A Battery Management Method and System for New Energy Vehicles

Abstract

The present invention relates to the technical field of battery management, and particularly to a battery management method and system for new energy vehicles. The method includes: extracting road condition data and battery operation state data from the operation log data of new energy vehicles, and performing battery dynamic output power mapping between different road conditions to obtain battery output power data with road condition differences; calculating the instantaneous power growth rate between different road conditions for the battery output power data with road condition differences, and identifying the critical interval of connection point failure to obtain the critical interval of connection point failure; adjusting the controllable output power of the battery between different road conditions according to the critical interval of connection point failure to obtain normalized battery output power adjustment data; learning the battery output control logic for the normalized battery output power adjustment data, and designing an automated logic firmware to obtain the battery output control logic firmware. The present invention makes the battery management technology more perfect through the optimization of the battery management technology.

Description

Technical Field

[0001] The present invention relates to the technical field of battery management, and particularly to a battery management method and system for new energy vehicles. Background Art

[0002] With the rapid development of new energy vehicles, the progress of battery technology has become one of the key factors in improving vehicle performance and extending service life. In this context, the battery management system (BMS), as one of the core technologies of new energy vehicles, plays an increasingly important role. The main task of the battery management system is to monitor, manage, and optimize the state of the battery in real time to ensure the safe, stable, and efficient operation of the battery. In new energy vehicles, especially in high-performance models or electric vehicles used for long-distance driving, the battery faces multiple challenges such as frequent acceleration, deceleration, and different environmental temperatures, which have a significant impact on the working state, life, charge-discharge efficiency, and safety of the battery. In actual use, the batteries of new energy vehicles often face the problem of fluctuating power demands. For example, during rapid acceleration, the battery needs to provide a large amount of instantaneous power, which can cause large current fluctuations inside the battery, leading to problems such as increased battery temperature rise and thermal runaway. When decelerating or braking, the battery needs to quickly recover energy and charge. These frequent power fluctuations pose huge challenges to the battery load, temperature control, life management, and safety. Especially under high-speed driving or intense driving conditions, local temperature rise inside the battery is more likely to cause thermal runaway, and in extreme cases, lead to battery fires or explosions. Therefore, how to accurately control the charge-discharge process of the battery, balance the temperature inside the battery, and extend the service life of the battery has become a key problem that the battery management system needs to solve. However, there are problems with the traditional battery management method for new energy vehicles that it cannot accurately analyze the thermal failure state of the battery connection points caused by frequent power fluctuations, and the battery output power cannot be well adapted to the road condition changes for stable control. Summary of the Invention

[0003] Based on this, it is necessary to provide a battery management method and system for new energy vehicles to solve at least one of the above technical problems.

[0004] To achieve the above object, a battery management method for new energy vehicles, the method includes the following steps:

[0005] Step S1: Obtain the operation log data of new energy vehicles; separately extract road condition data and battery operation status data from the operation log data of new energy vehicles, and obtain road condition data and battery operation status data respectively; perform battery dynamic output power mapping between different road conditions on the battery operation status data according to the road condition data, and obtain battery output power data with road condition differences;

[0006] Step S2: Calculate the instantaneous power growth rate between different road conditions for the battery output power data with road condition differences, and obtain the instantaneous power growth rate with road condition differences; conduct heat load increment conduction analysis between battery connection points according to the instantaneous power growth rate with road condition differences, and obtain connection point heat load increment conduction data; identify the connection point failure critical interval based on the connection point heat load increment conduction data, and obtain the connection point failure critical interval;

[0007] Step S3: Adjust the controllable battery output power between different road conditions for the battery output power data with road condition differences according to the connection point failure critical interval, and obtain normalized battery output power adjustment data;

[0008] Step S4: Learn the battery output control logic for the connection point failure critical interval and the normalized battery output power adjustment data through the policy gradient algorithm, and obtain battery output control logic data; design an automated logic firmware based on the battery output control logic data, obtain the battery output control logic firmware, and embed the battery output control logic firmware into the in-vehicle terminal of the new energy vehicle to perform battery management for the new energy vehicle.

[0009] Preferably, step S1 includes the following steps:

[0010] Step S11: Obtain the operation log data of new energy vehicles;

[0011] Step S12: Clean the operation log data of new energy vehicles to obtain cleaned operation log data;

[0012] Step S13: Separately extract road condition data and battery operation status data from the cleaned operation log data, and obtain road condition data and battery operation status data respectively;

[0013] Step S14: Fill in the time series missing values for the battery operation status data to obtain battery operation status missing value filled data;

[0014] Step S15: Perform battery dynamic output power mapping between different road conditions on the battery operation status missing value filled data according to the road condition data, and obtain battery output power data with road condition differences.

[0015] Preferably, step S2 includes the following steps:

[0016] Step S21: Calculate the instantaneous power growth rate between different road conditions for the battery output power data of road condition differences, and obtain the instantaneous power growth rate of road condition differences;

[0017] Step S22: Conduct a simulation of the battery's instantaneous temperature rise response based on the instantaneous power growth rate of road condition differences, and obtain the battery's instantaneous temperature rise response data;

[0018] Step S23: Obtain the battery design data of new energy vehicles;

[0019] Step S24: Conduct an analysis of the heat load increment conduction between battery connection points on the battery design data of new energy vehicles based on the instantaneous power growth rate of road condition differences and the battery's instantaneous temperature rise response data, and obtain the connection point heat load increment conduction data;

[0020] Step S25: Identify the heat load superimposed connection points from the connection point heat load increment conduction data, and obtain the heat load superimposed connection points;

[0021] Step S26: Identify the critical failure interval of the connection points for the heat load superimposed connection points based on the connection point heat load increment conduction data and the battery design data of new energy vehicles, and obtain the critical failure interval of the connection points.

[0022] Preferably, step S23 includes the following steps:

[0023] Step S231: Conduct a topological connection structure analysis on the battery design data of new energy vehicles, and obtain the battery topological connection structure data;

[0024] Step S232: Conduct a heat flow direction analysis on the battery topological connection structure data based on the battery's instantaneous temperature rise response data, and obtain the connection structure heat flow direction data;

[0025] Step S233: Conduct an analysis of the temperature difference distribution of the connection points on the connection structure heat flow direction data, and obtain the connection point temperature difference distribution data;

[0026] Step S234: Calculate the local connection point temperature difference increment series on the connection point temperature difference distribution data based on the instantaneous power growth rate of road condition differences, and obtain the local connection point temperature difference increment series;

[0027] Step S235: Conduct an analysis of the heat load increment conduction between battery connection points on the battery topological connection structure data based on the local connection point temperature difference increment series and the connection point temperature difference distribution data, and obtain the connection point heat load increment conduction data.

[0028] Preferably, step S26 includes the following steps:

[0029] Step S261: Conduct an analysis of the material characteristics of the battery connection lines on the battery design data of new energy vehicles, and obtain the battery connection line material characteristic data;

[0030] Step S262: Based on the connection point heat load increment conduction data, perform a thermal stress vector calculation on the heat load superposition connection point to obtain the superposition connection point thermal stress vector data;

[0031] Step S263: According to the superposition connection point thermal stress vector data, perform a thermal expansion plastic strain fitting on the battery connection line material characteristic data to obtain the thermal expansion plastic strain fitting data;

[0032] Step S264: Perform an overloading tolerance calculation on the superposition connection point resistance for the superposition connection point thermal stress vector data to obtain the superposition connection point resistance overloading tolerance data;

[0033] Step S265: Based on the thermal expansion plastic strain fitting data and the superposition connection point resistance overloading tolerance data, identify the connection point failure critical interval for the heat load superposition connection point to obtain the connection point failure critical interval.

[0034] Preferably, step S263 includes the following steps:

[0035] Extract the specific heat capacity of the material for the battery connection line material characteristic data to obtain the line material specific heat capacity;

[0036] Perform a thermal energy average difference calculation in the stress direction on the superposition connection point thermal stress vector data to obtain the thermal energy average difference data in the stress direction;

[0037] Based on the thermal energy average difference data in the stress direction, perform a plastic yield limit simulation on the line material specific heat capacity to obtain the thermal energy average difference material plastic yield data;

[0038] Based on the superposition connection point thermal stress vector data, perform a plastic anisotropy analysis on the thermal energy average difference material plastic yield data to obtain the material plastic yield anisotropy data;

[0039] Based on the thermal energy average difference material plastic yield data, the material plastic yield anisotropy data, and the thermal energy average difference data in the stress direction, perform a thermal expansion plastic strain fitting to obtain the thermal expansion plastic strain fitting data.

[0040] Preferably, step S264 includes the following steps:

[0041] Perform an analysis on the change in the connection point heat flux density for the superposition connection point thermal stress vector data to obtain the connection point heat flux density change data;

[0042] Perform a density increment trend analysis on the connection point heat flux density change data to obtain the heat flux density increment trend data;

[0043] Based on the heat flux density increment trend data, perform a segmented mapping of the connection point resistance gradient fluctuation on the connection point heat flux density change data to obtain the resistance gradient segmented fluctuation data;

[0044] Perform overload limit numerical calculation on the segmented fluctuating data of the resistance gradient to obtain the resistance overload limit value;

[0045] Based on the resistance overload limit value and the segmented fluctuating data of the resistance gradient, perform the calculation of the resistance overload tolerance at the superimposed connection point for the thermal stress vector data of the superimposed connection point to obtain the resistance overload tolerance data at the superimposed connection point.

