RU2850935C2 - Method and device for calibrating torque of engine and vehicle controller - Google Patents

Abstract

FIELD: vehicles.

SUBSTANCE: in the method of calibrating the engine torque, data on the target engine torque and the signal torque of the hybrid vehicle generator are collected. The deviation of the hybrid vehicle torque is calculated in accordance with the target engine torque and the signal torque of the generator. A self-learning calculation is performed based on the torque deviation to obtain the required engine torque, and the engine signal torque is calibrated based on the required engine torque to match the engine signal torque, the generator signal torque, and the target engine torque of the hybrid vehicle.

EFFECT: torque accuracy.

13 cl, 20 dwg

Description

Field of technology to which the invention relates

The present application relates to the field of vehicle control and, in particular, to a method and device for calibrating engine torque and to a vehicle controller.

State of the art

In the prior art, the engine torque accuracy refers to the difference between the torque signal (abbreviated as signal torque) sent by the engine via the Controller Area Network (CAN) bus and the actual engine torque (abbreviated as actual torque), which is expressed as a percentage, and its calculation formula is: (signal torque - actual torque) / signal torque × 100%. The engine signal torque is calculated using a torque model. It has been found that, due to manufacturing variations in the manufacture of the engine and the intake and exhaust system, the engine torque accuracy is generally within a range of ±10% when applied to the entire vehicle by performing a wheel edge force test on a chassis dynamometer.

In the development of hybrid vehicle technology, the entire vehicle controller requires increasingly higher engine torque accuracy, and the engine torque accuracy must be within a range of ±3%. In hybrid vehicles with the P13 configuration and the PEEB (Range-Extended) configuration, the engine is mechanically connected to the generator, and the generator torque is relatively accurate, with a torque accuracy of within ±3%. Prior art calibration methods cannot solve the problem of torque deviation caused by manufacturing variations or environmental changes, and require manual calibration, which is not well developed.

In relation to the problems described above, effective and accurate solutions have not yet been found.

Disclosure of the essence of the invention

The present application provides a method and apparatus for calibrating the torque of an engine and a vehicle controller to solve the technical problems existing in the prior art.

According to an embodiment of the present application, a method for calibrating engine torque is provided, comprising the steps of: collecting data of a target engine torque and a signal torque of a generator of a hybrid vehicle; calculating a torque deviation value of the hybrid vehicle in accordance with the target engine torque and the signal torque of the generator; performing a self-learning calculation based on the torque deviation to obtain a desired engine torque of the hybrid vehicle, and calibrating the signal torque of the engine based on the desired engine torque to ensure consistency between the signal torque of the engine, the signal torque of the generator and the target engine torque of the hybrid vehicle.

Additionally, calculating the hybrid vehicle torque deviation in accordance with the target engine torque and the signal torque of the generator comprises the steps of: subtracting the signal torque of the generator from the stored signal torque of the engine of the immediately preceding control cycle to obtain the calculated deviation of the target engine torque; subtracting the signal torque of the generator from the target engine torque to obtain the calculated value of the engine control torque deviation; where the torque deviations comprise the target engine torque deviation, the deviation of the actual engine torque and the deviation of the engine control torque.

Additionally, performing a self-learning calculation based on the torque deviation for obtaining the desired torque of the hybrid vehicle engine comprises the steps of: determining the location of the self-learning area according to the current parameter of the hybrid vehicle engine; deciding whether to permit recording the self-learning torque value in the self-learning area, based on the deviation of the target engine torque and the deviation of the actual engine torque; when permitting recording the self-learning torque value in the self-learning area, performing a self-learning torque calculation within the self-learning area based on the deviation of the engine control torque, and storing the self-learning torque value in a corresponding memory device; reading the self-learning torque value from the memory device and adding the self-learning torque to the target engine torque to obtain the desired torque of the engine.

Additionally, determining the location of the self-learning area according to the current parameter of the hybrid vehicle engine comprises the steps of: setting parameter thresholds for a plurality of engine operating condition parameters, where the parameter thresholds comprise X engine speed thresholds, Y engine load thresholds and Z engine temperature thresholds, and X, Y and Z are all positive integers greater than 0; dividing and obtaining a plurality of engine memories using the plurality of parameter thresholds, where each memory device is configured to store a value during self-learning; obtaining the current parameters of the hybrid vehicle engine; obtaining parameter indices in accordance with the results of comparison between the current engine parameters and the corresponding parameter thresholds, where the parameter indices comprise an engine speed index, an engine load index and an engine temperature index, and the parameter index is used to represent the threshold interval in which the current parameter is located; determining the location of the memory device ordinal number in the self-learning area using the parameter indices.

Additionally, dividing and obtaining a plurality of engine memories using a plurality of parameter thresholds comprises a step of: dividing the engine operating region into (X+1) × (Y+1) regions and dividing and obtaining a plurality of engine memories by (X+1) × (Y+1) × Z.

Additionally, determining the location of the serial numbers of the memory devices of the self-learning area using the parameter indices comprises a step in which: the serial number i of the memory device is calculated using the following formula: i = engine speed index × Z + engine load index × (X + 1) × Z + engine temperature index.

Additionally, deciding whether to allow recording the self-learning torque value in the self-learning area based on the target engine torque deviation and the actual engine torque deviation comprises the steps of: determining stability flags of the hybrid vehicle, where the stability flags comprise a target engine torque stability flag, an engine speed stability flag and a generator torque stability flag; deciding whether all the stability flags are in the set state, and deciding whether the real-time operating mode of the hybrid vehicle satisfies all of the following specified conditions: the engine coolant temperature is greater than a specified value; the engine finishes starting and continues to operate for a certain time; the engine is in the torque control mode; the absolute value of the target torque deviation is less than or equal to the first threshold, the absolute value of the actual engine torque deviation is greater than or equal to the second threshold; the target engine torque is greater than or equal to the third threshold; If the stability flags are all set and the real-time operating state of the hybrid vehicle satisfies all the specified conditions, it is determined that self-learning recording is permitted.

Additionally, performing the self-learning torque calculation within the self-learning area based on the engine control torque deviation and storing the self-learning torque value in the corresponding memory device comprises the steps of: deciding whether the engine control torque deviation is greater than or equal to the deviation threshold, and comparing the self-learning torque value in the self-learning area with the threshold; if the engine control torque deviation is greater than or equal to the first deviation threshold, and the self-learning torque value is less than the upper limit value, determining the self-learning state as the first self-learning state; if the engine control torque deviation is less than or equal to the second deviation threshold, and the self-learning torque value is greater than the lower limit value, determining the self-learning state as the second self-learning state; If the deviation of the motor control torque is less than the first deviation threshold, or the deviation of the motor control torque is greater than the second deviation threshold, or the self-learning torque value is equal to the upper limit value or the lower limit value, determining the self-learning state as the third self-learning state; if the first self-learning state occurs, increasing the self-learning torque value at a given speed; if the second self-learning state occurs, decreasing the self-learning torque value at a given speed; if the third self-learning state occurs, storing the current self-learning torque in the self-learning area; updating in real time the corresponding self-learning torque value in the memory device of the self-learning area.

