Light truck power battery charge and discharge endurance test method based on computer processing

Abstract

The invention discloses a light truck power battery charge and discharge endurance test method based on computer processing, and relates to the technical field of power battery test and evaluation, and the method comprises the steps: S1, automatically generating an aging evaluation model based on battery performance historical data, and S2, testing a power battery according to a set charge and discharge cycle, s3, automatically adjusting test conditions based on the aging evaluation model to adapt to the aging state of the battery; S4, recording various parameters in the test process and analyzing and evaluating the durability of the battery; according to the light truck power battery charge and discharge endurance test method based on computer processing, rapid processing and trend analysis of battery performance data are realized, high-value data support is provided for subsequent battery research and development and optimization, and the practicability of the test method is further enhanced; the problem that the actual use scene of the light truck is difficult to accurately simulate due to the lack of automatic evaluation means and dynamic adjustment function in the existing power battery charging and discharging endurance test method is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power battery testing and evaluation, and in particular to a method for charging and discharging endurance testing of a light truck power battery based on computer processing. Background Art

[0002] In the process of power battery development and verification, durability testing is an important part of evaluating battery performance and service life. Traditional durability testing methods usually observe performance changes by repeatedly charging and discharging the battery.

[0003] However, the existing test methods have some obvious limitations: First, the test conditions are usually pre-set fixed parameters (such as current, voltage and temperature), and lack the ability to dynamically adjust according to the actual aging degree of the battery. Such fixed parameter test conditions are difficult to simulate the complex working conditions and usage scenarios of power batteries in the actual operation of light trucks, resulting in a large deviation between the test results and the actual usage environment. Secondly, most of the current test methods rely on manual evaluation of the aging state of the battery, and the evaluation criteria are often based on a single parameter (such as capacity decay or internal resistance increase), which cannot fully reflect the comprehensive characteristics of battery aging. This evaluation method is not only inefficient, but also prone to introduce subjective errors, thereby affecting the accuracy of the test results. In addition, the existing test system lacks the ability to intelligently optimize the test conditions and cannot adjust the charge and discharge parameters based on real-time monitoring data to simulate the load changes of the battery at different aging stages. This makes the test process unable to dynamically adapt to changes in battery performance, further weakening the scientific nature and reference value of the test results. In summary, the existing power battery charge and discharge durability test method is difficult to accurately simulate the actual usage scenarios of light trucks due to the lack of automated evaluation methods and dynamic adjustment functions. Summary of the invention

[0004] The purpose of the present invention is to provide a light truck power battery charge and discharge endurance test method based on computer processing, so as to solve the problem that the existing power battery charge and discharge endurance test method is difficult to accurately simulate the actual use scenarios of light truck vehicles due to the lack of automated evaluation means and dynamic adjustment functions.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for charging and discharging endurance test of a light truck power battery based on computer processing, the method comprising: S1. Automatically generate an aging assessment model based on battery performance historical data, including collecting battery performance historical data and fitting historical data. The specific formula is: ; Where v represents the performance degradation rate of the battery in the current charge and discharge cycle, V maxIt indicates the limiting speed at which the battery's performance deteriorates, N indicates the number of charge and discharge cycles the battery has experienced, and B indicates the index value of the battery's anti-aging ability; Generate a battery aging assessment model to predict how battery capacity will change over future cycles S2. Test the power battery according to the set charge and discharge cycle; S3. Automatically adjust the test conditions based on the aging assessment model to adapt to the battery aging state, including monitoring the fluctuation of battery performance through the aging assessment model and calculating the adjusted test parameters. The specific formula is: ; Among them, x new represents the test parameter value after dynamic adjustment, x old represents the current test parameter value, A represents the adjustment amplitude of the test parameter, f represents the frequency of dynamic change of battery performance, t represents time, and g represents the initial state offset value of battery performance fluctuation; S4. Record various parameters during the test and analyze and evaluate battery durability.

[0006] Preferably, S2 includes: Determine the charge and discharge test parameters, assign weights to each parameter, and calculate the efficiency of the parameter combination under the current test conditions. The specific formula is: ; Where P(t) represents the efficiency under the current test conditions, P0 represents the benchmark performance value, β represents the comprehensive impact of different charge and discharge test parameters on battery performance, and w i represents the weight of the i-th trial parameter, x i represents the current value of the i-th experimental parameter, n represents the total number of experimental parameters, and i represents the number of the experimental parameter.

