A computer-based method for the charge and discharge durability test of light truck power batteries

Abstract

The present invention discloses a method for the charge and discharge durability test of a light truck power battery based on computer processing, which relates to the technical field of power battery testing and evaluation. The method includes: S1, automatically generating an aging evaluation model based on the historical data of battery performance; S2, conducting tests on the power battery according to the set charge and discharge cycles; S3, automatically adjusting the test conditions based on the aging evaluation model to adapt to the battery aging state; S4, recording various parameters during the test process and analyzing and evaluating the battery durability. This method for the charge and discharge durability test of a light truck power battery based on computer processing 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 practicability of the test method, and solves the problem that the existing charge and discharge durability test methods for power batteries are difficult to accurately simulate the actual use scenarios of light truck vehicles due to the lack of automatic evaluation means and dynamic adjustment functions.

Description

Technical Field

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

[0002] In the process of research and development and verification of power batteries, the durability test is an important link to evaluate the battery performance and service life. Traditional durability test methods usually perform repeated charge and discharge cycles on the battery to observe its performance changes.

[0003] However, there are some obvious limitations in the existing test methods: First, the test conditions are usually preset fixed parameters (such as current, voltage and temperature), lacking 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 actual light truck operation, resulting in a large deviation between the test results and the real usage environment. Second, most of the current test methods rely on manual evaluation of the battery aging state, and the evaluation criteria are often based on a single parameter (such as capacity attenuation or internal resistance increase), which cannot comprehensively reflect the comprehensive characteristics of battery aging. This evaluation method is not only inefficient, but also prone to introducing subjective errors, thus affecting the accuracy of the test results. In addition, the existing test systems lack the ability to intelligently optimize the test conditions and cannot adjust the charge and discharge parameters according to the 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 the 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 methods are difficult to accurately simulate the actual usage scenarios of light trucks due to the lack of automated evaluation means and dynamic adjustment functions. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for charging and discharging durability test of light truck power batteries based on computer processing, so as to solve the problem that the existing power battery charge and discharge durability test methods are difficult to accurately simulate the actual usage scenarios of light trucks due to the lack of automated evaluation means and dynamic adjustment functions.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A method for charging and discharging durability test of light truck power batteries based on computer processing, the method includes:

[0006] S1. Automatically generate an aging evaluation model based on the historical data of battery performance, including collecting the historical data of battery performance and fitting the historical data, and the specific formula is: ;

[0007] wherein, v represents the performance degradation rate of the battery in the current charge and discharge cycle, V maxIt represents the limit speed when the performance of the battery deteriorates, N represents the number of charge and discharge cycles the battery has experienced, and B represents the index value of the battery's anti-aging ability;

[0008] Generate a battery aging evaluation model to predict the change trend of the battery capacity in future cycles

[0009] S2. Conduct tests on the power battery according to the set charge and discharge cycles;

[0010] S3. Automatically adjust the test conditions based on the aging evaluation model to adapt to the battery aging state, including monitoring the fluctuations in battery performance through the aging evaluation model, calculating the adjusted test parameters. The specific formula is:

[0011] ;

[0012] Among them, x new represents the value of the test parameter 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 the dynamic change of the battery performance, t represents time, and g represents the initial state offset value of the battery performance fluctuation;

[0013] S4. Record various parameters during the test and analyze and evaluate the battery durability.

[0014] Preferably, the S2 includes:

[0015] Determine the charge and discharge test parameters, assign weights to each parameter, and calculate the efficiency under the current test conditions of the parameter combination. The specific formula is: ;

[0016] Among them, P(t) represents the efficiency under the current test conditions, P0 represents the reference performance value, β represents the comprehensive influence degree value of different charge and discharge test parameters on the battery performance, w i represents the weight of the i-th test parameter, x i represents the current value of the i-th test parameter, n represents the total number of test parameters, and i represents the number of the experimental parameter.

[0017] Preferably, the S4 includes:

[0018] Record the battery performance parameters each time during the test, calculate the performance change between two consecutive records, and obtain the average fluctuation degree value of the battery performance change. The specific formula is:

[0019] ;

[0020] Among them, R(t) represents the average fluctuation degree value of the battery performance change, y j represents the battery performance value of the j-th record, y j－1$E_{j - 1}$ represents the battery performance value recorded at the $(j - 1)$-th time, $m$ represents the total number of records, $t$ represents time, and $j$ represents the number of the record times.

