CN118636732A - A low-speed electric vehicle battery charging monitoring control method and system - Google Patents
A low-speed electric vehicle battery charging monitoring control method and system Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/62—Monitoring or controlling charging stations in response to charging parameters, e.g. current, voltage or electrical charge
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L3/00—Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
- B60L3/12—Recording operating variables ; Monitoring of operating variables
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
- B60L58/18—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries of two or more battery modules
- B60L58/22—Balancing the charge of battery modules
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/54—Drive Train control parameters related to batteries
- B60L2240/545—Temperature
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/54—Drive Train control parameters related to batteries
- B60L2240/547—Voltage
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L2240/00—Control parameters of input or output; Target parameters
- B60L2240/40—Drive Train control parameters
- B60L2240/54—Drive Train control parameters related to batteries
- B60L2240/549—Current
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Abstract
本发明涉及电池充电监测控制技术领域，具体为一种低速电动车电池充电监测控制方法及系统，系统包括：电池状态监测模块，基于电池组的初始状态，进行电压、电流和温度参数的监测和记录。本发明中，通过对电池组的电压、电流和温度参数的实时监测和记录，能够有效识别电池状态的变化并及时调整电流分配，从而保持电池组在最佳状态下运行。基于电池状态数据和临界状态信息，动态调节电流分配，通过负载均衡算法优化多个电池组的电流分配，提高了充电效率和安全性。在充电过程中模拟多种场景，通过历史数据和专家知识计算每个场景的风险水平，有助于提前识别潜在风险，增强了系统的安全性。
The present invention relates to the technical field of battery charging monitoring and control, and specifically to a method and system for monitoring and controlling the charging of a battery of a low-speed electric vehicle. The system includes: a battery status monitoring module, which monitors and records the voltage, current and temperature parameters based on the initial state of the battery pack. In the present invention, by real-time monitoring and recording of the voltage, current and temperature parameters of the battery pack, it is possible to effectively identify changes in the battery status and adjust the current distribution in time, thereby keeping the battery pack running in the best state. Based on the battery status data and critical state information, the current distribution is dynamically adjusted, and the current distribution of multiple battery packs is optimized through a load balancing algorithm, thereby improving the charging efficiency and safety. During the charging process, multiple scenarios are simulated, and the risk level of each scenario is calculated through historical data and expert knowledge, which helps to identify potential risks in advance and enhance the safety of the system.
Description
Technical Field
The invention relates to the technical field of battery charging monitoring control, in particular to a low-speed electric vehicle battery charging monitoring control method and system.
Background
The technical field of battery charging monitoring control mainly relates to monitoring and control of a battery in a charging process so as to ensure the safety, service life and charging efficiency of the battery. Techniques in this field include battery state monitoring, battery Management Systems (BMS), charger design, and charging algorithms, among others. The battery state monitoring is to monitor parameters such as voltage, current, temperature and the like of the battery in real time through a sensor and a data acquisition system, and identify the state and the performance of the battery. The battery management system is responsible for charging, discharging and balancing control of the battery, ensures voltage balance of each battery unit and prevents overcharge, overdischarge and overheat phenomena. The charger design covers charging circuits and charging mode designs of different types of batteries (such as lithium ion batteries, lead-acid batteries and the like), and the charging algorithm optimizes current and voltage control in the charging process, so that the charging efficiency and the service life of the batteries are improved.
However, when the existing battery management system is used for coping with complex charging scenes, a risk assessment mechanism based on historical data and expert knowledge is lacking, and potential risks are difficult to identify and prevent in advance. In addition, existing systems mostly ignore the user's analysis of charging behavior, failing to provide a personalized charge optimization strategy, resulting in a failure to maximize charging efficiency and battery life. The prior art often adopts fixed charging parameters in the charging process, and lacks the capability of optimizing adjustment based on real-time data, so that the charging process is not efficient and safe enough. The above defects may cause problems of overcharge, overdischarge, overheat and the like of the battery in practical application, and affect the safety and the service life of the battery.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a low-speed electric vehicle battery charging monitoring control method and system.