[0046] Preferably, the present invention further provides a new energy vehicle battery management system for executing the new energy vehicle battery management method as described above. The new energy vehicle battery management system includes:

[0047] A battery dynamic output power mapping module for obtaining the operation log data of the new energy vehicle; extracting the road condition data and the battery operation state data from the operation log data of the new energy vehicle respectively to obtain the road condition data and the battery operation state data; performing battery dynamic output power mapping between different road conditions on the battery operation state data according to the road condition data to obtain the battery output power data with road condition differences;

[0048] A connection point failure critical identification module for calculating the instantaneous power growth rate between different road conditions for the battery output power data with road condition differences to obtain the instantaneous power growth rate with road condition differences; performing thermal load increment conduction analysis between battery connection points according to the instantaneous power growth rate with road condition differences to obtain the connection point thermal load increment conduction data; identifying the connection point failure critical interval based on the connection point thermal load increment conduction data to obtain the connection point failure critical interval;

[0049] A battery output controllable power adjustment module for adjusting the battery output controllable power between different road conditions on the battery output power data with road condition differences according to the connection point failure critical interval to obtain the normalized battery output power adjustment data;

[0050] An automated power output execution module for learning the battery output control logic for the connection point failure critical interval and the normalized battery output power adjustment data through a policy gradient algorithm to obtain the battery output control logic data; designing an automated logic firmware based on the battery output control logic data to obtain the battery output control logic firmware, and embedding the battery output control logic firmware into the on-vehicle terminal of the new energy vehicle to execute the new energy vehicle battery management.

[0051] Advantages of the present invention: The present invention optimizes a traditional new energy vehicle battery management method, solves the problems that the traditional new energy vehicle battery management method cannot accurately analyze the thermal failure state of battery connection points caused by frequent power fluctuations, and the battery output power cannot be well adapted to the road condition changes for stable control, improves the accuracy of analyzing the thermal failure state of battery connection points caused by frequent power fluctuations, and enhances the ability of the battery output power to be stably adapted to road condition changes for control. Brief Description of the Drawings

[0052] Figure 1 It is a schematic diagram of the step flow of a new energy vehicle battery management method;

[0053] Figure 2 It is Figure 1 a detailed implementation step flow diagram of step S2 in

[0054] The realization, functional features and advantages of the present invention will be further described with reference to the embodiments and the drawings. Detailed Embodiments

[0055] To achieve the above object, please refer to Figures 1 to 2 , a new energy vehicle battery management method, the method includes the following steps:

[0056] Step S1: Obtain the operation log data of the new energy vehicle; extract the road condition data and the battery operation state data from the operation log data of the new energy vehicle respectively, and obtain the road condition data and the battery operation state data respectively; map the battery dynamic output power between different road conditions according to the road condition data, and obtain the battery output power data with road condition differences;

[0057] Step S2: Calculate the instantaneous power growth rate between different road conditions for the battery output power data with road condition differences, and obtain the instantaneous power growth rate with road condition differences; analyze the heat load increment conduction between battery connection points according to the instantaneous power growth rate with road condition differences, and obtain the connection point heat load increment conduction data; identify the connection point failure critical interval based on the connection point heat load increment conduction data, and obtain the connection point failure critical interval;

[0058] Step S3: Adjust the battery output controllable power between different road conditions for the battery output power data with road condition differences according to the connection point failure critical interval, and obtain the normalized battery output power adjustment data;

[0059] Step S4: Use the policy gradient algorithm to learn the battery output control logic from the connection point failure critical interval and the normalized data of the battery output power adjustment, and obtain the battery output control logic data; design the automation logic firmware based on the battery output control logic data to obtain the battery output control logic firmware, and embed the battery output control logic firmware into the in-vehicle terminal of the new energy vehicle to perform the battery management of the new energy vehicle.

[0060] In the embodiment of the present invention, refer to Figure 1 As described, it is a schematic diagram of the step flow of a battery management method for a new energy vehicle according to the present invention. In this example, the battery management method for the new energy vehicle includes the following steps:

[0061] Step S1: Obtain the operation log data of the new energy vehicle; extract the road condition data and the battery operation state data from the operation log data of the new energy vehicle respectively, and obtain the road condition data and the battery operation state data respectively; map the battery dynamic output power between different road conditions according to the road condition data to obtain the battery output power data with road condition differences;

[0062] In the embodiments of the present invention, first, it is necessary to obtain the operation log data of new energy vehicles. These data are sourced from the vehicle-mounted sensor system and include various parameter information of vehicle driving, such as speed, acceleration, battery voltage, current, temperature, driving time, road conditions, etc. When obtaining these data, it is first necessary to export the real-time data from the vehicle-mounted computing unit through the communication interface to ensure the synchronization of the time stamps of the data and the vehicle status, and avoid data inconsistency caused by time differences. Next, data cleaning is performed to remove noise and outliers, and a threshold-based screening method is used to eliminate unreasonable values. For example, when there are abnormal fluctuations in the battery voltage beyond the normal operating range, these data need to be excluded to ensure the accuracy of subsequent analysis. The result after data cleaning will form the "cleaned operation log data". After obtaining the cleaned log data, the road condition data and the battery operation status data are extracted according to the time series of vehicle driving. The extraction method of road condition data mainly relies on the vehicle GPS information and the external road condition sensor data. The GPS provides the real-time position of the vehicle, and the external sensor determines the current road condition (such as flat, ramp, rough, etc.) by detecting the surrounding environment. At the same time, the battery operation status data is obtained through the vehicle-mounted battery management system (BMS), including indicators such as battery voltage, current, and temperature. These data are respectively stored as "road condition data" and "battery operation status data". Next, according to the road condition data and the battery operation status data, the dynamic output power of the battery is calculated using a simple mathematical mapping relationship. First, according to the changes in different road conditions (such as changes in vehicle driving speed or ramp undulations), the load output of the battery under different road conditions is determined. By establishing a relationship model between battery power and vehicle load, the dynamic output power of the battery under each road condition is calculated, and finally, the "battery output power data with road condition differences" is obtained.

[0063] Step S2: Calculate the instantaneous power growth rate between different road conditions for the battery output power data with road condition differences to obtain the instantaneous power growth rate with road condition differences; conduct an analysis of the heat load increment conduction between battery connection points based on the instantaneous power growth rate with road condition differences to obtain the heat load increment conduction data of the connection points; identify the critical failure interval of the connection points based on the heat load increment conduction data of the connection points;

[0064] In the embodiments of the present invention, the instantaneous growth rate of the battery power at each moment in the "battery output power data with road condition differences" is calculated. To this end, the numerical difference method is used to discretize the power change. Subsequently, based on the obtained instantaneous power growth rate, the analysis of the heat load increment conduction between the battery connection points is carried out. To perform this analysis, first, a heat conduction model inside the battery system needs to be constructed, taking into account the physical properties of the battery, such as the thermal conductivity, thermal expansion coefficient, etc. Through thermodynamic equations (such as Fourier's law of heat conduction), combined with the instantaneous power growth rate data with road condition differences, the heat load change between each connection point of the battery is calculated. In particular, the high-power output interval should be focused on because the temperature rise in this area is the most significant and is likely to cause overheating of the battery connection points. In specific implementation, the numerical simulation method is used to calculate the heat flow with the help of battery design data (such as the connection structure and material of the battery), and the "connection point heat load increment conduction data" is obtained. This data describes the heat load distribution between each connection point of the battery under different power outputs and can effectively help identify potential thermal runaway areas. Then, based on the "connection point heat load increment conduction data", by analyzing the cumulative effect of the heat load, the failure critical interval of the battery connection points is identified. To achieve this goal, the heat load of each connection point needs to be accumulated and compared with the maximum bearing capacity during battery design. If the heat load of some connection points exceeds their maximum bearing capacity, then these connection points enter the failure critical interval. This process is achieved through multiple iterative calculations of the temperature change and heat conduction characteristics of each connection point, and finally the "connection point failure critical interval" is obtained.

[0065] Step S3: Adjust the controllable power of the battery output between different road conditions according to the connection point failure critical interval to obtain the normalized battery output power adjustment data;

[0066] In the embodiments of the present invention, according to the critical interval of connection point failure, the controllable power of battery output for different road conditions is adjusted for the battery output power data of different road conditions, and the normalized data of battery output power adjustment is obtained. First, the heat load critical points of the battery system under different driving conditions are identified from the critical interval data of connection point failure obtained in step S2. These critical points represent the risk of overheating or performance degradation of the battery at a specific time and power output. On this basis, the battery output power is adjusted for different road conditions (such as slopes, flat roads, hard brakes, etc.). Under each road condition, according to the heat load limit and power demand difference, a step-by-step adjustment strategy is applied to adjust the output power of the battery to ensure that the battery temperature remains within a safe range. The specific adjustment method is as follows: according to different road conditions, through a preset control function, the power output of the battery under each road condition is adjusted respectively. This control function calculates the maximum allowable power output value based on the real-time battery power data and the critical interval data of failure. Under each road condition, the normalized method is used to map the adjusted power value to a standardized range (for example, between 0 and 1), ensuring that all data is on the same scale for subsequent processing. The normalized power adjustment data is the normalized data of battery output power adjustment. This data provides a controllable range for the battery power output when the battery management system adapts to different road conditions for further optimization and control.