[0007] Preferably, S4 includes: Record the battery performance parameters each time during the test, calculate the performance change between two consecutive records, and find the average fluctuation value of the battery performance change. The specific formula is: ; Among them, R(t) represents the average fluctuation value of battery performance change, y j represents the battery performance value recorded for the jth time, y j－1 represents the battery performance value recorded for the j-1th time, m represents the total number of records, t represents the time, and j represents the number of records.

[0008] Preferably, the S1 also includes monitoring and collecting various performance indicators of the power battery from the time of activation to each test, analyzing the change trend of various performance indicators, identifying aging characteristic parameters, and constructing a battery aging status evaluation model based on the identified characteristic parameters, setting a judgment threshold T for the aging status, and when the performance degradation ratio exceeds the threshold T, that is, , the battery is judged to have reached the aging state and the model parameters are automatically updated, where E0 represents the performance parameters in the initial state, E represents the performance parameters in the current state, and T represents the judgment threshold of the aging state.

[0009] Preferably, the construction of the battery aging status evaluation model based on the identified characteristic parameters includes collecting performance data from multiple similar products under similar working conditions, applying a machine learning algorithm, selecting an appropriate feature extraction technology to reduce the dimensionality of the data, optimizing the parameters of the trained machine learning model, and calculating the output probability. Based on Q=f(U)≥0.85, the result closest to the ideal state is selected as the evaluation benchmark, wherein U represents the input vector of the characteristic parameters, f represents the mapping function obtained by learning, and Q represents the output probability.

[0010] Preferably, S2 also includes determining test parameters, which include charging current, discharge depth and voltage range, running the test according to the set number of cycles and parameter conditions, and recording the change data of battery performance.

[0011] Preferably, the S4 records various parameters during the test, including recording the capacity, internal resistance and efficiency change of the battery after each charge and discharge cycle, calculating the fluctuation range of various performance parameters, analyzing the fluctuation trend of various performance parameters and identifying the stage of rapid decline in battery durability.

[0012] Preferably, the weight w of the i-th test parameter in S2 is i The determination is based on the following principles: analyze historical test data, calculate the contribution of each test parameter to battery performance, standardize the weights using a normalization method so that the sum of all test parameter weights equals 1, and optimize the test conditions by dynamically adjusting the weights to adapt to battery performance requirements under different working conditions.

[0013] Preferably, the test parameters in S2 also include the battery charging and discharging temperature range and cycle time interval.

[0014] Preferably, the charge and discharge temperature range in S2 is set by environmental control, including a minimum temperature and a maximum temperature, to simulate battery performance under different environmental conditions.

[0015] It can be seen from the above technical solution that the present invention has the following beneficial effects: The light truck power battery charge and discharge durability test method based on computer processing automatically generates an aging evaluation model based on battery performance history data, tests the power battery according to the set charge and discharge cycle, automatically adjusts the test conditions based on the aging evaluation model to adapt to the battery aging state, records various parameters during the test and analyzes and evaluates the battery durability, effectively simulates the load change of the battery in the actual use scenario, improves the authenticity and scientificity of the test results, can more comprehensively and objectively reflect the battery aging state, improves the accuracy and reliability of the test results, not only improves the test efficiency, but also can simulate the complex use conditions of the battery at different aging stages, enhances the adaptability and pertinence of the test, significantly reduces the subjective error introduced by manual operation in the traditional method, improves the consistency and objectivity of the evaluation results, simplifies the test operation process, realizes the rapid processing and trend analysis of battery performance data, provides high-value data support for subsequent battery research and development and optimization, further enhances the practicality of the test method, and solves the problem that the existing power battery charge and discharge durability test method is difficult to accurately simulate the actual use scenario of light truck vehicles due to the lack of automated evaluation means and dynamic adjustment function. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] like Figure 1 As shown, the present invention provides a technical solution: a light truck power battery charge and discharge endurance test method based on computer processing, the method comprising: S1. Automatically generate an aging assessment model based on battery performance historical data, including collecting battery performance historical data and fitting historical data. The specific formula is: ; Where v represents the performance degradation rate of the battery in the current charge and discharge cycle, V max It indicates the limiting speed at which the battery's performance deteriorates, N indicates the number of charge and discharge cycles the battery has experienced, and B indicates the index value of the battery's anti-aging ability; Generate a battery aging assessment model to predict how battery capacity will change over future cycles S2. Test the power battery according to the set charge and discharge cycle; S3. Automatically adjust the test conditions based on the aging assessment model to adapt to the battery aging state, including monitoring the fluctuation of battery performance through the aging assessment model and calculating the adjusted test parameters. The specific formula is: ; Among them, x new represents the test parameter value after dynamic adjustment, x old represents the current test parameter value, A represents the adjustment amplitude of the test parameter, f represents the frequency of dynamic change of battery performance, t represents time, and g represents the initial state offset value of battery performance fluctuation; S4. Record various parameters during the test and analyze and evaluate battery durability.