[0021] Preferably, S1 further includes monitoring and collecting various performance indicators of the power battery from activation to each test time point, analyzing the change trends of various performance indicators, identifying aging characteristic parameters, constructing an aging state evaluation model of the battery based on the identified characteristic parameters, setting a judgment threshold $T$ for the aging state. When the performance degradation ratio exceeds the threshold $T$, that is , it is determined that the battery reaches the aging state and the model parameters are automatically updated, where $E_0$ represents the performance parameter in the initial state, $E$ represents the performance parameter in the current state, and $T$ represents the judgment threshold for the aging state.

[0022] Preferably, constructing the aging state evaluation model of the battery based on the identified characteristic parameters includes collecting performance data of multiple similar products under similar working conditions, applying machine learning algorithms, selecting appropriate feature extraction techniques to perform dimensionality reduction processing on the data, optimizing the parameters of the trained machine learning model, calculating the output probability, and based on $Q = f(U)\geq0.85$, selecting the result closest to the ideal state as the evaluation benchmark, where $U$ represents the input vector of the characteristic parameters, $f$ represents the mapping function obtained through learning, and $Q$ represents the output probability.

[0023] Preferably, S2 further includes determining test parameters, where the test parameters 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 the battery performance.

[0024] Preferably, in S4, various parameters during the test are recorded, including recording the change values of the battery capacity, internal resistance, and efficiency after each charge and discharge cycle, calculating the fluctuation amplitude of each performance parameter, analyzing the fluctuation trend of each performance parameter, and identifying the stage where the battery durability performance rapidly decreases.

[0025] Preferably, the weight $w$ i of the $i$-th test parameter in S2 is determined based on the following principles: analyzing historical test data, calculating the contribution degree of each test parameter to the battery performance, using a normalization method to standardize the weights so that the sum of all test parameter weights is equal to 1, and optimizing the test conditions by dynamically adjusting the weights to meet the battery performance requirements under different working conditions.

[0026] Preferably, the test parameters in S2 further include the charge and discharge temperature range and cycle time interval of the battery.

[0027] Preferably, the charge and discharge temperature range in S2 is set through environmental control, including the lowest temperature and the highest temperature, to simulate the battery performance under different environmental conditions.

[0028] As can be seen from the above technical solutions, the present invention has the following beneficial effects:

[0029] The light truck power battery charge and discharge durability test method based on computer processing automatically generates an aging evaluation model based on the historical battery performance data, conducts tests on the power battery according to the set charge and discharge cycles, automatically adjusts the test conditions based on the aging evaluation model to adapt to the battery aging state, records various parameters during the test process and analyzes and evaluates the battery durability, effectively simulates the load changes of the battery in the real usage scenario, improves the authenticity and scientific nature of the test results, can more comprehensively and objectively reflect the aging state of the battery, improves the accuracy and reliability of the test results, not only improves the test efficiency, but also can simulate the complex usage conditions of the battery at different aging stages, enhances the adaptability and pertinence of the test, significantly reduces the subjective errors introduced by manual operations in the traditional method, improves the consistency and objectivity of the evaluation results, simplifies the test operation process at the same time, realizes the rapid processing and trend analysis of the 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 methods are difficult to accurately simulate the actual usage scenario of light truck vehicles due to the lack of automatic evaluation means and dynamic adjustment functions. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0032] As Figure 1 shown, the present invention provides a technical solution: a light truck power battery charge and discharge durability test method based on computer processing, the method includes:

[0033] S1. Automatically generate an aging evaluation model based on the historical battery performance data, including collecting the historical battery performance data and fitting the historical data. The specific formula is: ;

[0034] wherein, v represents the performance degradation rate of the battery in the current charge and discharge cycle, V max represents the ultimate speed of the battery performance when deteriorating, N represents the number of charge and discharge cycles experienced by the battery, and B represents the index value of the battery's anti-aging ability;

[0035] Generate a battery aging assessment model to predict the change trend of battery capacity in future cycles

[0036] S2. Conduct tests on the power battery according to the set charge and discharge cycles;

[0037] S3. Automatically adjust the test conditions based on the aging assessment model to adapt to the battery aging state, including monitoring the fluctuations of battery performance through the aging assessment model, calculating the adjusted test parameters, and the specific formula is:

[0038] ;

[0039] where x new represents the value of the test parameter 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;

[0040] S4. Record various parameters during the test and analyze and evaluate the battery durability.