In order to achieve the above purpose, the present invention adopts the following technical scheme: a low-speed electric vehicle battery charge monitoring control system includes:
The battery state monitoring module is used for monitoring and recording voltage, current and temperature parameters based on the initial state of the battery pack, acquiring battery state data through a sensor, monitoring battery state change, comparing the voltage difference and the current difference of the battery pack, setting the variables of the battery pack, adjusting current distribution, recording the current distribution state and acquiring critical state information;
The current distribution adjusting module is used for dynamically adjusting current distribution based on the battery state data and the critical state information, monitoring voltage, current and temperature data of the battery packs through the sensors, calculating current distribution, and adjusting current distribution of the battery packs through a load balancing algorithm to generate a current distribution scheme;
The risk assessment module is used for simulating various charging scenes based on the current distribution scheme, generating random numbers by setting voltage, current, temperature and environmental condition parameters in a charging process, simulating the charging scenes, constructing a charging process scene set, calculating the risk level of each scene by using historical data and expert knowledge, and generating a risk assessment result;
The behavior analysis module is used for analyzing a charging behavior mode based on the risk assessment result, constructing a charging behavior sequence by collecting user charging records including charging time, charging duration and charging amount, selecting a sequence pattern mining algorithm, performing pattern mining on the charging behavior sequence, identifying a common charging behavior mode, analyzing user charging habit and requirement according to the mined charging behavior mode, classifying the charging behavior mode, identifying a good charging strategy and a bad charging habit, and generating a charging optimization strategy;
and the charging control module is used for implementing the charging control strategy based on the charging optimization strategy, adjusting charging voltage and current parameters according to the state estimation result of the regression tree model, updating parameters by adopting a differential evolution algorithm, and circularly optimizing the charging control strategy according to real-time data to generate a charging control scheme.
As a further aspect of the invention: the battery state monitoring module includes:
The voltage monitoring sub-module is used for monitoring and recording voltage parameters based on the initial state of the battery pack, acquiring voltage data of the battery pack through a sensor, monitoring voltage change of the battery pack, comparing voltage difference of the battery pack, setting variables of the battery pack and generating voltage state information;
the current monitoring sub-module is used for monitoring and recording current parameters based on the voltage state information, acquiring current data of the battery pack through a sensor, monitoring current change of the battery pack, comparing current difference of the battery pack, adjusting current distribution and generating current state information;
And the temperature monitoring sub-module is used for monitoring and recording temperature parameters based on the current state information, acquiring temperature data of the battery pack through a sensor, monitoring battery temperature change, judging whether the battery temperature change reaches a critical state, recording the current distribution state and generating critical state information.
As a further aspect of the invention: the current distribution adjustment module includes:
The real-time monitoring sub-module is used for carrying out real-time monitoring on the voltage, the current and the temperature data of the battery pack based on the critical state information, acquiring real-time data through sensor monitoring and generating real-time monitoring data;
The calculation and adjustment sub-module is used for calculating current distribution based on the real-time monitoring data, dynamically adjusting the current distribution by comparing the state change of the battery pack and generating current adjustment data;
And the load balancing sub-module is used for carrying out current distribution adjustment based on the current adjustment data, distributing the currents of the plurality of battery packs through a load balancing algorithm and generating a current distribution scheme.
As a further aspect of the invention: the risk assessment module includes:
The scene simulation sub-module is used for simulating a charging scene based on the current distribution scheme, generating random numbers by setting voltage, current, temperature and environmental condition parameters, simulating a charging scene, constructing a charging process scene set and generating simulated scene data;
The risk calculation sub-module is used for calculating the risk level of each scene by utilizing historical data and expert knowledge based on the simulated scene data and generating a risk calculation result;
And the risk analysis sub-module is used for carrying out statistical analysis based on the risk calculation result, identifying a risk scene and key risk factors and generating a risk assessment result.
As a further aspect of the invention: the behavior analysis module comprises:
The record acquisition sub-module is used for acquiring charging time, charging duration and charging amount data based on a user charging record and a risk evaluation result, constructing a charging behavior sequence and generating charging record data;
The pattern mining sub-module is used for selecting a sequence pattern mining algorithm based on the charging record data, performing pattern mining on a charging behavior sequence, identifying a common charging behavior pattern and generating behavior pattern data;
And the mode analysis sub-module is used for analyzing the charging habit and the requirement of the user, classifying the charging behavior modes, identifying the efficient charging strategy and the bad charging habit and generating a charging optimization strategy based on the behavior mode data.
As a further aspect of the invention: the charge control module includes:
the parameter adjustment sub-module is used for adjusting charging parameters based on the charging optimization strategy, applying the optimization strategy in real time through a sensor, adjusting charging voltage and current parameters according to a state estimation result, and generating parameter adjustment data;
The policy application sub-module is used for implementing a charging control policy based on the parameter adjustment data, and generating policy application data by adjusting the charging control policy through the data;
And the control optimization sub-module is used for monitoring and adjusting based on the strategy application data, optimizing the charging control strategy and generating a charging control scheme.