[0067] Step S4: Through the policy gradient algorithm, learn the battery output control logic for the critical interval of connection point failure and the normalized data of battery output power adjustment, and obtain the battery output control logic data; based on the battery output control logic data, design the automated logic firmware, obtain the battery output control logic firmware, and embed the battery output control logic firmware into the on-vehicle terminal of the new energy vehicle to perform the battery management of the new energy vehicle.

[0068] In the embodiments of the present invention, the battery output control logic is learned by using a policy gradient algorithm for the connection point failure critical interval and the battery output power adjustment normalized data, and the battery output control logic data is obtained. First, a reinforcement learning framework based on policy gradient is defined to learn the optimal battery output control strategy under different connection point failure critical intervals and road conditions. In this framework, the state space is composed of the connection point failure critical interval and the battery output power adjustment normalized data, and the action space is composed of controllable battery power output values. In the reinforcement learning algorithm, the policy gradient algorithm trains the decision-making process at each moment, adjusts the parameters of the policy network, and optimizes the battery output control strategy. By continuously interacting with the environment (i.e., the operation of the battery system), the power output control logic of the battery is adjusted so that the battery system operates safely and reliably in different usage scenarios. The algorithm calculates the gradient through backpropagation and selects appropriate actions (adjusting the power output) according to the current state (such as the failure critical interval and battery power data). This process undergoes multiple rounds of training until the policy converges to the optimal control strategy. The policy gradient algorithm can effectively capture the dynamic response relationship of the battery in complex driving scenarios, thereby providing the best power control decision for the battery management system. The learned control strategy is the battery output control logic data, which can reflect the power adjustment requirements of the battery under different road conditions and failure intervals and provide data support for subsequent firmware design. Based on the battery output control logic data, an automated logic firmware design is carried out to obtain the battery output control logic firmware. In the firmware design stage, first, the battery control logic learned by the policy gradient algorithm is converted into battery management firmware code. This firmware design includes: mapping the battery output control logic into an embedded code form, defining control functions, state machines, scheduling policies, etc., to ensure efficient execution on the vehicle terminal. According to the specific requirements of the hardware platform, the execution efficiency of the firmware is optimized to ensure real-time response to changes in battery power and timely adjustment of the output power to prevent exceeding the thermal load range. Finally, the battery output control logic firmware is embedded into the new energy vehicle on-vehicle terminal to perform the battery management of the new energy vehicle.

[0069] Step S1 includes the following steps:

[0070] Step S11: Obtain the operation log data of the new energy vehicle;

[0071] Step S12: Clean the operation log data of the new energy vehicle to obtain the cleaned operation log data;

[0072] Step S13: Extract the road condition data and the battery operation state data from the cleaned operation log data respectively to obtain the road condition data and the battery operation state data;

[0073] Step S14: Fill in the time-series missing values of the battery operating status data to obtain the battery operating status missing value filled data;

[0074] Step S15: Map the battery dynamic output power among different road conditions for the battery operating status missing value filled data according to the road condition data to obtain the battery output power data with road condition differences.

[0075] In the embodiments of the present invention, when obtaining the operation log data of new energy vehicles, first, real-time operation data including battery performance, road conditions, vehicle speed, driving mileage, acceleration and deceleration data, etc. is extracted from the on-vehicle terminal system. These data are usually collected through the vehicle's control system, sensors and GPS module. The data includes information such as battery voltage, current, temperature, power output at each moment, as well as the driving position of the vehicle, road type (such as urban roads, highways, mountain roads, etc.) and dynamic parameters of the vehicle (such as vehicle speed, acceleration). By docking with the vehicle's communication protocol, the system will automatically obtain this data from the on-vehicle system, ensuring that the timestamp of each data point is consistent with the actual driving time of the vehicle, thus providing an accurate basis for subsequent data processing. When performing data cleaning on the operation log data of new energy vehicles, first check the integrity of the data, identify and delete invalid data caused by sensor failures, communication interruptions or system anomalies. Then, deal with outliers and extreme points to ensure the validity of the data. For example, if the battery voltage shows an abnormally high or low value, or the vehicle speed shows a negative number, then these data will be marked as abnormal and removed from the dataset. In addition, data formatting is also required to make data from different sources (such as GPS position data, sensor data, etc.) have a unified time format and unit system, and perform necessary interpolation to fill in missing data to ensure the consistency of the entire dataset. The result after data cleaning is the operation log cleaning data, which provides an accurate basis for subsequent extraction and analysis. Extract road condition data and battery operation status data from the operation log cleaning data respectively. For the extraction of road condition data, first extract the current driving road type and its characteristics, such as whether it is an urban road, a highway, a mountain road, etc. based on the on-vehicle GPS system and geographic information system (GIS) data. By comparing the real-time vehicle speed and acceleration data, judge different situations of the road conditions, such as whether there is traffic congestion, uphill, downhill or sharp turns, etc., and then extract the corresponding road condition data. For the extraction of battery operation status data, mainly analyze parameters such as the voltage, current, temperature and power output of the battery system, and identify the working status of the battery under different working conditions, especially the power demand and the change of battery temperature. During the extraction process of these two types of data, ensure that all data points are aligned through timestamps so that different data can be accurately associated in subsequent analysis. When filling in missing values in the time series of battery operation status data, first identify the time periods with missing battery data. The missing data appears due to reasons such as sensor failures or data transmission interruptions. To fill in these missing values, interpolation methods such as linear interpolation, cubic spline interpolation, etc. are used to estimate the missing data based on the valid data at the previous and subsequent moments. During the interpolation process, algorithms that do not rely on external models are preferred to ensure simple and intuitive processing. For key parameters such as battery temperature, voltage and current, time series analysis methods are used to predict missing values based on the change trends in adjacent periods.For example, if the battery current data at a certain time point is missing, linear interpolation can be used to fill it based on the rate of change of the current values before and after. If the missing time period is relatively long, a weighted average method will be adopted to fill the missing data, thus ensuring the continuity and accuracy of the filled data. The finally obtained missing filled data of the battery operating state will be synchronized with other data to ensure the temporal consistency of all data. When performing battery dynamic output power mapping between different road conditions for the missing filled data of the battery operating state based on road condition data, first, the driving characteristics corresponding to each road condition are combined with the battery operating state data, considering the impact of different road conditions on the battery output power. For example, when driving on mountain roads, the vehicle needs more power to climb slopes, so the battery output power needs to be increased; while on flat roads, the power output of the battery needs to be relatively reduced to avoid unnecessary energy consumption. Using experience-based rules or the results of previous on-site tests, a set of mapping relationships are defined that can associate the driving modes under different road conditions with the battery output power requirements. Specifically, according to the real-time changes in road conditions (such as vehicle speed, slope, etc.) and the real-time state of the battery (such as the remaining battery charge, temperature, etc.), a simple weighted algorithm is used to dynamically adjust the power output of the battery to obtain the battery output power data for different road conditions. These data can accurately reflect the power requirements of the battery under different driving scenarios and provide a basis for subsequent power regulation.

[0076] Step S2 includes the following steps:

[0077] Step S21: Calculate the instantaneous power growth rate between different road conditions for the battery output power data for different road conditions to obtain the instantaneous power growth rate for different road conditions;

[0078] Step S22: Simulate the battery instantaneous temperature rise response based on the instantaneous power growth rate for different road conditions to obtain the battery instantaneous temperature rise response data;

[0079] Step S23: Obtain the battery design data of the new energy vehicle;

[0080] Step S24: Conduct an analysis of the heat load increment conduction between battery connection points on the battery design data of the new energy vehicle based on the instantaneous power growth rate for different road conditions and the battery instantaneous temperature rise response data to obtain the connection point heat load increment conduction data;

[0081] Step S25: Identify the heat load superimposed connection points for the connection point heat load increment conduction data to obtain the heat load superimposed connection points;

[0082] Step S26: Identify the critical failure interval of the connection points for the heat load superimposed connection points based on the connection point heat load increment conduction data and the battery design data of the new energy vehicle to obtain the critical failure interval of the connection points.

