New energy automobile battery device and control method thereof

Abstract

The invention relates to the technical field of battery control, in particular to a new energy automobile battery device and a control method thereof. The method comprises the following steps that dynamic parameter monitoring is conducted on an automobile battery, a battery state parameter set is obtained, and the battery state parameter set comprises a residual electric quantity continuous parameter, a voltage continuous parameter, a current continuous parameter and a temperature continuous parameter; according to the battery state parameter set, working condition identification is carried out on the automobile battery, a battery working condition type is obtained, and the battery working condition type is any one of a discharging working condition, a charging working condition and a standing working condition; if the battery working condition type is a discharge working condition, performing driving behavior data acquisition on the target automobile to obtain a driving behavior data set; and performing driving behavior pattern recognition on the target automobile based on the driving behavior data set to obtain a driving pattern type. The use efficiency and the safety of the battery are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery control, and in particular to a new energy vehicle battery device and a control method thereof. Background Art

[0002] In the field of new energy vehicles, battery devices are the core power source, and their performance directly affects the vehicle's endurance, charging efficiency, and overall energy efficiency. With the development of technology, the intelligence level of battery management systems (BMS) continues to improve, but existing technologies still have certain limitations in battery status monitoring, fault diagnosis, and energy management. Especially under high-load operation and extreme environments, how to ensure the stability and safety of the battery system has become a technical problem.

[0003] Modern battery device management systems often encounter the problem of difficulty in balancing energy management and battery life optimization when facing multiple tasks in parallel. For example, the battery device needs to dynamically adjust the energy output in different vehicle operating modes to meet the power demand, but excessive pursuit of efficient output can easily lead to excessive discharge of the battery, thereby accelerating its aging. In addition, the chemical properties of different types of batteries vary greatly, and a single control method often cannot take into account the use requirements of all battery types, which puts higher requirements on the universality of the system. Summary of the invention

[0004] Based on this, it is necessary for the present invention to provide a new energy vehicle battery device and a control method thereof to solve at least one of the above technical problems.

[0005] To achieve the above object, a new energy vehicle battery device control method comprises the following steps:

[0006] Step S1: Dynamically monitoring the parameters of the vehicle battery to obtain a battery status parameter set, wherein the battery status parameter set includes a remaining power continuous parameter, a voltage continuous parameter, a current continuous parameter, and a temperature continuous parameter; identifying the working condition of the vehicle battery according to the battery status parameter set to obtain a battery working condition type, wherein the battery working condition type is any one of a discharge working condition, a charging working condition, and a static working condition;

[0007] Step S2: if the battery operating condition type is a discharge operating condition, driving behavior data of the target vehicle is collected to obtain a driving behavior data set; based on the driving behavior data set, a driving behavior pattern of the target vehicle is identified to obtain a driving pattern type;

[0008] Step S3: dynamically allocating power to the vehicle battery according to the driving mode type to obtain a battery power configuration plan; dynamically adjusting the power output of the vehicle battery according to the battery power configuration plan;

[0009] Step S4: If the battery operating condition type is a charging condition, a temperature trend analysis is performed on the temperature continuous parameter to obtain a temperature change trend; a charging strategy of the vehicle battery is adjusted according to the temperature change trend to obtain a charging adjustment strategy; and a charging control operation is performed on the vehicle battery according to the charging adjustment strategy;

[0010] Step S5: If the battery operating condition type is a static operating condition, a battery cell balance test is performed on the vehicle battery to obtain a battery cell balance state set; and a periodic micro-cycle charge and discharge operation is performed on the vehicle battery according to the battery cell balance state set.

[0011] The present invention significantly improves the accuracy of battery status monitoring by dynamically monitoring the key parameters of the battery, and provides reliable data support for the battery management system. At the same time, it can flexibly identify different working conditions of the battery, and adopt corresponding control strategies according to the real-time analysis results, thereby improving the efficiency and safety of the battery. Under the discharge condition, by collecting driving behavior data and identifying the driving mode, the present solution achieves the best match between energy output and driving demand, which not only improves the driving experience, but also extends the service life of the battery. In addition, the present solution dynamically adjusts the charging strategy through temperature change trend analysis under the charging condition, improves the safety and efficiency of the charging process, and effectively prevents problems such as overheating. Under the static condition, through the battery cell balance detection and regular micro-cycle charge and discharge operations, the battery cell balance management is enhanced, and the problems of performance degradation and shortened life are reduced. Taking into account the chemical characteristics of different types of batteries, the present solution improves the universality of the battery management system and adapts to a variety of battery types. In summary, the present invention effectively reduces battery aging, extends service life, and improves overall energy utilization efficiency, which helps to reduce energy consumption and operating costs.

[0012] Preferably, the present invention also provides a new energy vehicle battery device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, for executing the new energy vehicle battery device control method as described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0014] Figure 1 A schematic flow chart of the steps of a new energy vehicle battery device control method according to an embodiment is shown.

[0015] Figure 2 A detailed flow chart of step S4 of an embodiment is shown.

[0016] Figure 3A detailed flow chart of step S5 of an embodiment is shown. DETAILED DESCRIPTION

[0017] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0018] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0019] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0020] To achieve this, please refer to Figures 1 to 3 The present invention provides a new energy vehicle battery device control method, comprising the following steps:

[0021] Step S1: Dynamically monitoring the parameters of the vehicle battery to obtain a battery status parameter set, wherein the battery status parameter set includes a remaining power continuous parameter, a voltage continuous parameter, a current continuous parameter, and a temperature continuous parameter; identifying the working condition of the vehicle battery according to the battery status parameter set to obtain a battery working condition type, wherein the battery working condition type is any one of a discharge working condition, a charging working condition, and a static working condition;

[0022] Step S2: if the battery operating condition type is a discharge operating condition, driving behavior data of the target vehicle is collected to obtain a driving behavior data set; based on the driving behavior data set, a driving behavior pattern of the target vehicle is identified to obtain a driving pattern type;

[0023] Step S3: dynamically allocating power to the vehicle battery according to the driving mode type to obtain a battery power configuration plan; dynamically adjusting the power output of the vehicle battery according to the battery power configuration plan;

[0024] Step S4: If the battery operating condition type is a charging condition, a temperature trend analysis is performed on the temperature continuous parameter to obtain a temperature change trend; a charging strategy of the vehicle battery is adjusted according to the temperature change trend to obtain a charging adjustment strategy; and a charging control operation is performed on the vehicle battery according to the charging adjustment strategy;

[0025] Step S5: If the battery operating condition type is a static operating condition, a battery cell balance test is performed on the vehicle battery to obtain a battery cell balance state set; and a periodic micro-cycle charge and discharge operation is performed on the vehicle battery according to the battery cell balance state set.

[0026] In this embodiment, it is assumed that the BMS has performed comprehensive dynamic parameter monitoring of the vehicle battery through a series of sensors and data acquisition systems. These sensors include sensors for measuring the remaining battery capacity (SOC), voltage sensors for monitoring battery voltage, current sensors for recording battery current, and temperature sensors for detecting battery temperature. The data of these sensors are transmitted to the BMS in real time to form a battery state parameter set. First, the BMS uses these parameter sets to identify the current working condition of the battery. The real-time data is compared with the preset working condition characteristics to determine whether the battery is in a discharge condition, a charging condition, or a static condition. For example, if the current sensor shows a positive current, indicating that the battery is discharging, and the data of the voltage and temperature sensors indicate that the battery performance is normal, the BMS will identify the battery condition as a discharge condition. After identifying the discharge condition, the BMS will activate the driving behavior data acquisition system, which includes accelerometers, GPS, vehicle speed sensors, etc., to collect driving behavior data sets. These data are used to analyze driving modes, such as smooth driving, aggressive driving, etc. Once the driving mode type is determined, the BMS will perform dynamic power allocation based on this information. This involves adjusting the output power of the battery to adapt to different driving needs. For example, if an aggressive driving mode is identified, the BMS will increase the output power of the battery. For charging conditions, the BMS pays special attention to continuous temperature parameters and performs temperature trend analysis. This can be achieved by analyzing the data from the temperature sensor to determine whether the battery is heating or cooling. Based on this analysis, the BMS will formulate a charging strategy, such as adjusting the charging current or charging time, to optimize the charging process and protect the battery. Finally, if the battery is in a static condition, the BMS will perform a battery cell balance detection. This usually involves comparing the voltage and temperature of each battery cell to identify cells with uneven performance. The BMS will then perform regular micro-cycle charge and discharge operations based on this data to maintain the performance and life of the battery pack.