As a further aspect of the invention: the critical state information comprises a voltage difference, a current difference and a temperature change, the current distribution scheme comprises a current distribution parameter, a voltage adjustment value and load distribution data, the risk assessment result comprises a risk level, a key risk factor and a risk scene, the charging optimization strategy comprises the steps of adjusting charging time, optimizing charging rate and improving charging habit, and the charging control scheme comprises a voltage adjustment parameter, a current optimization value and a charging adjustment item.
A battery charging monitoring control method of a low-speed electric vehicle comprises the following steps:
s1, based on the initial state of a battery pack, adopting a sensor monitoring technology, acquiring battery state data in real time by monitoring voltage, current and temperature parameters, monitoring battery state change, comparing voltage difference and current difference of the battery pack, setting the variables of the battery pack, adjusting current distribution, recording the current distribution state, and generating critical state information;
s2, based on the critical state information, a dynamic current adjustment method is adopted, voltage, current and temperature data of the battery packs are monitored through a sensor, current distribution is calculated, and current distribution of the plurality of battery packs is adjusted through a load balancing algorithm, so that a current distribution scheme is generated;
S3, based on the current distribution scheme, a scene simulation method is adopted, a random number is generated by setting voltage, current, temperature and environmental condition parameters in a charging process, a charging scene is simulated, a charging process scene set is constructed, and the risk level of each scene is calculated by utilizing historical data and expert knowledge to generate a risk assessment result;
S4, collecting user charging records, including charging time, charging duration and charging quantity, by adopting a sequence mode mining method based on the risk assessment result, constructing a charging behavior sequence, identifying common charging behavior modes, analyzing user charging habits and requirements, classifying the charging behavior modes, identifying good charging strategies and bad charging habits, generating a charging optimization strategy, adjusting charging voltage and current parameters according to a state estimation result of a regression tree model through implementation of the charging control strategy, circularly optimizing the charging control strategy, and generating a charging control scheme.
Compared with the prior art, the invention has the advantages and positive effects that:
According to the invention, through real-time monitoring and recording of the voltage, current and temperature parameters of the battery pack, the change of the battery state can be effectively identified, and the current distribution can be timely adjusted, so that the battery pack can be kept to operate in an optimal state. Based on battery state data and critical state information, current distribution is dynamically adjusted, and current distribution of a plurality of battery packs is optimized through a load balancing algorithm, so that charging efficiency and safety are improved. And in the charging process, multiple scenes are simulated, and the risk level of each scene is calculated through historical data and expert knowledge, so that potential risks can be recognized in advance, and the safety of the system is enhanced. By analyzing the charging behavior mode of the user, the charging habit of the user can be identified, the charging strategy is optimized, and the service life of the battery is prolonged. Based on the charging optimization strategy, charging voltage and current parameters are adjusted in real time, and the parameters are updated through a differential evolution algorithm, so that the charging control strategy can be continuously optimized, and the high efficiency and the safety of the charging process are ensured.
Drawings
FIG. 1 is a flow chart of the system of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Example 1
Referring to fig. 1, the present invention provides a technical solution: a low-speed electric vehicle battery charge monitoring control system includes:
The battery state monitoring module is used for monitoring and recording voltage, current and temperature parameters based on the initial state of the battery pack, acquiring battery state data through a sensor, monitoring battery state change, comparing the voltage difference and the current difference of the battery pack, setting the variables of the battery pack, adjusting current distribution, recording the current distribution state and acquiring critical state information;
the current distribution adjusting module is used for dynamically adjusting current distribution based on battery state data and critical state information, monitoring voltage, current and temperature data of the battery packs through the sensors, calculating current distribution, and adjusting current distribution of the battery packs through a load balancing algorithm to generate a current distribution scheme;
the risk assessment module is used for simulating various charging scenes based on a current distribution scheme, generating random numbers by setting voltage, current, temperature and environmental condition parameters in a charging process, simulating the charging scenes, constructing a charging process scene set, calculating the risk level of each scene by using historical data and expert knowledge, and generating a risk assessment result;
The behavior analysis module is used for analyzing a charging behavior mode based on a risk assessment result, constructing a charging behavior sequence by collecting a user charging record comprising charging time, charging duration and charging amount, selecting a sequence mode mining algorithm, performing mode mining on the charging behavior sequence, identifying a common charging behavior mode, analyzing a user charging habit and a requirement according to the mined charging behavior mode, classifying the charging behavior mode, identifying a good charging strategy and a bad charging habit, and generating a charging optimization strategy;
The charging control module is used for implementing the charging control strategy based on the charging optimization strategy, adjusting charging voltage and current parameters according to the state estimation result of the regression tree model, updating parameters by adopting a differential evolution algorithm, and generating a charging control scheme according to the real-time data cycle optimization charging control strategy.
