 210 and step 220.

A set of energy consumption impact factors for the vehicle is obtained 201.

The energy consumption influencing factor set may be one or more of the five types of energy consumption influencing factors described above, wherein the specific meanings of the energy consumption factors of different types are described above, and are not described herein again for brevity.

Optionally, the step 210 further includes: acquiring an energy consumption influence data set; normalizing the data in the energy consumption influence data set to obtain the energy consumption influence parameter set, wherein different types of energy consumption influence data in the energy consumption influence data set are used for calculating one or more energy consumption influence parameters in the energy consumption influence parameter set; and calculating an energy consumption influence factor set based on the energy consumption influence parameter set, wherein different types of energy consumption influence parameters in the energy consumption influence parameter set are used for calculating different energy consumption influence factors in the energy consumption influence parameter factor set.

As described above, if the energy consumption influencing parameter in the energy consumption influencing parameter set is the external temperature non-suitability degree influencing parameter, the energy consumption influencing data corresponding to the energy consumption influencing parameter includes the external temperature non-suitability degree influencing data, such as the external environment temperature of the vehicle in the preset mileage. If the energy consumption influence parameters in the energy consumption influence parameter set are weather influence parameters, the energy consumption influence data corresponding to the energy consumption influence parameters are weather influence data, such as the gear where the wiper is located in the preset mileage and the working time of the wiper at each gear. If the energy consumption influence parameters in the energy consumption influence parameter set are day and night influence parameters, the energy consumption influence data corresponding to the energy consumption influence parameters are day and night influence data, such as the gear of the headlamp in the preset mileage, the working time of the headlamp in each gear, and the like. If the energy consumption influence parameter in the energy consumption influence parameter set is the wind speed influence parameter, the energy consumption influence data corresponding to the energy consumption influence parameter is the wind speed influence data, such as the projection of the wind speed on the vehicle course. If the energy consumption influence parameter in the energy consumption influence parameter set is the windowing influence parameter, the energy consumption influence data corresponding to the energy consumption influence parameter is the windowing influence data, such as the wind resistance score of the vehicle window. For the corresponding relationship between the remaining energy consumption impact parameters and the energy consumption impact data, please refer to the above description, and for brevity, the detailed description is omitted here.

Optionally, the normalization processing includes linear normalization processing and nonlinear normalization processing.

The linear normalization process can be understood as the energy consumption influence data x_i' by the formula

Linear normalization processing is carried out to obtain energy consumption influence parameters y, p_iRepresenting energy consumption impact data x_i' probability distribution in preset mileage, a ' represents a lower limit value of variation of the variable i, b ' represents an upper limit value of variation of the variable i, M, N, m and z are constants and can be configured by a manufacturer.

Optionally, the energy consumption influence parameters obtained based on the above linear normalization processing may include an external temperature non-fitness degree influence parameter, a weather influence parameter, a day and night influence parameter, a windowing influence parameter, and the like. It should be noted that, in the above-described processing formula based on linear normalization, the constants M, N, M, and z are already configured, and for simplicity, the constant of 0 is omitted from the formula.

The above non-linear normalization process can be understood as influencing the energy consumption by the data x_i"passing formula

And z ═ f (x)_i") to obtain energy consumption influence parameters y, z represents all energy consumption influence data x in the preset journey_i"the statistical sum, J, Q, h and slope, is constant, and J, Q, h, slope and θ (J, Q, h, slope) can be configured by the vendor.

Optionally, the energy consumption impact parameters obtained based on the nonlinear normalization processing may include wind speed impact parameters, vehicle aging degree impact parameters, and the like. It should be noted that, in the above-described processing formula based on nonlinear normalization, the above-mentioned constants are already configured, and for simplicity, the constant of 0 is omitted from the formula.

202, inputting the energy consumption influence factor set into an energy consumption scoring model to determine a score corresponding to the number of one or more types of energy consumption influence factors in the energy consumption influence factor set, wherein the score corresponding to the one or more types of energy consumption influence factors is used for indicating the influence degree of the corresponding energy consumption influence factors in the preset journey on the energy consumption of the vehicle.

Optionally, the energy consumption scoring model is

Wherein x is_jDenotes the j energy consumption influence factor, w_jiThe weight of the ith energy consumption influence parameter representing the jth energy consumption influence factor, nj represents the total number of the energy consumption influence parameters corresponding to the energy consumption influence factors, b_jRepresents the intercept of the jth energy consumption impact factor; f. of_j() Representing a monotonically increasing function.