[0027] Preferably, step S1 comprises the following steps:

[0028] Step S11: Dynamically monitoring the parameters of the vehicle battery to obtain a battery status parameter set, wherein the battery status parameter set includes a remaining power continuous parameter, a voltage continuous parameter, a current continuous parameter, and a temperature continuous parameter;

[0029] Specifically, high-precision sensors can be used to monitor the remaining power, voltage, current, and temperature of the battery in real time. These sensors are integrated into the battery management system (BMS) and can collect data at a high frequency (for example, once per second). The remaining power parameter is obtained by measuring the state of charge (SOC) of the battery, which is calculated by the integration of voltage and current. The voltage continuity parameter is measured by the battery terminal voltage sensor, while the current continuity parameter measures the charge and discharge current of the battery through the current sensor. The temperature continuity parameter is obtained by the temperature sensor installed near the battery cell. All these parameter data are transmitted to the central processing unit of the BMS in real time, and finally a set of battery status parameters is obtained.

[0030] Step S12: Calculating the voltage change rate of the vehicle battery according to the voltage continuity parameter to obtain the voltage change rate parameter;

[0031] Specifically, the voltage change can be obtained by calculating the difference between the voltage values ​​at adjacent time points based on the voltage continuity parameter. Subsequently, this change is divided by the time interval to obtain the voltage change rate. This change rate parameter reflects the dynamic change characteristics of the battery voltage over time, which is crucial for evaluating the health of the battery and predicting its behavior. The calculation formula for the voltage change rate can be expressed as: voltage change rate = (V(t2)-V(t1)) / (t2-t1), where V(t1) and V(t2) are the voltage measurements at t1 and t2, respectively.

[0032] Step S13: calculating the current fluctuation amplitude of the vehicle battery according to the current continuity parameter to obtain the current fluctuation amplitude parameter;

[0033] Specifically, the standard deviation or variance of the current continuous parameters can be calculated to quantify the degree of current fluctuation. The current fluctuation amplitude parameter can reflect the stability of the battery current during the charging and discharging process. For the battery management system, this is an important indicator for evaluating battery performance and preventing potential failures. The calculation formula for the current fluctuation amplitude can be expressed as the standard deviation of the current value σ(I), where I is the set of current measurement values.

[0034] Step S14: dynamically matching the voltage change rate parameter and the current fluctuation amplitude parameter with a preset operating condition determination parameter set to obtain a battery operating condition type, wherein the battery operating condition type is any one of a discharge operating condition, a charge operating condition and a static operating condition.

[0035] Specifically, the voltage change rate parameter and the current fluctuation amplitude parameter can be compared with the preset operating condition determination parameter set to determine the battery operating condition type in a dynamic condition matching manner. The preset operating condition determination parameter set contains a series of thresholds for distinguishing different operating conditions, such as the typical range of voltage change rate and current fluctuation amplitude for discharge conditions, charging conditions, and static conditions. The BMS can determine the current working state of the battery by comparing the parameters calculated in real time with these thresholds. For example, if the voltage change rate is low and the current fluctuation amplitude is small, the battery is in a static condition; if the voltage change rate is high and the current fluctuation amplitude is large, it is in a discharge condition or a charging condition. This dynamic condition matching method can accurately identify the battery's operating condition in real time.

[0036] The present invention significantly improves the accuracy of battery status assessment by continuously monitoring the voltage and current of the battery and calculating its rate of change and fluctuation amplitude. This enhances the sensitivity of battery operating condition identification, and can accurately and in real time determine whether the battery is in a discharge condition, a charging condition, or a static condition, thereby implementing precise battery management. The response speed of the battery management system is optimized through a dynamic condition matching mechanism, ensuring a rapid response to changes in battery status and timely adjustment of control strategies. Through continuous monitoring and dynamic analysis, the present invention improves the adaptability of battery usage strategies, and can provide reasonable usage strategies based on the performance changes of the battery under different usage conditions. At the same time, accurate operating condition identification helps to promptly detect and prevent abnormal battery conditions, thereby enhancing the safety and reliability of the battery. Accurate monitoring and intelligent management help to avoid overcharging, over-discharging, and temperature anomalies, thereby extending the service life of the battery.

[0037] Preferably, step S2 comprises the following steps:

[0038] Step S21: if the battery operating condition type is a discharge operating condition, driving behavior data of the target vehicle is collected to obtain a driving behavior data set, wherein the driving behavior data set includes vehicle acceleration data, vehicle braking data, vehicle steering data, vehicle speed data, and vehicle route data;

[0039] Specifically, when the battery operating condition type is identified as a discharge condition, the battery management system (BMS) triggers the driving behavior data collection module to start working. This module uses multiple sensors on the vehicle to collect data, including accelerometers, brake pressure sensors, steering angle sensors, vehicle speed sensors and GPS positioning systems. The accelerometer provides acceleration data of the vehicle in three axes, the brake pressure sensor measures the braking force, the steering angle sensor records the steering wheel rotation angle, the vehicle speed sensor provides the real-time speed of the vehicle, and the GPS positioning system tracks the vehicle's route and position. All of these data are collected in real time and transmitted to the data processing unit of the BMS through the vehicle communication network, and finally a driving behavior data set is obtained, wherein the driving behavior data set includes vehicle acceleration data, vehicle braking data, vehicle steering data, vehicle speed data and vehicle route data.

[0040] Step S22: aligning the driving behavior data set with timestamps to obtain a synchronized driving behavior data set, wherein the synchronized driving behavior data set includes synchronized vehicle acceleration data, synchronized vehicle braking data, synchronized vehicle steering data, synchronized vehicle speed data, and synchronized vehicle route data;

[0041] Specifically, the driving behavior dataset can be timestamped. This means that each data point is assigned a precise time stamp that corresponds to a global clock or other synchronization signal. For example, when the vehicle speed sensor detects a change in vehicle speed, the data point is timestamped to indicate the specific time when the change occurred. Similarly, acceleration, braking, steering, and route data are also timestamped. In this way, the BMS can ensure that all datasets are synchronized in time, and ultimately obtain a synchronized driving behavior dataset.

[0042] Step S23: performing acceleration state statistics on the synchronous vehicle acceleration data according to a preset acceleration state gradient threshold to obtain acceleration state frequency data;

[0043] Specifically, the synchronous vehicle acceleration data can be analyzed according to the preset acceleration state gradient thresholds. These thresholds are pre-set to distinguish different acceleration states, such as smooth acceleration, slow acceleration, and sudden acceleration. The BMS calculates the rate of change of acceleration at each time point and compares it with the threshold. For example, if the rate of change of acceleration is lower than a certain threshold, it is classified as smooth acceleration; if the rate of change of acceleration is higher than another threshold, it is classified as sudden acceleration. By performing this classification on the acceleration data at each time point, the BMS can count the frequency of occurrence of each acceleration state and obtain the acceleration state frequency data.

[0044] Step S24: performing braking frequency statistics on the synchronous automobile braking data to obtain braking frequency data;

[0045] Specifically, the synchronized vehicle braking data records the start and end of each braking event, which is defined as the driver pressing the brake pedal to activate the braking system. The BMS records all braking events that occur within a specific time period (for example, within an hour) and then calculates the total number of braking times. This value is divided by the total length of the time period, and the quotient obtained is the braking frequency data, which reflects the frequency of the driver's braking operation during the time period.