[0019] The core of this implementation method is to analyze and model the historical data of battery performance through computer processing technology to generate an evaluation model that can reflect the aging trend of the battery. The model summarizes and predicts the changing laws of battery performance based on historical data, and can capture the aging characteristics of the battery such as capacity decline and internal resistance increase during use. In the experiment, the battery was preliminarily tested under the set charge and discharge cycle conditions, and the performance changes of the battery were analyzed by real-time data collection combined with the aging evaluation model. As the degree of battery aging increases, the model can provide suggestions for adjusting the test parameters to adapt to the actual state of the battery. This way of dynamically adjusting the test conditions not only makes the test more suitable for the actual use scenario of the battery, but also helps technicians quickly discover the key performance attenuation points of the battery. Through detailed recording and analysis of the test data, the entire system realizes a comprehensive evaluation of the battery durability performance, providing a scientific basis for the performance of the battery in real applications. This method makes up for the inability to dynamically adjust the test conditions in traditional test methods, and provides more efficient and accurate technical support for the research and development of power batteries.

[0020] Traditional power battery tests usually require a lot of time and resources, while this method reduces the number of invalid tests and improves test efficiency through accurate prediction of computer models. The function of dynamically adjusting test parameters enables the test to focus on the key performance change points of the battery more quickly, thereby saving time and labor costs. The test parameters will be dynamically adjusted as the battery aging state changes, so that the test conditions can simulate the complex environment of the battery in actual use. This method avoids the deviation caused by fixed parameter settings in traditional tests and improves the scientificity and reliability of the data. By combining the aging evaluation model with the test data, the performance of the battery in long-term charge and discharge cycles, such as capacity decay rate, voltage retention rate and internal resistance change trend, can be comprehensively analyzed. This multi-dimensional evaluation method provides precise technical support for battery R&D personnel and also provides a basis for improving battery design. This method is not only suitable for laboratory research, but also can provide a reference for battery life management in actual use, guide users to optimize charging and discharging strategies, extend the service life of the battery, and reduce economic losses caused by battery performance decay. All test data are recorded and analyzed in detail, which can not only provide support for current tests, but also form battery performance big data, providing technical reference for future new battery design and improvement. With these data, researchers can more accurately optimize battery materials and structures, and promote the further development of power battery technology.

[0021] S2 includes determining the charge and discharge test parameters, assigning weights to each parameter, and calculating the efficiency of the parameter combination under the current test conditions. The specific formula is: ; Where P(t) represents the efficiency under the current test conditions, P0 represents the benchmark performance value, β represents the comprehensive impact of different charge and discharge test parameters on battery performance, and w i represents the weight of the i-th trial parameter, x i represents the current value of the i-th experimental parameter, n represents the total number of experimental parameters, and i represents the number of the experimental parameter.

[0022] In the endurance test of power batteries, the combination of charging and discharging parameters has an important influence on the test results. Different parameters (such as charging current, discharge voltage and number of cycles, etc.) have different effects on battery performance, so it is necessary to quantify the contribution of each parameter to the overall test efficiency by weight distribution. This embodiment defines multiple test parameters and their weights, and calculates the comprehensive efficiency in combination with the current test conditions to optimize the test configuration. The initial efficiency is determined by a fixed benchmark performance value, on which basis, it is adjusted according to the contribution of different parameters to the performance. The importance of each test parameter is represented by a weight, and its value is determined based on historical test data or professional experience. Through dynamic analysis of parameter utility, the test method can achieve optimal performance under a variety of combination conditions, thereby reflecting the performance of the battery in complex usage scenarios. This method can not only improve the pertinence and efficiency of the test, but also provide a quantitative basis for the selection of test conditions. As the test progresses, the weight and current value of the parameter can be adjusted in real time according to the battery aging state, further improving the test accuracy. This embodiment uses a quantitative method to calculate the comprehensive effect of the test parameters, avoiding the deviation that may be caused by subjective setting of test conditions in traditional methods. Through the evaluation of weight distribution and combination effect, the test conditions can be optimized more scientifically and the reliability of test data can be ensured. Through the calculation of weight distribution and comprehensive contribution value, the parameter combination with the greatest impact on the test can be quickly determined, thereby reducing the number of unnecessary tests and resource consumption, and greatly improving the test efficiency. This method can flexibly adjust the weights and combinations of parameters according to different battery types and test requirements to meet the needs of different application scenarios. For example, in fast-charging battery tests, the weight of charging current can be increased; in long-life battery tests, the weights of discharge voltage and number of cycles can be highlighted. By quantifying and adjusting the comprehensive impact of each parameter, this method can more comprehensively and accurately evaluate the durability of the battery and provide accurate data support for battery research and development and performance optimization. The weight distribution and efficiency calculation process can be combined with computer automation processing to make the management and decision-making of the test more intelligent. The test personnel can quickly adjust the test conditions according to the calculation results to achieve an accurate and efficient test process.