Note that the weight w is_jiIntercept b_jThe trained parameters can be issued to the vehicle-mounted execution device through the cloud. For a specific training process, please refer to the following description.

And 203, displaying the score corresponding to each energy consumption influence factor in the energy consumption influence factor set to a user of the vehicle through a display interface.

The display interface may be a display screen of the vehicle itself, or may also be a display screen of a terminal device bound to the vehicle, which is not limited in the embodiment of the present application.

Because the number of the energy consumption influence factors is large, the scoring which needs to be displayed by a user is complex, and in order to simplify the scoring display, the 5 energy consumption influence factors are divided into objective energy consumption influence factor scoring and subjective energy consumption influence factor scoring.

The objective energy consumption impact factor may include a first type energy consumption impact factor x₁(environmental impact factor), fourth type energy consumption impact factor x₄(vehicle condition influencing factor) and a fifth type energy consumption influencing factor x₅(road condition influencing factor).

Optionally, the objective energy consumption impact factor grade_objCan be graded by objective energy consumption influence scoring formula_obj＝(w₁×x₁)+(w₄×x₄)+(w₅×x₅) Calculation of w₁Representing the first class of energy consumption influencing factor x₁Weight of (1), w₄Representing the fourth class of energy consumption influencing factor x₄Weight of (1), w₅Representing the first class of energy consumption influencing factor x₅The weight of (c).

The subjective energy consumption impact factor may include a secondLike energy consumption influence factor x₂(comfort habit influencing factor), third class energy consumption influencing factor x₃(driving behavior influencing factor).

Optionally, the subjective energy consumption impact factor grade_subCan be graded by subjective energy consumption influence scoring formula_sub＝(w₂×x₂)+(w₃×x₃) Can be calculated by the formula, w₂Representing the second type of energy consumption impact factor x₂Weight of (1), w₃Representing the third class of energy consumption impact factor x₃The weight of (c).

It should be noted that the weights in the above two scoring formulas may also be issued by the cloud to the vehicle-mounted executing device according to the trained parameters. For a specific training process, please refer to the following description.

In addition, the higher the score corresponding to the energy consumption influence factor is, the larger the energy consumption of the energy consumption influence factor is. The lower the score corresponding to the energy consumption influence factor is, the smaller the energy consumption of the energy consumption influence factor is.

The following describes a flow of the score calculating method according to the embodiment of the present application with reference to fig. 3 as an example of calculating the scores of the above 5 energy consumption impact factors. The method shown in fig. 3 includes steps 310 to 330.

As mentioned above, the first type of energy consumption impact factor x₁Including an external temperature non-suitability degree influence parameter x₁₁Weather affecting parameter x₁₂Day and night influence parameter x₁₃And a wind speed influencing parameter x₁₄And 4 energy consumption influencing parameters are obtained. Class II energy consumption influencing factor x₂Including a windowing affecting parameter x₂₁Air conditioner dependent influence parameter x₂₂Air conditioner temperature preference degree influence parameter x₂₃And an air conditioner windshield preference degree influence parameter x₂₄And 4 energy consumption influencing parameters are obtained. Class III energy consumption factor x₃Including the influence parameter x of acceleration and deceleration frequency degree₃₁And acceleration/deceleration intensity influence parameter x₃₂Non-economic vehicle speed duration influence parameter x₃₃And a high speed influencing parameter x₃₄And 4 energy consumption influencing parameters are obtained. Class IV energy consumption Effect factor x₄Including vehicle load influencing parameter x₄₁And a vehicle aging degree influence parameter x₄₂And 2 energy consumption influencing parameters. Class V energy consumption Effect factor x₅Including a road grade impact parameter x₅₁And a road undulation degree influence parameter x₅₂。

A corresponding energy consumption impact factor is calculated based on the energy consumption impact parameter 310. The corresponding relationship between the energy consumption influencing parameter and the energy consumption influencing factor can be referred to as the above introduction.

And 320, inputting the calculated energy consumption influence factors into the subjective energy consumption influence scoring formula, and calculating subjective energy consumption influence scoring, or inputting the calculated energy consumption influence factors into the objective energy consumption influence scoring formula, and calculating objective energy consumption influence scoring.