[0046] Step S25: performing braking force statistics on the target vehicle to obtain braking force statistical data;

[0047] Specifically, the BMS can be used to analyze the braking force recorded by the brake pressure sensor in each braking event. The system (BMS) calculates the peak braking force of each braking event and summarizes the peak braking force of all braking events to obtain the braking force statistics for the entire observation period.

[0048] Step S26: performing statistics on the duration of a single braking of the synchronous automobile braking data to obtain braking duration distribution data, and performing braking intensity classification on the braking behavior according to the braking frequency data, the braking force statistics data and the braking duration distribution data to obtain braking behavior intensity data, wherein the braking behavior intensity data is any one of light braking, medium braking and emergency braking;

[0049] Specifically, the synchronous automobile braking data records the duration of each braking event, that is, the length of time from the start of braking to the end of braking. That is, including the duration data of all braking events, the synchronous automobile braking data is statistically analyzed to obtain the distribution data of braking duration. Next, combined with the braking frequency data and braking force statistics, the BMS will classify the braking behavior according to the preset classification criteria. For example, light braking corresponds to a shorter braking duration and a lower braking force; medium braking corresponds to a medium braking duration and braking force; and emergency braking corresponds to a longer braking duration and a higher braking force. Through this classification, the braking behavior intensity data can be obtained.

[0050] Step S27: performing driving behavior pattern recognition on the target vehicle according to the synchronous vehicle steering data, the synchronous vehicle speed data, the synchronous vehicle route data, the acceleration state frequency data and the braking behavior intensity data to obtain the driving pattern type.

[0051] Specifically, please refer to the sub-steps of step S27 for the detailed implementation process of this embodiment.

[0052] The present invention significantly improves the accuracy of driving behavior recognition by comprehensively collecting and analyzing the acceleration, braking, steering, speed and route data of the car, laying a solid data foundation for the accurate recognition of driving behavior patterns. The synchronization of data is enhanced by the application of timestamp alignment, ensuring the accuracy and reliability of the analysis results. By statistics and classification of key parameters such as acceleration, braking frequency, braking force and braking duration, the characteristics of driving behavior are deeply excavated and the extraction of driving behavior characteristics is optimized. At the same time, through detailed driving mode classification, the refinement of recognition is improved, and more personalized driving mode recognition is achieved. The integration of these technologies enhances the adaptability of battery power management, and can dynamically adjust power output according to the driver's driving habits and real-time road conditions, thereby improving energy utilization efficiency.

[0053] Preferably, step S27 includes the following steps:

[0054] Step S271: performing acceleration behavior classification on the acceleration state frequency data to obtain acceleration behavior data, wherein the acceleration behavior data is any one of steady acceleration, slow acceleration and rapid acceleration;

[0055] Specifically, acceleration data can be classified as smooth acceleration, slow acceleration, or sudden acceleration according to preset acceleration thresholds. For example, smooth acceleration is defined as acceleration values ​​varying within a specific small range, which is determined based on vehicle manufacturer recommendations and driving comfort studies. Slow acceleration corresponds to a gradual increase in acceleration values ​​within a wider range, while sudden acceleration corresponds to a rapid rise in acceleration values ​​to a higher level. The BMS calculates the frequency of each acceleration behavior over the entire observation period and integrates these frequency data into acceleration behavior data.

[0056] Step S272: Calculating the steering amplitude and change rate of the synchronous automobile steering data to obtain steering change rate data and steering amplitude data;

[0057] Specifically, the BMS processes the synchronized vehicle steering data collected from the steering angle sensor. The system calculates the steering amplitude, which is determined by measuring the angle of the steering wheel from a neutral position (usually the straight-ahead position). At the same time, the system also calculates the steering rate of change, which is the rate at which the steering angle changes over time. These calculations involve differentiating the steering angle data at consecutive time points to obtain the steering angle velocity.

[0058] Step S273: performing steering behavior classification on the steering change rate data and the steering amplitude data to obtain steering behavior data, wherein the steering behavior data is any one of straight-line driving, slow turning and sharp turning;

[0059] Specifically, the driver's steering behavior can be classified based on the steering change rate data and the steering amplitude data. A series of predefined thresholds are set to distinguish between straight driving, slow turning and sharp turning. For example, if the steering change rate is low and the steering amplitude is small, the behavior is classified as straight driving. If the steering change rate is high but the steering amplitude is still in the medium range, it is classified as slow turning. Sharp turns correspond to high steering change rates and large steering amplitudes. The BMS analyzes this data to determine the frequency of each steering behavior within a specific time period and integrates this information into steering behavior data.

[0060] Step S274: Plotting a speed-time curve for the synchronous vehicle speed data to obtain a vehicle speed dynamic curve;

[0061] Specifically, a speed-time curve may be drawn based on the synchronous vehicle speed data, with time being used as the horizontal coordinate and the vehicle speed being used as the vertical coordinate, and a vehicle speed dynamic curve may be drawn on a graph.

[0062] Step S275: Calculating the speed smoothness index of the vehicle speed dynamic curve to obtain speed smoothness distribution data, and classifying the speed state of the vehicle driving speed according to the speed smoothness distribution data to obtain speed behavior data, wherein the speed behavior data is any one of a uniform speed driving stage, an acceleration stage, and a deceleration stage;

[0063] Specifically, the speed-time curve can be further analyzed to calculate the speed smoothness index. This index is obtained by measuring the continuity and consistency of speed changes, which involves differential and integral operations on speed data. The higher the speed smoothness index, the smoother the vehicle's driving, without drastic speed changes. Based on this index, the BMS will classify the vehicle's driving state into a uniform driving phase, an acceleration phase, or a deceleration phase. For example, if the speed smoothness index is within a preset high value range, it is classified as uniform driving; if the index rises rapidly, it means that the vehicle is accelerating; if the index drops rapidly, it means that the vehicle is decelerating.

[0064] Step S276: calculating the path curvature and turning frequency of the driving path based on the synchronized automobile route data to obtain path curvature data and turning frequency data, and classifying the driving environment by road type according to the path curvature data and turning frequency data to obtain driving environment data, wherein the driving environment data is any one or more of urban roads, highways and country roads;

[0065] Specifically, the BMS analyzes synchronized car route data collected from GPS and other sensors to calculate the curvature and turning frequency of the driving path. The path curvature is determined by measuring the frequency and amplitude of the vehicle's direction changes during driving, while the turning frequency is obtained by counting the number of times the vehicle changes direction within a specific time period. For example, high curvature and high turning frequency indicate that the vehicle is driving on an urban road, while low curvature and low turning frequency indicate that the vehicle is driving on a highway. Based on this data, the BMS classifies the driving environment and obtains driving environment data, such as urban roads, highways, or country roads.

[0066] Step S278: Match the acceleration behavior data, steering behavior data, speed behavior data and driving environment data with the preset driving mode determination rule set item by item to obtain the driving mode type, wherein the driving mode type is any one of economical, standard, sporty or aggressive.

[0067] Specifically, the acceleration behavior data, steering behavior data, speed behavior data, and driving environment data can be analyzed and matched by predefined logic judgment rules to determine the driving mode type. First, the BMS checks the acceleration behavior data to determine whether the vehicle often accelerates smoothly. If the data shows that the vehicle accelerates smoothly most of the time, it indicates that the driver tends to drive in an energy-saving and efficient manner, which is consistent with the economic driving mode. Next, the BMS analyzes the steering behavior data. If the steering behavior data shows that the vehicle is driving in a straight line or turning slowly most of the time, it means that the driver has adopted a more conservative and stable driving strategy, which is also consistent with the characteristics of the economic or standard driving mode. Then, the BMS considers the speed behavior data. If the speed data shows that the vehicle is driving at a constant speed most of the time, or the speed does not change much, this is usually related to the economic driving mode. If the data shows that the vehicle frequently accelerates and decelerates sharply, this matches the sporty or aggressive driving mode. Finally, the BMS will combine the driving environment data to further refine the judgment of the driving mode. For example, if the vehicle mainly drives on urban roads and the driving speed does not change much, this is more inclined to the economic or standard driving mode. On the contrary, if the vehicle is often driven on the highway and the speed changes frequently, this is more in line with a sporty or aggressive driving mode. Through the above steps, BMS will comprehensively consider all data and preset rules to finally determine the driving mode type. For example, if the data during a driving process shows smooth acceleration, slow turning, constant speed driving, and mainly driving on urban roads, BMS will judge this driving mode as economical.