[0083] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0084] Step S21: Calculate the instantaneous power growth rate between different road conditions for the battery output power data of road condition differences, and obtain the instantaneous power growth rate of road condition differences;

[0085] In the embodiment of the present invention, when calculating the instantaneous power growth rate between different road conditions for the battery output power data of road condition differences, it is first necessary to obtain the battery power output data of the vehicle under different road conditions. These data include the battery output power of the vehicle under different driving states (such as acceleration, deceleration, climbing, flat road driving, etc.). For the battery output power at each time point, calculate the change rate of power between adjacent time points, that is, the instantaneous power growth rate. Specifically, the instantaneous power growth rate is the ratio of the change in battery power between two adjacent time points. Specifically, the instantaneous power growth rate is equal to the ratio of the power change amount to the time interval. That is, by calculating the change amount of the battery power output between adjacent moments and dividing it by the corresponding time difference, the instantaneous power growth rate is obtained. For example, on a steep slope section, the battery power output will increase rapidly, and the instantaneous power growth rate is relatively high; while on a flat road, the power change is relatively stable, and the instantaneous power growth rate is relatively low. This calculation result provides basic data for subsequent temperature rise response simulation and thermal load analysis.

[0086] Step S22: Simulate the instantaneous temperature rise response of the battery according to the instantaneous power growth rate of road condition differences, and obtain the battery instantaneous temperature rise response data;

[0087] In the embodiment of the present invention, when simulating the instantaneous temperature rise response of the battery according to the instantaneous power growth rate of road condition differences, it is first necessary to consider the thermal characteristics of the battery. The temperature rise of the battery is closely related to the power output of the battery. In the case of high power output, the temperature of the battery will rise rapidly. Based on the instantaneous power growth rate, calculate the instantaneous temperature rise of the battery. The instantaneous temperature rise of the battery can be calculated through the relationship between the power output of the battery, the internal resistance of the battery, the mass, and the specific heat capacity. Specifically, the temperature rise of the battery is equal to the product of the battery output power and the internal resistance of the battery, and then divided by the product of the mass and specific heat capacity of the battery. This formula represents the relationship between the battery temperature rise and the battery design parameters (such as internal resistance, mass, specific heat capacity) under a given power output condition.

[0088] Step S23: Obtain the battery design data of new energy vehicles;

[0089] In the embodiments of the present invention, when obtaining the battery design data of new energy vehicles, relevant data is mainly obtained from the technical documents provided by battery manufacturers and the battery management system (BMS). The battery design data includes the physical characteristics of the battery (such as the size of a single battery cell, internal resistance, thermal conductivity, etc.), performance parameters (such as maximum output power, operating voltage range, temperature range, etc.), as well as the connection method and electrical characteristics between each battery unit. These design data are the basic data for battery performance evaluation and thermal management analysis. By combining the battery design data with the actual vehicle operation conditions, the performance and thermal response of the battery under different road conditions can be more accurately evaluated.

[0090] Step S24: Perform a thermal load increment conduction analysis between the battery connection points on the battery design data of new energy vehicles according to the instantaneous power growth rate difference of road conditions and the battery instantaneous temperature rise response data, and obtain the connection point thermal load increment conduction data;

[0091] In the embodiments of the present invention, when performing a thermal load increment conduction analysis between the battery connection points on the battery design data of new energy vehicles according to the instantaneous power growth rate difference of road conditions and the battery instantaneous temperature rise response data, first, a thermal conduction model of each connection point inside the battery (such as the connection between the battery cell and the battery management system, connection wires, contact points, etc.) needs to be established. Based on the instantaneous power change of the battery and the corresponding temperature rise response data, combined with the thermal conduction path inside the battery, analyze the thermal load change at different connection points. The thermal load conduction amount is the heat transfer process through the connection points inside the battery, which depends on the thermal conductivity of the material, contact area, temperature difference, and the length of the thermal conduction path. Specifically, the thermal conduction amount is equal to the product of the thermal conductivity and the contact area, multiplied by the temperature difference between both ends of the connection point, and then divided by the length of the thermal conduction path. This formula reflects how the heat transfer from one point to another is related to material properties, structural dimensions, and temperature difference. Through this method, the thermal load change between the battery connection points under different road conditions can be calculated, and it can be identified which connection points are subject to larger thermal load changes, providing accurate data support for the thermal management of the battery.

[0092] Step S25: Identify the thermal load superimposed connection points for the connection point thermal load increment conduction data, and obtain the thermal load superimposed connection points;

[0093] In the embodiments of the present invention, when identifying the heat load superposition connection points for the heat load increment conduction data of the connection points, it is first necessary to conduct a cumulative analysis of the heat loads of all connection points. In a battery system, the heat loads of different connection points will be superimposed. Especially during high-power output, certain connection points will bear higher heat loads. By analyzing the heat load increment data of each connection point, the connection points with relatively significant heat load superposition are identified. These connection points are usually located at key positions between battery modules or between the battery management system and battery cells. The superposition analysis can be completed through the cumulative calculation of the heat load, that is, by integrating the heat load changes in each period and combining the physical structure of the battery to determine which connection points have a greater heat load risk.

[0094] Step S26: Based on the heat load increment conduction data of the connection points and the battery design data of new energy vehicles, identify the critical failure interval of the connection points for the heat load superposition connection points, and obtain the critical failure interval of the connection points.

[0095] In the embodiments of the present invention, when identifying the critical failure interval of the connection points for the heat load superposition connection points based on the heat load increment conduction data of the connection points and the battery design data of new energy vehicles, it is first necessary to calculate the temperature critical value of each connection point according to the design parameters of the battery, combined with its heat load and temperature rise response. In the design of battery connection points, each connection point has a temperature range. When the temperature exceeds this range, electrical failure or thermal runaway will occur at the connection point. By analyzing the data of the heat load superposition connection points, it is identified which connection points are most likely to fail at different temperatures. By combining the heat load with the battery design data, the critical failure interval of each connection point can be obtained, that is, the critical value range of temperature and heat load. Once the operating temperature or heat load of the battery connection point exceeds this critical interval, the battery system will activate the warning mechanism and take corresponding protection measures.

[0096] Step S23 includes the following steps:

[0097] Step S231: Conduct a topological connection structure analysis on the battery design data of new energy vehicles to obtain battery topological connection structure data;

[0098] Step S232: Conduct a heat flow direction analysis on the battery topological connection structure data according to the instantaneous temperature rise response data of the battery to obtain connection structure heat flow direction data;

[0099] Step S233: Conduct a connection point temperature difference distribution analysis on the connection structure heat flow direction data to obtain connection point temperature difference distribution data;

[0100] Step S234: Based on the instantaneous power growth rate of road condition differences, calculate the local connection point temperature difference increment series for the connection point temperature difference distribution data to obtain the local connection point temperature difference increment series;

[0101] Step S235: Conduct a thermal load increment conduction analysis between battery connection points on the battery topology connection structure data according to the local connection point temperature difference increment series and the connection point temperature difference distribution data, and obtain the connection point thermal load increment conduction data.

[0102] In the embodiments of the present invention, the analysis of the topological connection structure of the battery is to determine the distribution and mutual relationship of battery cells and connection points by analyzing the geometric relationship between each battery cell and connection point inside the battery. First, obtain the physical layout data of the battery cells, including the position, size, and connection method of each battery cell. Then, use topological methods to model these battery cells, identify the connection relationships between battery cells, and obtain battery topology structure data. Specifically, by analyzing the current path and current flow direction of the battery cells, determine the connection mode between battery cells, so as to obtain the topological structure diagram of the battery pack. The calibration of each connection point, the interaction of current and voltage between battery cells, and their arrangement will all affect the overall structure of the battery. This data can provide a basis for subsequent thermal analysis and temperature management. The instantaneous temperature rise response data of the battery is used to analyze the temperature change of the battery under different load conditions. In this step, first, obtain the instantaneous temperature rise response data of the battery, which reflects the instantaneous change of the internal temperature of the battery under a specific power output. Then, combine these temperature rise data with the topological connection structure data of the battery to conduct a heat flow direction analysis. The specific method is based on the principle of heat conduction, analyzing the conduction direction and path of heat inside the battery from one connection point to another. By using parameters such as thermal conductivity, contact area, and temperature difference, calculate how heat flows inside the battery, and determine the main direction of heat flow and its conduction path. Through this analysis, it is possible to identify which connection points and battery cells are the key nodes of heat conduction, and obtain the corresponding heat flow direction data, which provides a basis for subsequent heat load calculation and temperature rise control. After obtaining the heat flow direction data, the next step is to analyze the temperature difference distribution of connection points. The temperature difference distribution of the battery pack directly affects its thermal management performance and battery life. In this step, based on the heat flow direction data, analyze the temperature difference distribution between each connection point inside the battery. First, simulate and calculate the temperature of each connection point, considering the heat conduction direction, transfer speed in the heat flow direction data, as well as the heat capacity and thermal conductivity characteristics of each point, to calculate the temperature change of each connection point. Then, by comparing the temperature differences between different connection points, obtain the temperature difference distribution of the connection points. This analysis helps to identify which connection points have larger temperature differences and which connection points are in positions with higher heat loads, thus providing a basis for subsequent thermal management decisions. According to the instantaneous power growth rate of road condition differences, combined with the temperature rise response data of the battery, calculate the temperature difference increment series of each connection point inside the battery under different road condition conditions. The core of this step is to identify how the temperature differences of each connection point of the battery system change under different driving environments. By obtaining the instantaneous power growth rate of road condition differences in real time (i.e., the impact of different driving conditions on the battery power output), combine these change rates with the previously obtained connection point temperature difference distribution data.Based on the change in the power growth rate, the temperature responses of different connection points can be simulated, and the incremental series of temperature differences can be calculated. The variation of this series reflects the amplitude and speed of the battery temperature change under different road conditions, can identify the connection points with larger temperature changes under certain specific conditions, and evaluate their thermal failure risks. Finally, based on the calculated local connection point temperature difference incremental series and the connection point temperature difference distribution data, a detailed thermal load incremental conduction analysis is carried out on the battery topology connection structure. Specifically, according to the local temperature difference incremental series, the change in the thermal load borne by each connection point can be further analyzed. The thermal load incremental conduction analysis will calculate how heat conducts from high-temperature connection points to low-temperature connection points inside the battery by considering the topology structure data of the battery. When calculating, factors such as the thermal conductivity of each connection point, the distance to other connection points, the heat conduction path, and the material properties of the contact points need to be considered. The result of this analysis is the connection point thermal load incremental conduction data, which can accurately reflect how heat is transferred between connection points inside the battery, thus providing an important basis for subsequent battery temperature management and protection measures.