Additionally, the calibration of the signal torque based on the required engine torque comprises the steps of: ensuring that the engine of the hybrid vehicle operates in accordance with the required engine torque, detecting the actual engine torque and determining the actual torque as the estimated engine torque; subtracting the self-learning torque value from the estimated engine torque to obtain the signal engine torque.

According to another embodiment of the present application, an engine torque calibration device is presented, comprising: a data acquisition module configured to acquire data of a target engine torque and a signal torque of a generator of a hybrid vehicle; a calculation module configured to calculate a torque deviation of the hybrid vehicle in accordance with the target engine torque and the signal torque of the generator; a calibration module configured to calculate in self-learning based on the torque deviation to obtain a desired engine torque of the hybrid vehicle and to calibrate the signal torque of the engine based on the desired engine torque in order to make the signal torque of the engine, the signal torque of the generator and the target engine torque of the hybrid vehicle consistent.

Additionally, the computing module comprises: a first computing device configured to subtract the stored signal torque of the engine of the immediately preceding control cycle from the target torque of the engine to obtain the deviation of the target torque of the engine; a second computing device configured to subtract the signal torque of the generator from the stored signal torque of the engine of the immediately preceding control cycle to obtain the deviation of the actual torque of the engine; a third computing device configured to subtract the signal torque of the generator from the target torque of the engine to obtain the calculated deviation of the engine control torque; wherein the torque deviations comprise the deviation of the target torque of the engine, the deviation of the actual torque of the engine, and the deviation of the engine control torque.

Additionally, the calibration module comprises: a location determination unit configured to determine the location of a self-learning area in accordance with a current parameter of the hybrid vehicle engine; a decision-making device configured to decide whether to permit recording of a self-learning torque value in the self-learning area based on the deviation of the target engine torque and the deviation of the actual engine torque; an execution unit configured to calculate the self-learning torque within the self-learning area based on the deviation of the engine control torque and to store the self-learning torque value in a corresponding memory device if recording of the self-learning torque value in the self-learning area is permitted; a first calibration unit configured to read the self-learning torque value from the memory device and to add the self-learning torque value to the target engine torque to obtain the required engine torque.

Additionally, the location determination unit comprises: a setting subunit configured to set parameter thresholds for a plurality of engine operating mode parameters, where the parameter thresholds comprise X engine speed thresholds, Y engine load thresholds and Z engine temperature thresholds, where X, Y and Z are all positive integers greater than 0; a dividing subunit configured to divide and obtain a plurality of engine memories using a plurality of parameter thresholds, wherein each memory device is configured to store a self-learning value; a data acquisition subunit configured to obtain current engine parameters of the hybrid vehicle; a comparison subunit configured to compare the current engine parameters with the corresponding parameter thresholds to obtain parameter indices, wherein the parameter indices comprise an engine speed index, an engine load index and an engine temperature index, and the parameter index is used to represent a threshold interval in which the current engine parameter is located; a location determination subunit configured to determine the location of the serial number of the self-learning area memory device using the parameter indices.

Additionally, the dividing subunit is also configured to divide the engine working area into (X+1) × (Y+1) areas and divide and obtain a plurality of engine memories into (X+1) × (Y+1) × Z.

Additionally, the location determination subunit is also configured to calculate the serial number i of the memory device using the following formula: i = engine speed index × Z + engine load index × (X+1) × Z + engine temperature index.

Additionally, the decision-making unit comprises: a first determining subunit configured to determine the stability flags of the hybrid vehicle, wherein the stability flags comprise a target engine torque stability flag, an engine speed stability flag, and a generator torque stability flag; a decision-making subunit configured to decide whether all the stability flags are in the set state and whether the operating mode of the hybrid vehicle in real time satisfies all the following specified conditions: the engine coolant temperature is greater than the set value; the engine has finished starting and is running for a certain period of time; the engine is in the torque control mode; the absolute value of the deviation of the target engine torque is less than or equal to the first threshold; the absolute value of the deviation of the actual engine torque is greater than or equal to the second threshold; the target engine torque is greater than or equal to the third threshold; a second determining subunit configured to determine that the self-learning recording is permitted if all the stability flags are in the set state and the operating mode of the hybrid vehicle in real time satisfies all the specified conditions.

Additionally, the execution unit comprises a processing subunit configured to decide whether the engine control torque deviation is greater than or equal to a deviation threshold and to compare the self-learning torque value in the self-learning area with the threshold; a determining subunit configured to determine the self-learning state as the first self-learning state, if the engine control torque deviation is greater than or equal to the first deviation threshold and the self-learning torque value is less than an upper limit value; determining the self-learning state as the second self-learning state, if the engine control torque deviation is less than or equal to the second deviation threshold and the self-learning torque value is greater than a lower limit value; determining the self-learning state as the third self-learning state, if the engine control torque deviation is less than the first deviation threshold, or the engine control torque deviation is greater than the second deviation threshold, or the self-learning torque value is equal to the upper limit value or the lower limit value; a learning subunit configured to increase the value of the self-learning torque in accordance with the specified speed if the first self-learning state occurs; to decrease the value of the self-learning torque in accordance with the specified speed if the second self-learning state occurs; to store the current value of the self-learning torque in the self-learning area if the third self-learning state occurs; and an updating subunit configured to update in real time the corresponding value of the self-learning torque in the memory device of the self-learning area.

Additionally, the calibration module comprises an operating unit configured to control the hybrid vehicle engine in accordance with the required engine torque, detect the actual engine torque, and determine the actual torque as the estimated engine torque; a second calibration unit configured to subtract the self-learning torque value from the estimated engine torque to obtain the signal engine torque.

In accordance with another approach of embodiments of the present application, a vehicle controller is also provided, comprising: a generator control unit configured to transmit a signal torque of the generator to an engine control unit; an engine control unit configured to receive a signal torque of the generator transmitted by the generator control unit, an example of a target torque of the engine transmitted by the control unit of the entire vehicle, and to perform the method according to the above-mentioned embodiments; wherein the control unit of the entire vehicle is configured to receive a signal torque of the engine transmitted by the engine control unit.

According to another approach of embodiments of the present application, a storage medium is provided that contains a program stored thereon, and the above-mentioned steps are performed when the program is executed.

According to another embodiment of the present application, an electronic device is provided comprising a processor, a communication interface, memory, and a communication bus, wherein the processor, communication interface, and memory communicate with each other via the communication bus. The memory is configured to store a computer program, and the processor is configured to execute the steps of the aforementioned method when executing the program stored in the memory.

An embodiment of the present application also provides a computer program product containing instructions. The computer program product contains a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method are performed.

This application has the following useful results.