[0068] The present invention significantly improves the battery management system's accurate understanding of driving behavior characteristics through refined driving behavior classification and comprehensive driving mode recognition. It can dynamically adjust the battery's power output according to different driving modes such as economical, standard, sporty or aggressive, optimize energy utilization, and thus improve battery performance and life. At the same time, this personalized driving mode recognition enhances the driving experience and meets the needs of different drivers. In-depth analysis of driving behavior helps to promptly discover and prevent potential unsafe factors, such as sudden acceleration or sharp turns, significantly improving driving safety. This solution also provides important data support for intelligent driving systems and promotes the development of intelligent driving technology. By accurately identifying driving modes, this solution helps to reduce ineffective energy consumption and improve the overall energy efficiency of new energy vehicles.

[0069] Preferably, step S3 comprises the following steps:

[0070] Step S31: extracting driving characteristic parameters of the target vehicle according to the driving mode type to obtain a driving characteristic parameter set, wherein the driving characteristic parameter set includes average acceleration data, maximum acceleration data, average vehicle speed data, maximum vehicle speed data, acceleration change rate data and vehicle speed fluctuation data;

[0071] Specifically, the BMS first determines the driving mode type, such as economical, standard, sporty or aggressive. Next, based on this driving mode type, a set of driving characteristic parameters is extracted from the previously collected driving behavior data. These parameters include average acceleration data, which is obtained by calculating the average value of all acceleration data over a period of time; maximum acceleration data, that is, the maximum acceleration value recorded during the period; average vehicle speed data, which is obtained by calculating the average value of all vehicle speed data; maximum vehicle speed data, that is, the highest speed value recorded; acceleration change rate data, which is obtained by calculating the rate of change of the acceleration value over time; and vehicle speed volatility data, which is obtained by analyzing the fluctuation range and frequency of the vehicle speed data. These parameters are combined to form a driving characteristic parameter set.

[0072] Step S32: collecting the total mass of the target vehicle to obtain the total mass data of the vehicle;

[0073] Specifically, the BMS collects the total mass of the vehicle. This involves reading data from the vehicle's weighing system or using sensors built into the vehicle (such as load sensors) to estimate the vehicle's current total mass. In some vehicles, this data can be obtained directly from the on-board diagnostic system (OBD) or manually entered through the vehicle's information system. After obtaining the vehicle's total mass data, the BMS will store it in the system.

[0074] Step S33: acquiring the air resistance coefficient of the target vehicle according to the driving environment data to obtain the driving air resistance coefficient;

[0075] Specifically, BMS will obtain the air resistance coefficient of the target car based on the driving environment data. This coefficient reflects the amount of resistance encountered by the vehicle when it is driving in the air, and it is related to the shape, surface characteristics and speed of the vehicle. BMS uses a pre-set database that contains the air resistance coefficients of different types of vehicles at different speeds. The system will retrieve the corresponding air resistance coefficient from the database based on the current vehicle type and speed. In addition, if the vehicle is equipped with real-time wind speed and wind direction sensors, BMS can also combine these data to adjust the air resistance coefficient to more accurately reflect the actual driving conditions and finally obtain the air resistance coefficient.

[0076] Step S34: acquiring the rolling resistance coefficient of the target vehicle according to the driving environment data to obtain the driving rolling resistance coefficient;

[0077] Specifically, the rolling resistance coefficient of a car is determined during the design phase of the car, which includes measuring the friction between the tire and the road under different road conditions (such as dry asphalt road, wet cement road, etc.). These test results will be recorded and used to establish a basic database of rolling resistance coefficients. Based on the test results of the car's design phase, a database containing rolling resistance coefficients under different tire types, road conditions and vehicle speeds is established. This database will serve as a reference for the BMS to obtain the rolling resistance coefficient. In actual driving, the BMS will retrieve the corresponding rolling resistance coefficient from the database based on the vehicle's real-time driving environment data, such as the current road type and vehicle speed. For example, if the vehicle is driving at 60 km / h on a dry asphalt road, the BMS will find the rolling resistance coefficient that matches these conditions from the database. Although the rolling resistance coefficient has been determined during the design phase, the actual driving conditions will change, such as wet road surfaces or tire wear. Therefore, the BMS will regularly update the rolling resistance coefficient to adapt to these changes. This can be achieved through the vehicle's sensor data (such as tire pressure sensors) and driver input (such as road conditions reported through the vehicle information system). After obtaining the accurate rolling resistance coefficient, BMS will use it in the vehicle's energy management strategy.

[0078] Step S35: constructing a real-time electrochemical model of the vehicle battery based on the battery state parameter set to obtain a real-time electrochemical model of the battery;

[0079] Specifically, the battery state parameter set can be used to estimate the current state of the battery, including state of charge (SOC), state of health (SOH), and internal resistance. By monitoring these parameters in real time, the BMS can build an electrochemical model that reflects the current behavior and performance of the battery. This model includes electrochemical parameters such as the battery's open circuit voltage, charge transfer reaction rate, and electrolyte concentration changes.

[0080] Step S36: evaluating the power output range of the vehicle battery based on the real-time electrochemical model of the battery and according to the continuous parameter of the remaining power, to obtain the available power range of the battery;

[0081] Specifically, the BMS collects key parameters of the battery in real time through sensors connected to the battery, including but not limited to SOC (remaining power continuous parameter), voltage, current, internal resistance and temperature. These data are acquired through high-precision sensors and data acquisition systems (such as ADCs-analog-to-digital converters) and transmitted to the central processing unit of the BMS. The BMS has a built-in real-time electrochemical model of the battery, which predicts the behavior of the battery based on the physical and chemical characteristics of the battery. This model includes parameters such as the open circuit voltage of the battery, charge transfer reactions, and changes in electrolyte concentration. The BMS uses this model to simulate the discharge and charging behavior of the battery at different SOC levels. The BMS pays special attention to the SOC parameter because it is directly related to the remaining energy and power output capacity of the battery. Based on the battery's electrochemical model and current SOC, the BMS calculates the maximum instantaneous power that the battery can provide without compromising the battery life. This involves considering the battery's thermal management limitations and voltage limitations. The BMS also calculates the minimum power that the battery can provide while ensuring battery performance and safety. When calculating the power output range, the BMS also considers the battery's safety thresholds and operating limitations. For example, the battery has higher power output capability at high SOC levels, but the BMS will limit the maximum charge rate to prevent overcharging. Similarly, at low SOC levels, the BMS will limit the minimum discharge rate to avoid over-discharging.

[0082] Step S37: dynamically allocate power to the vehicle battery according to the driving characteristic parameter set, the vehicle gross mass data, the driving air resistance coefficient, the driving rolling resistance coefficient and the battery available power range to obtain a battery power configuration plan; dynamically adjust the power output of the vehicle battery according to the battery power configuration plan.

[0083] Specifically, please refer to the sub-steps of step S37 for the detailed implementation process of this embodiment.