[0103] Step S26 includes the following steps:

[0104] Step S261: Analyze the material characteristics of the battery connection lines for the new energy vehicle battery design data to obtain the battery connection line material characteristic data;

[0105] Step S262: Calculate the thermal stress vector of the thermal load superimposed connection points based on the connection point thermal load incremental conduction data to obtain the superimposed connection point thermal stress vector data;

[0106] Step S263: Fit the thermal expansion plastic strain to the battery connection line material characteristic data according to the superimposed connection point thermal stress vector data to obtain the thermal expansion plastic strain fitting data;

[0107] Step S264: Calculate the superimposed point resistance overload tolerance for the superimposed connection point thermal stress vector data to obtain the superimposed point resistance overload tolerance data;

[0108] Step S265: Identify the connection point failure critical interval for the thermal load superimposed connection points based on the thermal expansion plastic strain fitting data and the superimposed point resistance overload tolerance data to obtain the connection point failure critical interval.

[0109] In the embodiments of the present invention, the material property analysis of the battery connection circuit is to understand its thermal stability and electrical performance under different working conditions by evaluating the physical properties of the wire materials used in the battery design. First, obtain the material information used in the battery design drawings, including the detailed specifications of the metal or synthetic materials used in each battery connection circuit, such as copper, aluminum or other conductive materials, as well as parameters such as their thermal conductivity, electrical resistivity, and coefficient of thermal expansion. By analyzing the thermophysical properties of these materials in detail and using the theories of heat conduction and electrical conductance, calculate the performance changes of the materials at different temperatures. Further, combined with the characteristics of the current flow path in the battery design, evaluate the temperature response of the materials during high-power output of the battery. These material property data provide a basis for subsequent heat load conduction, thermal stress analysis, and resistance overload tolerance calculation. After obtaining the heat load increment conduction data, it is necessary to further analyze the thermal stress caused by the heat load increment at each connection point. According to the heat flow path inside the battery and the heat load increment at different connection points, calculate the thermal stress distribution at each connection point. Using the basic principles of thermodynamics and elasticity, analyze the heat load at each connection point to determine the thermal stress vector, that is, the direction and magnitude of the stress caused by the heat load. Specifically, based on the temperature field and the coefficient of thermal expansion of the material, use the thermal stress equation to calculate the thermal stress vector at each connection point. At the battery connection point, the thermal stress vector represents the direction and magnitude of the material stress caused by the temperature difference, and then obtain the thermal stress vector data of the superimposed connection points. This data can reveal which connection points are most likely to have stress concentration problems under high heat load, providing a direction for subsequent optimization design. The purpose of thermal expansion plastic strain fitting is to analyze the plastic deformation of the battery connection circuit under different heat load conditions through the deformation of the material caused by thermal stress. In this step, based on the thermal stress vector data of the superimposed connection points obtained from the previous step and combined with the characteristic data of the battery connection circuit material, perform a thermal expansion plastic strain fitting analysis of the material. First, based on the coefficient of thermal expansion and the thermal stress vector of the material, calculate the expansion amount of the connection circuit material under temperature change. Further, apply the theory of plasticity mechanics, consider the plastic deformation of the material at high temperature, and determine the critical value of its plastic strain. Through the fitting formula, obtain the plastic strain curve of the material under different stress states under a certain heat load condition. This fitting process combines the thermal expansion behavior and the yield limit of the material, can predict the deformation problems that occur in the battery connection circuit in a high-temperature environment, and obtain the thermal expansion plastic strain fitting data for subsequent connection point failure risk assessment. The calculation of the resistance overload tolerance of the superimposed point is mainly used to evaluate the resistance change of the battery connection point under high heat load, and then predict the overload risk when current passes through. In this step, first, based on the thermal stress vector data of the superimposed connection points obtained previously, determine the thermal stress situation and its resistance change at each connection point. Since the resistance of the wire material at the battery connection point will change at high temperature, it is necessary to consider the influence of thermal stress on the material resistance.The specific method is to apply the relationship formula between resistance and temperature: , where is the resistance at temperature , is the resistance at the reference temperature, is the temperature coefficient of the material, is the reference temperature. Calculate the resistance values of the connection points under different thermal stress conditions according to the formula. Then, evaluate the resistance overload tolerance when current passes through, that is, calculate the maximum resistance change that the connection point can withstand under given current and temperature conditions, and obtain the data of the resistance overload tolerance of the overlapping point. These data help to predict whether there is a risk of current overload caused by high temperature at the battery connection point and provide necessary protection measures. After obtaining the thermal expansion plastic strain fitting data and the resistance overload tolerance data of the overlapping point, the next step is to identify the critical interval of connection point failure. This analysis determines which connection points may fail due to excessive temperature and thermal stress by comprehensively considering the plastic deformation ability of the material and the tolerance of resistance change. First, based on the thermal expansion plastic strain fitting data, identify the temperature ranges in which the connection points undergo plastic deformation, and compare them with the resistance overload tolerance data of the overlapping point to evaluate whether the resistance change will exceed the tolerance range at the corresponding temperature. If a certain connection point exceeds both the plastic deformation limit of the material and the resistance overload tolerance under specific thermal load conditions, it means that the connection point fails under these conditions. By this method, the critical temperature interval of connection point failure, that is, the critical interval of connection point failure, can be accurately identified. This data provides a basis for the thermal management and protection mechanism of the battery, avoids the problem of connection point failure caused by overheating, and ensures the safety and stability of the battery system.

[0110] Another embodiment of step S264. When calculating the overload tolerance of the superimposed point resistance, it is first necessary to evaluate the thermal load of the battery connection point. By monitoring the current and resistance at the battery connection point, the heat generated due to the current flowing through the connection point is calculated. When the current passes through the connection point, the resistance is heated due to the flow of the current, thereby generating a thermal load. This thermal load depends on the magnitude of the current and the characteristics of the connection point resistance. Next, it is necessary to understand the maximum temperature tolerance of the battery connection point. This ability is usually determined by the thermal stability of the material. For example, copper, aluminum or other alloy materials are used for the battery connection point, and different materials have different temperature tolerance limits. By consulting the thermal characteristic data of the material, the maximum temperature that the battery connection point can withstand during operation is determined. When the current passes through the battery connection point, the resistance increases with the increase in temperature. As the temperature further increases, the resistance of the battery connection point gradually increases, which will cause more heat to be generated by the current at the connection point. If the current is too large or the duration is too long, the temperature of the battery connection point will exceed the tolerance limit of its material, and then an overload phenomenon will occur. The overload situation usually manifests as overheating of the battery connection point or the resistance value exceeding the safe range, resulting in a decrease in battery performance or damage to the connection point. For each connection point, it is necessary to determine the upper resistance limit that it can withstand under different current conditions. By monitoring the change trends of the actual current load and resistance, it can be identified when the connection point will enter the overload state. For example, a current continuously exceeding the maximum withstand current will cause the temperature of the connection point to continuously rise and eventually exceed the design range, resulting in an overload. Through the dynamic relationship between current and temperature, the safe operating range of each connection point under given working conditions is calculated. By evaluating the thermal load situation and temperature rise trend of the battery connection point, a tolerance threshold is set, which represents the maximum resistance overload tolerance that the connection point can withstand. When the current load of the battery connection point exceeds this tolerance threshold, the system will trigger an alarm or adjust the current output to prevent the battery from overheating and failing.

[0111] Step S263 includes the following steps:

[0112] Extract the specific heat capacity of the material from the material characteristic data of the battery connection line to obtain the specific heat capacity of the line material;

[0113] Calculate the average thermal energy difference in the stress direction for the superimposed connection point thermal stress vector data to obtain the average thermal energy difference data in the stress direction;

[0114] Perform a plastic yield limit simulation on the specific heat capacity of the line material based on the average thermal energy difference data in the stress direction to obtain the plastic yield data of the average thermal energy difference material;

[0115] Perform a plastic anisotropy analysis on the plastic yield data of the average thermal energy difference material based on the superimposed connection point thermal stress vector data to obtain the plastic yield anisotropy data of the material;

[0116] Based on the plastic yield data of the thermal energy difference material, the anisotropy data of the material's plastic yield, and the thermal energy difference data in the stress direction, a fitting of the thermal expansion plastic strain is carried out to obtain the fitting data of the thermal expansion plastic strain.