[0023] S4 includes recording the battery performance parameters each time during the test, calculating the performance change between two consecutive records, and finding the average fluctuation value of the battery performance change. The specific formula is: ; Among them, R(t) represents the average fluctuation value of battery performance change, y j represents the battery performance value recorded for the jth time, y j－1 represents the battery performance value recorded for the j-1th time, m represents the total number of records, t represents the time, and j represents the number of records.

[0024] During the durability test of power batteries, changes in battery performance directly reflect their aging degree and durability. In order to accurately evaluate the attenuation characteristics of the battery, this embodiment records the key performance parameters of the battery during the test (such as battery capacity, voltage, internal resistance, etc.), analyzes the performance differences between consecutive recording points, and thus quantifies the fluctuation range of battery performance changes. The recorded data are arranged in chronological order, and the performance value recorded each time reflects the operating state of the battery at a specific time point. The difference between two consecutive records represents the rate of change of battery performance over time. By calculating the average change range of all recording points, the overall fluctuation trend of battery performance changes can be obtained. This method can comprehensively and dynamically reflect the performance attenuation law of the battery, and provide a reliable basis for subsequent battery life prediction and design optimization. In addition, this embodiment emphasizes the accuracy and continuity of the record, and uses high-precision sensors and data acquisition equipment to ensure the accurate recording of performance parameters. Combined with computer processing technology, data processing and analysis can be completed quickly, the battery status can be monitored in real time, and abnormal conditions can be found. By recording the battery performance parameters each time during the test, the state changes of the battery in the charge and discharge cycle can be understood in real time, which helps to timely discover abnormal performance fluctuations and improve the safety and reliability of the test. This implementation method can quantify the performance changes between consecutive recording points and calculate the average fluctuation range, providing an intuitive numerical indicator. This evaluation method is more comprehensive than the traditional single parameter recording and can better reflect the dynamic change characteristics of battery performance. By analyzing the fluctuation law of battery performance, it is possible to accurately predict the life and attenuation trend of the battery in long-term use, provide a scientific basis for the design optimization of power batteries, and extend their service life. Using computer processing technology to perform batch analysis of recorded performance parameters can improve data processing efficiency, reduce errors caused by manual operations, and make test results more scientific and reliable. This method automatically records and analyzes data, avoiding a large amount of manual recording and subsequent data collation in traditional experiments, significantly improving test efficiency, and providing data support for subsequent optimization of test conditions.

[0025] S1 also includes monitoring and collecting various performance indicators of the power battery from the time of activation to each test, analyzing the change trend of various performance indicators, identifying aging characteristic parameters, and building a battery aging status evaluation model based on the identified characteristic parameters, setting the judgment threshold T of the aging status, and when the performance degradation ratio exceeds the threshold T, that is, , it is determined that the battery has reached the aging state and the model parameters are automatically updated, where E0 represents the performance parameters in the initial state, E represents the performance parameters in the current state, and T represents the judgment threshold of the aging state. This embodiment collects the performance indicators of the power battery in real time after it is enabled, and constructs an evaluation model for evaluating the battery aging state, ensuring that the test can fully cover the entire process of the battery from the initial state to the aging state. The test system will regularly collect indicator data related to battery performance, including capacity, internal resistance, voltage, efficiency, etc., and record the performance change trend of the battery at each stage. By performing statistical analysis and feature extraction on these data, parameters closely related to battery aging, such as capacity decay rate, internal resistance increase, etc., are identified. Based on the identified key parameters, a battery aging state evaluation model is constructed to quantify the degree of performance degradation of the battery during the aging process. In order to facilitate the judgment of whether the battery has entered the aging state, this method sets a clear aging state judgment threshold. When the decline ratio of a key performance parameter exceeds the preset threshold T, the system determines that the battery has reached the aging state. At this time, the evaluation model will automatically update the key parameters to further optimize the monitoring and prediction of the aging state. The entire process achieves real-time and accuracy through computer processing and automatic model updating, making the experiment more efficient and scientific.

