Battery aging test method and system for optimizing charging strategy of vehicle charger

Abstract

The invention discloses a battery aging test method and system for optimizing a charging strategy of an electric vehicle charger, and the method comprises the steps: collecting real driving information, constructing a load spectrum, defining an accelerated aging load function, and enabling a battery to generate equivalent cumulative aging damage within a preset time, and controlling the testing device to accelerate aging of the battery to a target state, testing the battery in the state by using multiple charging strategies, recording parameters, and optimizing the charging strategies after analysis and evaluation. The system comprises a load simulation module, a charging module, a data acquisition module, a control module and a processing module, and can realize load output, multi-strategy charging, parameter acquisition and data analysis optimization. According to the scheme, the data of the batteries in different aging states can be quickly obtained, the test period is shortened, the charging strategy research and development efficiency and accuracy are improved, the battery safety is guaranteed, and support is provided for efficient use and management of the batteries of the electric vehicles.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric vehicle charging test, in particular to a battery aging test method and system for optimizing vehicle charger charging strategy. BACKGROUND

[0002] In the technical field of electric vehicles, the optimization of charging strategy needs to closely match the characteristics of the battery at different aging stages to achieve a balance between charging efficiency, safety and battery life. However, there is a core problem in the prior art: the aging process of the battery in real use scenarios is extremely slow, usually taking several years, and is significantly different from individual to individual due to complex factors such as driving habits and environmental temperature, making it difficult to quickly obtain battery performance data under different aging states. This problem directly restricts the development and optimization efficiency of the charging strategy. Traditional testing methods either rely on long-term real vehicle operation to accumulate data, resulting in a long cycle, or use simplified laboratory aging simulation, such as fixed charge-discharge cycles, to obtain data that deviates greatly from real aging characteristics. Both methods cannot efficiently and accurately support the optimization of charging strategies for different aging stages, making existing charging strategies often difficult to meet the actual needs of new batteries and aging batteries. In addition, the existing technology lacks the ability to accurately simulate real driving conditions, and cannot accurately reflect the differential impact of different driving scenarios on battery aging, resulting in a large deviation between laboratory test results and actual use effects. At the same time, existing test systems often cannot simultaneously meet the needs of accelerated aging test and multi-environment charging strategy evaluation, making it difficult to comprehensively evaluate the performance of charging strategies under different temperature conditions. These problems seriously hinder the progress of electric vehicle charging technology, and there is an urgent need for a comprehensive solution that can accurately simulate real use scenarios, quickly generate equivalent aging states, and support multi-environment charging strategy evaluation. SUMMARY

[0003] The purpose of the present application is to provide a battery aging test method and system for optimizing electric vehicle charger charging strategy, which has the advantages of being able to efficiently and accurately simulate the battery aging process under real use scenarios, quickly generate equivalent aging states, and support charging strategy optimization.

[0004] To solve the above technical problems, the present application solves the problem by the following technical scheme: a battery aging test method for optimizing electric vehicle charger charging strategy, comprising the following steps: Load spectrum construction step: collecting driving information of a target electric vehicle model under real driving conditions, the driving information at least including speed sequence, mileage data, corresponding motor load data, road condition information and environmental temperature data; The acceleration aging function definition step is to construct the driving information collected in the load spectrum construction step, and construct an acceleration aging load function, wherein the acceleration aging load function refers to a definition rule of an electrical load parameter sequence capable of causing the battery to produce an equivalent cumulative aging damage to the real driving state within a preset time, wherein the electrical load parameter sequence includes curves of current, voltage and power changing over time, and load adjustment parameters under different environmental temperatures; and the equivalent cumulative aging damage refers to damage equivalent to the cumulative aging effect caused by the real driving state on the battery. The acceleration aging execution step is to connect a new battery to be tested or a battery with a known initial state to a test device, control the test device to cyclically apply the electrical load parameter sequence according to the acceleration aging load function, and accelerate the battery to a predetermined target aging state. The charging strategy test step is to configure a plurality of charging strategies to be evaluated for the battery reaching the target aging state in the acceleration aging execution step, and test the charging strategies by using a charging unit, and record charging-related parameters in the charging process, wherein the charging-related parameters at least include charging efficiency, battery temperature rise, charging time, and final state of charge. The strategy evaluation and optimization step is to analyze and compare the charging-related parameters of different charging strategies recorded in the charging strategy test step, evaluate the charging efficiency, temperature rise control effect and influence on battery health of each charging strategy, and optimize the charging strategy actually applied to the electric vehicle charger.

[0005] By using the above technical solution, the load spectrum is constructed by collecting rich driving information of the target electric vehicle in the real driving state, which can accurately reflect the complex working conditions of the battery actually used, and provide a reliable basis for subsequent aging test; the acceleration aging load function constructed based on this can cause the battery to produce an equivalent cumulative aging damage to the real driving state within a preset time, effectively solving the problem that the battery is slowly aged and it is difficult to quickly obtain data of different aging states, and greatly shortening the test period; connecting the battery to be tested to the test device and cyclically applying the electrical load according to the function can quickly obtain battery samples with different aging degrees, and significantly improve the research and development efficiency; configuring a plurality of charging strategies to be evaluated for the battery reaching the target aging state, testing the charging strategies, and recording related parameters, and evaluating the performance of each strategy and optimizing the charging strategy actually applied by analyzing and comparing can improve the charging efficiency, improve the temperature rise control effect, and protect the battery health.

[0006] The application further provides that, in the acceleration aging function definition step, the method for constructing the acceleration aging load function includes: characteristic extraction is performed on the driving information to identify key working condition segments having a significant influence on battery aging, wherein the key working condition segments include acceleration, deceleration, high-speed cruising working conditions and corresponding load characteristics; The load cycle sequence generating the equivalent cumulative aging damage is designed by one or more of increasing the repetition frequency of the key operating condition segments, amplifying the intensity of the load characteristics, or shortening the rest time between the key operating condition segments.

[0007] By adopting the technical scheme, in the accelerated aging function defining step, the driving information is characterized, the key operating condition segments significantly affecting battery aging, such as acceleration, deceleration, high-speed cruising, and corresponding load characteristics are identified, and the load cycle sequence is designed by increasing the repetition frequency of the key operating condition segments, amplifying the intensity of the load characteristics, or shortening the rest time between the key operating condition segments. This way can more targetedly simulate the operating conditions prone to aging of the battery in actual use, enhance the accelerated aging effect, and make the aging test more efficient and accurately reflect the actual aging condition of the battery.