[0117] In the embodiments of the present invention, by analyzing the physical properties of the materials of the battery connection lines, the specific heat capacity data of the materials are extracted. The specific heat capacity is the amount of heat required for a material to raise the temperature per unit mass by one unit. For battery connection lines, common materials such as copper and aluminum, their specific heat capacity values directly affect their heat energy transfer and thermal expansion characteristics. In this step, according to the chemical composition and temperature change characteristics of the materials, the specific heat capacity of each connection material is extracted through experimental data or by referring to materials. The extraction process is based on standard thermal property testing methods, such as differential scanning calorimetry (DSC), to measure the specific heat capacity value of the material within a specific temperature range. The finally obtained material specific heat capacity data include the specific heat capacity values of each battery connection line material. According to the thermal stress vector data of the superimposed connection points, the calculation of the average difference in heat energy in the stress direction is carried out. At the battery connection points, due to the thermal expansion effect and external current load, the thermal stress distribution at the battery connection points is non-uniform. The thermal stress vector represents the stress direction and magnitude caused by temperature change at each connection point. In the calculation process, first, it is necessary to measure the heat energy distribution of each connection point in different stress directions through the existing thermal stress vector data. Then, calculate the difference in heat energy in the stress direction and determine the uniform distribution of heat energy. Finally, the obtained average difference in heat energy data can reflect the non-uniformity of heat energy distribution inside the connection points, providing an important basis for subsequent analysis. According to the average difference in heat energy data in the stress direction, the plastic yield limit of the line material is further simulated. The plastic yield limit refers to the maximum stress value at which a material can undergo permanent deformation without fracture under the action of an external load. The simulation of this step is based on the yield behavior of the material under the influence of heat energy, combined with the temperature and stress states, and evaluates the plastic yield limit of the material under specific conditions through the relationship between thermal stress and material yield strength. In this process, the simulation methods adopted include the stress-strain curve model and the thermodynamic model, and the critical points of material yield under different temperature and stress conditions are analyzed through numerical calculation to obtain the plastic yield data under the condition of average difference in heat energy. After obtaining the plastic yield data of the material, the anisotropy analysis of material plastic yield is carried out. Anisotropy refers to the difference in physical properties of a material in different directions. In this analysis, considering the structural complexity of the battery connection points, the yield strength of the connection line material is different in different directions. Using the simulation results of the plastic yield limit, the stress and plastic deformation capabilities of the material in different directions (such as transverse and longitudinal) are evaluated, and the stress response of the material in different directions is analyzed. This analysis evaluates the difference in yield behavior of the material under thermal load by constructing an anisotropic stress-strain model, and finally obtains the anisotropy data of material plastic yield. Based on the average difference in heat energy, plastic yield data, and anisotropy analysis results of the material, the fitting of thermal expansion plastic strain is carried out. The fitting of thermal expansion plastic strain aims to analyze the plastic deformation characteristics of the material under temperature change and thermal stress.By establishing a thermal expansion plastic strain model, considering factors such as the thermal expansion coefficient, yield strength, and anisotropy of the material, these parameters are combined with the data of the average difference in thermal energy and the data of the plastic yield limit to perform fitting calculations of strain. During the fitting process, through data interpolation and regression analysis of the stress responses at different temperatures, the thermal expansion plastic strain fitting data of each material are obtained. These data can provide detailed support for the deformation characteristics of the material in the subsequent failure analysis of the battery connection points.

[0118] Step S264 includes the following steps:

[0119] Perform an analysis of the change in the heat flux density at the connection point on the superimposed connection point thermal stress vector data to obtain the data of the change in the heat flux density at the connection point;

[0120] Perform a trend analysis of the density increment on the data of the change in the heat flux density at the connection point to obtain the data of the trend of the heat flux density increment;

[0121] Perform a piecewise mapping of the connection point resistance gradient fluctuation on the data of the change in the heat flux density at the connection point according to the data of the trend of the heat flux density increment to obtain the data of the piecewise fluctuation of the resistance gradient;

[0122] Perform an overload limit numerical calculation on the data of the piecewise fluctuation of the resistance gradient to obtain the numerical value of the resistance overload limit;

[0123] Perform an overload tolerance calculation of the superimposed point resistance on the superimposed connection point thermal stress vector data based on the numerical value of the resistance overload limit and the data of the piecewise fluctuation of the resistance gradient to obtain the data of the overload tolerance of the superimposed point resistance.

[0124] In the embodiments of the present invention, first, a change analysis of the heat flux density at the superimposed connection points is performed on the thermal stress vector data of the connection points. The thermal stress vector data is obtained through the previous step and represents the direction and magnitude of the thermal stress generated due to temperature changes at different connection points. The heat flux density at each connection point can be deduced from the relationship between the thermal stress vector and the material thermal conductivity. The purpose of this analysis is to reveal the trend of the heat flux density at the connection points changing with time and environmental conditions. During the specific operation, first, the heat flux density at each connection point at different time steps is calculated by numerical methods (such as the finite difference method), and the change of the heat flux density is dynamically tracked according to the actual operating conditions. Finally, the heat flux density change data of each connection point is obtained, including the time series of the heat flux density and the change amplitude under different conditions. After obtaining the heat flux density change data of the connection points, a density increment trend analysis is performed. This step calculates the heat flux density increment at each time point by performing a difference analysis on the time series of the heat flux density change data and further analyzes the trend of the increment. Specifically, the difference method (such as the first-order difference method) is used to calculate the increment of the heat flux density at each time point, and the increment value is smoothed to eliminate short-term fluctuations caused by data noise. Through statistical methods, the long-term and short-term change trends of the heat flux density increment are analyzed to reveal the increasing and decreasing law of the heat flux density at the connection points with time, and then the heat flux density increment trend data is obtained. These data provide a basis for subsequent heat flux density fluctuation analysis and resistance gradient calculation. According to the heat flux density increment trend data, a segmented mapping of the heat flux density change at the connection points is performed for the resistance gradient fluctuation. The resistance gradient describes the rate of resistance change caused by non-uniform resistance when current passes through the connection points. First, according to the increment trend of the heat flux density, the temperature change situation of each connection point is determined, so as to deduce the resistance change of the connection points at different times. Then, the resistance change of the connection points is combined with the trend of their heat flux density change and divided into multiple fluctuation intervals. Within each fluctuation interval, the resistance gradient of the connection points shows different fluctuation characteristics. Using the data fitting method, the resistance gradient fluctuation data is divided into multiple stage fluctuation intervals, and finally the resistance gradient segmented fluctuation data is obtained. This data reflects the fluctuation behavior of the connection point resistance under different heat flux density conditions and provides the necessary data support for subsequent resistance overload calculation. After obtaining the resistance gradient segmented fluctuation data, an overload limit numerical calculation is performed. The overload limit value refers to the maximum tolerable load before the resistance of the connection point changes to the critical value when it withstands a certain current and thermal stress. In this step, the overload limit of the connection points under different working conditions is calculated through the relationship between the current load and the resistance gradient. The power loss of each connection point at different current intensities is calculated using the relationship formula between resistance and current, and the corresponding overload limit value is calculated according to the changes of current and resistance.During this process, the influence of thermal stress on resistance was considered. By combining experimental measurement and theoretical derivation, the overload tolerance limits of each connection point under different conditions were obtained. Finally, based on the resistance overload limit values and the piecewise fluctuating data of the resistance gradient, the resistance overload tolerance of the superimposed connection points was calculated. The resistance overload tolerance refers to the maximum current fluctuation range that the connection point can withstand during actual operation. This step considered multiple factors, including the resistance of the connection point, heat flux density, temperature change, etc. By combining the resistance gradient fluctuation data with the overload limit values, the overload tolerance of each connection point under different working conditions was further analyzed. During specific operation, through data fusion and optimization algorithms, the resistance overload tolerance of each connection point was calculated, and the risk points of resistance overload under specific conditions were identified. Finally, the resistance overload tolerance data of the superimposed points were obtained, providing important information about the tolerance ability of the connection points for the battery management system.

[0125] Step S3 includes the following steps:

[0126] Step S31: Conduct a lower-limit passing output power analysis of the battery output power data for different road conditions to obtain the lower-limit passing output power between different road conditions;

[0127] Step S32: Adjust the lower-limit passing output power between different road conditions according to the connection point failure critical interval to obtain the battery output controllable power adjustment data;

[0128] Step S33: Normalize the battery output controllable power adjustment data to obtain the normalized battery output power adjustment data.