[0026] Based on the identified characteristic parameters, building a battery aging status evaluation model includes collecting performance data from multiple similar products under similar working conditions, applying machine learning algorithms, selecting appropriate feature extraction techniques to reduce the dimensionality of the data, optimizing parameters of the trained machine learning model, and calculating the output probability. Based on Q=f(U)≥0.85, the result closest to the ideal state is selected as the evaluation benchmark, where U represents the input vector of characteristic parameters, f represents the mapping function obtained through learning, and Q represents the output probability.

[0027] This embodiment provides a more accurate analysis tool for the durability test of power batteries by constructing an aging state evaluation model based on a machine learning algorithm. First, performance data of multiple similar battery products under similar working conditions are collected. These data may include battery capacity, internal resistance, voltage curve, etc., as well as external influencing factors such as ambient temperature and working current. In order to reduce the data dimension and highlight the key features related to battery aging, feature extraction technology is applied to process the original data, such as principal component analysis or recursive feature elimination, to remove redundant information and retain the core variables related to aging characteristics. Subsequently, the processed data is used as input to train the battery aging state evaluation model using a machine learning algorithm (such as a support vector machine, a random forest, or a deep learning network). During the model training process, the prediction accuracy and robustness of the model are improved by optimizing parameters (such as learning rate, regularization parameter, etc.). The evaluation model outputs the probability value of the battery aging state. When the output probability meets the preset conditions, it is judged that its state is close to the ideal state, and the result is used as the benchmark for aging state evaluation. This evaluation benchmark provides a standardized reference value for subsequent analysis to ensure the scientificity and consistency of the experiment.

[0028] S2 also includes determining the test parameters, which include charging current, discharge depth and voltage range, running the test according to the set number of cycles and parameter conditions, and recording the change data of battery performance. The charge and discharge endurance test of power batteries is intended to evaluate the performance attenuation law of batteries under specific conditions. This embodiment simulates the actual use environment of the battery by accurately setting the test parameters to ensure that the test data can effectively reflect the change trend of battery performance with the number of cycles. First, according to the design characteristics and application scenarios of the battery, the key parameters of the test are determined, including charging current, discharge depth and voltage range. The charging current determines the battery charging rate, the discharge depth reflects the capacity range of the battery for a single use, and the voltage range is used to limit the maximum and minimum voltage values ​​during the charging and discharging process. The setting of each parameter is based on the characteristics of the battery and the expected use scenario to ensure the scientificity and rationality of the test conditions. During the test, the battery is charged and discharged according to the set parameter conditions, and the test environment (such as temperature and humidity) is strictly controlled to minimize the impact of external factors on the test results. During operation, the system collects and records the change data of battery performance in real time, such as capacity retention rate, voltage change, internal resistance increase, etc. These data provide a reliable basis for subsequent analysis and can be used to dynamically adjust test parameters and optimize test processes. In this way, we can fully understand the durability of the battery under set conditions and provide accurate experimental data support for the development and optimization of power batteries.

[0029] Recording various parameters during the test in S4 includes recording the capacity, internal resistance and efficiency change values ​​of the battery after each charge and discharge cycle, calculating the fluctuation range of various performance parameters, analyzing the fluctuation trend and identifying the stage of rapid decline in battery durability. The durability of the power battery is closely related to its capacity retention rate, internal resistance increase and efficiency change during the charge and discharge cycle. This embodiment records the key performance parameters after each cycle in detail, calculates its fluctuation range, and analyzes the fluctuation trend to identify the stage of rapid decline in battery durability. During the test, the system records the capacity (i.e., the actual capacity during discharge), internal resistance (reflecting the internal conductivity of the battery) and efficiency (energy conversion efficiency during charge and discharge) of the battery one by one. The recorded data is used to calculate the change value between the current cycle and the previous cycle, and evaluate the fluctuation range of these change values. This method can quantify the performance stability of the battery during the test. Furthermore, the test system performs trend analysis on all recorded change values ​​to identify the critical point of performance parameter fluctuation. For example, when the capacity decline rate is significantly accelerated, the internal resistance rises sharply or the efficiency decreases significantly, it can be judged that the battery has entered the stage of rapid decline in durability. This stage is often an important signal that the battery is about to fail. By identifying this stage in advance, it can provide important reference for battery design improvement and optimization of usage strategy. In addition, the automatic recording and analysis of data is controlled by computer, ensuring high accuracy and real-time performance, making the test process more scientific and efficient.

[0030] The weight w of the i-th trial parameter in S2 i The determination is based on the following principles: analyze historical test data, calculate the contribution of each test parameter to battery performance, standardize the weights using a normalization method so that the sum of all test parameter weights equals 1, and optimize the test conditions by dynamically adjusting the weights to adapt to battery performance requirements under different working conditions.