1. In this application, by adopting the generator signal torque as the calibration reference point for self-learning the engine torque, the required engine torque is automatically adjusted and the engine signal torque is calibrated, ensuring that the target engine torque, engine signal torque, and generator signal torque are close to each other. This solves problems such as poor oil consumption performance and poor drive performance in the operating state at extremely low temperatures due to the relatively large actual engine torque, and the problem of unbalanced electric power during emission circuit testing due to the relatively small engine torque.

2. By improving the torque accuracy, the oil consumption characteristic of the vehicle is improved, the low temperature performance of the drive of the vehicle is improved, and it is easier to balance the supply and demand of electric power.

Brief description of the drawings

The accompanying drawings described herein are provided to provide a further understanding of the present application and are a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute unduly limiting limitations of the present application. In the accompanying drawings:

Fig. 1 is a block diagram of the structure of the technical means of the engine control module, corresponding to an embodiment of the present application;

Fig. 2 is a flow chart of the sequence of steps of the method for calibrating engine torque in accordance with an embodiment of the present application;

Fig. 3 is a logic diagram for calculating the target engine torque deviation in an embodiment of the present application;

Fig. 4 is a logical diagram for calculating the deviation of the actual engine torque in the present application;

Fig. 5 is a logic diagram for calculating the deviation of the engine control torque in the present application;

Fig. 6 is a logical diagram for calculating the engine speed index in the present application;

Fig. 7 is a logical diagram for calculating the engine load index in the present application;

Fig. 8 is a logical diagram for calculating the engine intake air temperature index in the present application;

Fig. 9 is a schematic diagram of the working area of the engine corresponding to the serial number of the memory device in the present application;

Fig. 10 is a logic diagram for calculating the engine speed stability flag in the present application;

Fig. 11 is a logic diagram for calculating the engine target torque stability flag in the present application;

Fig. 12 is a logic diagram for calculating the generator torque stability flag in the present application;

Fig. 13 is a logical diagram of the self-learning state calculation in the present application;

Fig. 14 is a logic diagram for calculating the self-learning value when the self-learning state is 1 in the present application;

Fig. 15 is a logic diagram for calculating the self-learning value when the self-learning state is 2 in the present application;

Fig. 16 is a logic diagram for calculating the self-learning value reading flag in the present application;

Fig. 17 is a logical diagram for calculating the reading value during self-training in the present application;

Fig. 18 is a flow chart of the sequence of operations of an embodiment of the present application;

Fig. 19 is a structural block diagram of an engine torque calibration device in accordance with an embodiment of the present application;

Fig. 20 is a diagram of a vehicle controller in an embodiment of the present application.

Implementation of the invention

In order to make the solutions of the present application more understandable to those skilled in the art, the technical solutions of the embodiments of the present application will be described clearly and completely in conjunction with the accompanying drawings in the embodiments of the present application. It is obvious that the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts should fall within the scope of protection of the present application. It should be noted that, unless there is a contradiction, the embodiments of the present application and the features in the embodiments can be combined with each other.

It should be noted that terms such as "first" and "second" in the description and claims of the present application, as well as those appearing in the aforementioned accompanying drawings, are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence thereof. It should be understood that terms used in this manner may be interchangeable under appropriate circumstances, so that the embodiments of the present application described herein may be implemented in a different order than those shown or described herein. Furthermore, the terms "including" and "having" and any variations thereof are intended to be non-exclusive inclusive. For example, processes, methods, products, or devices that comprise a sequence of steps or blocks should not be limited to those steps or blocks explicitly listed, but may also comprise other steps or blocks that are not explicitly listed or inherent in these processes, methods, products, or devices.

Option 1 of implementation

The embodiment of the method presented in embodiment 1 of the present application can be implemented in a vehicle terminal, a vehicle control module, an engine control module (engine control unit), a hybrid vehicle or similar processing means. Taking the implementation of the engine control module as an example, Fig. 1 shows a block diagram of the structure of the hardware of the engine control module corresponding to an embodiment of the present application. As shown in Fig. 1, the engine control module can comprise one or more (only one is shown in Fig. 1) processors 102 (the processor 102 may comprise, but is not limited to, a processing device such as a microprocessor (Microcontroller Unit, abbreviated as MCU) or a programmable logic integrated circuit (Field-Programmable Gate Array, FPGA)) and a memory 104 for storing data. Alternatively, the engine control module can also comprise a transmitting device 106 for performing communication functions and an input/output device 108. Those skilled in the art will appreciate that the structure shown in Fig. 1 is merely schematic and does not limit the structure of the engine control module illustrated above. For example, the engine control module may also contain more or fewer components than shown in Fig. 1, or may have a different configuration relative to that shown in Fig. 1.

Memory 104 may be configured to store a program of the engine control module, for example, a program and software modules of the application, such as a program of the engine control module corresponding to the method for calibrating the engine torque in an embodiment of the present application. Processor 102 executes various functional applications and data processing by executing the program of the engine control module stored in memory 104, that is, implementing the method described above. Memory 104 may comprise high-speed random access memory and may also comprise long-term memory, such as one or more magnetic storage devices, flash memory, or other long-term solid-state memory devices. In some examples, memory 104 may further comprise memory devices remotely located relative to processor 102, and these remote memory devices may be connected to the engine control module via a network. Examples of the aforementioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

The transmitting device 106 is configured to receive or transmit data via a network. Specific examples of the aforementioned network may include a wireless network provided by the engine control module's communications provider. In one example, the transmitting device 106 comprises a network adapter (Network Interface Controller, abbreviated NIC) that can connect to other network devices via a base station to enable internet communication. In one example, the transmitting device 106 may be a radio frequency (abbreviated RF) module configured to enable wireless internet communication.

This embodiment provides a method for calibrating engine torque. Fig. 2 is a flow chart of the method for calibrating engine torque according to an embodiment of the present application. As shown in Fig. 2, this process comprises the following steps.

Step S202: Acquiring target engine torque and signal generator torque data of the hybrid vehicle.

The hybrid vehicle in this embodiment is a vehicle equipped with two power units, namely, an engine and a generator, and may be a plug-in hybrid electric vehicle (abbreviated PHEV), a mid-range hybrid electric vehicle (abbreviated MNEV), a full hybrid electric vehicle (abbreviated FHEV), and other types of hybrid vehicles.

Fig. 20 shows a signal transmission diagram in an embodiment of the present application. The engine control unit receives the target engine torque sent by the complete vehicle control unit and the signal generator torque sent by the generator control unit. After the engine control unit generates a calibrated signal engine torque, the signal engine torque is sent to the complete vehicle control unit.

Step S204: calculating the torque deviation of the hybrid vehicle in accordance with the target engine torque and the signal torque of the generator.

The torque deviation in this embodiment may be a target engine torque deviation, an actual engine torque deviation, or an engine control torque deviation.

Step S206: performing a self-learning calculation based on the torque deviation to obtain a target engine torque of the hybrid vehicle, and calibrating the signal engine torque based on the target engine torque to make the signal engine torque, the signal generator torque, and the target engine torque of the hybrid vehicle match.