[0084] The present invention realizes dynamic allocation and adjustment of battery power by accurately analyzing the characteristics of driving behavior, such as average acceleration and vehicle speed, combined with the physical characteristics of the vehicle and real-time battery status evaluation. This not only optimizes energy use, improves battery operating efficiency and vehicle performance, but also significantly improves energy utilization efficiency, extends battery life, and increases the vehicle's cruising range. At the same time, dynamic power output adjustment ensures the smoothness and response speed of the driving experience and meets the needs of different driving scenarios. By real-time monitoring of the battery status and timely adjustment of power output, overcharging and over-discharging are effectively avoided, the safe use of the battery is guaranteed, and the risk of thermal runaway is reduced. The present invention also particularly strengthens the adaptability to complex road conditions and changeable driving environments, provides a suitable battery management strategy, and enhances the overall adaptability and reliability of the vehicle.

[0085] Preferably, step S37 includes the following steps:

[0086] Step S371: Acquire driving environment data, and construct a driving scene for the target vehicle according to the driving environment data to obtain a driving scene virtual environment;

[0087] Specifically, it can be determined based on the driving environment data whether the vehicle is currently traveling on an urban road, a highway, or a country road. Assuming that the vehicle is traveling on an urban road, the BMS collects data about traffic flow, road type (such as asphalt or cement), traffic signals (such as the location and status of traffic lights), and obstacles (such as pedestrians, bicycles, or other vehicles). Using this data, the BMS builds a virtual environment for the driving scenario that simulates the actual conditions around the vehicle. In this virtual environment, the BMS takes into account the characteristics of urban roads, such as frequent starts and stops, multiple traffic signals, and complex traffic flows. This driving scenario virtual environment provides the BMS with a framework for simulating and predicting the behavior of the vehicle in the current environment.

[0088] Step S372: Acquire synchronized vehicle route data, and perform environment-route fusion modeling on the driving scene virtual environment according to the synchronized vehicle route data to obtain a driving route environment model;

[0089] Specifically, synchronized car route data can be obtained, which includes the vehicle's GPS trajectory, the geometric features of the road (such as curves, slopes), and the attributes of the road (such as pavement material and lane width). Assuming that the vehicle is driving on a country road with multiple curves and slope changes, the BMS will combine these route features with the previously constructed virtual environment. The BMS will analyze the vehicle's current position and the road conditions ahead, such as the curvature of the curve and the size of the slope, and how these conditions affect the vehicle's driving speed and power requirements. Based on these analyses, the BMS builds a driving route environment model that describes in detail the dynamic characteristics and environmental factors of the vehicle when driving on a specific route.

[0090] Step S373: Perform dynamic modeling on the target vehicle to obtain a vehicle dynamic model;

[0091] Specifically, the vehicle dynamics model can be constructed by considering the physical parameters of the target vehicle, such as vehicle mass, tire characteristics, suspension system characteristics, aerodynamic characteristics, etc. The dynamics model includes the longitudinal dynamics of the vehicle (related to speed and acceleration), lateral dynamics (related to steering and lateral force), and rolling dynamics (related to the tilt and roll of the vehicle).

[0092] Step S374: real-time positioning of the target vehicle to obtain the real-time geographic coordinates of the vehicle, and estimating the remaining travel time of the target vehicle based on the synchronized vehicle route data and the real-time geographic coordinates of the vehicle to obtain the remaining travel time data;

[0093] Specifically, the vehicle's GPS system can be used to locate the target car in real time and obtain the car's real-time geographic coordinates, including longitude, latitude and altitude information. These coordinates reflect the precise position of the vehicle on the road. Subsequently, the BMS combines the synchronized car route data to obtain information such as the total length of the route, the characteristics of each section (such as straight roads, curves, slopes), and traffic conditions. Using this data, the BMS can estimate the remaining travel time for the vehicle to reach its destination. This estimate takes into account the vehicle's current speed, the complexity of the route, traffic conditions, and delay factors. For example, if the vehicle is traveling on a highway and the route data indicates that there is traffic congestion ahead, the BMS will adjust the estimate of the remaining travel time to reflect this situation. In this way, the BMS obtains the remaining travel time data.

[0094] Step S375: simulating the driving trajectory of the target vehicle based on the driving route environment model and the vehicle dynamics model according to the driving characteristic parameter set, the vehicle gross mass data, the driving air resistance coefficient, the driving rolling resistance coefficient and the remaining travel time data to obtain driving trajectory simulation data;

[0095] Specifically, the target vehicle's driving trajectory can be simulated based on the driving route environment model and the vehicle dynamics model, combined with the driving characteristic parameter set, the vehicle's total mass data, the driving air resistance coefficient, the driving rolling resistance coefficient, and the remaining travel time data. This simulation process involves predicting the vehicle's expected behavior on different road sections, including acceleration on straight roads, turning on curves, climbing or descending on ramps, etc. BMS will use the vehicle dynamics model to predict the vehicle's power demand and energy consumption under these different road conditions. At the same time, BMS will also consider the impact of the vehicle's total mass on acceleration and climbing ability, and the impact of the air resistance coefficient and rolling resistance coefficient on energy consumption. By combining these factors, BMS can simulate the vehicle's driving trajectory in the remaining journey, including speed changes, acceleration events, and energy requirements, and finally obtain driving trajectory simulation data.

[0096] Step S376: dynamically distribute power to the vehicle battery according to the available power range of the battery and the driving trajectory simulation data to obtain a battery power configuration plan, and dynamically adjust the power output of the vehicle battery according to the battery power configuration plan.

[0097] Specifically, please refer to the sub-steps of step S376 for the detailed implementation process of this embodiment.

[0098] The present invention provides a simulation environment that is close to actual driving conditions and real background information by accurately constructing driving scenarios and modeling the driving route environment in detail. The simulation of vehicle motion characteristics is enhanced through in-depth understanding of dynamic modeling, providing a scientific basis for precise control. Accurate estimation of real-time positioning and travel time enables the solution to dynamically adapt to routes and traffic conditions and optimize driving strategies. High-precision driving trajectory simulation provides accurate prediction data for battery power management, and dynamic power allocation based on this optimizes the efficient use of energy. Intelligent regulation of battery power output further ensures that the car achieves optimal energy management based on actual driving needs and battery status. In summary, the present invention improves driving safety and comfort, and enhances the adaptability and reliability of the car to changing driving environments and complex route conditions, providing the driver with a safer, more comfortable and efficient driving experience.

[0099] Preferably, step S376 includes the following steps:

[0100] Step S3761: identifying high power demand areas on the driving trajectory simulation data to obtain a high power demand area set;

[0101] Specifically, the driving trajectory simulation data can be analyzed to identify areas with higher battery power demand during vehicle driving. These areas include steep uphill sections, sections that require rapid acceleration to overtake, or sections of continuous driving at higher speeds on highways. The BMS sets specific power thresholds to define "high power demand areas" based on the vehicle dynamics model and historical driving data. Once certain parts of the simulation data exceed this threshold, these parts will be marked as high power demand areas and added to the high power demand area set.

[0102] Step S3762: performing driving duration statistics on the high power demand area set based on the driving trajectory simulation data to obtain a high power demand duration set;

[0103] Specifically, the driving duration of each high power demand area can be counted based on the driving trajectory simulation data. This involves measuring the start and end time of each high power demand event in the simulation data and calculating the time difference between them. In this way, the BMS can obtain the duration of each high power demand area and summarize these time data to form a high power demand duration set.

[0104] Step S3763: mapping the remaining travel time data to a high power demand time according to the high power demand duration set to obtain a high power demand time distribution diagram;

[0105] Specifically, the specific duration of each high power demand area can be compared with the vehicle's real-time location and the remaining travel schedule to determine the specific time location of these high power demand events throughout the vehicle's entire journey. In this way, the BMS can generate a high power demand time distribution map, which shows which time periods the vehicle will experience high power demand throughout the remaining journey.