[0041] The core of this embodiment lies in the analysis and modeling of the historical data of battery performance through computer processing technology to generate an assessment model that can reflect the battery aging trend. This model summarizes and predicts the change law of battery performance based on historical data, and can capture the aging characteristics such as capacity decline and internal resistance increase of the battery during use. In the test, the battery is initially tested under the set charge and discharge cycle conditions, and the performance change of the battery is analyzed by combining the real-time collected data with the aging assessment model. As the battery aging degree increases, the model can provide adjustment suggestions for 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 in line with the actual use scenario of the battery, but also helps technicians quickly discover the key performance decay points of the battery. Through the detailed recording and analysis of the test data, the entire system realizes a comprehensive assessment of the battery durability performance, providing a scientific basis for the performance of the battery in real applications. This method makes up for the deficiency of the traditional test method that cannot dynamically adjust the test conditions, and provides more efficient and accurate technical support for the research and development of power batteries.

[0042] Traditional power battery tests usually require a large amount of time and resources. However, this method reduces the number of ineffective tests through the accurate prediction of computer models, improving the test efficiency. The function of dynamically adjusting test parameters enables the test to focus more quickly on the key performance change points of the battery, thus saving time and labor costs. The test parameters will be dynamically adjusted according to the change of the battery aging state, enabling the test conditions to simulate the complex environment of the battery in actual use. This way avoids the deviation caused by fixed parameter settings in traditional tests, improving the scientificity and reliability of the data. By combining the aging assessment model with the test data, it is possible to comprehensively analyze the performance of the battery during long-term charge and discharge cycles, such as the capacity attenuation rate, voltage retention rate, and internal resistance change trend. This multi-dimensional assessment method provides precise technical support for battery R & D personnel and also provides a basis for improving battery design. This method is not only applicable to laboratory research but also can provide reference for battery life management in actual use, guiding users to optimize charge and discharge strategies, extend the service life of the battery, and reduce economic losses caused by battery performance attenuation. All test data are recorded and analyzed in detail, which can not only support the current test but also form big data on battery performance, providing technical reference for future new battery design and improvement. Through these data, R & D personnel can more accurately optimize battery materials and structures, promoting the further development of power battery technology.

[0043] S2 includes determining 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: ;

[0044] Among them, P(t) represents the efficiency under the current test conditions, P0 represents the benchmark performance value, β represents the comprehensive influence degree value of different charge and discharge test parameters on the battery performance, w i represents the weight of the i-th test parameter, x i represents the current value of the i-th test parameter, n represents the total number of test parameters, and i represents the number of the experimental parameter.

[0045] In the durability test of power batteries, the combination of charge and discharge parameters has an important impact on the test results. Different parameters (such as charging current, discharge voltage, and number of cycles, etc.) have different degrees of influence on battery performance. Therefore, it is necessary to quantify the contribution value of each parameter to the overall test efficiency through weight allocation. In this embodiment, by defining multiple test parameters and their weights, the comprehensive efficiency is calculated in combination with the current test conditions to optimize the test configuration. The initial efficiency is determined by a fixed reference performance value, and on this basis, it is adjusted according to the contribution degree of different parameters to performance. The importance of each test parameter is represented by a weight, and its value is determined according to historical test data or professional experience. Through the dynamic analysis of parameter utility, the test method can achieve the best performance under various 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 weights and current values of the parameters can be adjusted in real time according to the battery aging state to further improve the test accuracy. This embodiment calculates the comprehensive effect of test parameters in a quantitative manner, avoiding the deviation that may be brought by subjectively setting test conditions in traditional methods. Through the evaluation of weight allocation and combined effect, the test conditions can be optimized more scientifically to ensure the reliability of test data. By calculating the weight allocation and comprehensive contribution value, the parameter combination with the greatest impact on the test can be quickly determined, thereby reducing unnecessary test times and resource consumption and greatly improving the test efficiency. This method can flexibly adjust the weights and their combination methods of parameters according to different battery types and test requirements to meet the needs of different application scenarios. For example, in the fast charge battery test, the weight of the charging current can be increased; in the long-life battery test, the weights of the discharge voltage and number of cycles can be emphasized. By quantifying and adjusting the comprehensive influence of each parameter, this method can more comprehensively and accurately evaluate the durability performance of the battery and provide accurate data support for battery R & D and performance optimization. The weight allocation 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.