[0008] The application further provides that, in the accelerated aging executing step, the battery state is further monitored in real time, and the battery state at least includes the real-time temperature and the internal resistance change rate of the battery. The preset adjustment rule is determined based on a comparison result of the battery state parameter and a preset threshold value, and when the real-time temperature or the internal resistance change rate of the battery exceeds the corresponding threshold value, the electrical load parameter sequence is adjusted correspondingly. By dynamically optimizing the accelerated aging load function, the safety of the battery in the accelerated aging process is ensured, and the equivalent cumulative aging damage is generated.

[0009] By adopting the technical scheme, the real-time monitoring and safety guarantee are realized: in the accelerated aging executing step, the battery state is monitored in real time, and according to the preset adjustment rule: based on a comparison result of the battery state parameter and a preset threshold value, when the real-time temperature or the internal resistance change rate of the battery exceeds the corresponding threshold value, the electrical load parameter sequence is adjusted correspondingly. By dynamically optimizing the accelerated aging load function, the safety of the battery in the accelerated aging process is ensured, and the equivalent cumulative aging damage is generated, so that the damage of the battery caused by overheating or other abnormal states in the test process is effectively avoided, and the reliability and safety of the test are ensured.

[0010] The application further provides that, in the charging strategy testing step, low-temperature, normal-temperature, and high-temperature environments are configured, and charging tests are performed in the low-temperature, normal-temperature, and high-temperature environments, and the recorded charging related parameters further include the internal pressure change and voltage fluctuation of the battery; the internal pressure change of the battery is collected by a pressure sensor, and the voltage fluctuation is calculated by monitoring the instantaneous voltage value.

[0011] By adopting the above technical scheme, multi-environment testing and comprehensive data collection: in the charging strategy testing step, low-temperature, normal-temperature and high-temperature environments are configured for charging testing, and additional charging related parameters such as battery internal pressure change and voltage fluctuation are recorded, for example, the battery internal pressure change is collected through a pressure sensor, and the voltage fluctuation is calculated through monitoring the instantaneous voltage value, the multi-environment testing can more comprehensively evaluate the performance of the charging strategy under different temperature conditions, and collecting more diversified parameters helps to deeply analyze the state change of the battery in the charging process, thereby providing rich data support for more accurate optimization of the charging strategy.

[0012] The application is further provided that: in the strategy evaluation and optimization step, the cycle life prediction model of the battery is combined for evaluation, the cycle life prediction model is a model for predicting the cycle number of the battery based on the chemical properties, aging state parameters and historical charging and discharging data of the battery, the evaluation content further includes the economy of long-term use of each charging strategy, and an optimal charging strategy recommendation table under different aging states is generated.

[0013] By adopting the above technical scheme, combined with life prediction and economy evaluation: in the strategy evaluation and optimization step, the cycle life prediction model based on the chemical properties, aging state parameters and historical charging and discharging data of the battery is combined for evaluation, the economy of long-term use of each charging strategy is evaluated at the same time, and an optimal charging strategy recommendation table under different aging states is generated. This not only evaluates the charging strategy from the perspective of battery life, but also considers the long-term use cost, thereby providing more comprehensive and practical charging strategy suggestions for users, and helping to realize the whole life cycle optimization management of electric vehicle charging.

[0014] The application is further provided that: it further includes an accelerated and basic aging mapping relationship establishing step, including: An accelerated aging data recording sub-step: recording the performance attenuation data of the battery during the execution of the accelerated aging execution step, the performance attenuation data including a capacity attenuation rate and an internal resistance growth rate; A basic and accelerated aging data comparative analysis sub-step: comparing and analyzing the performance attenuation data recorded in the accelerated aging data recording sub-step with basic aging data obtained by long-term running of the same type of battery under a standard load corresponding to the driving information; An equivalent aging mapping relationship modeling sub-step: based on the comparative analysis result of the basic and accelerated aging data comparative analysis sub-step, a mapping relationship model between the action time or cycle number of the accelerated aging load function and the equivalent real driving mileage or equivalent real running time is established.

[0015] By adopting the technical scheme, the aging mapping relationship is established: the battery performance attenuation data is recorded through the accelerated aging data recording substep, and the basic aging data obtained by long-term operation of the same type of battery under a standard load is compared and analyzed, and then a mapping relationship model between the action time or cycle number of the accelerated aging load function and the equivalent real driving mileage or equivalent real running time is established. The mapping relationship model helps to accurately quantify the corresponding relationship between the accelerated aging test and the battery aging in the actual use scenario, so that the charging strategy based on the accelerated aging test can be better applied to the actual situation, and the accuracy and practicality of the strategy are improved.

[0016] The application further provides that the system comprises: a load simulation module configured to programmatically output the electrical load parameter sequence required by the accelerated aging load function; a charging module provided with a charging interface for connecting the battery to be tested, and configured to charge the battery connected to the battery interface according to a plurality of charging strategies to be evaluated; a data acquisition module configured to acquire the voltage, current and temperature parameters of the battery in real time during the test and the electrical load parameters output by the load simulation module; a control and processing module in communication connection with the load simulation module, the charging module and the data acquisition module, wherein the control and processing module is used to store driving information, accelerated aging load function definition and charging strategies to be evaluated, control the load simulation module to perform accelerated aging, control the charging module to perform charging test, receive and process the data of the data acquisition module, analyze and compare the charging-related parameters of different charging strategies, and perform charging strategy evaluation and optimization.

[0017] By adopting the technical scheme, the system integration and function implementation are achieved: the test system comprises the load simulation module, the charging module, the data acquisition module and the control and processing module, and the modules work cooperatively to realize full-process automatic operation from battery accelerated aging test to charging strategy evaluation and optimization, improve the test efficiency and accuracy, and provide a complete system solution for charging strategy research and development.

[0018] The application further provides that the system further comprises an environment simulation module in cooperation with the load simulation module and the charging module, and configured to adjust the temperature of the test environment and simulate the use environment under different climate conditions, and the load simulation module further has the ability to dynamically adjust the output load according to the real-time state of the battery, and can simulate the load characteristics under different environmental temperatures.

[0019] By adopting the above technical scheme, the environmental simulation and load dynamic adjustment: the system increases an environmental simulation module to adjust the test environment temperature, simulates the use environment under different climate conditions, and the load simulation module has the ability to dynamically adjust the output load according to the real-time state of the battery, simulates the load characteristics under different environmental temperatures. This makes the test system can more realistically simulate the working state of the battery under various actual environments, comprehensively evaluate the performance of the charging strategy under the influence of different environmental factors, and improve the comprehensiveness and adaptability of the charging strategy optimization.