[0106] Step S3764: performing peak power estimation on the high power demand area set based on the driving trajectory simulation data to obtain peak power demand data;

[0107] Specifically, driving trajectory simulation data can be collected from the BMS. This data includes information such as the vehicle's position, speed, acceleration, and the slope of the route. By analyzing the driving trajectory data, high power demand areas such as steep slopes and sharp turns are identified. The vehicle dynamics model is used to estimate the power required under specific driving conditions. This model takes into account factors such as the vehicle's mass, air resistance, rolling resistance, and slope resistance. For example, the following formula can be used to estimate the power required for a vehicle to overcome rolling resistance and air resistance on a level road: Among them, P roll+air is the power required to overcome rolling resistance and air resistance, F rollis the rolling resistance, v is the vehicle speed, ρ is the air density, A is the front area of ​​the vehicle, C d is the air resistance coefficient. Slope resistance calculation: For a road section with a slope, calculate the additional power required for the slope resistance: P grade =m·g·sin(θ)·v; where P grade is the additional power required for slope resistance, m is the vehicle mass, g is the acceleration due to gravity, sin(θ) is the slope angle, and v is the vehicle speed. Peak power demand estimation: Add all the above power requirements and consider the additional power required for vehicle acceleration to obtain the peak power demand in the high power demand area: P peak =P roll+air +P grade +P peace ; Among them, P peace is the power required for acceleration, which can be calculated based on the acceleration and mass of the vehicle. The acceleration power P of the vehicle peace It can be calculated by the following formula: Where m is the mass of the vehicle (in kilograms, kg), and a is the acceleration of the vehicle (in meters per second squared, m / s 2 ), v is the instantaneous speed of the vehicle (unit: meters per second, m / s). The calculated peak power demand data is recorded and the peak power demand data is generated.

[0108] Step S3765: if the peak power demand data is within the battery available power range, the peak power demand data is directly used as the maximum available power data; otherwise, the maximum battery available power within the battery available power range is used as the maximum available power data;

[0109] Specifically, if the peak power demand data is within the battery available power range, the peak power demand data is directly used as the maximum available power data; otherwise, the maximum battery available power within the battery available power range is used as the maximum available power data.

[0110] Step S3766: acquiring average vehicle speed data, estimating the cruising power of the vehicle battery according to the average vehicle speed data, and obtaining cruising power demand data;

[0111] Specifically, the average vehicle speed data can be obtained through the vehicle speed sensor, and then, a pre-set table lookup method is used to find the power demand of the vehicle battery in the cruising state according to the average vehicle speed data, and finally the cruising power demand data is obtained.

[0112] Step S3767: Calculate the power buffer zone for the maximum available power data and the cruise power demand data to obtain the power buffer zone;

[0113] Specifically, the maximum available power data and the cruise power demand data can be compared to calculate a power buffer. This buffer is to ensure that the battery has enough power margin to cope with sudden high power demands, such as sudden acceleration or climbing, under any driving conditions. The BMS uses a simple mathematical model to calculate this buffer, for example, by subtracting the cruise power demand from the maximum available power, and then adjusting the difference according to a preset percentage. This percentage can be determined based on empirical data or the discharge characteristics of the battery.

[0114] Step S3768: Generate a dynamic power curve for the vehicle battery based on the high power demand time distribution diagram according to the maximum available power data, the cruising power demand data and the power buffer, obtain a dynamic power output curve, and use the dynamic power output curve as a battery power configuration scheme, and adjust the dynamic power output of the vehicle battery according to the battery power configuration scheme.

[0115] Specifically, a dynamic programming algorithm can be used to generate a dynamic power output curve for the vehicle battery based on the high power demand time distribution map, the maximum available power data, the cruising power demand data, and the power buffer zone. This algorithm takes into account the power demand of the vehicle in different time periods and ensures that the power output of the battery does not exceed the available power range of the battery while meeting these demands. For example, if the high power demand time distribution map shows that the vehicle is about to enter a high power demand area, the BMS will adjust the output of the battery through the algorithm to prepare to provide additional power. At the same time, the algorithm will also consider the power buffer zone to ensure that the battery still has enough power margin after the high power demand. The generated dynamic power output curve is then used as a battery power configuration plan, and the BMS adjusts the power output of the battery in real time according to this plan to optimize the performance of the vehicle and the service life of the battery. This process involves data processing and algorithm calculations, which are performed by a microcontroller or computing module in the BMS.

[0116] The present invention analyzes driving trajectory simulation data to accurately identify and optimize responses to high-power demand areas, thereby ensuring power support for key areas. By intelligently managing peak power and combining it with the available power range of the battery, it is ensured that power demands are met while not exceeding safety and performance limits. By accurately estimating cruising power demands and setting a reasonable power buffer zone, this solution ensures safety while meeting power demands. The dynamically generated power output curve provides a scientific basis for battery power configuration, improves driving performance and safety, and enhances the service life and reliability of the battery. In summary, the present invention improves energy utilization efficiency, supports complex road conditions and changing driving environments, and enhances the adaptability and reliability of the vehicle through precise power demand analysis and dynamic management.

[0117] Preferably, step S4 comprises the following steps:

[0118] Step S41: if the battery operating condition type is a charging condition, a temperature trend analysis is performed on the temperature continuous parameter to obtain a temperature change trend, wherein the temperature change trend is any one of a temperature rising trend, a temperature falling trend and a temperature stable state;

[0119] Specifically, if the battery operating condition type is a charging condition, the BMS will start the temperature monitoring system, which includes multiple temperature sensors installed on the battery cells. These sensors measure the temperature of the battery in real time and send the data to the BMS. The BMS uses this data to analyze the continuous changes in temperature to determine the trend of temperature changes. In specific operations, the BMS will use a time series analysis method to calculate the moving average or derivative of the temperature data to identify whether the temperature is rising, falling, or remaining stable. For example, if the temperature derivative (temperature change rate) is positive, it indicates that the battery temperature is rising; if it is negative, it indicates that the temperature is falling; if it is close to zero, it indicates that the temperature is stable.

[0120] Step S42: If the temperature change trend is a temperature rising trend, a thermal management optimization strategy is formulated for the vehicle battery to obtain a charging adjustment strategy;

[0121] Specifically, if the temperature change trend is a rising trend, the process of formulating a thermal management optimization strategy is triggered. The BMS will adopt methods such as increasing the power of the cooling system, adjusting the size of the charging current, or changing the charging strategy. For example, the BMS activates the liquid cooling system to remove the heat generated by the battery through the circulation of coolant. Alternatively, the BMS will reduce the charging current to reduce the heat generation of the battery. These operations can be achieved through the control logic built into the BMS, which dynamically adjusts the charging parameters based on the temperature feedback of the battery.

[0122] Step S43: If the temperature change trend is a temperature decreasing trend or a temperature stable state, a fast charging optimization strategy is formulated for the vehicle battery to obtain a charging adjustment strategy.

[0123] Specifically, if the temperature change trend is a downward trend or a stable temperature state, the BMS will increase the charging current or voltage to shorten the charging time. At the same time, the BMS will monitor the temperature of the battery to ensure that overheating does not occur during fast charging. For example, the BMS uses a predefined charging curve that adjusts the charging parameters based on the battery's charge state and temperature. If a temperature drop is detected, the BMS will decide to maintain or slightly increase the charging current to take advantage of the fact that the battery can accept a higher charging rate at a lower temperature. If the temperature is stable, the BMS will continue to execute the current charging strategy while maintaining real-time monitoring of the battery status to ensure that the battery remains in the best condition during the charging process. These operations can also be implemented through the BMS's built-in software control logic, which dynamically adjusts the charging parameters based on the battery's real-time data.