[0031] In the power battery charge and discharge endurance test, the degree of influence of test parameters (such as charging current, discharge depth, voltage range, etc.) on battery performance may be different, so it is necessary to assign weights to each test parameter to quantify its contribution to the overall test results. This embodiment determines the test parameter weights through data analysis and normalization, and dynamically optimizes the weights to adapt to the performance requirements of the battery under different operating conditions. First, the system analyzes historical test data, extracts the relationship between each test parameter and battery performance (such as capacity retention rate, efficiency, etc.), and quantifies the contribution of each test parameter to battery performance. For example, evaluate the influence of charging current on battery capacity retention rate, or the influence of discharge depth on battery internal resistance change. After obtaining the contribution of each parameter, the weight w is normalized. iStandardization is performed so that the sum of all weights is equal to 1. This standardization process not only ensures the reasonable distribution of weights, but also facilitates the dynamic adjustment of the contribution of different parameters in the subsequent optimization of test conditions. In order to adapt to the performance requirements of the battery under different working conditions, the weight distribution adopts a dynamic adjustment mechanism. During the test, the weight distribution is adjusted to optimize the test conditions based on the real-time recorded battery performance data. For example, in high-rate discharge tests, the weight of the depth of discharge is increased; in long-life tests, the influence of the charging current is emphasized. Through this dynamic optimization, the test conditions can be more in line with the needs of actual working conditions, thereby improving the scientificity and accuracy of the test.

[0032] The test parameters in S2 include the battery's charging current and charging voltage, the battery's discharge current and discharge depth, the battery's charge and discharge temperature range, and the cycle time interval.

[0033] In the charge and discharge endurance test of power batteries, the test parameters have an important impact on the scientificity and accuracy of the test results. This embodiment fully simulates the actual use conditions of the battery and evaluates its durability performance by setting multiple key test parameters such as charging current, charging voltage, discharge current, discharge depth, charge and discharge temperature range, and cycle time interval.

[0034] The charging current determines the battery charging rate, which is usually controlled in constant current-constant voltage mode. The setting of the charging voltage range ensures that the charging process is safe and effective, while avoiding the impact of overcharging on battery life. Reasonable charging parameters help evaluate the capacity retention and thermal management performance of the battery at different charging rates. The discharge current reflects the energy output capacity of the battery under load conditions, and the discharge depth indicates the proportion of the capacity that can be released by the battery during a single discharge. By adjusting the discharge current and discharge depth, the performance of the battery under high-rate discharge or partial discharge conditions can be simulated, providing data support for battery optimization in different application scenarios. Temperature is an important factor affecting battery performance and life. By controlling the charging and discharging temperature range in the test, the stability and safety of the battery performance under extreme temperature environments (such as high temperature and low temperature) can be evaluated, and the impact of different temperature conditions on the battery aging rate can be analyzed. The cycle time interval is used to control the interval time of the battery charge and discharge cycle, which can simulate the switching state of the battery between continuous operation and static rest in actual use. Reasonable time interval settings can reflect the recovery characteristics of the battery and its impact on durability. Through precise control and combined testing of the above parameters, this implementation can fully simulate the operating status of the battery in different usage scenarios, reveal the performance change rules under various working conditions, and provide a scientific basis for the design and optimization of the battery.

[0035] The battery performance parameters in S4 include the battery capacity change, the battery internal resistance change, the battery charging efficiency and discharge efficiency, and the temperature change during the battery operation. During the durability test of the power battery, the monitoring of battery performance parameters is the key to evaluating the battery operating status and attenuation characteristics. This embodiment fully grasps the performance of the battery in the charge and discharge cycle by monitoring the battery capacity change, internal resistance change, charging efficiency and discharge efficiency, and temperature change during operation, and deeply analyzes the change law of its durability performance.

[0036] Battery capacity is a direct reflection of the battery's ability to store electrical energy. By recording the capacity change after each cycle, the battery's capacity retention rate can be intuitively evaluated and the accelerated stage of capacity decay can be identified. Internal resistance is a key indicator of the battery's internal conductivity, and its change directly affects the battery's output power and efficiency. The increase in internal resistance is usually accompanied by battery aging. By continuously monitoring the change in internal resistance, the battery's health status can be evaluated in a timely manner. The charging efficiency and discharge efficiency reflect the energy loss of the battery during the charging and discharging process, respectively. The decline in efficiency may be a manifestation of aging of internal battery materials or improper thermal management. Monitoring these parameters can provide a basis for optimizing battery design and use strategies. Temperature has an important impact on battery performance and safety. High temperature may accelerate battery aging or cause thermal runaway, while low temperature will reduce the battery's energy efficiency and discharge capacity. By recording the temperature changes during battery operation in real time, the impact of temperature on battery performance can be evaluated and the test conditions can be optimized. Through a comprehensive analysis of the above performance parameters, this embodiment can comprehensively evaluate the durability of the battery and reveal the key influencing factors in the battery degradation process through trend identification and data mining. This provides a scientific basis for the life prediction and optimization design of power batteries.