[0020] The application further provides that the data acquisition module includes a plurality of high-precision sensors for acquiring different parameters of the battery, and has an abnormal data alarm function.

[0021] By adopting the above technical scheme, high-precision data acquisition and abnormal alarm: the data acquisition module uses a plurality of high-precision sensors to acquire different parameters of the battery, and has an abnormal data alarm function. When the collected data exceeds the preset threshold, an alarm signal is sent to the control and processing module in a timely manner. The high-precision sensor ensures the accuracy of the collected data, providing a reliable basis for subsequent analysis, and the abnormal alarm function can timely detect abnormal conditions during the test, ensuring the safety and smooth progress of the test, and avoiding false test conclusions or equipment damage caused by abnormal data.

[0022] The application further provides that the control and processing module further includes a human-computer interaction interface for displaying real-time data, test progress, analysis results and optimized charging strategy during the test, supporting user input of test parameters, selection of charging strategies to be evaluated and setting of target aging state.

[0023] By adopting the above technical scheme, human-computer interaction and convenient operation: the control and processing module is equipped with a human-computer interaction interface for displaying real-time data, test progress, analysis results and optimized charging strategy during the test, supporting user input of test parameters, selection of charging strategies to be evaluated and setting of target aging state. The human-computer interaction interface provides an intuitive and convenient operation mode for the user, facilitating the user to participate in the test process, understand the test progress and results in a timely manner, and flexibly adjust the test settings according to the needs, improving the ease of use and user experience of the test system.

[0024] The application has the following technical effects: from the test efficiency, by constructing a real load spectrum, defining an accelerated aging function and performing accelerated aging test, the battery aging test period is greatly shortened, solving the problem of slow battery real aging and difficulty in quickly obtaining different aging state data in the traditional method, and combining key working condition identification and strengthening, the aging test is more efficient and practical, and the charging strategy development efficiency is significantly improved.

[0025] In terms of testing reliability and safety, real-time monitoring of battery status and dynamic adjustment of load parameters effectively avoid damage to the battery due to abnormal state during accelerated aging process, ensuring the reliability of the test; high-precision data acquisition ensures the accuracy of the data, and the abnormal alarm function further ensures the safety of the test, providing a reliable basis for subsequent analysis.

[0026] In terms of comprehensiveness and accuracy of charging strategy evaluation, the charging test in multiple environments combined with comprehensive parameter collection can comprehensively evaluate the performance of charging strategies in different environments; combined with the cycle life prediction model and economic evaluation, the generated optimal charging strategy recommendation table is more comprehensive and practical, which is helpful to realize the whole life cycle optimization management of electric vehicle charging.

[0027] In terms of system practicality and operability, the complete test system realizes the whole process automation from battery accelerated aging to charging strategy evaluation optimization, the environment simulation and load dynamic adjustment function improves the comprehensiveness and adaptability of strategy optimization, and the man-machine interface improves the usability and user experience of the system, so that the whole technical scheme is more convenient and efficient in practical application.

[0028] In summary, the battery aging test method and system for optimizing the charging strategy of the electric vehicle charger provided by the present application can generate equivalent real aging battery status within a predetermined time by constructing a real driving load spectrum and an accelerated aging function, combined with multi-environment charging test and dynamic optimization mechanism, solves the problems of long test cycle and large simulation deviation of traditional test, has the advantages of being able to efficiently and accurately simulate the battery aging process in real use scenarios, quickly generate equivalent aging state, and support charging strategy optimization. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 is a step schematic diagram of the battery aging test method for optimizing the charging strategy of the vehicle charger; Figure 2 is a structural schematic diagram of the test system for optimizing the charging strategy of the vehicle charger. DETAILED DESCRIPTION

[0030] The present application will be further described in detail below in combination with the drawings and examples.

[0031] Example: The optimization of the charging strategy of the electric vehicle needs to adapt to the characteristics of the battery at different aging stages, but the battery aging process in real scenarios is slow and affected by complex factors, resulting in long test data acquisition period and insufficient accuracy, and the traditional method relies on long-term real vehicle operation or simplified laboratory simulation, which is difficult to balance efficiency and authenticity, restricting the research and optimization of the charging strategy.

[0032] A method is needed to quickly generate an equivalent aging state and test the charging strategy, the core idea is to build a load spectrum through real driving data, design an accelerated aging function to simulate the damage of long-term use to the battery, and perform aging test in a controlled environment, so as to shorten the data collection period, further consider verifying the adaptability of the charging strategy under different environmental conditions, and establish a multi-dimensional evaluation system to optimize the strategy.

[0033] The present application proposes a method including load spectrum construction, accelerated aging function definition, accelerated aging execution, charging strategy test and strategy evaluation and optimization, the load spectrum construction acquires real driving speed, mileage, motor load, road condition and environmental temperature data to form input parameters reflecting actual use scenarios, the accelerated aging function designs electrical load parameter sequence based on the load spectrum to produce equivalent cumulative aging damage within a predetermined time, the test device loads the battery according to the function to accelerate aging to a target state, then performs multi-strategy charging test on the aged battery and records key parameters, and finally optimizes the charging strategy by analyzing and comparing.

[0034] Load spectrum construction refers to obtaining vehicle driving information through vehicle-mounted sensors or historical operation logs, which can be realized by using an accelerometer, a GPS module and a battery management system to ensure that the test scenario is consistent with the actual use conditions. The accelerated aging function definition is to convert real driving load into a reproducible electrical parameter sequence in the laboratory, which can be realized by adjusting the current fluctuation amplitude, prolonging the high-power load duration or shortening the charge-discharge interval, so that the battery reaches the equivalent aging effect in a short time. The equivalent cumulative aging damage is verified by comparing the capacity decay rate and internal resistance change rate of accelerated aging and natural aging, and the damage degree is quantified by electrochemical impedance spectrum analysis or capacity calibration method. The charging strategy test configures multiple charging modes, which can be realized by using constant current and constant voltage, pulse charging or temperature adaptive charging scheme, and the influence of different strategies on the aged battery is evaluated by switching charging parameters.