In this embodiment, the fact that the engine signal torque, the generator signal torque, and the target engine torque of the hybrid vehicle are consistent means that the values of these three parameters are close (the difference between them is less than the minimum threshold), and these three values tend to be consistent through calibration.

In the above steps, the target engine torque and signal torque of the generator of the hybrid vehicle are collected; the torque deviation of the hybrid vehicle is calculated according to the target engine torque and the signal torque of the generator; a self-learning calculation is performed based on the torque deviation to obtain the required engine torque of the hybrid vehicle; and the signal torque of the engine is calibrated based on the required engine torque to make the signal torque of the engine, the signal torque of the generator, and the target engine torque of the hybrid vehicle consistent. The technical problem existing in the prior art, namely, that the torque accuracy is low due to the production variation in the manufacture of the engine or due to the change in the environment of the hybrid vehicle, is solved, and the user experience is improved. By using the generator signal torque as the calibration reference point for engine torque self-learning, engine torque deviation learning can be automatically performed after the self-calibration operating mode permission condition is met. The required engine torque is automatically adjusted and the engine signal torque is calibrated, realizing that the target engine torque, engine signal torque, and generator signal torque values are consistent. The problems of poor oil consumption characteristics and poor drive performance in the extreme low-temperature operating mode due to the relatively large actual engine torque are solved, as well as the problem of poor power consumption during emission circuit testing due to the relatively small actual engine torque. By improving torque accuracy, the vehicle's oil consumption characteristics are improved, vehicle handling performance at low temperatures is improved, and the power demand is more easily met.

In implementing this embodiment, calculating the torque deviation of the hybrid vehicle in accordance with the target torque and the signal torque of the signal comprises the steps of: subtracting the stored signal torque of the engine immediately preceding the control cycle from the target torque of the engine to obtain the deviation of the target torque of the engine; subtracting the signal torque of the generator from the stored signal torque of the engine immediately preceding the control cycle to obtain the deviation of the actual torque of the engine; subtracting the signal torque of the generator from the target torque of the engine to obtain the calculated deviation of the engine control torque; where the torque deviations comprise the deviation of the target torque of the engine, the deviation of the actual torque of the engine and the deviation of the engine control torque.

When calculating torque deviation, this embodiment involves calculating the following three torque deviations: target engine torque deviation, actual engine torque deviation, and engine control torque deviation. A detailed explanation is provided below.

Fig. 3 shows a logic flow chart for calculating the target engine torque deviation in an embodiment of the present application. The signal engine torque (of the immediately preceding torque control cycle), obtained after low-pass filtering (calibrated filtering time T2), is subtracted from the target engine torque obtained after low-pass filtering (calibrated filtering time T1) to obtain the calculated target engine torque deviation, called deviation A.

Fig. 4 shows the logic diagram for calculating the deviation of the actual engine torque in the presented application. The logic for calculating the deviation of the actual engine torque is shown in Fig. 4. The signal torque of the generator, obtained after low-pass filtering (filtering time T4, which can be calibrated), is subtracted from the signal torque of the engine (or the immediately preceding torque control cycle), obtained after low-pass filtering (filtering time T3, which can be calibrated), to obtain the calculated deviation of the actual engine torque, called deviation B.

Fig. 5 in the present application shows a logic circuit for calculating the deviation of the engine control torque. The logic for calculating the deviation of the engine control torque is shown in Fig. 5. The signal torque of the generator, obtained after low-pass filtering (filtering time T6, which can be calibrated), is subtracted from the target torque of the engine, obtained after low-pass filtering (filtering time T5, which can be calibrated), to obtain the calculated deviation of the engine control torque, called deviation C.

In implementing this embodiment, performing a self-learning calculation based on a torque deviation in order to obtain a desired engine torque of a hybrid vehicle comprises the steps of: determining a location of a self-learning area according to a current engine parameter of the hybrid vehicle; deciding whether it is permitted to write a self-learning torque value into the self-learning area based on the deviation of the target engine torque and the deviation of the actual engine torque; if writing a self-learning torque value into the self-learning area is permitted, performing a self-learning torque calculation within the self-learning area based on the deviation of the engine control torque, and storing the self-learning torque value in a corresponding memory device; reading the self-learning torque value from the memory device and adding the self-learning torque value to the target engine torque in order to obtain the desired engine torque.

In an example based on the above-mentioned implementation, determining the location of a self-learning area corresponding to a current parameter of the hybrid vehicle engine comprises the steps of: establishing parameter thresholds for a plurality of engine operating mode parameters, where the parameter thresholds comprise X engine speed thresholds, Y engine load thresholds and Z engine temperature thresholds, wherein X, Y and Z are all positive integers greater than 0; dividing and obtaining a plurality of engine memories using the plurality of parameter thresholds, where each memory device is configured to store a self-learning value; collecting the current engine parameters of the hybrid vehicle; obtaining parameter indices in accordance with a comparison result between the current engine parameters and the corresponding parameter thresholds, where the parameter indices comprise an engine speed index, an engine load index and an engine temperature index, and the parameter index is used to represent a threshold interval in which the current engine parameter is located; determining the location of the serial number of the self-learning area memory device using the parameter indices.

Alternatively, dividing and obtaining a plurality of engine memories using a plurality of parameter thresholds comprises the steps of: dividing the engine operating region into (X+1) × (Y+1) regions; dividing and obtaining a plurality of engine memories into (X+1) × (Y+1) × Z regions.

For example, X, Y, and Z are equal to 4, 3, and 3, respectively. By setting 4 engine speed thresholds, 3 engine load thresholds, and 3 temperature thresholds, the engine operating area is divided into 20 regions with a total of 60 memory devices.

Alternatively, determining the location of the serial number of the self-learning area storage device using the parameter indices comprises the steps of: calculating the serial number i of the storage device using the following formula: i = engine speed index × Z + engine load index × (X+1) × Z + engine temperature index.

Fig. 6 in the present application shows a logic circuit for calculating the engine speed index. Four thresholds N1, N2, N3 and N4 of the engine speed and a hysteresis Nhy of the speed threshold are set. If the engine speed is greater than the threshold N4, the engine speed index is 4. If the engine speed is less than or equal to (N4-Nhy) and the engine speed is greater than N3, the engine speed index is 3. If the engine speed is less than or equal to (N3-Nhy) and the engine speed is greater than N2, the engine speed index is 2. If the engine speed is less than or equal to (N2-Nhy) and the engine speed is greater than N1, the engine speed index is 1. If the engine speed is less than or equal to (N1-Nhy), the engine speed index is 0.

Fig. 7 in the present application shows a logic circuit for calculating the engine load index. As shown in Fig. 7, three engine load thresholds R1, R2 and R3 are set, and there is a hysteresis Rhy of the load threshold. If the engine load is greater than the threshold R3, the engine load index is 3. If the engine load is less than or equal to (R3 - Rhy) and the engine load is greater than R2, the engine load index is 2. If the engine load is less than or equal to (R2 - Rhy) and the engine load is greater than R1, the engine load index is 1. If the engine load is less than or equal to (R1 - Rhy), the engine load index is 0.