And 330, outputting the subjective energy consumption influence score and the objective energy consumption influence score.

Generally, after the score of the energy consumption impact factors is calculated, the ranking of the energy consumption impact factors can be presented to the user based on the scoring result, so that the user can clearly identify the energy consumption impact factors which have larger impact on the energy consumption. Of course, the scoring ranking of the energy consumption impact parameters may also be presented directly to the user, so that the user may specify the energy consumption impact parameters that have a greater impact on energy consumption. Due to the fact that the number of the energy consumption influence parameters is large, only the energy consumption influence parameters with the top grades can be displayed to the user.

It should be noted that, the higher the score corresponding to the energy consumption impact parameter is, the larger the energy consumption of the energy consumption impact parameter is. The lower the score corresponding to the energy consumption impact parameter, the smaller the amount of energy consumed by the energy consumption impact parameter.

To further enhance the user experience, driving economy advice may also be presented on the user interface. For example, when the score of the air conditioning dependent impact parameter ranks higher, the driving economy advice may be displayed on the user interface: this time, the air conditioner consumes more energy, and the air conditioner is required to be properly reduced in starting time. For another example, when the score of the air conditioner temperature preference impact parameter ranks higher, the driving economy advice may be displayed on the user interface: if the temperature of the air conditioner is lower, the temperature of the air conditioner should be properly increased. For another example, when the score of the acceleration/deceleration severity influence parameter is ranked higher, the driving economy advice may be displayed on the user interface: this acceleration and deceleration is severe, and please reduce the severe driving properly.

A schematic diagram of a user interface of an embodiment of the present application is described below in conjunction with fig. 4 and 5. It should be noted that the user interfaces shown in fig. 4 and 5 are only for ease of understanding.

FIG. 4 is a schematic diagram of a user interface of an embodiment of the application. Through the above-described score calculation method, the scores of the energy consumption impact factors are shown in fig. 4, the score of the driving behavior impact factor is 65 (see 412), the score of the comfort habit impact factor is 250 (see 413), the score of the road condition impact factor is 20 (see 415), the score of the environment impact factor is 40 (see 416), and the score of the vehicle operating condition impact factor is 25 (see 417). And the subjective energy consumption impact score was 56 (see 411), the objective energy consumption impact score was 35 (see 414), and the hundred kilometers energy consumption was 29.6kwh/100 km.

Through the scoring calculation method introduced above, the scores of the energy consumption influence parameters are ranked as 420, that is, the score of the external temperature unsuitable temperature influence parameter is higher than the score of the air conditioner dependent influence parameter, the score of the air conditioner dependent influence parameter is higher than the score of the air conditioner temperature preference degree influence parameter, the score of the temperature preference degree influence parameter is higher than the score of the acceleration and deceleration intensity influence parameter, and the score of the temperature preference degree influence parameter is higher than the score of the wind speed influence parameter. Accordingly, driving economy advice 430 is also given in the user interface 400 based on the energy consumption impact parameter score ranking.

It should be noted that the user interface may also display other additional functions, for example, displaying a one-touch share 440 to share the scoring result displayed by the user interface to other users. As another example, a history view 450 may also be displayed to view previous scoring results.

FIG. 5 is a schematic view of a user interface of another embodiment of the present application. It should be noted that elements in the user interface shown in fig. 4 having the same functions as elements in the functional interface shown in fig. 5 have the same numbers, and please refer to the above description for the functions, which are not described in detail below for brevity.

Fig. 5 also shows, on the basis of fig. 4, the scores of the energy consumption impact parameters that impact the subjective energy consumption. Wherein energy consumption impact parameters associated with the subjective energy consumption impact score may be shown at 511 to 518. For example, the score of the air conditioning dependency impact parameter may be shown at 511, the air conditioning temperature preference degree impact parameter may be shown at 512, the air conditioning windshield preference degree impact parameter may be shown at 513, the acceleration and deceleration frequency degree impact parameter may be shown at 514, the acceleration and deceleration severity impact parameter may be shown at 515, the non-economic vehicle speed duration impact parameter may be shown at 516, the high speed impact parameter may be shown at 517, and the windowing impact parameter may be shown at 518.