The critical state information comprises voltage differences, current differences and temperature changes, the current distribution scheme comprises current distribution parameters, voltage adjustment values and load distribution data, the risk assessment result comprises a risk level, key risk factors and a risk scene, the charging optimization strategy comprises the steps of adjusting charging time, optimizing charging rate and improving charging habit, and the charging control scheme comprises voltage adjustment parameters, current optimization values and charging adjustment items.
In the battery state monitoring module, the initial state of the battery pack is set, the voltage, current and temperature parameters are monitored and recorded, the high-precision sensor is adopted to acquire battery state data, the battery state change is monitored, the voltage difference and the current difference of the battery pack are compared, key variables including the voltage difference and the current difference are set, the system can dynamically adjust current distribution according to the variables, the current distribution state is recorded, critical state information is acquired, the data format acquired by the sensor in real time is a time stamp, a voltage value, a current value and a temperature value, the system can accurately track the real-time state of the battery pack through continuous monitoring and recording of the data, the charging strategy is dynamically adjusted, various parameters of the battery pack are ensured to be kept in a safe range, and a detailed battery pack state record file is generated.
In the current distribution adjusting module, dynamic adjustment of current distribution is carried out based on battery state data and critical state information, voltage, current and temperature data of battery packs are monitored through a high-precision sensor, voltage values, current values and temperature values of the battery packs are obtained in real time, current distribution of the battery packs is calculated, current, voltage and temperature parameters of the battery packs are compared, data summarization is carried out, current distribution of a plurality of battery packs is adjusted according to a load balancing algorithm, load balancing of each battery pack is ensured, a detailed current distribution scheme is generated, and the scheme comprises current distribution proportion of the battery packs and the adjusted current values so as to ensure stability and efficiency of a charging process.
In the risk assessment module, simulation of various charging scenes is carried out based on a current distribution scheme, random numbers are generated by setting parameters of voltage, current, temperature and environmental conditions in a charging process, different charging scenes are simulated, a scene set of the charging process is constructed, the scene set comprises a plurality of parameter combinations of the voltage, the current, the temperature, the environmental conditions and the like, the risk level of each scene is calculated by utilizing historical data and expert knowledge, risk calculation comprises assessment of conditions of overcharge, undercharge, overheating and the like of a battery of each scene, a detailed risk assessment result is generated, and a risk assessment result file comprises risk grades of each scene and detailed risk analysis data and provides reference basis for subsequent charging control.
In the charging behavior analysis module, based on a risk evaluation result, analysis of a charging behavior pattern is performed, a charging behavior sequence is constructed by collecting user charging records including charging time, charging duration and charging amount, a data format includes a time stamp, the charging amount, the charging duration and the like, a sequence pattern mining algorithm is selected, pattern mining is performed on the charging behavior sequence, common charging behavior patterns are identified, then charging habits and requirements of users are analyzed according to the mined charging behavior patterns, the charging behavior patterns are classified, good charging strategies and bad charging habits are identified, a detailed charging optimization strategy file is generated, the file includes personalized charging suggestions and optimization schemes for different users, and user charging experience and charging efficiency are improved.
In the charging control module, the implementation of the charging control strategy is carried out based on the charging optimization strategy, the charging voltage and current parameters are adjusted according to the state estimation result of the regression tree model, the parameter update is carried out through the differential evolution algorithm, the charging control strategy is circularly optimized according to real-time data, the data format comprises a time stamp, the charging voltage, the charging current and the battery temperature, the charging process can be continuously optimized by the system through real-time monitoring and adjustment, a detailed charging control scheme is generated, the scheme comprises specific charging parameter adjustment values and charging steps, the high efficiency and safety of the charging process are ensured, and the service life of the battery is prolonged to the maximum extent.
In this embodiment, the battery state monitoring module includes:
The voltage monitoring sub-module is used for monitoring and recording voltage parameters based on the initial state of the battery pack, acquiring voltage data of the battery pack through a sensor, monitoring voltage change of the battery pack, comparing voltage difference of the battery pack, setting variables of the battery pack and generating voltage state information;
The current monitoring sub-module is used for monitoring and recording current parameters based on the voltage state information, acquiring current data of the battery pack through the sensor, monitoring current change of the battery pack, comparing current difference of the battery pack, adjusting current distribution and generating current state information;
And the temperature monitoring sub-module is used for monitoring and recording temperature parameters based on the current state information, acquiring temperature data of the battery pack through the sensor, monitoring the temperature change of the battery, judging whether the current state reaches a critical state, recording the current distribution state and generating critical state information.
