Power battery performance evaluation method and system and storage medium

Abstract

The invention discloses a power battery performance evaluation method and system and a storage medium. The method comprises the following steps: acquiring multi-dimensional performance indexes such as cycle life attenuation rate, charge-discharge efficiency, rate capability retention rate and low-temperature capacity retention rate of a battery; standardizing each index, and automatically calculating the objective weight based on the information entropy of the index data so as to eliminate the subjective deviation of manually setting the weight; performing weighted summation on the standardized index values and the corresponding weights to obtain a comprehensive evaluation value of the battery performance; and finally dividing performance grades according to a preset threshold. The technical problems that a traditional evaluation method is single in dimension and subjective in weight are solved, comprehensive, objective and quantitative scientific evaluation of the performance of the power battery is achieved, and the accuracy of the evaluation result and the engineering practicability are remarkably improved.

Description

Technical Field

[0001] This application relates to the field of new energy power battery testing, specifically to a power battery performance evaluation method, system, and storage medium. Background Technology

[0002] With the rapid development of new energy technologies, the accuracy and comprehensiveness of the performance evaluation of power batteries, as core energy storage components, directly affects the reliability, service life, and safety risks of equipment. Existing battery testing technologies have achieved the measurement of basic parameters (such as voltage, current, and internal resistance) and performance judgment through simple formulas or empirical models. For example, the remaining capacity of a battery can be assessed by the capacity after a single discharge, and the degree of battery aging can be judged by changes in internal resistance.

[0003] However, traditional testing methods have significant limitations. Most methods rely on a single metric for evaluation, ignoring the multifaceted nature of battery performance. For example, some methods only measure the static value of the battery's internal resistance at a certain temperature, without considering the dynamic effect of temperature on internal resistance, or rely solely on visual inspection to identify whether the battery is bulging or has surface defects. These single-metric evaluation methods are insufficient for a comprehensive assessment of the battery.

[0004] Existing technologies lack comprehensive quantitative models. Even when some solutions attempt to combine multiple parameters, the weighting logic of each parameter is not clearly defined, resulting in highly subjective evaluation results that are difficult to standardize and promote. Summary of the Invention

[0005] This application provides a method, system, and storage medium for evaluating the performance of power batteries, which can solve the technical problem that existing power battery performance evaluation comprehensive quantitative models, even if some solutions attempt to combine multiple parameters, do not clearly define the weight allocation logic of each parameter, resulting in highly subjective evaluation results and difficulty in standardization and promotion.

[0006] In a first aspect, embodiments of this application provide a method for evaluating the performance of a power battery, comprising: Acquire data from multiple test samples; each test sample includes the cycle life degradation rate, charge / discharge efficiency, rate performance retention rate, and low-temperature capacity retention rate of the power battery under test. For each indicator of each test sample data, the dimensions are processed, and then the data is standardized to obtain a dataset with unified standards. The information entropy of each indicator of each test sample data in the dataset is calculated, and the corresponding weight is calculated based on the information entropy of each indicator. The comprehensive evaluation value is obtained by weighting and summing the index values ​​of each test sample data in the dataset with their corresponding weights. The performance level of the power battery is determined based on the comprehensive evaluation value and according to the preset performance level classification rules.

[0007] Preferably, each indicator of each test sample data is processed to have a unified dimension, and then standardized to obtain a standardized dataset. This process includes the following steps: For each indicator of each test sample data, a unified dimension processing is performed to ensure that the value of each indicator of each test sample data is within the standard range; the standard range is [0,1]. For each test sample data after uniform dimension processing, the charge / discharge efficiency, rate performance retention rate and low temperature capacity retention rate are all standardized according to Formula 1. The cycle life decay rate of each test sample data after uniform dimension processing is standardized according to Formula 2. Each metric of each test sample data after standardization is used as a dataset.

[0008] Preferably, Formula 1 is: ; in, For the j-th indicator of the i-th test sample data, , These are the maximum and minimum values ​​selected from all test sample data for the j-th indicator, respectively. The value of the j-th indicator of the i-th test sample data after standardization.

[0009] Preferably, the second formula is:

[0010] in, For the j-th indicator of the i-th test sample data, , These are the maximum and minimum values ​​selected from all test sample data for the j-th indicator, respectively. The value of the j-th indicator of the i-th test sample data after standardization.

[0011] Preferably, the information entropy of each indicator in each test sample data in the dataset is calculated, and the corresponding weight is calculated based on the information entropy of each indicator. This specifically includes the following steps: Calculate the first data point of each test sample in the dataset according to Formula 3. j Information entropy of the indicator Formula 3 is: ; Calculate information entropy according to formula four. Corresponding difference coefficient Formula four is: ; The first data point of each test sample is calculated according to Formula 5. jWeights corresponding to each indicator Formula 5 is: , Let be the weight of the j-th indicator, and .