[0124] The present invention realizes accurate temperature change monitoring by real-time monitoring of the battery temperature and analyzing its change trend. When the battery temperature rises, the thermal management optimization strategy is implemented in time to effectively control the temperature, prevent overheating, and ensure the safety of the charging process and battery performance. For temperature drops or stable states, the fast charging optimization strategy is used to improve the charging efficiency and shorten the charging time, thereby improving the user experience. By adopting corresponding charging strategies according to different temperature change trends, it helps to reduce the thermal stress and chemical stress of the battery and extend the battery life. The charging strategy of this solution is intelligent and can be flexibly adjusted according to real-time temperature changes, improve energy utilization efficiency, enhance charging safety, and adapt to different environmental conditions to ensure that the battery can be charged safely and efficiently in various climate environments. At the same time, continuous temperature monitoring and timely adjustment of the charging strategy help maintain the health of the battery and maintain the long-term stability of battery performance.

[0125] Preferably, step S5 comprises the following steps:

[0126] Step S51: performing multi-parameter real-time monitoring on each battery cell in the vehicle battery to obtain a battery cell multi-dimensional parameter matrix, wherein the battery cell multi-dimensional parameter matrix includes an instantaneous voltage value, an instantaneous current value, and an instantaneous temperature value of each battery cell;

[0127] Specifically, multiple sensors distributed in the automotive battery pack can be used to monitor each battery cell in real time. These sensors include voltage sensors, current sensors, and temperature sensors, which measure the instantaneous voltage, instantaneous current, and instantaneous temperature of the battery cell, respectively. These data are transmitted to the data processing unit of the BMS in real time through the sensor interface. The BMS organizes these data into a multi-dimensional parameter matrix of battery cells, in which each row represents a battery cell and each column represents a parameter (voltage, current, and temperature). For example, if the battery pack consists of 96 battery cells, then this matrix will contain 96 rows of data, each with the voltage, current, and temperature readings of the cell.

[0128] Step S52: performing statistical analysis on the battery cells in the vehicle battery based on the battery cell multi-dimensional parameter matrix to obtain battery cell parameter statistical results, wherein the battery cell parameter statistical results include battery cell average voltage value, battery cell average temperature value and battery cell average internal resistance value;

[0129] Specifically, the multi-dimensional parameter matrix of battery cells can be statistically analyzed. This includes calculating the average voltage value, average temperature value, and average internal resistance value of each battery cell. These statistical results are obtained by taking the average of all readings of each cell within a certain period of time. For example, the BMS will calculate the average of the past hour or hundreds of data points. These statistical results help the BMS understand the overall health and performance of the battery pack.

[0130] Step S53: judging the abnormality of each battery cell in the automobile battery according to the instantaneous voltage value, the instantaneous current value, the instantaneous temperature value, the average voltage value of the battery cell, the average temperature value of the battery cell and the average internal resistance value of the battery cell, and obtaining a battery cell balance state set, wherein the battery cell balance state set includes balance state data of each battery cell, and the balance state data is any one or more of a voltage deviation value, a temperature deviation threshold value and a current deviation value;

[0131] Specifically, the statistical results obtained in step S52 can be used in combination with the instantaneous parameters monitored in real time to determine the abnormality of each battery cell. This includes comparing the differences between the voltage, current and temperature parameters of each cell and the average value, and whether these differences exceed the preset thresholds. For example, if the voltage deviation value of a battery cell exceeds the set voltage deviation threshold, or the temperature deviation threshold exceeds the allowable temperature variation range, the BMS will mark this cell as abnormal.

[0132] Step S54: performing periodic micro-cycle charging and discharging operations on the vehicle battery according to the battery cell balancing state set.

[0133] Specifically, the BMS can perform regular micro-cycle charge and discharge operations on the vehicle battery according to the battery cell balance status set. This refers to the additional charge and discharge of battery cells that are identified as unbalanced during the charging or discharging process of the battery pack to restore their performance and status. For example, if the voltage of a battery cell is lower than that of other cells, the BMS will increase the charging current to this cell until its voltage reaches a level that matches that of other cells in the group. Conversely, if the voltage of a cell is too high, the BMS will reduce its charging current or discharge it lightly.

[0134] The present invention realizes comprehensive battery health monitoring by real-time monitoring of multiple parameters of battery cells, timely discovers and handles potential problems, thereby ensuring the health of the battery. By using statistical methods to conduct in-depth analysis of battery data, detailed statistical results of battery performance can be obtained. Through accurate abnormality detection capabilities, abnormal conditions of battery cells such as voltage deviation, temperature deviation and current deviation can be identified, effectively preventing the occurrence of battery problems. Regular micro-cycle charging and discharging operations help maintain the balance between battery cells, avoid performance degradation, and extend battery life. In summary, the present invention ensures the stability and reliability of electric vehicle power output by improving battery efficiency and enhancing safety.

[0135] Preferably, the present invention also provides a new energy vehicle battery device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, for executing the new energy vehicle battery device control method as described above.

[0136] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0137] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A new energy vehicle battery device control method, characterized in that: The following steps are involved: Step S1: Dynamically monitoring the parameters of the vehicle battery to obtain a battery status parameter set, wherein the battery status parameter set includes a remaining power continuous parameter, a voltage continuous parameter, a current continuous parameter, and a temperature continuous parameter; identifying the working condition of the vehicle battery according to the battery status parameter set to obtain a battery working condition type, wherein the battery working condition type is any one of a discharge working condition, a charging working condition, and a static working condition; Step S2: if the battery operating condition type is a discharge operating condition, driving behavior data of the target vehicle is collected to obtain a driving behavior data set; based on the driving behavior data set, a driving behavior pattern of the target vehicle is identified to obtain a driving pattern type; Step S3: dynamically allocating power to the vehicle battery according to the driving mode type to obtain a battery power configuration plan; dynamically adjusting the power output of the vehicle battery according to the battery power configuration plan; Step S4: If the battery operating condition type is a charging condition, a temperature trend analysis is performed on the temperature continuous parameter to obtain a temperature change trend; a charging strategy of the vehicle battery is adjusted according to the temperature change trend to obtain a charging adjustment strategy; and a charging control operation is performed on the vehicle battery according to the charging adjustment strategy; Step S5: If the battery operating condition type is a static operating condition, a battery cell balance test is performed on the vehicle battery to obtain a battery cell balance state set; and a periodic micro-cycle charge and discharge operation is performed on the vehicle battery according to the battery cell balance state set.

2. The new energy vehicle battery device control method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Dynamically monitoring the parameters of the vehicle battery to obtain a battery status parameter set, wherein the battery status parameter set includes a remaining power continuous parameter, a voltage continuous parameter, a current continuous parameter, and a temperature continuous parameter; Step S12: Calculating the voltage change rate of the vehicle battery according to the voltage continuity parameter to obtain the voltage change rate parameter; Step S13: calculating the current fluctuation amplitude of the vehicle battery according to the current continuity parameter to obtain the current fluctuation amplitude parameter; Step S14: dynamically matching the voltage change rate parameter and the current fluctuation amplitude parameter with a preset operating condition determination parameter set to obtain a battery operating condition type, wherein the battery operating condition type is any one of a discharge operating condition, a charge operating condition and a static operating condition.

3. The new energy vehicle battery device control method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: if the battery operating condition type is a discharge operating condition, driving behavior data of the target vehicle is collected to obtain a driving behavior data set, wherein the driving behavior data set includes vehicle acceleration data, vehicle braking data, vehicle steering data, vehicle speed data, and vehicle route data; Step S22: aligning the driving behavior data set with timestamps to obtain a synchronized driving behavior data set, wherein the synchronized driving behavior data set includes synchronized vehicle acceleration data, synchronized vehicle braking data, synchronized vehicle steering data, synchronized vehicle speed data, and synchronized vehicle route data; Step S23: performing acceleration state statistics on the synchronous vehicle acceleration data according to a preset acceleration state gradient threshold to obtain acceleration state frequency data; Step S24: performing braking frequency statistics on the synchronous automobile braking data to obtain braking frequency data; Step S25: performing braking force statistics on the target vehicle to obtain braking force statistical data; Step S26: performing statistics on the duration of a single braking of the synchronous automobile braking data to obtain braking duration distribution data, and performing braking intensity classification on the braking behavior according to the braking frequency data, the braking force statistics data and the braking duration distribution data to obtain braking behavior intensity data, wherein the braking behavior intensity data is any one of light braking, medium braking and emergency braking; Step S27: performing driving behavior pattern recognition on the target vehicle according to the synchronous vehicle steering data, the synchronous vehicle speed data, the synchronous vehicle route data, the acceleration state frequency data and the braking behavior intensity data to obtain the driving pattern type.