In the voltage monitoring sub-module, the real-time monitoring and recording of the voltage parameters of the battery pack are carried out through the sensor, and the data format is a time stamp and a voltage value. Firstly, setting initial voltage parameters based on the initial state of the battery pack, collecting voltage data of the battery pack by a sensor at fixed time intervals, recording the voltage data in a data log, continuously monitoring the change of the battery voltage by a system, comparing the current voltage value with the voltage value of the previous time point in real time, and calculating a voltage difference. The voltage difference is calculated by simple subtraction to obtain the voltage variation value of the battery pack in a specific time interval. According to the set threshold, when the voltage difference exceeds the preset threshold, the system automatically adjusts the variable of the battery pack, sets a new voltage state, and generates a voltage state information file, wherein the file comprises a time stamp, a voltage value and the voltage difference. The voltage state information is used for the subsequent current monitoring and temperature monitoring sub-modules to provide data support for the real-time voltage change of the battery pack.
In the current monitoring sub-module, the real-time monitoring and recording of the current parameters of the battery pack are carried out through the sensor, and the data format is a time stamp and a current value. Based on the voltage state information, the sensor collects current data of the battery pack in real time, the current data are recorded in a data log, the system continuously monitors the change of the battery current, the current value is compared with the current value at the previous time point, and the current difference is calculated. The calculation of the current difference obtains the current variation value of the battery pack in a specific time interval through simple subtraction operation. According to the set threshold value, when the current difference exceeds the preset threshold value, the system automatically adjusts current distribution, balances the load of each battery pack by adjusting the current distribution proportion, and generates a current state information file which comprises a time stamp, a current value and the current difference. The current state information is used for a subsequent temperature monitoring sub-module to provide data support for real-time current change of the battery pack.
In the temperature monitoring sub-module, the temperature parameters of the battery pack are monitored and recorded in real time through the sensor, and the data format is a time stamp and a temperature value. Based on the current state information, the sensor collects temperature data of the battery pack in real time, the temperature data are recorded in a data log, the system continuously monitors the change of the battery temperature, and the current temperature value and the temperature value of the previous time point are compared to judge whether the critical state is reached. The temperature judgment is carried out by comparing the current temperature value with a preset temperature threshold value, and when the temperature reaches or exceeds the critical temperature, the system records the current distribution state and generates a critical state information file which comprises a time stamp, the temperature value and the current distribution state. The critical state information is used for triggering subsequent emergency response measures, so that the battery pack is ensured to run in a safe temperature range, and data support of real-time temperature change of the battery pack is provided.
In this embodiment, the current distribution adjustment module includes:
The real-time monitoring sub-module is used for carrying out real-time monitoring on the voltage, the current and the temperature data of the battery pack based on the critical state information, acquiring the real-time data through sensor monitoring and generating real-time monitoring data;
The calculation and adjustment sub-module is used for calculating current distribution based on the real-time monitoring data, dynamically adjusting the current distribution by comparing the state change of the battery pack, and generating current adjustment data;
And the load balancing sub-module is used for carrying out current distribution adjustment based on the current adjustment data, distributing the currents of the plurality of battery packs through a load balancing algorithm and generating a current distribution scheme.
In the real-time monitoring sub-module, the voltage, current and temperature data of the battery pack are monitored in real time based on the critical state information. And a high-precision sensor is adopted to continuously acquire real-time data of the battery pack, and the data is transmitted to a central processing unit through a data acquisition system. The format of the real-time monitoring data includes a time stamp, a voltage value, a current value and a temperature value. In the data acquisition process, the system calibrates and filters each data point to eliminate noise and errors and ensure the accuracy and reliability of the data. And displaying and recording the data in real time through monitoring software to generate a detailed real-time monitoring data file. The file contains voltage, current and temperature changes per second, providing the basis data for subsequent current regulation. The real-time monitoring data plays a key role in real-time analysis, and abnormal conditions such as voltage dip or abnormal temperature rise can be found in time, so that the system can respond quickly.