Fig. 8 in this application shows the logic diagram for calculating the engine supply air temperature index. As shown in Fig. 8, two thresholds D1 and D2 of the engine supply air temperature are set, as well as a hysteresis Dhy of the temperature threshold. If the engine supply air temperature is greater than the threshold D2, the supply air temperature index is 2. If the engine supply air temperature is less than or equal to (D2 - Dhy) and the supply air temperature is greater than D1, the supply air temperature index is 1. If the supply air temperature is less than or equal to (D1 - Dhy), the supply air temperature index is 0.

The serial number i of the memory device is determined in accordance with the engine speed index, engine load index, and engine temperature index. The formula for calculating the serial number of the memory device is: i = engine speed index × 3 + engine load index × 15 + engine temperature index.

At a given engine operating mode (engine speed, engine load, intake air temperature), the corresponding memory device can be found according to the memory device's serial number, and each memory device has a self-learning value stored therein. Fig. 9 in this application schematically illustrates the engine operating region corresponding to the memory device's serial number. A schematic diagram of the division of engine operating modes corresponding to different memory device serial numbers is shown in Fig. 9.

In an example based on the implementation described above, deciding whether to allow recording a self-learning torque value in a self-learning area comprises the steps of: determining stability flags of a hybrid vehicle, where the stability flags comprise a target engine torque stability flag, an engine speed stability flag, and a generator torque stability flag; determining whether all the stability flags are in the set state, and deciding whether the real-time operating mode of the hybrid vehicle satisfies all of the following specified conditions: the engine coolant temperature is above the set value; the engine finishes starting and runs for a certain time; the engine is in the torque control mode; the absolute value of the target engine torque deviation is less than or equal to the first threshold; the absolute value of the real engine torque deviation is greater than or equal to the second threshold; the target engine torque is greater than or equal to the third threshold; if all the stability flags are in the set state and the real-time operating mode of the hybrid vehicle satisfies all of the specified conditions, deciding that self-learning recording is permitted.

In this example, the hybrid vehicle stability flags include an engine speed stability flag, an engine target torque stability flag, and a generator torque stability flag. A detailed explanation is provided below.

Fig. 10 in the present application shows a logic circuit for calculating the engine speed stability flag. The engine speed stability flag is determined by the engine speed variation and the corresponding threshold. The engine speed before A calculation periods is subtracted from the engine speed (i.e., their current engine speed) to obtain the engine speed variation. The engine speed variation is first processed by a low-pass filter (the filter constant is equal to m1), and then the absolute value is processed to obtain the processed engine speed variation. If the processed engine speed variation is less than or equal to the threshold Nd, the engine speed stability flag is set. If the processed engine speed variation is greater than the threshold Nd, the engine speed stability flag is reset to its initial state.

Fig. 11 in the present application shows a logic circuit for calculating the engine target torque stability flag. The engine target torque stability flag is determined by the engine target torque variation and a corresponding threshold. The engine target torque before B calculation periods is subtracted from the engine target torque to obtain the engine target torque variation. The engine target torque variation is first processed by a low-pass filter (the filter constant is equal to m2), and then the absolute value is processed to obtain the processed engine target torque variation. If the processed engine target torque variation is less than or equal to the threshold Tqd1, the engine target torque stability flag is set. If the processed engine target torque variation is greater than the threshold Tqd1, the engine target torque stability flag is reset to its initial state.

Fig. 12 in the present application shows the logic circuit for calculating the generator torque stability flag. The generator torque stability flag is determined by the generator torque variation and the corresponding threshold. The generator torque before C calculation periods is subtracted from the generator torque to obtain the generator torque variation. The generator torque variation is first processed by a low-pass filter (the filter constant is equal to m3), and then the absolute value is processed to obtain the processed generator torque variation. If the processed generator torque variation is less than or equal to the threshold Tqd2, the generator torque stability flag is set. If the processed generator torque variation is greater than the threshold Tqd2, the generator torque stability flag is reset to its initial state.

When all nine conditions below are met, the self-training write enable flag is set. When not all nine conditions below are met, the self-training write enable flag is reset. All conditions include:

Condition 1: The engine target torque stability flag is set;

Condition 2: The engine speed stability flag is set;

Condition 3: The generator torque stability flag is set;

condition 4: engine coolant temperature is greater than threshold D3;

Condition 5: The engine has finished starting and has been running for a certain period of time T7;

Condition 6: The engine is in torque control mode;

condition 7: the absolute value of the torque deviation A is less than or equal to the threshold Tq1;

condition 8: the absolute value of the torque deviation B is greater than or equal to the threshold Tq2;

Condition 9: The target motor torque is greater than or equal to the threshold Tq3.

In the example based on the above implementation, performing the learning torque calculation within the learning region and storing the learning value in the corresponding memory device comprises the steps of: deciding whether the engine control torque deviation is greater than or equal to the deviation threshold, and comparing the learning torque value in the learning region with the threshold; if the engine control torque deviation is greater than or equal to the first deviation threshold and the learning torque value is less than the upper limit value, determining the learning state as the first learning state; if the engine control torque deviation is less than or equal to the second deviation threshold and the learning torque value is greater than the lower limit threshold, determining the learning state as the second learning state; If the deviation of the engine control torque is less than the first deviation threshold, or if the deviation of the engine control torque is greater than the second deviation threshold, or if the self-learning torque value is equal to the upper limit value or the lower limit value, determining the self-learning state as the third self-learning state; if the state is the first self-learning state, increasing the self-learning torque value at a given speed; if the state is the second self-learning state, decreasing the torque value at a given speed; if the state is the third self-learning state, storing the current torque value of the self-learning area; updating the corresponding self-learning torque value in real time in the memory device of the self-learning area.

Fig. 13 shows the logic diagram of the self-learning state calculation in the present application, which is used to explain the process of making a decision on the self-learning state. If the self-learning write enable flag is in the set state, the torque deviation C is greater than or equal to the torque threshold Tq4, and the self-learning value is less than the upper limit TqUp, then the self-learning state is 1 (the first self-learning state). If the self-learning write enable flag is in the set state, the torque deviation C is less than or equal to the threshold Tq5, and the self-learning value is greater than the lower limit TqDw, then the self-learning state is 2 (the second self-learning state). In all other cases, the self-learning state is 0 (the third self-learning state).

Fig. 14 in the present application shows a logic circuit for calculating the self-learning value when the self-learning state is 1, and Fig. 15 in the present application shows a logic circuit for calculating the self-learning value when the self-learning state is 2, which are respectively used to explain the processes of calculating the self-learning torque in different states. The initial self-learning value is 0; if the self-learning state is 1, as shown in Fig. 14, the self-learning value increases by itself at the rate of increasing UpRate; if the self-learning state is 2, as shown in Fig. 15, the self-learning value decreases by itself at the rate of decreasing DwRate; if the self-learning state is 0, the self-learning value remains unchanged as in the previous case; the self-learning value is updated in the memory in real time and is saved and will not be erased when the power is turned on and off and in other situations.