[0035] The load spectrum construction step integrates multi-dimensional driving data to generate a dynamic load model, providing input reference for accelerated aging. The accelerated aging function strengthens key working condition segments and optimizes the load sequence to quickly accumulate equivalent damage to the battery in the laboratory environment. The test device monitors the battery temperature and internal resistance when applying load according to the function, dynamically adjusts the parameters to prevent overload, and performs charging test under different temperature environments after reaching the target aging state, collects charging efficiency, temperature rise and other parameters, and finally selects the optimized scheme considering efficiency and safety by comparing the performance data of each strategy under the same aging state.

[0036] The conventional method adopts a fixed cycle mode and cannot reflect the load fluctuation of real driving, and the method can keep the aging process authenticity through a dynamic load spectrum, the prior art lacks correlation analysis of the aging state and the charging strategy, the scheme establishes a closed-loop test system from accelerated aging to strategy evaluation, realizes data-driven strategy optimization, and in addition, the multi-environment test module overcomes the limitation of single temperature condition test, and more comprehensively evaluates the adaptability of the charging strategy.

[0037] The application effectively shortens the battery aging test period to the level of several weeks, while ensuring that the aging state is equivalent to real use, quantifies the performance difference of different charging strategies at a specific aging stage, provides accurate optimization basis for the charger software, avoids battery overload damage during the test process through a dynamic load adjustment mechanism, improves test safety and repeatability, and a multi-dimensional parameter acquisition and analysis system provides data support for global optimization of the charging strategy.

[0038] The application also proposes a method for constructing an accelerated aging load function, which includes feature extraction on driving information, identifying key working condition segments that significantly affect battery aging, and key working condition segments including acceleration, deceleration, high-speed cruising working conditions and their corresponding load characteristics; one or more of increasing the repetition frequency of the key working condition segments, amplifying the intensity of the load characteristics, or shortening the rest time between the key working condition segments, to design a load cycle sequence that can produce equivalent cumulative aging damage.

[0039] The key working condition segment refers to a typical driving scene that has a major impact on battery aging selected from real driving information, which can be realized by a data segmentation algorithm combined with a battery aging sensitivity model, for example, after dividing the driving data by a sliding window, a high-damage segment is identified by an aging damage contribution evaluation model, wherein the load cycle sequence refers to a periodic load combination formed by the key working condition segments after parameter enhancement, which can be generated by a time compression algorithm or a load superposition algorithm, for example, the identified acceleration working condition current curve amplitude is increased and the deceleration working condition with shortened interval is alternately arranged.

[0040] After the driving information is preprocessed and input into the feature extraction module, the speed sequence and load data are segmented by a sliding window, the damage contribution degree of each segment is calculated by combining a battery aging mechanism model, the key working condition segments with a damage contribution degree higher than a set threshold are selected, and further, the load parameters of the key working condition segments are enhanced, for example, the current peak value of the acceleration working condition is increased, and the repetition number of the working condition is increased from 5 times per hour in the real scene to 20 times, the load cycle sequence generated in this way significantly shortens the aging test period while maintaining equivalent cumulative aging damage.

[0041] The traditional method usually adopts fixed charge-discharge cycles or single working condition repeated loading, which cannot reflect the combined influence of multiple working conditions alternation on aging in real driving, and the scheme can reflect the combined influence of multiple working conditions alternation on aging in real driving through dynamic strengthening of key working condition parameters, maintain the diversity characteristics of real aging damage, realize acceleration effect through load cycle optimization, and avoid test result deviation caused by simplified model.

[0042] The application can quickly reproduce the influence of real driving scene on battery aging in a laboratory environment, provide an aging battery sample highly consistent with actual use for testing of different charging strategies, and solve the problems of long cycle or distorted data of the traditional testing method.

[0043] In the accelerated aging execution step, the battery state is monitored in real time, and the battery state at least includes the real-time temperature and the internal resistance change rate of the battery; the preset adjustment rule is determined based on the comparison result of the battery state parameter and the preset threshold value, when the real-time temperature or the internal resistance change rate of the battery exceeds the corresponding threshold value, the corresponding electrical load parameter sequence is adjusted; the accelerated aging load function is dynamically optimized to ensure that the battery is safe and produces equivalent cumulative aging damage during the accelerated aging process, the real-time temperature of the battery refers to the dynamic change value of the surface or internal temperature of the battery during the accelerated aging process, which can be collected in real time by using a pasted temperature sensor or an embedded thermocouple, and is used to judge whether the battery is in an overheating state, the internal resistance change rate refers to the growth rate of the internal resistance value per unit time of the battery during the charging and discharging process, which can be measured by using an alternating current impedance method or a direct current pulse method, and is used to represent the aging degree of the battery, the dynamic optimization of the accelerated aging load function refers to an algorithm for dynamically correcting the electrical load parameter sequence according to the real-time monitored battery state parameter, which can be realized by using a feedback control model or an adaptive adjustment mechanism, and is used to balance the accelerated aging efficiency and the safety boundary of the battery.

[0044] In the accelerated aging process, the battery is connected to the test device and the electrical load parameter sequence is applied, the temperature sensor continuously collects the surface temperature of the battery, and the internal resistance monitoring module measures the internal resistance value of the battery at fixed time intervals, when the temperature exceeds the preset threshold value, for example, reaches 50 degrees Celsius, the test device automatically reduces the load current or prolongs the load interval time; when the internal resistance change rate exceeds 5% per hour, the test device adjusts the voltage output amplitude or shortens the duration of the high load working condition, by inputting the real-time data into the dynamic optimization algorithm, the corrected load parameter sequence is generated, for example, the original three consecutive acceleration working conditions are adjusted to two acceleration working conditions and a cooling stage is added, thereby avoiding battery overheating or abnormal growth of internal resistance under the premise of maintaining equivalent cumulative aging damage.

[0045] The conventional accelerated aging test usually adopts a fixed load cycle and lacks real-time state monitoring, and when the battery is overloaded due to individual differences or environmental fluctuations, timely intervention cannot be made, which may cause damage to the battery or distortion of the aging data. The present scheme can adjust the load parameters according to the actual response of the battery by introducing a real-time monitoring and dynamic optimization mechanism, which not only ensures the safety of the test process, but also ensures the equivalence of the aging damage and the real driving state.

[0046] The present application solves the problem of overload risk and aging data deviation caused by uncontrollable battery state in accelerated aging test, and realizes accurate simulation of battery aging process under real use scenario within the safety threshold, providing reliable aging state samples for subsequent charging strategy test.