[0129] In the embodiments of the present invention, first, it is necessary to analyze the battery output power data collected in advance according to the road condition differences, and identify and extract the fluctuations of the battery output power under different road conditions. Specifically, the output power of the battery is affected by various factors, including the slope of the road, the road surface conditions (such as flat, rough, slippery, etc.), traffic flow, and driving speed. Through quantitative analysis of these influencing factors, the minimum value of the battery output power under each road condition, that is, the "lower limit passing output power", is determined. This analysis method first statistically processes the battery output power data under different road conditions, including calculating the minimum value of the power under each road condition. For different road condition intervals, a segmented data processing method is adopted. By performing a sliding window analysis on the battery output power data, the change trend of the battery output power under each section of the road condition is analyzed, so as to extract the lower limit passing output power of each section. The calculated lower limit value will be used as the basis for adjusting the subsequent battery output control strategy to ensure that the vehicle can still drive stably under the most adverse road conditions. Adjust the lower limit passing output power under different road conditions according to the connection point failure critical interval, so as to ensure that the output power of the battery under various road conditions can meet the basic requirements of vehicle driving, and avoid overheating of the battery or failure of the connection point due to excessive power requirements. It is necessary to carefully analyze the lower limit passing output power data under each road condition, and combine the data of the connection point failure critical interval to determine the power adjustment strategy for each section of the road. The connection point failure critical interval refers to the threshold at which certain connection points in the battery system fail under different temperatures or loads. This data is obtained by long-term monitoring of parameters such as the thermal stress and temperature rise of the battery. In this step, the output power under different road conditions is dynamically adjusted according to the failure critical interval. For the lower limit power under each road condition, combined with the actual working state and failure threshold of the battery connection point, a power adjustment factor is designed, which takes into account the requirements of the current road condition and the bearing capacity of the battery connection point. For example, if the battery output power on a certain section of the road exceeds the thermal load bearing capacity of the connection point, the battery output power should be appropriately reduced to avoid overloading of the connection point. Finally, according to the output power requirements of each road condition and the thermal load bearing capacity of the battery connection point, the lower limit passing output power data is adjusted to obtain the battery output controllable power adjustment data. Normalize the battery output controllable power adjustment data obtained in step S32 so that this data can adapt to different working environment conditions and be compatible with the data of other system modules. The main purpose of normalization is to convert the adjusted power data into a unified dimension for the subsequent application of control algorithms and hardware systems. Specifically, when operating, first standardize the battery output controllable power adjustment data. Commonly used standardization methods include min-max normalization and Z-score normalization.In this embodiment, the min-max normalization method is adopted to linearly transform the power adjustment data, and the adjusted power data is mapped into the interval of [0, 1], ensuring that all power data is within the same dimension range. This process not only enables the unified processing of power data under different battery usage states, but also makes the power demands of different vehicles under different road conditions comparable. The normalized battery output power adjustment normalized data will become the input data for the control strategy in the next-step battery management system, providing a basis for achieving precise control and energy efficiency optimization. In addition, the normalized data can interact more effectively with other system modules (such as the temperature control system and the power system), improving the overall performance and system stability of new energy vehicles.

[0130] Step S32 includes the following steps:

[0131] Step S321: Calculate the instantaneous power output bearing upper limit between different road conditions according to the connection point failure critical interval for the lower limit passing output power between different road conditions, and obtain the instantaneous power output bearing upper limit data;

[0132] Step S322: Analyze the output upper limit continuous controllable time of the instantaneous power output bearing upper limit data based on the connection point failure critical interval between different road conditions, and obtain the output upper limit continuous controllable time data;

[0133] Step S323: Perform battery output voltage load balancing processing according to the instantaneous power output bearing upper limit data and the output upper limit continuous controllable time data, and obtain the battery output voltage load balancing data;

[0134] Step S324: Adjust the lower limit passing output power between different road conditions for the battery output controllable power between different road conditions according to the instantaneous power output bearing upper limit data, the output upper limit continuous controllable time data, and the battery output voltage load balancing data, and obtain the battery output controllable power adjustment data.

[0135] In the embodiments of the present invention, according to the critical failure interval of the connection point, the upper limit of the instantaneous power output of the battery under different road conditions is calculated. First, it is necessary to obtain the instantaneous output power of the battery under each road condition and analyze the bearing capacity of the battery under the current power load in combination with the critical failure interval of the battery connection point. The critical failure interval of the connection point refers to the maximum heat load and current peak that the battery connection point can withstand. When the instantaneous power output of the battery exceeds this threshold, the connection point will fail due to overload. Specifically, when implementing, first classify and analyze the lower-limit passing output power data under different road conditions and calibrate the power requirements under each road condition. Subsequently, according to the critical failure interval of the connection point, calculate the maximum instantaneous power output that the battery can withstand under different road conditions. At this time, use the real-time monitoring data of the heat load and current fluctuation to determine the upper limit of the bearing power according to the critical failure interval. This upper limit value will be used as a basic parameter for control and management in the battery system to ensure that under any road condition, the battery output power will not exceed its safe bearing capacity, thereby avoiding battery damage or performance degradation caused by overload. First, it is necessary to analyze the duration of the upper limit of the instantaneous power output borne by the battery under different road conditions. This process is closely related to the critical failure interval of the connection point because when the battery connection point bears an instantaneous power load, there is a certain duration threshold, and exceeding this threshold will cause overheating or connection point failure. Based on the upper limit data of the instantaneous power output obtained in step S321, in combination with the heat capacity characteristics of the connection point and the heat conduction performance of the battery material, calculate the maximum duration for which the battery can continuously output power under different road conditions. This analysis is based on the heat load bearing capacity of the connection point, and the power output in different time periods is deduced through the heat balance equation. Specifically, first, by analyzing the relationship between the instantaneous power output of the battery under different road conditions and the critical failure interval of the connection point, determine the maximum time for which the output power of the battery can be continuously maintained. Subsequently, according to the power requirements and duration limitations under different road conditions, obtain the upper limit duration controllable time data, which represents the maximum time range for which the battery output power can be maintained under a certain specific road condition. According to the upper limit data of the instantaneous power output and the upper limit duration controllable time data obtained in steps S321 and S322, perform load balancing processing on the output voltage of the battery. Under different road conditions, the output power and voltage of the battery will fluctuate with the change of the load, and the battery management system needs to ensure that the output voltage of the battery remains within a stable range under different working conditions to avoid affecting the battery life or vehicle performance due to too high or too low voltage. First, it is necessary to use the upper limit data of the instantaneous power output to estimate the power requirements under different road conditions and analyze the voltage change trend of the battery under these conditions in combination with the upper limit duration controllable time data. According to the relationship between the power output and voltage of the battery (such as calculating the relationship between power and voltage through Ohm's law), adjust the voltage of the battery to ensure that the battery can stably output under the maximum bearing power.Specifically, if the output power of the battery approaches its load limit and lasts for a long time, the battery management system will appropriately adjust the output voltage of the battery to reduce the excessive load caused by too high voltage or the system instability caused by too low voltage. By integrating the data in steps S321, S322, and S323, the dynamic adjustment of the controllable output power of the battery is performed for the lower limit passing output power under different road conditions. According to the instantaneous power output load limit of the battery, the continuous controllable time of the output limit, and the battery voltage load balance, the output power of the battery under each road condition is adjusted to ensure that the battery can meet the vehicle driving requirements and avoid overloading or battery damage. First, combine the load limit, duration, and voltage load balance data obtained in steps S321, S322, and S323 to reasonably adjust the output power of the battery under each road condition. By calculating the power demand of each section and the load capacity of the battery, and comprehensively considering the output stability and safety of the battery under different road conditions, the final adjusted data of the controllable output power of the battery is obtained. During this process, the battery management system will make precise adjustments according to real-time data to ensure that the battery will not exceed its safe output range under any conditions, thereby maximizing the service life of the battery and ensuring the driving stability of the vehicle.

[0136] The present invention also provides a battery management system for a new energy vehicle, which is used to execute the new energy vehicle battery management method described above. The battery management system for a new energy vehicle includes:

[0137] A battery dynamic output power mapping module, which is used to obtain the operation log data of the new energy vehicle; extract the road condition data and the battery operation state data from the operation log data of the new energy vehicle respectively, and obtain the road condition data and the battery operation state data respectively; perform a battery dynamic output power mapping between different road conditions on the battery operation state data according to the road condition data, and obtain the battery output power data with road condition differences;

[0138] A connection point failure critical identification module, which is used to calculate the instantaneous power growth rate between different road conditions for the battery output power data with road condition differences, and obtain the instantaneous power growth rate with road condition differences; perform a heat load increment conduction analysis between battery connection points according to the instantaneous power growth rate with road condition differences, and obtain the connection point heat load increment conduction data; identify the connection point failure critical interval based on the connection point heat load increment conduction data, and obtain the connection point failure critical interval;

[0139] A battery output controllable power adjustment module, which is used to adjust the controllable output power of the battery between different road conditions for the battery output power data with road condition differences according to the connection point failure critical interval, and obtain the normalized data of the adjusted battery output power;

[0140] An automated power output execution module is used to learn the battery output control logic through a policy gradient algorithm for the connection point failure critical interval and the normalized data of the battery output power adjustment, so as to obtain the battery output control logic data; based on the battery output control logic data, an automated logic firmware is designed to obtain the battery output control logic firmware, and the battery output control logic firmware is embedded into the in-vehicle terminal of the new energy vehicle to perform the battery management of the new energy vehicle.