Alternatively, after the torque value is obtained by the self-learning calculation, a self-learning value reading step may be performed, comprising deciding whether to allow the self-learning value reading and calculating the self-learning reading value.

Fig. 16 in the present application shows a logic diagram for calculating the self-learning value reading flag, which is used to explain the process of making a decision on reading during self-learning. In the case where the engine starting completion flag is in the set state and the delay time T8 has elapsed, if the steady state selection switch SW1 is set to 0, then the self-learning reading flag is set. In the case where the engine starting completion flag is in the set state and the delay time T8 has elapsed, if the steady state selection switch SW1 is set to 1 and the engine torque stability flag is in the set state, then the self-learning reading flag is set. In other cases, the self-learning reading flag is reset to the initial state.

Fig. 17 in the present application shows a logic diagram for calculating the self-learning read value, which is used to explain the process of the self-learning read value. If the self-learning read flag is in the reset state, the self-learning read value is 0. If the self-learning read flag is 1 (e.g., the set state), the self-learning read value is obtained by the self-learning read value from each memory device subjected to low-pass filtering (filtering time T9).

In this embodiment, calibrating the signal torque of the engine based on the required engine torque comprises the steps of: operating the engine of the hybrid vehicle in accordance with the required engine torque, detecting the actual engine torque and determining the actual torque as the estimated engine torque; subtracting the self-learning torque from the estimated engine torque to obtain the signal torque of the engine.

Fig. 18 shows a flow chart of an embodiment of the present application, which represents a method for self-learning engine torque. The engine controller collects target engine torque and signal generator torque data via CAN bus communication, performs torque self-learning according to torque deviation, and adjusts the required engine torque to match the signal engine torque, signal generator torque, and target engine torque. The process comprises the following steps.

S1: Calculation of torque deviation.

S2: Recording data during self-training.

Alternatively, stage S2 contains three sub-stages: S21, which divides the self-learning area; S22, which enables recording of self-learning data; S23, which calculates the self-learning torque.

S3: read self-training data.

Alternatively, step S3 contains two substeps: S31, which enables self-training reading; S32, which calculates the self-training reading value.

S4: Calculate the required torque.

The calculation logic for calculating the required motor torque in step S4 is that the required motor torque is equal to the target motor torque plus the self-learning reading value.

S5: Calculate the signal torque of the engine.

The calculation logic for calculating the signal torque of the motor in step S5 is that the signal torque of the motor is equal to the calculated torque of the motor minus the self-learning torque value.

In this embodiment, by using the generator signal torque as the calibration reference for engine torque self-learning, torque deviation learning can be automatically performed after the self-learning operation permission condition is met, and the required engine torque can be automatically adjusted, realizing that the target engine torque, engine signal torque, and generator signal torque are close, improving the engine torque accuracy. By improving torque accuracy, oil consumption characteristics can be improved, low-temperature drive performance can be improved, and power demand can be easily met.

Based on the description of the above-mentioned implementations, those skilled in the art can easily understand that the method corresponding to the above-mentioned embodiments can be implemented by means of software and the necessary general platform of technical means, and, of course, it can also be implemented by technical means, but in many cases the first method is better. Based on this understanding, the technical solution of the present application, in essence, or a part thereof, which contributes to the prior art, can be implemented in the form of a software product. The computer program product is stored on a storage medium (such as ROM/RAM, a magnetic disk, an optical disk) and contains several instructions that cause a terminal device (which can be a mobile phone, a computer, a server or a network device, etc.) to execute the methods described in various embodiments of the present application.

Option 2 of implementation

This embodiment also provides a motor torque calibration device capable of implementing the embodiment and preferred implementations presented above, and the description presented here is not repeated. As used below, the term "module" may be a combination of software and/or hardware that implements a given function. Although the device described in the following embodiment is preferably implemented by software, implementation by hardware or a combination of software and hardware is also possible and contemplated.

Fig. 19 shows a structural block diagram of an engine torque calibration device according to an embodiment of the present application. As shown in Fig. 19, the device comprises:

a data acquisition module 190 configured to acquire target engine torque data and signal generator torque data of the hybrid vehicle;

a calculation module 192 configured to calculate the torque deviation of the hybrid vehicle in accordance with the target torque of the engine and the signal torque of the generator;

A calibration module 194 configured to perform a self-learning calculation based on the torque deviation to obtain a desired engine torque of the hybrid vehicle, and to calibrate the signal torque of the engine based on the desired engine torque to make the signal torque of the engine, the signal torque of the generator and the target engine torque of the hybrid vehicle consistent.

Alternatively, the computing module comprises: a first computing device configured to subtract the stored signal torque of the engine immediately preceding the control cycle from the target torque of the engine in order to obtain the deviation of the target torque of the engine; a second computing device configured to subtract the signal torque of the generator from the stored signal torque of the engine signal immediately preceding the control cycle in order to obtain the deviation of the actual torque of the engine; a third computing device configured to subtract the signal torque of the generator from the target torque of the engine in order to obtain the calculated deviation of the engine control torque; where the torque deviations comprise the deviation of the target torque of the engine, the deviation of the actual torque of the engine and the deviation of the engine control torque.

Alternatively, the calibration module comprises: a location determination unit configured to determine the location of a self-learning area in accordance with a current parameter of the hybrid vehicle engine; a decision-making unit configured to decide whether to permit recording of a self-learning torque value in the self-learning area based on the deviation of the target engine torque and the deviation of the actual engine torque; an execution unit configured to calculate the self-learning torque within the self-learning area based on the deviation of the engine control torque, if it is permitted for the torque value to be recorded in the self-learning area, and to store the self-learning torque value in a corresponding memory device; a first calibration unit configured to read the self-learning torque value from the memory device and to add the self-learning torque value to the target engine torque to obtain the required engine torque.

As a variant, the location determination unit comprises: a setting subunit configured to set parameter thresholds for a plurality of engine operating mode parameters, where the parameter thresholds comprise X engine speed thresholds, Y engine load thresholds and Z engine temperature thresholds, and X, Y and Z are all positive integers greater than 0; a dividing subunit configured to divide and obtain a plurality of engine memories using a plurality of parameter thresholds, where each memory device is configured to store a self-learning value; a data acquisition subunit configured to obtain current engine parameters of the hybrid vehicle; a comparison subunit configured to compare the current engine parameters with the corresponding parameter thresholds in order to obtain parameter indices, where the parameter indices comprise an engine speed index, an engine load index and an engine temperature index, and the parameter index is used to represent a threshold interval in which the current engine parameter is located; a location determination subunit configured to determine the location of a memory device sequence number in the self-learning area using the parameter indices.

Alternatively, the dividing subunit is also configured to divide the engine operating area into (X+1) × (Y+1) areas; and to divide and obtain a plurality of engine memory devices into (X+1) × (Y+1) × Z areas.