4. The new energy vehicle battery device control method according to claim 3, characterized in that: Step S27 includes the following steps: Step S271: performing acceleration behavior classification on the acceleration state frequency data to obtain acceleration behavior data, wherein the acceleration behavior data is any one of steady acceleration, slow acceleration and rapid acceleration; Step S272: Calculating the steering amplitude and change rate of the synchronous automobile steering data to obtain steering change rate data and steering amplitude data; Step S273: performing steering behavior classification on the steering change rate data and the steering amplitude data to obtain steering behavior data, wherein the steering behavior data is any one of straight-line driving, slow turning and sharp turning; Step S274: Plotting a speed-time curve for the synchronous vehicle speed data to obtain a vehicle speed dynamic curve; Step S275: Calculating the speed smoothness index of the vehicle speed dynamic curve to obtain speed smoothness distribution data, and classifying the speed state of the vehicle driving speed according to the speed smoothness distribution data to obtain speed behavior data, wherein the speed behavior data is any one of a uniform speed driving stage, an acceleration stage, and a deceleration stage; Step S276: calculating the path curvature and turning frequency of the driving path based on the synchronized automobile route data to obtain path curvature data and turning frequency data, and classifying the driving environment by road type according to the path curvature data and turning frequency data to obtain driving environment data, wherein the driving environment data is any one or more of urban roads, highways and country roads; Step S278: Match the acceleration behavior data, steering behavior data, speed behavior data and driving environment data with the preset driving mode determination rule set item by item to obtain the driving mode type, wherein the driving mode type is any one of economical, standard, sporty or aggressive.

5. The new energy vehicle battery device control method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: extracting driving characteristic parameters of the target vehicle according to the driving mode type to obtain a driving characteristic parameter set, wherein the driving characteristic parameter set includes average acceleration data, maximum acceleration data, average vehicle speed data, maximum vehicle speed data, acceleration change rate data and vehicle speed fluctuation data; Step S32: collecting the total mass of the target vehicle to obtain the total mass data of the vehicle; Step S33: acquiring the air resistance coefficient of the target vehicle according to the driving environment data to obtain the driving air resistance coefficient; Step S34: acquiring the rolling resistance coefficient of the target vehicle according to the driving environment data to obtain the driving rolling resistance coefficient; Step S35: constructing a real-time electrochemical model of the vehicle battery based on the battery state parameter set to obtain a real-time electrochemical model of the battery; Step S36: evaluating the power output range of the vehicle battery based on the real-time electrochemical model of the battery and according to the continuous parameter of the remaining power, to obtain the available power range of the battery; Step S37: dynamically allocate power to the vehicle battery according to the driving characteristic parameter set, the vehicle gross mass data, the driving air resistance coefficient, the driving rolling resistance coefficient and the battery available power range to obtain a battery power configuration plan; dynamically adjust the power output of the vehicle battery according to the battery power configuration plan.

6. The new energy vehicle battery device control method according to claim 5, characterized in that: Step S37 includes the following steps: Step S371: Acquire driving environment data, and construct a driving scene for the target vehicle according to the driving environment data to obtain a driving scene virtual environment; Step S372: Acquire synchronized vehicle route data, and perform environment-route fusion modeling on the driving scene virtual environment according to the synchronized vehicle route data to obtain a driving route environment model; Step S373: Perform dynamic modeling on the target vehicle to obtain a vehicle dynamic model; Step S374: positioning the target vehicle in real time to obtain the real-time geographic coordinates of the vehicle, and estimating the remaining travel time of the target vehicle based on the synchronized vehicle route data and the real-time geographic coordinates of the vehicle to obtain the remaining travel time data; Step S375: simulating the driving trajectory of the target vehicle based on the driving route environment model and the vehicle dynamics model according to the driving characteristic parameter set, the vehicle gross mass data, the driving air resistance coefficient, the driving rolling resistance coefficient and the remaining travel time data to obtain driving trajectory simulation data; Step S376: dynamically distribute power to the vehicle battery according to the available power range of the battery and the driving trajectory simulation data to obtain a battery power configuration plan, and dynamically adjust the power output of the vehicle battery according to the battery power configuration plan.

7. The new energy vehicle battery device control method according to claim 6, characterized in that: Step S376 includes the following steps: Step S3761: identifying high power demand areas on the driving trajectory simulation data to obtain a high power demand area set; Step S3762: performing driving duration statistics on the high power demand area set based on the driving trajectory simulation data to obtain a high power demand duration set; Step S3763: mapping the remaining travel time data to a high power demand time according to the high power demand duration set to obtain a high power demand time distribution diagram; Step S3764: performing peak power estimation on the high power demand area set based on the driving trajectory simulation data to obtain peak power demand data; Step S3765: if the peak power demand data is within the battery available power range, the peak power demand data is directly used as the maximum available power data; otherwise, the maximum battery available power within the battery available power range is used as the maximum available power data; Step S3766: acquiring average vehicle speed data, estimating the cruising power of the vehicle battery according to the average vehicle speed data, and obtaining cruising power demand data; Step S3767: Calculate the power buffer zone for the maximum available power data and the cruise power demand data to obtain the power buffer zone; Step S3768: Generate a dynamic power curve for the vehicle battery based on the high power demand time distribution diagram according to the maximum available power data, the cruising power demand data and the power buffer, obtain a dynamic power output curve, and use the dynamic power output curve as a battery power configuration scheme, and adjust the dynamic power output of the vehicle battery according to the battery power configuration scheme.

8. The new energy vehicle battery device control method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: if the battery operating condition type is a charging condition, a temperature trend analysis is performed on the temperature continuous parameter to obtain a temperature change trend, wherein the temperature change trend is any one of a temperature rising trend, a temperature falling trend and a temperature stable state; Step S42: If the temperature change trend is a temperature rising trend, a thermal management optimization strategy is formulated for the vehicle battery to obtain a charging adjustment strategy; Step S43: If the temperature change trend is a temperature decreasing trend or a temperature stable state, a fast charging optimization strategy is formulated for the vehicle battery to obtain a charging adjustment strategy.

9. The new energy vehicle battery device control method according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: performing multi-parameter real-time monitoring on each battery cell in the vehicle battery to obtain a battery cell multi-dimensional parameter matrix, wherein the battery cell multi-dimensional parameter matrix includes an instantaneous voltage value, an instantaneous current value, and an instantaneous temperature value of each battery cell; Step S52: performing statistical analysis on the battery cells in the vehicle battery based on the battery cell multi-dimensional parameter matrix to obtain battery cell parameter statistical results, wherein the battery cell parameter statistical results include battery cell average voltage value, battery cell average temperature value and battery cell average internal resistance value; Step S53: judging the abnormality of each battery cell in the automobile battery according to the instantaneous voltage value, the instantaneous current value, the instantaneous temperature value, the average voltage value of the battery cell, the average temperature value of the battery cell and the average internal resistance value of the battery cell, and obtaining a battery cell balance state set, wherein the battery cell balance state set includes balance state data of each battery cell, and the balance state data is any one or more of a voltage deviation value, a temperature deviation threshold value and a current deviation value; Step S54: performing periodic micro-cycle charging and discharging operations on the vehicle battery according to the battery cell balancing state set.

10. A new energy vehicle battery device, characterized in that: It comprises a processor, a memory and a computer program stored in the memory and executable on the processor, and is used to execute the new energy vehicle battery device control method as described in any one of claims 1 to 8.

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