Energy consumption impact parameters associated with the objective energy consumption impact score may be shown at 521 through 528. For example, the outside temperature discomfort level affecting parameter may be shown at 521, the weather affecting parameter may be shown at 522, the day and night affecting parameter may be shown at 523, the wind speed affecting parameter may be shown at 524, the vehicle load affecting parameter may be shown at 525, the vehicle age affecting parameter may be shown at 526, the road grade affecting parameter may be shown at 527, and the road undulation level affecting parameter may be shown at 528.

The calculation process of the energy consumption scoring model and the method for presenting the score to the user in the embodiment of the present application are described above with reference to fig. 1 to 5. The following describes a training process of the energy consumption scoring model according to an embodiment of the present application with reference to fig. 6 to 7. For the convenience of understanding, an application scenario of the training method of the energy consumption scoring model according to the embodiment of the present application is described first with reference to fig. 6.

Fig. 6 is a schematic diagram of a system architecture to which an embodiment of the present application is applicable. The system 600 shown in fig. 6 includes a cloud execution device 610, a data storage system 620, and an in-vehicle device 630.

The execution device 610 is implemented by one or more servers, optionally in cooperation with other computing devices, such as: data storage, routers, load balancers, and the like; the execution device may be disposed onboard one physical site or distributed across multiple physical sites. The executing device onboard may use data in the data storage system 620 or call program code in the data storage system 620 to implement the process of calculating a score through the energy consumption scoring model, specifically, as described above, for example, steps 201 to 203. For brevity, further description is omitted.

The user may operate the respective in-vehicle device 630 to interact with the performance device 610. Each in-vehicle device may represent any computing device, such as an in-vehicle computing device, and so forth.

Each user's in-vehicle device may interact with the enforcement device 610 via a communication network of any communication mechanism/standard, such as a wide area network, a local area network, a peer-to-peer connection, etc., or any combination thereof.

In another implementation, one or more aspects of the execution device 610 may be implemented by each in-vehicle device, e.g., the in-vehicle device 301 may provide local data or feedback calculations for the execution device 210.

It is noted that all functions of the execution device 610 may also be implemented by the in-vehicle device. For example, the in-vehicle device 630 implements functions of the execution device 610 and provides services to its own user, or provides services to a user of the in-vehicle device 630.

The following describes a training method of the energy consumption scoring model according to the embodiment of the present application, based on the system shown in fig. 6. Fig. 7 is a schematic flow chart of a training method of an energy consumption scoring model according to an embodiment of the present application. The method shown in fig. 7 includes steps 710 to 720.

A training data set is obtained 710, the training data set including a plurality of energy consumption impact factor training data.

The energy consumption impact factor training data is training data used for training the energy consumption scoring model, and may be an energy consumption impact factor of the vehicle or energy consumption impact factors of other vehicles, which is not limited in the embodiment of the present application.

It should be noted that the meaning of the energy consumption impact factor training data may refer to the energy consumption impact factor mentioned above, and the calculation method of the energy consumption impact factor training data is also the same as the calculation method of the energy consumption impact factor mentioned above, and for brevity, detailed description is not repeated below.

And 720, inputting the training data set into an original energy consumption scoring model to obtain the energy consumption scoring model.

Generally, in order to improve the accuracy of the energy consumption scoring model, the energy consumption scoring model which is already issued to the vehicle-mounted device may be updated.

Alternatively, whether the energy consumption scoring model is updated may be determined based on the model error. For example, when the model error is greater than a preset value, a process of updating the energy consumption scoring model may be triggered.

Determining, wherein N represents the total number of samples in the training set, C_i-honstA true value, C, representing the energy consumption of said vehicle over a historical journey (for example, a hundred kilometres)_i-predictRepresenting a predicted value of energy consumption of the vehicle over a historical trip (e.g., hundreds of kilometers).

Optionally, a gradient descent algorithm may be adopted to dynamically train model parameters such as weights and intercept in the scoring calculation formula, and an energy consumption model in which energy consumption within the historical mileage is positively correlated with the multidimensional energy consumption factor is established. It should be noted that, in order to ensure a positive correlation, the following constraints are particularly applied to the objective and subjective energy consumption models: the weight w and the intercept b are non-negative numbers; f 1-f 6 are monotone increasing functions; and a 5-dimensional index range of [0,100 ].

The method for scoring the vehicle energy consumption according to the embodiment of the present application is described above with reference to fig. 1 to 7, and the apparatus according to the embodiment of the present application is described below with reference to fig. 8 to 9. It should be understood that, it should be noted that the apparatuses shown in fig. 8 to fig. 9 can implement the steps of the above-mentioned method, and are not described herein again for brevity.