Alternatively, the location determination subunit is configured to calculate the serial number i of the memory device using the following formula: i = engine speed index × Z + engine load index × (X+1) × Z + engine temperature index.

Alternatively, the decision-making unit comprises a first determining subunit configured to determine the stability flags of the hybrid vehicle, where the stability flags comprise a target engine torque stability flag, an engine speed stability flag, and a generator torque stability flag; a decision-making subunit configured to decide whether all the stability flags are in the set state, and whether the operating mode of the hybrid vehicle in real time satisfies all the following specified conditions: the engine coolant temperature is greater than the set value; the engine has finished starting and is running for a certain period of time; the engine is in the torque control mode; the absolute value of the target engine torque deviation is less than or equal to the first threshold; the absolute value of the actual engine torque deviation is greater than or equal to the second threshold; the target engine torque is greater than or equal to the third threshold; a second determining subunit configured to determine that the self-learning recording is permitted if all the stability flags are in the set state, and the operating mode of the hybrid vehicle in real time satisfies all the specified conditions.

Alternatively, the execution unit comprises: a processing subunit configured to decide whether the engine control torque deviation is greater than or equal to a deviation threshold and to compare the self-learning torque value in the self-learning area with the threshold; a determining subunit configured to determine the self-learning state as a first self-learning state if the engine control torque deviation is greater than or equal to a first deviation threshold and the self-learning torque value is less than an upper limit value; determining the self-learning state as a second self-learning state if the engine control torque deviation is less than or equal to a second deviation threshold and the self-learning torque value is greater than a lower limit value; determining the self-learning state as a third self-learning state if the engine control torque deviation is less than the first deviation threshold or the engine control torque deviation is greater than the second deviation threshold or the self-learning torque value is equal to the upper limit value or the lower limit value; A learning subunit configured to increase the self-learning torque value according to a given speed if this state is a first self-learning state; to decrease the self-learning torque value according to a given speed if this state is a second self-learning state; to store the current self-learning torque value in a self-learning area if this state is a third self-learning state; and an updating subunit configured to update in real time the corresponding self-learning torque value in a memory device of the self-learning area.

Alternatively, the calibration module comprises: an operating unit configured to control the hybrid vehicle engine in accordance with the required engine torque, detect the actual engine torque, and determine the actual torque as the estimated engine torque; a second calibration device configured to subtract the self-learning torque value from the estimated engine torque to obtain the signal engine torque.

In accordance with the approach of the embodiments of the present application, a vehicle controller is also provided. Fig. 20 schematically shows a diagram of a vehicle controller in an embodiment of the present application, which comprises: a generator control unit configured to transmit a signal torque of the generator to an engine control unit; an engine control unit configured to receive a signal torque of the generator transmitted by the generator control unit and a target torque of the engine transmitted by a control unit of the entire vehicle, including an engine torque calibration device in the above-mentioned embodiment; a control unit of the entire vehicle configured to receive a signal torque of the engine transmitted by the engine control unit.

It should be noted that the above modules may be implemented by software or hardware, and the latter may be implemented in the following ways (but not limited to): all of the above modules are located in the same processor; or all of the above modules are located in different processors in any combination.

Option 3 implementation

An embodiment of the present application also provides a storage medium on which a computer program is stored, and the computer program is configured to implement, during its execution, the steps of any of the above-mentioned embodiments of the method.

Alternatively, in this embodiment, the storage medium may be configured to store a computer program for executing the following steps:

S1: Collects the target engine torque data and the signal torque of the generator of the hybrid vehicle.

S2: calculate the torque deviation of the hybrid vehicle corresponding to the target engine torque and the signal torque of the generator.

S3: perform self-learning calculation based on the torque deviation to obtain the target torque of the hybrid vehicle engine, and calibrate the signal torque of the engine based on the target torque of the engine to make the signal torque of the engine, the signal torque of the generator, and the target torque of the hybrid vehicle engine consistent.

Alternatively, in this embodiment, the above-mentioned storage medium may comprise, but is not limited to, a USB flash memory, a read-only memory (abbreviated ROM), a random access memory (abbreviated RAM), a mobile hard disk, a magnetic disk, an optical disk, or other media on which a computer program can be stored.

An embodiment of the present application also provides an electronic device comprising a memory and a processor, wherein the computer program is stored in the memory and the processor is configured to operate the computer program to perform the steps of any of the above-mentioned embodiments of the method.

Alternatively, the above-mentioned electronic device may further comprise a transmitting device and an input-output device, where the transmitting device is connected to the above-mentioned processor and the input-output device is connected to the above-mentioned processor.

Alternatively, in this embodiment, the above-mentioned processor may be configured to execute the following steps via a computer program:

S1: Collects the target engine torque and signal generator torque data of the hybrid vehicle.

S2: calculate the torque deviation of the hybrid vehicle corresponding to the target engine torque and the signal torque of the generator.

S3: perform self-learning calculation based on the torque deviation to obtain the target torque of the hybrid vehicle engine, and calibrate the signal torque of the engine based on the target torque of the engine to make the signal torque of the engine, the signal torque of the generator, and the target torque of the hybrid vehicle engine consistent.

Alternatively, specific examples in this embodiment can be found in the examples described in the above-mentioned embodiments and in additional embodiments that will not be repeated here in this embodiment.

The reference numbers of the embodiments of the present application are for description only and do not represent the priority or non-priority of the embodiments.

In the above embodiments of the present application, the description of each embodiment has its own specific features. For details not disclosed in one embodiment, please refer to the corresponding descriptions of other embodiments.

In several embodiments presented by this application, it should be understood that the disclosed technical content can be implemented in other ways. The embodiments of the technical means described above are only schematic. For example, the division into blocks is only a division by logical functions, and in actual implementation, another division method is possible, for example, multiple blocks or components can be combined or integrated into another system, or some features can be ignored or not implemented. Furthermore, the interconnection or direct connection or connection by means of communication shown or discussed can be indirect connection or connection by means of communication through certain interfaces, blocks, or modules, which can have electrical or other forms.

Devices described as individual components may or may not be physically separate, and components depicted as blocks may or may not be physical blocks, meaning they may be located in a single location or distributed across multiple network devices. Some or all of the blocks may be selected based on the actual needs for implementing the solution objective of the embodiment.

Furthermore, the functional units in the embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically as a single unit, or two or more units may be integrated into a single unit. The aforementioned integrated unit may be implemented in the form of hardware or software in the functional units.

An integrated unit may be stored on a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as an independent product. Based on this understanding, the technical solution of the present application in essence, or a part thereof that contributes to the prior art, or all or a part of the technical solution, may be implemented in the form of a software product. The software product is stored on a storage medium and contains several instructions for causing a computing device (such as a personal computer, server, or network device, etc.) to perform all or some of the steps of the method described in various embodiments of the present application. The aforementioned storage medium comprises: USB flash memory, read-only memory (ROM), random access memory (RAM), mobile hard drive, magnetic disk, optical disk, or other medium on which program codes can be stored.