In the calculate conditioning sub-module, the current distribution is calculated by monitoring the data based on real time. Firstly, the system acquires real-time monitoring data and preprocesses the data, including removing abnormal values and smoothing data fluctuation. Then, the state change of the battery pack is analyzed by comparing the historical data with the current monitoring data. The system adopts a differential calculation method to calculate the current variation and the voltage variation of each battery pack, and judges the charging state of the battery pack according to the variation. The current distribution is dynamically adjusted according to the charging requirements of the battery pack. The specific operation comprises the steps of comparing the real-time current value with a preset ideal current value, and adjusting current output according to a comparison result. The adjusting process is realized by a PID controller, the PID controller calculates the adjustment quantity according to the current error value, and adjusts the current output in real time to generate current adjusting data. The data includes an adjusted current value and an adjusted voltage value for each battery pack, providing basis for the load balancing sub-module. The current regulation data ensures that each battery pack is charged in an optimal state, and the charging efficiency and the safety are improved.
In the load balancing sub-module, current distribution adjustment is performed by adjusting data based on the current. The system first obtains current regulation data including an adjusted current value and an adjusted voltage value for each battery pack. Then, the current distribution of the plurality of battery packs is calculated using a load balancing algorithm. The load balancing algorithm considers the current state and charging requirements of each battery pack, and evenly distributes current to each battery pack, so as to avoid overcharge or undercharge of a certain battery pack. The specific operation steps include first calculating the specific gravity of the load of each battery pack and adjusting the current distribution according to the specific gravity. And then, detecting the load change condition of each battery pack by monitoring data in real time, and dynamically adjusting current distribution. The load balancing algorithm continuously optimizes the current distribution scheme through iterative calculation, ensures the charge load balance of each battery pack, and generates the current distribution scheme. The current distribution scheme comprises a final current distribution value and a voltage adjustment value of each battery pack, so that reasonable current distribution in the charging process is ensured, and the overall charging efficiency and the service life of the battery packs are improved.
In this embodiment, the risk assessment module includes:
The scene simulation sub-module is used for simulating a charging scene based on a current distribution scheme, generating random numbers by setting voltage, current, temperature and environmental condition parameters, simulating a charging scene, constructing a charging process scene set and generating simulated scene data;
The risk calculation sub-module is used for calculating the risk level of each scene based on the simulated scene data by utilizing the historical data and expert knowledge and generating a risk calculation result;
and the risk analysis sub-module is used for carrying out statistical analysis based on the risk calculation result, identifying a risk scene and key risk factors and generating a risk assessment result.
In the scene simulation sub-module, voltage, current, temperature and environmental condition parameters are first set by a current distribution scheme. The specific value of each parameter is obtained through interface input or file reading, then the parameter is utilized to generate random numbers, a Monte Carlo method is adopted when the random numbers are generated, and a scene set of the charging process is constructed through a large number of random samples. When the method is specifically executed, firstly, input parameters are normalized, parameter values are mapped to the [0,1] interval, then random samples are generated according to a normal distribution function, and the samples are combined with the parameter values to form a simulated charging scene. And then, carrying out simulation calculation on each charging scene by adopting a simulation model, wherein the simulation model comprises mathematical description and thermodynamic model of a charging circuit, solving current, voltage and temperature distribution under each scene by a numerical solution method such as a finite element method, and finally generating a data file containing a plurality of scenes. Each data file records time series data of each parameter in the corresponding scene, and the simulated scene data provides a basis for subsequent risk calculation.
In the risk calculation sub-module, the risk level of each scene is calculated by using the simulated scene data and the history data in combination with expert knowledge. The specific method is to adopt a Bayesian network model, wherein the model represents expert knowledge and historical data through prior probability and conditional probability tables. Firstly, characteristic parameters are extracted from historical data, wherein the parameters comprise indexes such as overcharge voltage, overcurrent and overhigh temperature, and normalization processing is carried out. Then, a Bayesian network model is constructed by combining expert knowledge, the network nodes represent risk factors, the edges represent conditional probabilities, and the conditional probabilities among the nodes are obtained from historical data through a maximum likelihood estimation method. And then, inputting each simulated scene data into a Bayesian network to infer, and calculating posterior risk probability of each scene by using a forward-backward algorithm to generate a risk calculation result file. Each result file includes a risk level and risk factor weight for each scene, providing a data basis for subsequent risk analysis. The module realizes quantitative risk assessment of the simulated scene, is helpful for identifying the high risk scene and provides basis for risk decision.