[0046] S4 includes recording the battery performance parameters each time during the test process, calculating the performance change between two consecutive recordings, and obtaining the average fluctuation degree value of the battery performance change. The specific formula is:

[0047] ;

[0048] Among them, R(t) represents the average fluctuation degree value of the battery performance change, y j represents the battery performance value of the jth record, y j－1 represents the battery performance value of the (j - 1)th record, m represents the total number of records, t represents time, and j represents the numbering of the record times.

[0049] During the durability test of power batteries, the change in battery performance directly reflects its aging degree and durability. To accurately evaluate the attenuation characteristics of the battery, in this embodiment, by recording the key performance parameters of the battery during the test (such as battery capacity, voltage, internal resistance, etc.), and analyzing the performance differences between consecutive recording points, the fluctuation range of the battery performance change is quantified. The recorded data is arranged in chronological order, and each recorded performance value reflects the operating state of the battery at a specific time point. The difference between two consecutive recordings represents the rate of change of battery performance over time. By calculating the average change amplitude of all recording points, the overall fluctuation trend of the battery performance change can be obtained. This method can comprehensively and dynamically reflect the performance attenuation law of the battery, providing a reliable basis for subsequent battery life prediction and design optimization. In addition, this embodiment emphasizes the accuracy and continuity of recording, and uses high-precision sensors and data acquisition devices to ensure the accurate recording of performance parameters. Combined with computer processing technology, it can quickly complete the processing and analysis of data, monitor the battery status in real time, and detect abnormal situations. By recording the battery performance parameters each time during the test, the state change of the battery during the charge and discharge cycle can be understood in real time, which helps to detect abnormal performance fluctuations in time, improving the safety and reliability of the test. This embodiment can quantify the performance change between consecutive recording points and calculate the average fluctuation amplitude, providing an intuitive numerical index. 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 battery performance fluctuation law, the life and attenuation trend of the battery during long-term use can be accurately predicted, providing a scientific basis for the design optimization of power batteries and extending their service life. Using computer processing technology to batch analyze the recorded performance parameters can improve the data processing efficiency, reduce the errors caused by manual operations, and make the test results more scientific and credible. This method automatically records and analyzes data, avoiding a large amount of manual recording and subsequent data collation work in traditional tests, significantly improving the test efficiency, and providing data support for subsequent test condition optimization.

[0050] S1 further includes monitoring and collecting various performance indicators of the power battery from the start of use to the time point of each test, analyzing the change trends of various performance indicators, identifying aging characteristic parameters, constructing an aging state evaluation model of the battery based on the identified characteristic parameters, setting a judgment threshold T for the aging state. 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. Here, E0 represents the performance parameters in the initial state, E represents the performance parameters in the current state, and T represents the judgment threshold for the aging state. In this embodiment, by collecting the performance indicators of the power battery in real time after it is enabled, an evaluation model for evaluating the aging state of the battery is constructed to ensure that the test can comprehensively cover the whole process of the battery from the initial state to the aging state. The test system will regularly collect the index data related to the battery performance, including capacity, internal resistance, voltage, efficiency, etc., and record the performance change trend of the battery at each stage. Through statistical analysis and feature extraction of these data, the parameters closely related to the battery aging are identified, such as the capacity attenuation rate, the increase in internal resistance, etc. Based on the identified key parameters, an evaluation model for the aging state of the battery is constructed to quantify the degree of performance degradation of the battery during the aging process. To facilitate the judgment of whether the battery has entered the aging state, this method sets a clear judgment threshold for the aging state. When the decline ratio of a certain 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 whole process realizes real-time and accuracy through computer processing and automatic model update, making the test more efficient and scientific.