[0047] The present application also proposes to configure low-temperature, normal-temperature and high-temperature environments in the charging strategy test step, and to perform charging test under low-temperature, normal-temperature and high-temperature environments, and the recorded charging-related parameters also include battery internal pressure change and voltage fluctuation; the battery internal pressure change is collected by a pressure sensor, and the voltage fluctuation is calculated by monitoring the instantaneous voltage value, the low-temperature, normal-temperature and high-temperature environments refer to the temperature scenarios simulating the battery under different climate conditions, for example, the low-temperature can be-20℃, the normal-temperature can be 25℃, and the high-temperature can be 50℃; the temperature regulation can be realized by an environmental simulation cabin or a temperature control device, which is used to test the adaptability of the charging strategy under extreme temperature, the battery internal pressure change refers to the shell pressure change of the battery caused by internal chemical reaction or gas generation during charging, which can be realized by a pasted pressure sensor or an embedded pressure detection module for real-time collection, which is used to evaluate the battery structure stability and safety risk, the voltage fluctuation refers to the instantaneous change amplitude of the battery terminal voltage during charging, which can be obtained by a high-frequency sampling circuit to get the voltage waveform, and the standard deviation or peak-valley difference value is calculated, which is used to reflect the abnormality of the battery internal impedance change and electrochemical state.

[0048] In the charging strategy test step, the test environment is configured as low-temperature, normal-temperature and high-temperature conditions, for example, the temperature of the test cabin is set to-20℃, 25℃ and 50℃ respectively by a temperature control system, the battery to be tested executes the charging strategy to be evaluated under each temperature environment, and the charging unit charges according to the preset current and voltage curve, in this process, the pressure sensor is installed on the surface or inside of the battery shell to continuously collect pressure data; the voltage monitoring module records the instantaneous voltage value at a sampling frequency higher than 1kHz and calculates the fluctuation range, and the test data is transmitted to the processing unit for subsequent analysis of the influence of the charging strategy under different temperatures on the battery internal pressure and voltage stability.

[0049] The traditional charging strategy test is usually only carried out in a normal temperature environment, and cannot reflect the influence of extreme temperature on the charging process, and the key safety parameter of monitoring the internal pressure change of the battery is not monitored. The scheme can more comprehensively evaluate the performance difference of the charging strategy in different use scenarios by introducing multi-temperature environment test and pressure and voltage fluctuation monitoring, such as identifying the risk of battery swelling caused by rapid pressure increase in high temperature environment, or the overcharging problem caused by abnormal voltage fluctuation in low temperature environment.

[0050] The application can provide multi-dimensional test data for charging strategy optimization, such as screening the charging strategy with better temperature rise control by comparing the internal pressure change trend of the battery at different temperatures, and formulating the charging parameter adjustment rules suitable for different aging stages by analyzing the correlation between voltage fluctuation and battery aging state, so as to improve the safety and environmental adaptability of the charging strategy.

[0051] The application also proposes to combine the cycle life prediction model of the battery in the strategy evaluation and optimization steps for evaluation. The cycle life prediction model is a model for predicting the cycle number of the battery based on the chemical properties, aging state parameters and historical charging and discharging data of the battery. The evaluation content also includes the economy of long-term use of each charging strategy, and generates an optimal charging strategy recommendation table under different aging states.

[0052] The cycle life prediction model refers to a mathematical prediction tool established based on the material properties, aging degree and charging and discharging history of the battery. Specifically, a neural network model or a degradation model based on physical and chemical reactions can be used to realize the model, which is used to quantify the influence of different charging strategies on the remaining life of the battery. The economy evaluation of long-term use refers to the calculation of the battery maintenance cost, replacement cycle and energy consumption cost corresponding to the charging strategy, which can be realized by using the whole life cycle cost analysis method, and is used to measure the comprehensive economic value of the charging strategy in actual application. The optimal charging strategy recommendation table refers to a decision reference table generated according to the matching relationship between the battery aging state and the charging strategy performance, which can be realized by using a multi-dimensional parameter correlation analysis method, and is used to provide differentiated charging scheme selection basis for batteries in different health states.

[0053] After completing the charging test, the charging efficiency, temperature rise control and other parameters of each strategy are input into the cycle life prediction model to predict the remaining cycle number of the battery after long-term use of each strategy. At the same time, the charging time, energy consumption data and battery replacement cost are combined to calculate the comprehensive economic cost of each strategy in the whole life cycle. By comparing the life prediction value and economic indicators of each strategy under different aging states, the corresponding relationship table of aging degree and optimal charging strategy is established, for example, for a battery with capacity attenuation exceeding 20%, a strategy of reducing charging power but prolonging charging time is recommended to balance the life and economy.

[0054] The traditional method only selects the strategy based on short-term charging parameters, lacks systematic evaluation of the long-term life and use cost of the battery, and the scheme can quantize the cumulative influence of the charging strategy on the battery health state by introducing a cycle life prediction model and economic analysis, and can also consider the optimization demand of the operation cost, and provides a scientific decision basis for the battery in different aging stages.

[0055] The application realizes the cooperative optimization of long-term economic benefits of the charging strategy and the battery health management, solves the problem that the traditional test method cannot evaluate the whole cycle influence of the strategy, and can dynamically adjust the charging scheme according to the actual aging degree of the battery, effectively prolongs the service life of the battery and reduces the comprehensive operation cost.

[0056] The method further comprises a step of establishing an accelerated and basic aging mapping relationship, including an accelerated aging data recording sub-step, a basic and accelerated aging data comparative analysis sub-step, and an equivalent aging mapping relationship modeling sub-step. In the accelerated aging data recording sub-step, the performance degradation data of the battery is recorded during the execution process, including the capacity attenuation rate and the internal resistance growth rate. In the basic and accelerated aging data comparative analysis sub-step, the recorded accelerated aging performance degradation data is compared and analyzed with the basic aging data obtained by long-term operation of the same type of battery under standard load. In the equivalent aging mapping relationship modeling sub-step, a mapping relationship model between the action time or cycle number of the accelerated aging load function and the equivalent real driving mileage or equivalent real running time is established based on the comparative analysis result.

[0057] The performance degradation data refers to a quantitative parameter reflecting the aging degree of the battery, which can be realized by regularly measuring the capacity and internal resistance changes of the battery by using a capacity tester and an internal resistance tester, and is used to track the degradation trajectory of the battery in the accelerated aging process. The basic aging data refers to the aging history data of the battery under real use conditions, which can be realized by collecting long-term performance monitoring data of the battery during real vehicle operation, and is used as a reference for verifying the equivalence of accelerated aging. The equivalent aging mapping relationship model refers to a mathematical relationship between the laboratory accelerated aging parameters and the actual use parameters, which can be realized by using a regression analysis method to establish a conversion coefficient between the accelerated aging cycle number and the equivalent driving mileage, and is used to quantize the corresponding relationship between the accelerated aging test and the actual use.