Fig. 8 is a schematic diagram of a vehicle energy consumption scoring device according to an embodiment of the present application. The apparatus 800 shown in fig. 8 comprises: an acquisition unit 810 and a processing unit 820.

The acquiring unit 810 is configured to acquire an energy consumption influence factor set of the vehicle, where the energy consumption influence factor set includes a first type of energy consumption influence factor and/or a second type of energy consumption influence factor, the first type of energy consumption influence factor includes a windowing influence parameter, the windowing influence parameter is used to indicate a degree of influence of a wind resistance caused by opening of a window of the vehicle in a preset trip on energy consumption of the vehicle, and the second type of energy consumption influence factor indicates a degree of influence of a driving environment where the vehicle is located in the preset trip on energy consumption of the vehicle;

and the processing unit 820 is configured to input the energy consumption influence factors acquired by the acquisition unit into the energy consumption scoring model in a combined manner so as to determine a score corresponding to the number of one or more types of energy consumption influence factors in the energy consumption influence factor set, where the score corresponding to the one or more types of energy consumption influence factors is used to indicate the influence degree of the one or more types of energy consumption influence factors on the vehicle energy consumption in a preset journey.

Optionally, as an embodiment, the second type of energy consumption influencing factor includes one or more of an external temperature non-suitability degree influencing parameter, a weather influencing parameter, a day and night influencing parameter, or a wind speed influencing parameter, wherein the external temperature non-suitability degree influencing parameter is used for indicating the degree of influence of the ambient temperature outside a preset suitable temperature interval in a preset trip on the working state of the vehicle, the weather influencing parameter is used for indicating the degree of influence of the weather in the preset trip on the working state of the vehicle, the day and night influencing parameter is used for indicating the degree of influence of the day and night environment in the preset trip on the working state of the vehicle, and the wind speed influencing parameter is used for indicating the degree of influence of the wind speed in the preset trip on the working state of the vehicle.

Alternatively, as an embodiment, the external temperature non-suitability degree influence parameter is obtained by performing linear normalization processing on external temperature non-suitability degree data, which includes an external ambient temperature of the vehicle.

Alternatively, as an example, the degree of external temperature inadequacy affects the parameter x₁₁By the formula

It is determined that,and is

Wherein, T₁Lower limit, T, of the optimum working temperature range for the component to be preset₂Represents the upper limit of the preset proper working temperature interval of the component, n represents the total number of sampling points in the preset stroke, i represents the ith sampling point in the preset stroke, i is 1,2, …, n, delta t_maxIs a preset value, t_iRepresenting the external ambient temperature of the vehicle collected at the ith sample point.

Optionally, as an embodiment, the weather influence parameters are obtained by performing linear normalization processing on weather influence data, where the weather influence data includes one or more gears in which the wiper of the vehicle operates in a preset stroke and a working duration of each gear in the preset stroke.

Alternatively, as an example, the weather affecting parameter x₁₂By the formula

Determining wherein M_YIndicates the total number of the operating positions of the wiper of the vehicle, l indicates the l-th operating position of the wiper, and l is 1,2, …, M_Y，P_lExpresses the power consumption T required by the wiper to work at the I-th working gear in unit time_lRepresents the time length of the windscreen wiper working at the l-th gear position, P_maxThe power consumption corresponding to the first working gear of the windscreen wiper in unit time is represented, the first working gear is the working gear with the maximum energy consumption of the vehicle in all the working gears of the windscreen wiper, and T_maxRepresenting the total length of time required for the vehicle to travel the preset trip.

Optionally, as an embodiment, the diurnal influence parameter is obtained by performing linear normalization processing on diurnal influence data, where the diurnal influence data includes one or more gears in which the lamps of the vehicle operate in the preset trip and an operating time period in which the lamps are in each gear in the preset trip.

Optionally, as an embodiment, the circadian influence parameter x₁₃By the formula

Determining wherein M_DIndicating the total number of operating positions of the lamps of the vehicle, l ' indicating the l ' th operating position of the lamps, l ' 1,2, …, M_D，D_l'Represents the power consumption T required by the vehicle lamp to work in the l' th working gear in unit time_l'Indicating the duration of operation of the vehicle lamp in the l' th operating position, D_maxThe power consumption required by the vehicle lamp to work at the second working gear in unit time is shown, the second working gear is the working gear with the maximum power consumption of the vehicle in all the working gears of the vehicle lamp, and T_maxRepresenting the total length of time required for the vehicle to travel the preset trip.