In the risk analysis sub-module, a high-risk scene and key risk factors are identified by carrying out statistical analysis on a risk calculation result. Firstly, a risk calculation result file is read, the risk level and risk factors of each scene are extracted, and a risk database is constructed. And then, processing the risk database by adopting a cluster analysis method, and classifying scenes with similar risk levels into one category by using a K-means clustering algorithm. In the specific execution, firstly, the risk data is subjected to standardized processing, and the risk level and the factor weight are mapped to the same numerical interval. Then, initializing K cluster centers, and iteratively updating the cluster centers and distributing scenes to the nearest cluster centers until convergence. The clustering result identifies a high risk scene group. And the PCA is used for constructing a main risk factor combination by decomposing the characteristic values of the risk factor matrix and reserving the characteristic vectors corresponding to the main characteristic values. Finally, a risk assessment report is generated detailing the high risk scenario, the major risk factors, and the suggested risk mitigation measures. The module effectively identifies key risks, provides a targeted risk management scheme, and improves the safety and reliability of the charging system.
In this embodiment, the behavior analysis module includes:
The record acquisition sub-module is used for acquiring charging time, charging duration and charging amount data based on a user charging record and a risk evaluation result, constructing a charging behavior sequence and generating charging record data;
The pattern mining sub-module selects a sequence pattern mining algorithm based on the charging record data, performs pattern mining on the charging behavior sequence, identifies a common charging behavior pattern and generates behavior pattern data;
and the mode analysis sub-module is used for analyzing the charging habit and the requirement of the user based on the behavior mode data, classifying the charging behavior mode, identifying the efficient charging strategy and the bad charging habit and generating the charging optimization strategy.
In the record acquisition sub-module, the acquisition of the charging time, the charging duration and the charging amount data is performed based on the user charging record and the risk evaluation result, and the data format comprises a time stamp, a charging start time, a charging end time, the charging duration and the charging amount. First, the system extracts the charging time point and the corresponding charging duration of the user from the charging record, and records the starting and ending time of each charging and the total charging time. And then, the system records the electric quantity data transmitted in the charging process through the sensor, and calculates the total electric quantity of each charging. All the acquired data are constructed into a charging behavior sequence, and the sequence format is a time stamp, a corresponding charging duration and a corresponding charging amount. The system can clean and format the sequence data, remove abnormal data and noise, and ensure the accuracy and the integrity of the data. Finally, a charging record data file is generated, wherein the charging record data file records each charging action of a user, including time, duration and electric quantity, and data support is provided for subsequent pattern mining.
In the pattern mining sub-module, a sequence pattern mining algorithm is selected based on charging record data to perform pattern mining on a charging behavior sequence, wherein the data format is a charging behavior sequence and a time stamp. Firstly, the system extracts a charging behavior sequence of a user from a charging record data file, analyzes sequence data by utilizing a sequence pattern mining algorithm, and identifies common patterns in the sequence data. The pattern mining algorithm identifies patterns in which a user is charged frequently in a specific period of time, and the charging duration and the charging amount law by calculating frequent subsequences of the charging behavior sequence. By counting the frequency and duration of the patterns, the system generates charging behavior pattern data. The behavior pattern data format is a charging pattern and its occurrence frequency and duration. Finally, a behavior pattern data file is generated, which records the common charging patterns of the user and provides basic data for subsequent pattern analysis.
In the pattern analysis sub-module, the data format includes the charging behavior pattern and the statistical analysis result by analyzing the charging habit and the demand of the user based on the behavior pattern data. The system firstly extracts common charging modes from the behavior mode data file, and identifies the charging habit of the user, such as liking to charge in a specific time period, preference of charging duration and the like by analyzing the occurrence frequency and duration of the modes. The system classifies the patterns to distinguish efficient charging strategies from bad charging habits. The efficient charging strategy comprises moderate charging time of a user, reasonable charging frequency and the like, and the bad charging habit comprises overlong or overlong charging time, frequent charging and the like. And the system generates a detailed charging optimization strategy file according to the analysis result, wherein the file content comprises personalized charging suggestions, such as adjusting charging time, optimizing charging duration and the like. The charging optimization strategy file provides specific charging suggestions for the user, helps the user to develop good charging habits, and improves charging efficiency and battery life.
In this embodiment, the charging control module includes:
The parameter adjustment sub-module is used for adjusting charging parameters based on a charging optimization strategy, applying the optimization strategy in real time through a sensor, adjusting charging voltage and current parameters according to a state estimation result, and generating parameter adjustment data;
The policy application sub-module is used for implementing a charging control policy based on the parameter adjustment data, and generating policy application data by adjusting the charging control policy through the data;
and the control optimization sub-module is used for monitoring and adjusting based on the strategy application data, optimizing the charging control strategy and generating a charging control scheme.