[0037] The charge and discharge temperature range in S2 is set by environmental control, including the minimum temperature and the maximum temperature, to simulate the battery performance under different environmental conditions. Specifically, the environmental control system adjusts the temperature range of the test environment through high-precision temperature control equipment. The temperature control equipment can accurately set the minimum and maximum temperatures, for example, between -20°C and 60°C, to cover the extreme use conditions that light truck power batteries may face. During the charge and discharge process, the computer program monitors the charging current, discharge current and capacity changes of the battery under different temperature conditions in real time, records the test data and analyzes it to evaluate the performance stability and durability of the battery in various temperature environments. In addition, by controlling the temperature setting in stages, such as gradually transitioning from low temperature to high temperature or performing charge and discharge tests in a random temperature change manner, the complex environmental effects under actual working conditions can be further simulated, thereby comprehensively evaluating the battery performance. This charge and discharge temperature control method can not only improve the test accuracy, but also provide key data support for the design and optimization of power batteries.

[0038] Specifically: It can be explained according to the test data in Table 1: Table 1

[0039] Table Description: Experiment number: the number of each experiment; Cycle count: The number of charge and discharge cycles the battery has undergone; Charging current (A): The charging current of the battery during each charging process; Discharge depth (%): The discharge depth of each experiment, which indicates the percentage of battery capacity discharged; Battery capacity (Ah): the capacity of the battery in each experiment (unit: ampere-hour); Internal resistance (mΩ): The internal resistance of the battery at different experimental stages, in milliohms; Charging efficiency (%): the efficiency of converting electric energy during battery charging; Discharge efficiency (%): the efficiency of converting electric energy during battery discharge; Temperature (℃): The working environment temperature of the battery in the experiment; Capacity change (%): The percentage change of battery capacity compared to the initial state; Internal resistance change (%): the percentage change of the battery internal resistance compared to the initial state; Efficiency change (%): The percentage change in charge and discharge efficiency compared to the initial state; Battery aging status: The battery aging status is determined based on experimental data and is divided into "initial status", "normal status", "initial decline", "decline stage", "high-speed decline" or "complete decline"; Aging threshold (T): The battery aging state judgment threshold. When the performance degradation exceeds this threshold, the battery is considered to have reached the aging state. According to experimental data, as the number of charge and discharge cycles increases, the battery capacity, internal resistance, charging efficiency and discharge efficiency will change to a certain extent. By monitoring and analyzing these changes, the trend of battery performance degradation can be identified; After the number of cycles reaches 300, the battery begins to enter the decline stage, with obvious changes in capacity, increased internal resistance, and decreased efficiency. These parameters can be used as a basis for determining the battery aging state; The setting of the aging threshold (T) helps determine whether the battery has reached the aging state. For example, in the experiment, when the capacity changes by more than -30%, or the internal resistance increases by more than 100%, the battery is judged to have entered the aging state.

[0040] Battery durability evaluation instructions: High: The battery performs stably in multiple cycle tests, with small changes in capacity, internal resistance and efficiency, and has not entered the decay stage; Middle: The battery begins to show signs of degradation, such as a slight decrease in capacity and an increase in internal resistance, but can still maintain a certain performance; Low: The battery performance has declined significantly, the capacity has been greatly reduced, the internal resistance has increased sharply, the efficiency has been significantly reduced, and it is close to the aging state; Very low: The battery enters a state of complete decay, performance is severely reduced, and it almost loses its practical value; Battery durability takes into account multiple aspects of battery performance and provides a concise assessment of the battery's aging state, helping to quickly identify the battery's health and durability.