[0058] During the accelerated aging test process, the capacity decay rate and internal resistance growth rate of the battery are recorded in real time and form a data set, which is compared with the aging data corresponding to the real driving mileage accumulated by the same type of battery under standard load, and the correlation between the accelerated aging parameters and the actual use parameters is identified by statistical analysis method, for example, when it is found that the capacity decay rate generated by performing 100 accelerated aging cycles is equivalent to the decay level of 5000 kilometers of real vehicle driving, a linear proportional relationship between the cycle number and the equivalent mileage can be established, and the mapping model formed thereby can convert the laboratory test results into the equivalent aging state in the actual use scenario, providing accurate test data basis for charging strategy optimization.

[0059] The conventional method only performs aging test by fixing the cycle number or time, lacks correlation verification with actual use data, and thus the test results cannot accurately reflect the real aging characteristics, while the present scheme establishes the mapping relationship between the accelerated aging parameters and the real vehicle running data, so that the laboratory test results can be directly converted into equivalent driving mileage or running time, solving the problem of aging simulation distortion in the conventional test method.

[0060] The present application can effectively correlate the accelerated aging test results with the aging data in the actual use scenario, ensure that the test data truly reflects the aging characteristics of the battery at different use stages, and thus obtain the equivalent aging mapping relationship which can provide accurate test basis for charging strategy optimization, avoid charging strategy adaptation errors caused by aging simulation deviation, and improve the charging strategy development efficiency for batteries at different aging stages.

[0061] The present application also proposes a test system for optimizing the charging strategy of an electric vehicle charger, comprising a load simulation module, a charging module, a data acquisition module, and a control and processing module. The load simulation module is configured to programmably output the electrical load parameter sequence required by the accelerated aging load function. The charging module is provided with a charging interface for connecting the battery to be tested, and is configured to charge the battery connected to the battery interface according to a plurality of charging strategies to be evaluated. The data acquisition module is configured to acquire the voltage, current, temperature parameters of the battery during the test process and the electrical load parameters output by the load simulation module in real time. The control and processing module is in communication connection with the load simulation module, the charging module and the data acquisition module, and is used to store driving information, accelerated aging load function definition, and charging strategies to be evaluated, control the load simulation module to perform accelerated aging, control the charging module to perform charging test, receive and process the data of the data acquisition module, analyze and compare the charging related parameters of different charging strategies, and perform charging strategy evaluation and optimization.

[0062] The programmable output of the accelerated aging load function requires the electrical load parameter sequence to be output by presetting the program to control the output of the current, voltage and power curve that meets the requirements of accelerated aging. Specifically, it can be realized by combining a digital signal processor with a power amplifier. This feature enables the load simulation to accurately reproduce the dynamic load changes in real driving scenarios. The various charging strategies to be evaluated refer to differentiated charging schemes designed for batteries at different aging stages. Specifically, it can be realized by using constant current and constant voltage, pulse charging or temperature compensation charging protocols. This feature supports the comparative analysis of the applicability of different strategies on the same test platform. The real-time collection of voltage, current and temperature parameters of the battery during the test process refers to the synchronous acquisition of battery operating state data through a sensor network. Specifically, it can be realized by using high-precision Hall sensors, thermocouples and data acquisition cards. This feature provides a multi-dimensional quantitative basis for evaluating charging strategies.

[0063] The test system generates a dynamic load curve required for accelerated aging through the load simulation module, so that the battery reaches the target aging state in a short time. The charging module then executes the preset charging strategy on the aged battery. The data acquisition module synchronously records the electrical parameters and temperature changes during the charging process. The control and processing module analyzes the collected data and compares the differences in efficiency, temperature rise and battery health impact of different charging strategies. Finally, an optimized charging strategy scheme is generated. During the test process, the coordinated work of each module forms a closed loop for aging simulation and strategy verification, effectively shortening the test cycle.

[0064] Traditional test systems usually use fixed charge and discharge cycles for aging simulation, which cannot reflect the dynamic load characteristics in real driving and lack multi-dimensional data support for charging strategy evaluation. The present scheme integrates programmable load simulation and multi-strategy charging test functions, combined with real-time data acquisition and analysis, to more realistically simulate the battery aging process and accurately evaluate the long-term impact of charging strategies.

[0065] The present application realizes efficient simulation of battery aging state and rapid verification of charging strategy, solves the problems of long cycle and large data deviation of traditional test methods, and provides reliable technical support for optimizing the charging strategy of batteries at different aging stages through dynamic load reproduction and multi-strategy comparative analysis.

[0066] The test system also includes an environment simulation module that works with the load simulation module and the charging module to adjust the temperature of the test environment and simulate different climate conditions. The load simulation module also has the ability to dynamically adjust the output load according to the real-time state of the battery, which can simulate the load characteristics under different environmental temperatures.

[0067] The environment simulation module refers to a device capable of actively controlling the temperature of the test environment, which can be realized by using a thermostat or a temperature control cabin with temperature control function. Through the built-in heating element, refrigeration device and temperature sensor, a closed loop regulation is formed, so that the battery can be tested in low temperature, normal temperature and high temperature environment, thereby simulating the climate conditions in the real use scene. The ability of dynamic adjustment of output load refers to the function of automatically correcting the electrical load parameter sequence of the battery according to the real-time working parameters of the battery. Specifically, by collecting the battery temperature and internal resistance change rate data in real time, combining the preset load adjustment algorithm to dynamically adjust the current and voltage output curve, it can ensure that the load characteristics applied under different environmental temperatures are consistent with the battery load characteristics under the real driving state.

[0068] The environment simulation module adjusts the test environment to the target temperature range through the temperature control device, for example, controls the temperature in the cabin to any set value within the range of-20℃ to 50℃, while the load simulation module receives the real-time temperature data of the battery from the data acquisition module, adjusts the amplitude and duration of the output current based on the preset temperature-load mapping relationship. When the battery is tested in a high temperature environment, the load simulation module automatically reduces the current peak value to avoid overheating damage; in a low temperature environment, the duration of high power load is extended to simulate the influence of cold climate on battery performance. Therefore, the test system can accurately reproduce the influence of real driving conditions on battery aging under different temperature conditions.