In the parameter adjustment sub-module, adjustment of the charging parameters is performed by a charging optimization strategy. First, the system receives a charge optimization strategy, including specific voltage and current adjustment instructions. And using a high-precision sensor to monitor the voltage, current and temperature parameters of the battery in real time, wherein the data format is a time stamp, a voltage value, a current value and a temperature value. And according to the real-time data acquired by the sensor, the system estimates the current state of the battery through a state estimation model. The state estimation result includes the remaining capacity, the state of health, and the current charge efficiency of the battery. And according to the state estimation result, the system adjusts the charging voltage and current parameters in real time. The adjusting process adopts a feedback control mechanism, namely, the voltage and current values are dynamically adjusted according to real-time state feedback, so that the charging process is ensured to be in an optimal state. The system records all the adjusted parameters to generate parameter adjustment data, including adjusted voltage values, current values and time stamps. The parameter adjustment data provides a basis for subsequent policy application, ensuring accurate control and efficient execution of the charging process.
In the policy application submodule, a charging control policy is implemented by adjusting data based on the parameters. The system receives and analyzes the parameter adjustment data, specifically including the voltage value and the current value at each time point. According to the data, the system applies the charging control strategy to the actual charging process. The specific operation steps include that the output voltage and the current of the charging equipment are adjusted through the controller so that the output voltage and the current meet the instructions in the parameter adjustment data. Meanwhile, the system continuously monitors various parameters in the charging process, and ensures that the charging equipment works according to a set strategy. In the process, the system can adjust according to the real-time data to ensure the effective implementation of the charging strategy. The execution results of all the charging control operations are recorded, and policy application data is generated, including the actual charging voltage, current and corresponding charging state at each time point.
In the control optimization sub-module, the charging control strategy is optimized by monitoring and adjusting based on strategy application data. The system first obtains policy application data, specifically including the actual charging voltage, current, and state of charge for each point in time. According to the data, the system monitors the whole charging process and identifies any deviation from the expected condition. And adopting a control optimization model, analyzing the difference between strategy application data and an expected result, and judging the effectiveness of the current charging strategy. According to the analysis result, the system adjusts the charging control strategy, optimizes the charging parameters and ensures that the battery is charged in a safe and efficient state. The optimized charging control strategy is applied to the charging process again, and real-time monitoring and adjustment are performed to form a closed-loop control system. Finally, the system generates a charge control scheme comprising the optimized charge voltage, current parameters and adjustment records. The charging control scheme is used for guiding the subsequent charging operation, and ensures continuous optimization and efficient execution of the charging process.
A battery charging monitoring control method of a low-speed electric vehicle comprises the following steps:
Based on the initial state of the battery pack, a sensor monitoring technology is adopted, battery state data are obtained in real time through monitoring voltage, current and temperature parameters, battery state change is monitored, voltage difference and current difference of the battery pack are compared, the variables of the battery pack are set, current distribution is adjusted, the current distribution state is recorded, and critical state information is generated;
Based on critical state information, a dynamic current adjusting method is adopted, voltage, current and temperature data of the battery packs are monitored through a sensor, current distribution is calculated, and current distribution of the plurality of battery packs is adjusted through a load balancing algorithm, so that a current distribution scheme is generated;
Based on a current distribution scheme, a scene simulation method is adopted, a random number is generated by setting parameters of voltage, current, temperature and environmental conditions in a charging process, a charging scene is simulated, a charging process scene set is constructed, and the risk level of each scene is calculated by using historical data and expert knowledge to generate a risk assessment result;
Based on a risk assessment result, a sequence mode mining method is adopted, a user charging record is collected, the sequence of charging behaviors is constructed, a common charging behavior mode is identified, user charging habits and demands are analyzed, the charging behavior mode is classified, good charging strategies and bad charging habits are identified, a charging optimization strategy is generated, charging voltage and current parameters are adjusted according to a state estimation result of a regression tree model through implementation of the charging control strategy, and the charging control strategy is circularly optimized, so that a charging control scheme is generated.
The present invention is not limited to the above embodiments, and any equivalent embodiments which can be changed or modified by the technical disclosure described above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above embodiments according to the technical matter of the present invention will still fall within the scope of the technical disclosure.
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Denomination of invention: A low-speed electric vehicle battery charging monitoring and control method and system Granted publication date: 20241018 Pledgee: CHINA FOREIGN ECONOMY AND TRADE TRUST Co.,Ltd. Pledgor: Zhejiang Huicheng Cloud Energy Technology Co.,Ltd. Registration number: Y2025980056954 |