[0051] Based on the identified characteristic parameters, constructing an evaluation model for the aging state of the battery includes collecting the performance data of multiple similar products under similar working conditions, applying machine learning algorithms, selecting appropriate feature extraction techniques to perform dimensionality reduction processing on the data, implementing parameter optimization on the trained machine learning model, calculating the output probability, and then based on Q = f(U) ≥ 0.85, selecting the result closest to the ideal state as the evaluation benchmark, where U represents the input vector of the characteristic parameters, f represents the mapping function obtained through learning, and Q represents the output probability.

[0052] In this embodiment, an aging state evaluation model based on machine learning algorithms is constructed to provide a more accurate analysis tool for the durability test of power batteries. 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 environmental temperature and working current. In order to reduce the data dimension and highlight the key features related to battery aging, feature extraction techniques are 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, and a battery aging state evaluation model is trained using machine learning algorithms (such as support vector machines, random forests, or deep learning networks). During the model training process, by optimizing parameters (such as learning rate, regularization parameter, etc.), the prediction accuracy and robustness of the model are improved. 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 this result is used as the benchmark for aging state evaluation. This evaluation benchmark provides a standardized reference value for subsequent analysis, ensuring the scientificity and consistency of the test.

[0053] S2 also includes determining test parameters. The test parameters include charging current, depth of discharge, and voltage range. The test is run according to the set number of cycles and parameter conditions, and the change data of battery performance is recorded. The charge-discharge durability test of power batteries aims to evaluate the performance degradation law of batteries under specific conditions. In this embodiment, the real usage environment of the battery is simulated 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, depth of discharge, and voltage range. The charging current determines the charging rate of the battery, the depth of discharge reflects the capacity range of the battery used in a single time, and the voltage range is used to limit the maximum and minimum voltage values during the charge-discharge process. The setting of each parameter is based on the characteristics of the battery and the expected usage scenario to ensure the scientificity and rationality of the test conditions. During the test process, the battery is charged and discharged cyclically according to the set parameter conditions, and the test environment (such as temperature and humidity) is strictly controlled to minimize the influence of external factors on the test results. During the 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 the test parameters and optimize the test process. In this way, the durability performance of the battery under the set conditions can be comprehensively understood, providing accurate experimental data support for the development and optimization of power batteries.

[0054] In S4, various parameters during the test process are recorded, including the capacity, internal resistance, and efficiency change values of the battery after each charge-discharge cycle. The fluctuation range of each performance parameter is calculated, the fluctuation trend is analyzed, and the stage of rapid decline in the battery durability performance is identified. The durability performance of the power battery is closely related to its capacity retention rate, internal resistance increase, and efficiency change during the charge-discharge cycle. In this embodiment, by recording the key performance parameters after each cycle in detail, calculating their fluctuation range, and analyzing the fluctuation trend, the stage of rapid decline in the battery durability performance is identified. During the test, the system successively 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 the charge-discharge process) of the battery. The recorded data is used to calculate the change value between the current cycle and the previous cycle, and the fluctuation range of these change values is evaluated. This method can quantify the performance stability of the battery during the test. Further, the test system performs a trend analysis on all recorded change values to identify the critical points of performance parameter fluctuations. For example, when the capacity decline rate significantly accelerates, the internal resistance sharply rises, or the efficiency significantly decreases, it can be judged that the battery has entered the stage of rapid decline in durability performance. This stage is often an important signal that the battery is about to fail. By identifying this stage in advance, it can provide an important reference for battery design improvement and optimized usage strategies. In addition, the automatic recording and analysis of data are controlled by a computer, ensuring high precision and real-time performance, making the test process more scientific and efficient.

[0055] The weight w of the i-th test parameter in S2 i is determined based on the following principles: Analyze historical test data, calculate the contribution degree of each test parameter to the battery performance, use the normalization method to standardize the weight, make the sum of all test parameter weights equal to 1, and optimize the test conditions by dynamically adjusting the weight to meet the battery performance requirements under different working conditions.