[0041] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A light truck power battery charge and discharge endurance test method based on computer processing, characterized in that: The method comprises: S1. Automatically generate an aging assessment model based on battery performance historical data, including collecting battery performance historical data and fitting historical data. The specific formula is: ; Where v represents the performance degradation rate of the battery in the current charge and discharge cycle, V max It indicates the limiting speed at which the battery's performance deteriorates, N indicates the number of charge and discharge cycles the battery has experienced, and B indicates the index value of the battery's anti-aging ability; Generate a battery aging assessment model to predict the change trend of battery capacity in future cycles; S2. Test the power battery according to the set charge and discharge cycle; S3. Automatically adjust the test conditions based on the aging assessment model to adapt to the battery aging state, including monitoring the fluctuation of battery performance through the aging assessment model and calculating the adjusted test parameters. The specific formula is: ； Among them, x new represents the test parameter value after dynamic adjustment, x old represents the current test parameter value, A represents the adjustment amplitude of the test parameter, f represents the frequency of dynamic change of battery performance, t represents time, and g represents the initial state offset value of battery performance fluctuation; S4. Record various parameters during the test and analyze and evaluate battery durability.

2. A method for charging and discharging endurance test of a light truck power battery based on computer processing according to claim 1, characterized in that: The S2 includes: Determine the charge and discharge test parameters, assign weights to each parameter, and calculate the efficiency of the parameter combination under the current test conditions. The specific formula is: ; Where P(t) represents the efficiency under the current test conditions, P0 represents the benchmark performance value, β represents the comprehensive impact of different charge and discharge test parameters on battery performance, and w i represents the weight of the i-th trial parameter, x i represents the current value of the i-th experimental parameter, n represents the total number of experimental parameters, and i represents the number of the experimental parameter.

3. A method for charging and discharging endurance test of a light truck power battery based on computer processing according to claim 1, characterized in that: The S4 includes: Record the battery performance parameters each time during the test, calculate the performance change between two consecutive records, and find the average fluctuation value of the battery performance change. The specific formula is: ； Among them, R(t) represents the average fluctuation value of battery performance change, y j represents the battery performance value recorded for the jth time, y j－1 represents the battery performance value recorded for the j-1th time, m represents the total number of records, t represents the time, and j represents the number of records.

4. A method for charging and discharging endurance test of a light truck power battery based on computer processing according to claim 1, characterized in that: The S1 also includes monitoring and collecting various performance indicators of the power battery from the time of activation to each test, analyzing the change trend of various performance indicators, identifying aging characteristic parameters, and building a battery aging status evaluation model based on the identified characteristic parameters, setting a judgment threshold T for the aging status, and when the performance degradation ratio exceeds the threshold T, that is, , the battery is judged to have reached the aging state and the model parameters are automatically updated, where E0 represents the performance parameters in the initial state, E represents the performance parameters in the current state, and T represents the judgment threshold of the aging state.

5. A method for charging and discharging endurance test of a light truck power battery based on computer processing according to claim 4, characterized in that: The method of constructing a battery aging status evaluation model based on the identified characteristic parameters includes collecting performance data from multiple similar products under similar working conditions, applying a machine learning algorithm, selecting an appropriate feature extraction technology to reduce the dimensionality of the data, optimizing the parameters of the trained machine learning model, and calculating the output probability. Based on Q=f(U)≥0.85, the result closest to the ideal state is selected as the evaluation benchmark, wherein U represents the input vector of the characteristic parameters, f represents the mapping function obtained through learning, and Q represents the output probability.

6. A method for charging and discharging endurance test of a light truck power battery based on computer processing according to claim 1, characterized in that: The S2 also includes determining test parameters, which include charging current, discharge depth and voltage range, running the test according to the set number of cycles and parameter conditions, and recording the change data of battery performance.

7. A method for charging and discharging endurance test of a light truck power battery based on computer processing according to claim 1, characterized in that: The S4 records various parameters during the test, including recording the capacity, internal resistance and efficiency change values ​​of the battery after each charge and discharge cycle, analyzing the fluctuation trend of various performance parameters and identifying the stage where the battery durability performance decreases rapidly.

8. A method for charging and discharging endurance test of a light truck power battery based on computer processing according to claim 2, characterized in that: The weight w of the i-th experimental parameter in S2 i The determination is based on the following principles: analyze historical test data, calculate the contribution of each test parameter to battery performance, standardize the weights using a normalization method so that the sum of all test parameter weights equals 1, and optimize the test conditions by dynamically adjusting the weights to adapt to battery performance requirements under different working conditions.

9. A method for charging and discharging endurance test of a light truck power battery based on computer processing according to claim 2, characterized in that: The test parameters in S2 also include the battery charging and discharging temperature range and cycle time interval.

10. A method for charging and discharging endurance test of a light truck power battery based on computer processing according to claim 9, characterized in that: The charge and discharge temperature range in S2 is set by environmental control, including the minimum temperature and the maximum temperature, to simulate the battery performance under different environmental conditions.

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