[0069] The conventional test system can usually only simulate the load at a fixed temperature, and cannot reflect the comprehensive influence of temperature change on battery aging and charging strategy. The present scheme integrates the environment simulation module and the dynamic load adjustment function, realizes full-scene test coverage under multiple climate conditions, and makes the optimization result of the charging strategy closer to the actual application environment.

[0070] The present application can effectively simulate the aging process and charging performance change of the battery under different climate conditions, provide more comprehensive test data support for the optimization of the charging strategy, solve the test result deviation problem caused by the single environment factor of the traditional method, and significantly improve the adaptability and reliability of the charging strategy under different temperature scenes.

[0071] The data acquisition module includes a plurality of high-precision sensors for collecting different parameters of the battery, and has an abnormal data alarm function. When the collected data exceeds the preset threshold, an alarm signal is sent to the control and processing module in time.

[0072] The high-precision sensor refers to a sensor device with higher measurement accuracy than the conventional industrial standard, which can be specifically implemented by combining a temperature sensor, a voltage sensor, a current sensor, and a pressure sensor. The temperature sensor can be a platinum resistance type, the voltage sensor can be a Hall effect type, and the current sensor can be a combination of a shunt and an operational amplifier. These sensors are configured to synchronously collect the voltage, current, temperature, and internal pressure parameters of the battery. The high-precision characteristics ensure that the error between the collected data and the actual state is controlled within an allowable range. The abnormal data alarm function refers to a function of determining whether the collected data exceeds a safety threshold through a preset logic. This function can be implemented by a comparator circuit or an embedded software algorithm. For example, when the battery temperature exceeds 60 degrees Celsius or the voltage fluctuation amplitude exceeds 15% of the nominal value, an alarm signal is triggered. This function can prevent battery damage or equipment failure caused by abnormal data during testing by monitoring key parameters in real time.

[0073] During the battery accelerated aging and charging test, multiple high-precision sensors are configured to continuously collect the voltage, current, temperature, and internal pressure parameters of the battery. The collected data is transmitted to the control and processing module through a communication interface. At the same time, the embedded system compares the data with the preset threshold in real time. For example, during high-temperature environment testing, if the temperature sensor detects that the battery surface temperature reaches 65 degrees Celsius, which exceeds the preset threshold of 60 degrees Celsius, an alarm signal is immediately triggered and sent to the control module. At this time, the test process can be paused for heat dissipation intervention. This mechanism enables the test system to maintain the reliability of data collection under complex working conditions while ensuring the safety of the test process.

[0074] The pressure sensor can be a piezoelectric sensor with a range of 0-10 MPa and a sampling frequency of 100 Hz to adapt to the rapid changes in the internal pressure of the battery. Voltage fluctuation monitoring can be achieved through a high-speed ADC module with a sampling rate of, for example, 1 kHz to capture transient voltage fluctuations during charging and discharging. The transmission of the alarm signal can use the CAN bus protocol to ensure real-time signal transmission and anti-interference capability.

[0075] Traditional test systems usually use single sensors or low-precision sensor combinations, which have large data collection errors and lack real-time alarm mechanisms. For example, some existing solutions use thermocouple temperature sensors, which have difficulty meeting the high dynamic load test requirements in terms of response speed and accuracy. Abnormal data processing relies on manual monitoring. The present solution significantly improves the accuracy of data collection and the response speed of the system through the cooperative work of multiple types of high-precision sensors and the automatic alarm function.

[0076] The application can effectively solve the safety hazards caused by data collection errors or abnormal working conditions in the test process, ensure the reliability and repeatability of the accelerated aging and charging strategy test, and further reduce the risk of battery over-temperature or over-voltage in the test process. The introduction of the abnormal alarm function further reduces the risk of battery over-temperature or over-voltage in the test process, and provides a high-quality data basis for optimizing the charging strategy.

[0077] The control and processing module further comprises a human-computer interaction interface for displaying real-time data, test progress, analysis results and optimized charging strategy in the test process, supporting user input test parameters, selecting charging strategies to be evaluated and setting target aging states.

[0078] The human-computer interaction interface refers to a visual operation platform for realizing information interaction between the user and the test system, which can be realized by a touch screen or a graphical user interface combined with physical buttons. The test data and analysis results are displayed in real time to facilitate the operator to intuitively monitor the test process. The real-time data includes the change curves of battery voltage, current, temperature and load parameters, which can be transmitted in real time to the control and processing module by the sensors of the data acquisition module and presented in the form of dynamic charts in the interface to assist the user to judge whether the test state is abnormal. The test parameters include the set value of the target aging state, the cycle number of the accelerated aging load function, and the current and voltage threshold of the charging strategy, which can be configured through input boxes or drop-down menus to make the test process customizable. The selection of the charging strategy to be evaluated supports calling from the pre-stored strategy library or customizing the strategy parameters by the user, which can be realized by file import or manual input to ensure that the test covers various charging scenarios.

[0079] The human-computer interaction interface is integrated in the control and processing module. The target aging state parameters are set through the interface before the test starts, such as setting the battery capacity attenuation rate to 20% or the internal resistance growth rate to 30%. During the accelerated aging process, the interface displays the battery temperature, internal resistance change curve and load cycle number in real time, and marks the current test progress percentage. When entering the charging strategy test stage, the user can select the strategy to be evaluated from the pre-stored charging strategy list through the interface, such as constant current charging, pulse charging or temperature adaptive charging mode. After the test is completed, the optimized charging strategy and its corresponding charging efficiency and temperature rise control effect data are displayed in the form of a table. The user can adjust the strategy parameters according to the analysis results and retest.

[0080] The traditional test system usually relies on independent data recording devices and manual parameter configuration tools, and the operator needs to switch between multiple devices to monitor data and adjust strategies, resulting in low test efficiency and easy human error. The present scheme integrates the functions of parameter setting, strategy selection, data visualization and result analysis into a single operation terminal through a man-machine interaction interface, reducing the data transmission delay between devices, and reducing the operation complexity through a standardized input interface.

[0081] The present application solves the problem of low parameter configuration efficiency and insufficient transparency of the test process caused by poor interactivity of the traditional test system, realizes one-stop management of test parameters and real-time data visualization, significantly shortens the iteration period from data acquisition to strategy optimization, and at the same time, users can set custom charging strategies and target aging states to quickly verify the applicability of charging schemes in different scenarios, providing efficient support for charging strategy optimization in the battery aging stage.