[0056] In the charge-discharge durability test of the power battery, the influence degrees of test parameters (such as charging current, discharge depth, voltage range, etc.) on the battery performance may be different. Therefore, it is necessary to assign weights to each test parameter to quantify its contribution to the overall test result. In this embodiment, the weights of the test parameters are determined through data analysis and normalization processing, and the weights are dynamically optimized to meet the performance requirements of the battery under different working conditions. First, the system analyzes historical test data, extracts the relationship between each test parameter and the battery performance (such as capacity retention rate, efficiency, etc.), and quantifies the contribution degree of each test parameter to the battery performance. For example, evaluate the influence degree of the charging current on the capacity retention rate of the battery, or the influence range of the discharge depth on the change of the battery internal resistance. After obtaining the contribution degree of each parameter, the normalization method is used to process the weight w iPerform normalization so that the sum of all weights equals 1. This normalization not only ensures the reasonable distribution of weights but also facilitates the dynamic adjustment of the contributions of different parameters in subsequent test condition optimization. To meet the performance requirements of the battery under different working conditions, a dynamic adjustment mechanism is adopted for weight allocation. During the test process, according to the real-time recorded battery performance data, the weight allocation is adjusted to optimize the test conditions. For example, in high-rate discharge tests, increase the weight of discharge depth; in long-life tests, emphasize the influence of charging current. Through this dynamic optimization, the test conditions can better meet the requirements of actual working conditions, thus improving the scientificity and accuracy of the test.

[0057] The test parameters in S2 include the charging current and charging voltage of the battery, the discharging current and discharging depth of the battery, the charging and discharging temperature range of the battery, and the cycle time interval.

[0058] In the charge-discharge durability test of power batteries, the test parameters have an important impact on the scientificity and accuracy of the test results. In this embodiment, by setting multiple key test parameters such as charging current, charging voltage, discharging current, discharging depth, charging and discharging temperature range, and cycle time interval, the actual usage conditions of the battery are comprehensively simulated to evaluate its durability performance.

[0059] The charging current determines the charging rate of the battery and is usually controlled in a constant current-constant voltage mode. The setting of the charging voltage range ensures the safety and effectiveness of the charging process and at the same time avoids the impact of overcharging on the battery life. Reasonable charging parameters help to evaluate the capacity retention rate and thermal management performance of the battery at different charging rates. The discharging current reflects the energy output ability of the battery under load conditions, and the discharging depth represents the proportion of the capacity that can be released by the battery during a single discharge process. By adjusting the discharging current and discharging 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 at the same time, the influence of different temperature conditions on the battery aging rate can be analyzed. The cycle time interval is used to control the intermittent time of the battery charge-discharge cycle and can simulate the switching state between continuous operation and rest of the battery in actual use. A reasonable time interval setting can reflect the recovery characteristics of the battery and its impact on the durability performance. Through the precise control and combined test of the above parameters, this embodiment can comprehensively simulate the operating state of the battery under different usage scenarios, reveal the performance change laws of the battery under various working conditions, and provide a scientific basis for battery design and optimization.

[0060] In S4, the battery performance parameters include the capacity change of the battery, the internal resistance change of the battery, the charge efficiency and discharge efficiency of the battery, and the temperature change during the operation of the battery. During the durability test of the power battery, the monitoring of battery performance parameters is the key to evaluating the battery operation status and attenuation characteristics. In this embodiment, by monitoring the capacity change, internal resistance change, charge efficiency and discharge efficiency of the battery, as well as the temperature change during the operation, the performance of the battery during charge and discharge cycles is comprehensively grasped, and the change law of its durability performance is deeply analyzed.