Optionally, as an embodiment, the wind speed influencing parameter is obtained by performing nonlinear normalization processing on wind speed influencing data, where the wind speed influencing data includes a speed component obtained by projecting a wind speed in a preset trip on a heading of the vehicle.

Optionally, as an embodiment, the wind speed influencing parameter x₁₄By the formula

Determining, wherein,

n represents the total number of sample points within the preset run, i represents the ith sample point within the preset run, i is 1,2, …, n, vector

Representing the wind direction, vector, collected at the ith sample point

Indicates the heading, V, of the vehicle collected at the ith sample point_iRepresenting the wind speed, v, collected at the ith sample point_iRepresents the driving speed of the vehicle collected at the ith sampling point, and theta and e are preset constants.

Optionally, as an embodiment, the windowing influence parameter is obtained by performing linear normalization processing on windowing influence data, where the windowing influence data includes one or more window opening/closing state combinations of the vehicle collected within a preset mileage, and the window opening/closing state combination of the vehicle includes an opening/closing state of each window in the vehicle.

Optionally, as an embodiment, the windowing affecting parameter x₂₁By the formula

Determining wherein M_WIndicates the total number of window open-close state combinations of the vehicle, l ' indicates the l ' th open-close state combination of the window, and l ' is 1,2, …, M_W，R_iRepresents the power consumption of the vehicle consumed by the combination of the first' open-close state of the vehicle window in unit time, R_maxRepresents the power consumption, T, of the vehicle consumed by the combination of the first open and closed states of the vehicle window in unit time_maxThe total time required by the vehicle for running the preset travel is represented, and the first opening and closing state combination is the opening and closing state combination with the largest energy consumption in all opening and closing state combinations of the windows in the vehicle.

In an alternative embodiment, the processing unit 820 may be a processor 920 in a computing device, the obtaining unit 810 may be a communication interface 930 in the computing device, and the computing device may further include a memory 910, as specifically shown in fig. 9.

FIG. 9 is a schematic block diagram of a computing device of another embodiment of the present application. The computing device 900 shown in fig. 9 may include: memory 910, processor 920, and communication interface 930. Wherein, the memory 910, the processor 920, and the communication interface 930 are connected via an internal connection path, the memory 910 is configured to store instructions, and the processor 920 is configured to execute the instructions stored in the memory 920 to control the input/output interface 930 to receive/transmit at least part of the parameters of the second channel model. Optionally, the memory 910 may be coupled to the processor 920 via an interface, or may be integrated with the processor 920.

It is noted that the communication interface 930 implements communication between the communication device 900 and other devices or communication networks using transceiver means, such as, but not limited to, a transceiver. The communication interface 930 may also include an input/output interface (input/output interface).

In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 920. The method disclosed in the embodiments of the present application may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 910, and the processor 920 reads the information in the memory 910, and performs the steps of the above method in combination with the hardware thereof. To avoid repetition, it is not described in detail here.

It should be understood that in the embodiments of the present application, the processor may be a Central Processing Unit (CPU), and the processor may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

It will also be appreciated that in embodiments of the present application, the memory may comprise both read-only memory and random access memory, and may provide instructions and data to the processor. A portion of the processor may also include non-volatile random access memory. For example, the processor may also store information of the device type.

It should be understood that the term "and/or" herein is merely one type of association relationship that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.

It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A scoring method for vehicle energy consumption, comprising:

acquiring a set of energy consumption influencing factors of the vehicle, the energy consumption influencing factor set including a first type of energy consumption influencing factor and/or a second type of energy consumption influencing factor, the first type of energy consumption influencing factor including a window opening influence parameter, The window-opening influence parameter is used to indicate the degree of influence on the energy consumption of the vehicle caused by the size of the wind resistance caused by the opening of the window of the vehicle within the preset trip, and the second type of energy consumption influence factor indicates the pre-set value. Set the degree of influence of the driving environment of the vehicle on the energy consumption of the vehicle within the trip;

Input the energy consumption impact factor set into the energy consumption scoring model to determine the score corresponding to one or more types of energy consumption impact factors in the energy consumption impact factor set, and the corresponding one or more types of energy consumption impact factors The score is used to indicate the degree of influence of the one or more types of energy consumption influencing factors on the vehicle energy consumption within the preset trip.