[0141] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application document are intended to be encompassed within the present invention.

[0142] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can 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 these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A new energy vehicle battery management method, characterized in that: The following steps are involved: Step S1: Acquire new energy vehicle operation log data; extract road condition data and battery operation status data from the new energy vehicle operation log data to obtain road condition data and battery operation status data respectively; map the battery dynamic output power between different road conditions based on the road condition data to obtain battery output power data for different road conditions; Step S2: calculating the instantaneous power growth rate between different road conditions based on the road condition difference battery output power data to obtain the road condition difference instantaneous power growth rate; performing heat load incremental conduction analysis between battery connection points based on the road condition difference instantaneous power growth rate to obtain connection point heat load incremental conduction data; and identifying the connection point failure critical interval based on the connection point heat load incremental conduction data to obtain the connection point failure critical interval; Step S3: adjusting the battery output controllable power between different road conditions based on the road condition difference battery output power data according to the critical interval of connection point failure to obtain battery output power adjustment normalization data; Step S4: Using a policy gradient algorithm to learn the battery output control logic of the critical interval of connection point failure and the battery output power adjustment normalization data, obtain battery output control logic data; based on the battery output control logic data, perform automated logic firmware design to obtain battery output control logic firmware, and embed the battery output control logic firmware into the on-board terminal of the new energy vehicle to perform new energy vehicle battery management.

2. The new energy vehicle battery management method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire new energy vehicle operation log data; Step S12: Cleaning the new energy vehicle operation log data to obtain cleaned operation log data; Step S13: extracting road condition data and battery operating status data from the operation log cleaned data to obtain road condition data and battery operating status data respectively; Step S14: Filling the time series missing values of the battery operating status data to obtain battery operating status missing filled data; Step S15: mapping the battery dynamic output power between different road conditions for the battery operating status missing fill data according to the road condition data to obtain road condition difference battery output power data.

3. The new energy vehicle battery management method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: calculating the instantaneous power growth rate between different road conditions based on the road condition difference battery output power data to obtain the road condition difference instantaneous power growth rate; Step S22: simulating the instantaneous temperature rise response of the battery according to the instantaneous power growth rate of the road condition difference to obtain the instantaneous temperature rise response data of the battery; Step S23: Acquire new energy vehicle battery design data; Step S24: performing a heat load incremental conduction analysis between battery connection points on the new energy vehicle battery design data based on the instantaneous power growth rate of road condition differences and the battery instantaneous temperature rise response data to obtain connection point heat load incremental conduction data; Step S25: performing heat load superposition connection point identification on the connection point heat load incremental conduction data to obtain the heat load superposition connection point; Step S26: Based on the connection point heat load incremental conduction data and the new energy vehicle battery design data, the connection point failure critical interval is identified for the heat load superimposed connection point to obtain the connection point failure critical interval.

4. The new energy vehicle battery management method according to claim 3, characterized in that: Step S23 includes the following steps: Step S231: performing a topological connection structure analysis on the new energy vehicle battery design data to obtain battery topological connection structure data; Step S232: performing a heat flow guidance analysis on the battery topology connection structure data according to the battery instantaneous temperature rise response data to obtain connection structure heat flow guidance data; Step S233: performing connection point temperature difference distribution analysis on the connection structure heat flow guidance data to obtain connection point temperature difference distribution data; Step S234: calculating the incremental series of the local connection point temperature difference on the connection point temperature difference distribution data based on the instantaneous power growth rate of the road condition difference to obtain the incremental series of the local connection point temperature difference; Step S235: performing heat load incremental conduction analysis between battery connection points on the battery topology connection structure data according to the local connection point temperature difference incremental series and the connection point temperature difference distribution data to obtain connection point heat load incremental conduction data.

5. The new energy vehicle battery management method according to claim 3, characterized in that: Step S26 includes the following steps: Step S261: Analyze the material characteristics of the battery connection line of the new energy vehicle battery design data to obtain the material characteristic data of the battery connection line; Step S262: Calculating the thermal stress vector of the heat load superimposed connection point based on the connection point thermal load incremental conduction data to obtain the superimposed connection point thermal stress vector data; Step S263: performing thermal expansion plastic strain fitting on the material characteristic data of the battery connection line according to the superimposed connection point thermal stress vector data to obtain thermal expansion plastic strain fitting data; Step S264: performing superposition point resistance overload tolerance calculation on the superposition point thermal stress vector data to obtain superposition point resistance overload tolerance data; Step S265: identifying a critical failure interval of the connection point for the thermal load superposition connection point based on the thermal expansion plastic strain fitting data and the superposition point resistance overload tolerance data to obtain the critical failure interval of the connection point.

6. The new energy vehicle battery management method according to claim 5, characterized in that: Step S263 includes the following steps: Extract the material specific heat capacity of the battery connection circuit material characteristic data to obtain the circuit material specific heat capacity; The mean difference of thermal energy in stress direction is calculated for the thermal stress vector data of the superimposed connection points to obtain the mean difference of thermal energy in stress direction; According to the stress direction thermal energy average difference data, the plastic yield limit of the line material is simulated to obtain the plastic yield data of the thermal energy average difference material; Based on the superimposed connection point thermal stress vector data, the plastic anisotropy analysis is performed on the plastic yield data of the thermal energy average difference material to obtain the material plastic yield anisotropy data; Thermal expansion plastic strain fitting is performed based on the thermal energy mean difference material plastic yield data, material plastic yield anisotropy data and stress direction thermal energy mean difference data to obtain thermal expansion plastic strain fitting data.

7. The new energy vehicle battery management method according to claim 6, characterized in that: Step S264 includes the following steps: Perform connection point heat flux density change analysis on the superimposed connection point thermal stress vector data to obtain connection point heat flux density change data; Perform density increment trend analysis on the heat flux density change data of the connection point to obtain the heat flux density increment trend data; According to the heat flux density increment trend data, the connection point heat flux density change data is mapped into connection point resistance gradient fluctuation segments to obtain resistance gradient segment fluctuation data; Calculate the overload limit value of the resistance gradient segmented fluctuation data to obtain the resistance overload limit value; The resistance overload tolerance of the superposition point is calculated based on the resistance overload limit value and the resistance gradient segmented fluctuation data of the superposition connection point thermal stress vector data to obtain the superposition point resistance overload tolerance data.

8. The new energy vehicle battery management method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: analyzing the lower limit output power between different road conditions on the road condition difference battery output power data to obtain the lower limit output power between different road conditions; Step S32: adjusting the battery output controllable power between different road conditions based on the lower limit of the passing output power between different road conditions according to the critical interval of the connection point failure, and obtaining battery output controllable power adjustment data; Step S33: normalizing the battery output controllable power adjustment data to obtain battery output power adjustment normalized data.

9. The new energy vehicle battery management method according to claim 8, characterized in that: Step S32 includes the following steps: Step S321: calculating the instantaneous power output carrying upper limit between different road conditions for the lower limit traffic output power between different road conditions according to the critical interval of connection point failure, and obtaining instantaneous power output carrying upper limit data; Step S322: analyzing the output upper limit continuous controllable time under different road conditions on the instantaneous power output carrying upper limit data based on the critical interval of connection point failure to obtain output upper limit continuous controllable time data; Step S323: performing battery output voltage load balancing processing according to the instantaneous power output carrying upper limit data and the output upper limit continuous controllable time data to obtain battery output voltage load balancing data; Step S324: adjusting the battery output controllable power between different road conditions for the lower limit passing output power according to the instantaneous power output carrying upper limit data, the output upper limit continuous controllable time data, and the battery output voltage load balance data to obtain the battery output controllable power adjustment data.

10. A new energy vehicle battery management system, characterized in that: Used to execute the new energy vehicle battery management method according to claim 1, the new energy vehicle battery management system comprises: The battery dynamic output power mapping module is used to obtain the new energy vehicle operation log data; the road condition data and battery operation status data are extracted from the new energy vehicle operation log data to obtain the road condition data and battery operation status data respectively; the battery dynamic output power between different road conditions is mapped to the battery operation status data based on the road condition data to obtain the battery output power data for different road conditions; The connection point failure critical identification module is used to calculate the instantaneous power growth rate between different road conditions based on the road condition difference battery output power data to obtain the road condition difference instantaneous power growth rate; perform heat load incremental conduction analysis between battery connection points based on the road condition difference instantaneous power growth rate to obtain the connection point heat load incremental conduction data; and identify the connection point failure critical interval based on the connection point heat load incremental conduction data to obtain the connection point failure critical interval; The battery output controllable power adjustment module is used to adjust the battery output controllable power between different road conditions based on the critical interval of connection point failure and obtain the battery output power adjustment normalization data; The automated power output execution module is used to learn the battery output control logic based on the critical interval of connection point failure and the battery output power adjustment normalization data through a policy gradient algorithm to obtain the battery output control logic data; the automated logic firmware is designed based on the battery output control logic data to obtain the battery output control logic firmware, and the battery output control logic firmware is embedded into the on-board terminal of the new energy vehicle to perform new energy vehicle battery management.

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