[0061] The battery capacity is a direct reflection of the battery's ability to store electrical energy. By recording the capacity change after each cycle, the capacity retention rate of the battery can be intuitively evaluated, and the accelerated stage of capacity attenuation can be identified. The internal resistance is a key indicator of the internal conductivity of the battery, and its change directly affects the output power and efficiency of the battery. The increase in internal resistance is usually accompanied by battery aging. By continuously monitoring the change in internal resistance, the health status of the battery can be evaluated in a timely manner. The charge efficiency and discharge efficiency respectively reflect the energy loss of the battery during the charge and discharge processes. The decrease in efficiency may be an indication of aging of the internal materials of the battery or improper thermal management. Monitoring these parameters can provide a basis for optimizing the battery design and usage strategy. 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 energy efficiency and discharge capacity of the battery. By recording the temperature change during the operation of the battery in real time, the impact of temperature on battery performance can be evaluated and the test conditions can be optimized. Through the comprehensive analysis of the above performance parameters, this embodiment can comprehensively evaluate the durability performance of the battery, and reveal the key influencing factors during the battery decay process through trend identification and data mining. This provides a scientific basis for the life prediction and optimized design of power batteries.

[0062] The charge and discharge temperature range in S2 is set through 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 temperature and the maximum temperature, for example, between -20°C and 60°C, to cover the extreme usage conditions that the light truck power battery may face. During the charge and discharge process, the computer program monitors the charge current, discharge current and capacity change of the battery under different temperature conditions in real time, records the test data and conducts analysis to evaluate the performance stability and durability of the battery in various temperature environments. In addition, by performing segmented control on the temperature setting, such as gradually transitioning from low temperature to high temperature or conducting charge and discharge tests in a random temperature change manner, the complex environmental impacts under actual working conditions can be further simulated, so as to comprehensively evaluate 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.

[0063] Specifically: It can be illustrated based on the test data in Table 1:

[0064] Table 1

[0065]

[0066] Table description:

[0067] Experiment number: The number of each experiment;

[0068] Number of cycles: The number of charge-discharge cycles the battery has experienced;

[0069] Charging current (A): The charging current of the battery during each charging process;

[0070] Depth of discharge (%): The depth of discharge in each experiment, indicating the percentage of the battery's discharged capacity;

[0071] Battery capacity (Ah): The capacity of the battery in each experiment (unit: ampere-hour);

[0072] Internal resistance (mΩ): The internal resistance value of the battery at different experimental stages, with the unit of milliohm;

[0073] Charging efficiency (%): The electrical energy conversion efficiency during the battery charging process;

[0074] Discharging efficiency (%): The electrical energy conversion efficiency during the battery discharging process;

[0075] Temperature (°C): The working environmental temperature of the battery during the experiment;

[0076] Capacity change (%): The percentage change in the battery capacity compared to the initial state;

[0077] Internal resistance change (%): The percentage change in the battery internal resistance compared to the initial state;

[0078] Efficiency change (%): The percentage change in the charge-discharge efficiency compared to the initial state;

[0079] Battery aging state: Judging the battery aging state based on the experimental data, divided into "initial state", "normal state", "preliminary decline", "decline stage", "rapid decline" or "complete decline";

[0080] Aging threshold (T): The aging state judgment threshold of the battery. When the performance decay exceeds this threshold, the battery is considered to reach the aging state;

[0081] According to the experimental data, as the number of charge-discharge cycles increases, the capacity, internal resistance, charging efficiency, and discharging efficiency of the battery will all change to a certain extent. By monitoring and analyzing these changes, the trend of battery performance decline can be identified;

[0082] After the number of cycles reaches 300, the battery begins to enter the degradation stage, with obvious capacity changes, increased internal resistance, and decreased efficiency. These parameters can be used as the basis for determining the aging state of the battery;

[0083] The setting of the aging threshold (T) helps to determine whether the battery has reached the aging state. For example, in the experiment, when the capacity change exceeds -30% or the internal resistance increases by more than 100%, the battery is judged to have entered the aging state.

[0084] Battery durability assessment description:

[0085] High: The battery performs stably in multiple cycle tests, with small changes in capacity, internal resistance, and efficiency, and has not entered the degradation stage;

[0086] Medium: The battery begins to show signs of degradation, such as a slight decrease in capacity and an increase in internal resistance, but still maintains a certain performance;

[0087] Low: The performance of the battery decreases significantly, with a large reduction in capacity, a sharp increase in internal resistance, and a significant decrease in efficiency, approaching the aging state;

[0088] Very low: The battery enters the complete degradation state, with a serious decrease in performance and almost losing its practical value;

[0089] Battery durability comprehensively considers various factors of battery performance and provides a concise assessment of the aging state of the battery, which helps to quickly identify the health status and durability of the battery.