2. The method according to claim 1, wherein the second type of energy consumption influencing factor comprises one or more of an external temperature unsuitability influence parameter, a weather influence parameter, a day and night influence parameter or a wind speed influence parameter. a factor,

Wherein, the influence parameter of the unsuitability of the external temperature is used to indicate the influence degree of the ambient temperature outside the preset suitable temperature range in the preset travel on the working state of the vehicle,

The weather influence parameter is used to indicate the degree of influence of the weather in the preset trip on the working state of the vehicle,

The day and night influence parameter is used to indicate the degree of influence of the day and night environment on the working state of the vehicle within the preset itinerary,

The wind speed influence parameter is used to indicate the degree of influence of the wind speed in the preset trip on the working state of the vehicle.

3. The method according to claim 2, wherein the external temperature unsuitability influencing parameter is obtained by performing linear normalization processing on external temperature unsuitability data, and the external temperature unsuitability data includes the The external ambient temperature of the vehicle.

4. The method according to claim 3, wherein the unsuitable degree of external temperature affects the parameter x ₁₁ by formula

sure, and

Wherein, T1 represents the preset lower limit _of the suitable working temperature range of the component, _T2 represents the preset upper limit of the suitable working temperature range of the component, n represents the total number of sampling points in the preset stroke, i represents the ith sampling point within the preset itinerary, i=1,2,...,n, Δt _max is a preset value, and t _i represents the exterior of the vehicle collected at the ith sampling point ambient temperature.

5. The method according to any one of claims 2-4, wherein the weather influence parameter is obtained by performing linear normalization on weather influence data, and the weather influence data includes the preset itinerary One or more gears in which the wiper of the vehicle operates, and the working time of the wiper in each gear within the preset travel.

6. The method of claim 5, wherein the weather influence parameter x ₁₂ is determined by the formula

Determine, wherein, _MY represents the total number of working gears of the wiper of the vehicle, _l represents the first working gear of the _wiper , l=1, 2, . _The power consumption required for the wiper to work in the first working gear, T1 represents the duration of the wiper working in the first gear, and _Pmax represents the first working gear of the wiper corresponding to the unit time. power consumption, the first working gear is the working gear that consumes the largest energy consumption of the vehicle among all the working gears of the wiper, and _Tmax represents the total energy required for the vehicle to travel the preset travel. duration.

7. The method according to any one of claims 2-6, wherein the circadian influence parameter is obtained by performing linear normalization on circadian influence data, and the circadian influence data includes the preset itinerary One or more gears in which the lights of the vehicle work, and the working time of the lights in each gear within the preset range.

8. The method of claim 7, wherein the circadian influence parameter x ₁₃ is determined by the formula

Determine, wherein, MD represents the total number of working gears of the lights of the vehicle, l' represents the l'th working gear of the lights, _l '=1,2,..., _MD , _Dl _' represents the power consumption required for the vehicle lamp to work in the 1'th working gear per unit time, T _l' represents the duration of the vehicle lamp working in the l'th working gear, and _Dmax represents the unit time The power consumption required for the lamp to work in the second working gear, the second working gear is the working gear that consumes the largest energy consumption of the vehicle among all the working gears of the vehicle lamp, T _max Indicates the total time required for the vehicle to travel the preset trip.

9 . The method according to claim 2 , wherein the wind speed influence parameter is obtained by performing nonlinear normalization processing on wind speed influence data, and the wind speed influence data includes the preset The velocity component of the in-trip wind speed projected on the vehicle's heading.

10. The method of claim 9, wherein the wind speed influence parameter x ₁₄ is determined by the formula

OK, where,

n represents the total number of sampling points in the preset stroke, i represents the ith sampling point in the preset stroke, i=1,2,...,n, vector

Represents the wind direction collected at the ith sampling point, vector

represents the heading of the vehicle collected at the ith sampling point, V _i represents the wind speed collected at the _ith sampling point, vi represents the traveling speed of the vehicle collected at the ith sampling point, θ and e are preset constants.