Claims

1. A battery aging test method for optimizing vehicle charger charging strategies, characterized in that, Includes the following steps: Load spectrum construction steps: Collect driving information of the target electric vehicle model under real driving conditions. The driving information includes at least speed sequence, mileage data, corresponding motor load data, road condition information and ambient temperature data. Accelerated aging function definition steps: Based on the driving information collected in the load spectrum construction step, an accelerated aging load function is constructed. The accelerated aging load function refers to the definition rule of the electrical load parameter sequence that can cause the battery to produce equivalent cumulative aging damage to the actual driving conditions within a preset time. The electrical load parameter sequence includes the curves of current, voltage, and power changing over time, as well as the load adjustment parameters under different ambient temperatures. The equivalent cumulative aging damage is the damage that is equivalent to the cumulative aging effect caused to the battery by the actual driving conditions. Accelerated aging execution steps: Connect the new battery to be tested or the battery with a known initial state to the test device, and control the test device to cyclically apply the electrical load parameter sequence according to the accelerated aging load function, so that the battery is accelerated to the predetermined target aging state. Charging strategy test steps: For batteries that have reached the target aging state in the accelerated aging execution step, several charging strategies to be evaluated are configured, and charging tests are conducted on them through a charging unit. Charging-related parameters during the charging process are recorded. The charging-related parameters include at least charging efficiency, battery temperature rise, charging time, and the final state of charge reached. Strategy evaluation and optimization steps: Analyze and compare the charging-related parameters of different charging strategies recorded in the charging strategy test steps, evaluate the charging efficiency, temperature rise control effect and impact on battery health of each charging strategy, and optimize the charging strategy actually applied to electric vehicle chargers based on this.

2. The battery aging test method for optimizing vehicle charger charging strategy according to claim 1, characterized in that, In the step of defining the accelerated aging function, the method for constructing the accelerated aging load function includes: Feature extraction is performed on the driving information to identify key operating condition segments that significantly affect battery aging. These key operating condition segments include acceleration, deceleration, high-speed cruising, and their corresponding load characteristics. By increasing the repetition frequency of the critical operating condition segments, amplifying the intensity of the load characteristics, or shortening the rest time between the critical operating condition segments, a load cycle sequence that can produce the equivalent cumulative aging damage is designed.

3. The battery aging test method for optimizing vehicle charger charging strategy according to claim 1, characterized in that, The accelerated aging process also includes real-time monitoring of battery status, which includes at least the real-time battery temperature and the rate of change of internal resistance. The preset adjustment rules are determined based on the comparison results between battery state parameters and preset thresholds. When the real-time temperature or internal resistance change rate of the battery exceeds the corresponding threshold, the electrical load parameter sequence is adjusted accordingly. By dynamically optimizing the accelerated aging load function, we ensure that the battery is safe during accelerated aging and does not generate equivalent cumulative aging damage.

4. The battery aging test method for optimizing vehicle charger charging strategy according to claim 1, characterized in that, The charging strategy test steps include low temperature, normal temperature and high temperature environments, and charging tests are conducted in the low temperature, normal temperature and high temperature environments. The recorded charging-related parameters also include changes in battery internal pressure and voltage fluctuations. Among them, the internal pressure change of the battery is collected by a pressure sensor, and the voltage fluctuation is calculated by monitoring the instantaneous voltage value.

5. The battery aging test method for optimizing vehicle charger charging strategy according to claim 1, characterized in that, In the strategy evaluation and optimization step, a battery cycle life prediction model is configured for evaluation. The cycle life prediction model is a model built based on the battery's chemical characteristics, aging state parameters and historical charge and discharge data to predict the number of battery cycles. The evaluation also includes the long-term economics of each charging strategy and generates a recommended table of optimal charging strategies under different aging states.

6. The battery aging test method for optimizing vehicle charger charging strategy according to claim 1, characterized in that, It also includes steps to establish a mapping between accelerated and basic aging, including: Accelerated aging data recording sub-step: During the execution of the accelerated aging step, the performance degradation data of the battery is recorded, including the capacity degradation rate and the internal resistance growth rate. Basic and accelerated aging data comparison and analysis sub-step: Compare and analyze the performance degradation data recorded in the accelerated aging data recording sub-step with the basic aging data obtained by long-term operation of the same type of battery under standard load corresponding to driving information; Equivalent aging mapping modeling sub-step: Based on the comparative analysis results of the basic and accelerated aging data comparison analysis sub-step, establish a mapping relationship model between the application time or number of cycles of the accelerated aging load function and the equivalent real driving mileage or equivalent real running time.

7. A test system for optimizing vehicle charger charging strategies, comprising a battery aging test method for optimizing vehicle charger charging strategies as described in any one of claims 1-6, characterized in that, include: The load simulation module is configured to programmably output a sequence of electrical load parameters required by the accelerated aging load function; The charging module is equipped with a charging interface for connecting the battery to be tested, and is configured to charge the battery connected to the battery interface according to a variety of charging strategies to be evaluated. The data acquisition module is configured to collect the battery's voltage, current, and temperature parameters, as well as the electrical load parameters output by the load simulation module, in real time during the test. The control and processing module is communicatively connected to the load simulation module, the charging module, and the data acquisition module. The control and processing module is used to store driving information, define the accelerated aging load function, and the charging strategy to be evaluated. It controls the load simulation module to perform accelerated aging, controls the charging module to perform charging tests, receives and processes data from the data acquisition module, analyzes and compares the charging-related parameters of different charging strategies, and performs charging strategy evaluation and optimization.

8. The system according to claim 7, characterized in that, It also includes an environment simulation module, which works in conjunction with the load simulation module and the charging module. It is configured to adjust the temperature of the test environment to simulate the usage environment under different climatic conditions. The load simulation module also has the ability to dynamically adjust the output load according to the real-time status of the battery, and can simulate the load characteristics under different ambient temperatures.

9. The system according to claim 7, characterized in that, The data acquisition module includes multiple high-precision sensors, which are used to collect different parameters of the battery. It also has an abnormal data alarm function. When the collected data exceeds a preset threshold, it promptly sends an alarm signal to the control and processing module.

10. The system according to claim 7, characterized in that, The control and processing module also includes a human-machine interface for displaying real-time data, test progress, analysis results, and optimized charging strategies during the test process. It supports users in inputting test parameters, selecting the charging strategy to be evaluated, and setting the target aging state.

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