The above are merely preferred embodiments of the present application. It should be noted that certain improvements and refinements may be made by those skilled in the art without departing from the principles of the present application, and such improvements and refinements should also be considered as falling within the scope of protection of the present application.

Claims (48)

1. A method for calibrating engine torque, comprising the steps of: collect target engine torque and signal generator torque data of the hybrid vehicle; calculate the torque deviation of the hybrid vehicle according to the target torque of the engine and the signal torque of the generator; perform a self-learning calculation based on the torque deviation to obtain a target engine torque of the hybrid vehicle, and calibrate the signal torque of the engine based on the target engine torque to make the signal torque of the engine, the signal torque of the generator, and the target engine torque of the hybrid vehicle consistent. 2. The method according to claim 1, wherein calculating the torque deviation of the hybrid vehicle in accordance with the target engine torque and the signal generator torque comprises the steps of: subtracting the stored signal torque of the engine of the immediately preceding control cycle from the target torque of the engine to obtain the deviation of the target torque of the engine; subtract the generator signal torque from the stored engine signal torque of the immediately preceding control cycle to obtain the actual engine torque deviation; subtract the generator signal torque from the engine target torque to obtain the calculated engine control torque deviation; wherein the torque deviations contain the target engine torque deviation, the actual engine torque deviation, and the engine control torque deviation. 3. The method according to claim 1, wherein performing the calculation during self-learning based on the torque deviation to obtain the required torque of the hybrid vehicle engine comprises the steps of: determine the location of the self-learning area in accordance with the current parameter of the hybrid vehicle engine; decide whether to allow the recording of the self-learning torque value into the self-learning area based on the deviation of the target engine torque and the deviation of the actual engine torque; perform self-learning torque calculation within the self-learning area based on the deviation of the engine control torque if the recording of the torque value in the self-learning area is permitted, and store the self-learning torque value in the corresponding storage device; read the learning torque value from the memory device and add the learning torque value to the target motor torque to obtain the required motor torque. 4. The method according to claim 3, wherein determining the location of the self-learning area in accordance with the current parameter of the hybrid vehicle engine comprises the steps of: setting parameter thresholds for a plurality of engine operating mode parameters, wherein the parameter thresholds comprise X engine speed thresholds, Y engine load thresholds, and Z engine temperature thresholds, where X, Y, and Z are all positive integers greater than 0; dividing and obtaining a plurality of engine memories using a plurality of parameter thresholds, wherein each memory is configured to store a self-learning value; collect data on the current parameters of the hybrid vehicle engine; parameter indices are obtained in accordance with the results of the comparison between the current engine parameters and the corresponding parameter thresholds, wherein the parameter indices comprise an engine speed index, an engine load index and an engine temperature index, and the parameter index is used to represent the threshold interval in which the current engine parameter is located; determine the location of the serial number of the storage device in the self-training area using parameter indices. 5. The method according to claim 4, in which the separation and obtaining of a plurality of engine memories using a plurality of parameter thresholds comprises the steps of: divide the engine's working area into (X+1) × (Y+1) areas; divide and obtain a set of engine memories into (X+1) × (Y+1) × Z. 6. The method according to claim 4, in which determining the location of the serial number of the storage device in the self-training area using parameter indices comprises the steps of: calculate the serial number i of the storage device using the following formula: i = engine speed index × Z + engine load index × (X + 1) × Z + engine temperature index. 7. The method according to claim 3, in which the decision whether to allow recording of the self-learning torque value in the self-learning area based on the deviation of the target engine torque and the deviation of the actual engine torque comprises the steps of: determining stability flags of the hybrid vehicle, wherein the stability flags comprise a target engine torque stability flag, an engine speed stability flag, and a generator torque stability flag; deciding whether all the stability flags are in the set state, and deciding whether the real-time operating mode of the hybrid vehicle satisfies all of the following specified conditions: the engine coolant temperature is greater than a specified value; the engine finishes starting and continues to run for a certain period of time; the engine is in the torque control mode; the absolute value of the target torque deviation is less than or equal to the first threshold; the absolute value of the actual engine torque deviation is greater than or equal to the second threshold; the target engine torque is greater than or equal to the third threshold; determine that self-learning recording is permitted if all stability flags are set and the real-time operating mode of the hybrid vehicle satisfies all specified conditions. 8. The method according to claim 3, in which the calculation of the self-learning torque within the self-learning region based on the deviation of the engine control torque and the storage of the self-learning torque value in the corresponding memory device comprises the steps of: decide whether the deviation of the motor control torque is greater than or equal to the deviation threshold, and compare the self-learning torque value in the self-learning area with the threshold; if the deviation of the engine control torque is greater than or equal to the first deviation threshold and the self-learning torque value is less than the upper limit value, determining the self-learning state as the first self-learning state; if the deviation of the engine control torque is less than or equal to the second deviation threshold and the self-learning torque value is greater than the lower limit value, determining the self-learning state as the second self-learning state; if the deviation of the engine control torque is less than the first deviation threshold, or the deviation of the engine control torque is greater than the second deviation threshold, or the self-learning torque value is equal to the upper limit value or the lower limit value, determining the self-learning state as the third self-learning state; if the first self-learning state occurs, the value of the self-learning torque is increased at a given speed; if the second self-learning state occurs, the value of the self-learning torque is decreased at a given speed; if the third self-learning state occurs, the current self-learning torque is maintained in the self-learning area; update in real time the corresponding torque value during self-learning in the memory device in the self-learning area. 9. The method of claim 1, wherein calibrating the signal engine torque based on the desired engine torque comprises the steps of: controlling the engine of the hybrid vehicle according to the required engine torque, detecting the actual engine torque, and determining the actual torque as the estimated engine torque; subtract the self-learning torque value from the calculated motor torque to obtain the signal motor torque. 10. An engine torque calibration device comprising: a data acquisition module configured to acquire data of a target engine torque and a signal generator torque of a hybrid vehicle; a computing module configured to calculate a torque deviation of the hybrid vehicle in accordance with a target engine torque and a signal generator torque; a calibration module configured to calculate, in a self-learning manner, based on the torque deviation to obtain the desired engine torque of the hybrid vehicle and calibrate the signal torque of the engine based on the desired engine torque to make the signal torque of the engine, the signal torque of the generator and the target torque of the engine of the hybrid vehicle consistent. 11. A vehicle controller comprising: a generator control unit configured to transmit a signal torque of the generator to the engine control unit; an engine control unit configured to receive a signal generator torque transmitted by the generator control unit, to receive a target engine torque transmitted by the entire vehicle control unit, and to perform the method according to any one of paragraphs 1-9; wherein the control unit of the entire vehicle is configured to receive the engine torque signal transmitted by the engine control unit. 12. A storage medium on which a computer program is stored, wherein the computer program is configured to perform the method according to any of paragraphs 1-9 when it is executed. 13. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program for performing the method according to any of paragraphs 1-9.