11. The method according to any one of claims 1-10, wherein the windowing influence parameter is obtained by performing linear normalization on windowing influence data, and the windowing influence data includes the One or more window opening and closing state combinations of the vehicle collected within a preset mileage, where the vehicle window opening and closing state combinations include the opening and closing states of each window in the vehicle.

12. The method according to any one of claims 11, wherein the windowing influence parameter x ₂₁ is determined by the formula

Determine, where _MW represents the total number of window opening and closing state combinations of the vehicle, l” represents the l” th opening and closing state combination of the vehicle window, l”=1, 2, . . . , _MW , R _i represents the power consumption of the vehicle when the window of the vehicle is in the 1"th opening and closing state combination within a unit time, and _Rmax represents the first opening and closing state combination of the vehicle window in the unit time. The power consumption of the vehicle consumed, T _max represents the total time required for the vehicle to travel the preset trip, and the first combination of opening and closing states is the total number of opening and closing state combinations of the windows in the vehicle. The combination of opening and closing states that consumes the most energy.

13. A scoring device for vehicle energy consumption, comprising:

an obtaining unit, configured to obtain a set of energy consumption influencing factors of the vehicle, the energy consumption influencing factor set including the first type of energy consumption influencing factors and/or the second type of energy consumption influencing factors, the first type of energy consumption influencing factors including The window-opening influence parameter is used to indicate the influence degree of the wind resistance caused by the opening of the window of the vehicle within the preset trip on the energy consumption of the vehicle, and the second type of energy consumption influence The factor indicates the degree of influence of the driving environment where the vehicle is located in the preset trip on the energy consumption of the vehicle;

a processing unit, configured to input the energy consumption influencing factor set acquired by the acquiring unit into an energy consumption scoring model to determine the scores corresponding to one or more types of energy consumption influencing factors in the energy consumption influencing factor set, the The scores corresponding to one or more types of energy consumption influencing factors are used to indicate the degree of influence of the one or more types of energy consumption influencing factors on the vehicle energy consumption within the preset trip.

14. The device according to claim 13, wherein the second type of energy consumption influencing factor comprises one or more of an external temperature unsuitability influence parameter, a weather influence parameter, a day and night influence parameter or a wind speed influence parameter. a factor,

15 . The device according to claim 14 , wherein the external temperature unsuitability influencing parameter is obtained by performing linear normalization processing on external temperature unsuitability data, and the external temperature unsuitability data includes the The external ambient temperature of the vehicle.

16. The device according to claim 15, wherein the external temperature unsuitability influence parameter x ₁₁ is determined by the formula

sure, and

17. The apparatus according to any one of claims 14-16, wherein the weather influence parameter is obtained by performing linear normalization processing on weather influence data, and the weather influence data includes the preset itinerary One or more gears in which the wiper of the vehicle operates, and the working time of the wiper in each gear within the preset travel.

18. The apparatus of claim 17, wherein the weather-affecting parameter x ₁₂ is determined by the formula

19. The device according to any one of claims 14-18, wherein the circadian influence parameter is obtained by performing linear normalization on circadian influence data, and the circadian influence data includes the preset itinerary One or more gears in which the lights of the vehicle work, and the working time of the lights in each gear within the preset range.

20. The device of claim 19, wherein the day and night influence parameter x ₁₃ is determined by the formula

21. The device according to any one of claims 14-20, wherein the wind speed influence parameter is obtained by performing nonlinear normalization processing on wind speed influence data, and the wind speed influence data includes the preset The velocity component of the in-trip wind speed projected on the vehicle's heading.

22. The device of claim 21, wherein the wind speed influence parameter x ₁₄ is determined by the formula

OK, where,

Represents the wind direction collected at the ith sampling point, vector

23. The device according to any one of claims 13-22, wherein the windowing influence parameter is obtained by performing linear normalization on windowing influence data, and the windowing influence data includes the One or more window opening and closing state combinations of the vehicle collected within a preset mileage, where the vehicle window opening and closing state combinations include the opening and closing states of each window in the vehicle.

24. The device according to any one of claims 23, wherein the windowing influence parameter x ₂₁ is determined by the formula

25. A computing device, comprising: at least one processor and a memory, the at least one processor being coupled to the memory for reading and executing instructions in the memory to execute the claims The method of any one of 1-12.

26. A computer-readable medium, characterized in that the computer-readable medium stores program codes that, when the computer program codes are run on a computer, cause the computer to perform the method as claimed in any one of claims 1-12. method described.