[0090] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A computer - based method for the charge - discharge durability test of light - truck power batteries, characterized in that, The method includes: S1. Automatically generate an aging assessment model based on historical battery performance data, including collecting historical battery performance data and fitting the historical data. The specific formula is: ; Among them, v represents the performance degradation rate of the battery in the current charge-discharge cycle, V max represents the limit speed when the battery performance deteriorates, N represents the number of charge-discharge cycles experienced by the battery, and B represents the index value of the battery's anti-aging ability; Generating a battery aging evaluation model to predict the change trend of battery capacity in future cycles; S2. Conducting tests on the power battery according to the set charge-discharge cycles; S3. Automatically adjusting the test conditions based on the aging evaluation model to adapt to the battery aging state, including monitoring the performance fluctuations of the battery through the aging evaluation model and calculating the adjusted test parameters. The specific formula is: ； Among them, x new represents the value of the test parameter 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 the dynamic change of the battery performance, t represents time, and g represents the initial state offset value of the battery performance fluctuation; S4. Recording various parameters during the test and analyzing and evaluating the battery durability.

2. A method for the charge and discharge durability test of a light truck power battery based on computer processing according to claim 1, characterized in that: The S2 includes: Determine the charge-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 as follows: ; Among them, P(t) represents the efficiency under the current test conditions, P0 represents the reference performance value, β represents the comprehensive influence degree value of different charge and discharge test parameters on the battery performance, w i represents the weight of the i-th test parameter, x i represents the current value of the i-th test parameter, n represents the total number of test parameters, and i represents the number of the experimental parameter.

3. A light truck power battery charge and discharge durability test method based on computer processing according to claim 1, characterized in that: The S4 includes: Recording the battery performance parameters each time during the test, calculating the performance change between two consecutive recordings, and obtaining the average fluctuation degree value of the battery performance change. The specific formula is: ； Among them, R(t) represents the average fluctuation degree value of the battery performance change, and y j represents the battery performance value recorded at the j-th time, and y j－1 represents the battery performance value recorded at the (j - 1)-th time, m represents the total number of records, t represents time, and j represents the serial number of the record times.

4. A light truck power battery charge and discharge durability test method based on computer processing according to claim 1, characterized in that: The S1 further includes monitoring and collecting various performance indicators of the power battery from the start of use to each test time point, analyzing the change trends of the various performance indicators, identifying aging characteristic parameters, constructing an aging state evaluation model for the battery based on the identified characteristic parameters, setting a judgment threshold T for the aging state. 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. Among them, E0 represents the performance parameter in the initial state, E represents the performance parameter in the current state, and T represents the judgment threshold for the aging state.

5. A method for the charge and discharge durability test of a light truck power battery based on computer processing according to claim 4, characterized in that: Constructing an aging state evaluation model of the battery based on the identified characteristic parameters includes collecting performance data of multiple similar products under similar working conditions, applying machine learning algorithms, selecting appropriate feature extraction techniques to perform dimensionality reduction on the data, optimizing the parameters of the trained machine learning model, calculating the output probability, and then based on Q = f(U) ≥ 0.85, selecting the result closest to the ideal state as the evaluation benchmark, where 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 light truck power battery charge and discharge durability test based on computer processing according to claim 1, characterized in that: The S2 further includes determining the test parameters. The test parameters 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 the battery performance.

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

8. A method for light truck power battery charge and discharge durability test based on computer processing according to claim 2, characterized in that: The weight w of the i-th test parameter in S2 i is determined based on the following principles: Analyze historical test data, calculate the contribution of each test parameter to the battery performance, use the normalization method to standardize the weights so that the sum of all test parameter weights is equal to 1, and optimize the test conditions by dynamically adjusting the weights to meet the battery performance requirements under different working conditions.

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

10. A method for the charge and discharge durability test of a light truck power battery based on computer processing according to claim 9, characterized in that: The charge-discharge temperature range in the S2 is set through environmental control, including the lowest temperature and the highest temperature, to simulate the battery performance under different environmental conditions.

Images