[0012] Preferably, the comprehensive evaluation value is obtained by weighted summation of each indicator value and its corresponding weight for each test sample in the dataset, specifically including the following steps: Calculate the comprehensive evaluation value according to Formula Six; Formula six is: ; This is the comprehensive evaluation value.

[0013] Preferably, the performance level of the power battery is determined based on the comprehensive evaluation value and according to the preset performance level classification rules, specifically including the following steps: If the overall evaluation value is less than the first preset value, the performance level of the power battery is judged to be excellent. If the comprehensive evaluation value is greater than or equal to the first preset value and less than the second preset value, the performance level of the power battery is judged to be good. If the comprehensive evaluation value is greater than or equal to the second preset value and less than the third preset value, the performance level of the power battery is determined to be general. If the comprehensive evaluation value is greater than or equal to the third preset value and less than the fourth preset value, the performance level of the power battery is judged to be poor. If the comprehensive evaluation value is greater than the fourth preset value, the performance level of the power battery is determined to be very poor; where the first preset value is... The second preset value is The third preset value is The fourth preset value is .

[0014] Preferably, acquiring multiple test sample data includes the following steps: After performing N standard charge-discharge cycles on the battery under constant temperature conditions, the cycle life decay rate is obtained by calculating the ratio of the difference between the initial discharge capacity and the discharge capacity of the Nth cycle to the initial capacity. By performing a complete charge-discharge cycle on a fully charged battery under constant temperature conditions, the total input energy and total output energy are obtained by integration. Then, the ratio of the total output energy to the total input energy is calculated to obtain the charge-discharge efficiency. The above steps are repeated to obtain multiple charge-discharge efficiencies, and the average of the multiple charge-discharge efficiencies is taken as the final charge-discharge efficiency. By discharging a fully charged battery at 0.5C and 5C rates under constant temperature conditions, two discharge capacities are obtained. The ratio of these two discharge capacities is calculated to obtain the rate performance retention rate. The above steps are repeated to obtain multiple rate performance retention rates, and the average of the multiple rate performance retention rates is taken as the final rate performance retention rate. By placing fully charged batteries separately in room temperature and low temperature environments and then discharging them to the cutoff voltage at the same rate, the discharge capacity of each battery is recorded. The ratio of the two discharge capacities is calculated to obtain the low temperature capacity retention rate. The above steps are repeated to obtain multiple low temperature capacity retention rates, and then the average of the multiple low temperature capacity retention rates is taken as the final low temperature capacity retention rate.

[0015] Thirdly, embodiments of this application provide a power battery performance evaluation system, which includes: The first module is used to acquire multiple test sample data; each test sample data includes the cycle life decay rate, charge and discharge efficiency, rate performance retention rate and low temperature capacity retention rate of the power battery under test; The second module is used to process the dimensions of each indicator of each test sample data, and then perform standardization to obtain a dataset with unified standards; calculate the information entropy of each indicator of each test sample data in the dataset, and calculate the corresponding weight based on the information entropy of each indicator. The third module is used to sum the index values ​​of each test sample data in the dataset with their corresponding weights to obtain a comprehensive evaluation value. The fourth module is used to determine the performance level of the power battery based on the comprehensive evaluation value and according to the preset performance level classification rules.

[0016] Fourthly, embodiments of this application provide a computer-readable storage medium storing a power battery performance evaluation program, wherein when the power battery performance evaluation program is executed by a processor, it implements the steps of the above-described power battery performance evaluation method.

[0017] The beneficial effects of the technical solutions provided in this application include: The evaluation method explicitly requires the acquisition of four key performance indicators: cycle life degradation rate, charge / discharge efficiency, rate performance retention rate, and low-temperature capacity retention rate. This overcomes the limitations of existing technologies that often rely on a single or a few static parameters (such as initial capacity and internal resistance) for evaluation. These four indicators characterize the core performance of the battery from different dimensions: cycle life degradation rate reflects the capacity degradation characteristics of the battery during long-term use (lifetime dimension); charge / discharge efficiency reflects the energy loss of the battery during energy storage and release (efficiency dimension); rate performance retention rate reflects the battery's ability to perform high-power charge and discharge in a short period of time (power dimension); and low-temperature capacity retention rate reflects the battery's usable capacity in cold environments (environmental adaptability dimension). By comprehensively examining these four dimensions, the overall performance of the power battery under actual complex operating conditions can be reflected more comprehensively and realistically, significantly improving the completeness and scientific nature of the evaluation, and providing a more reliable basis for battery selection, application, and retirement judgment.

[0018] This method calculates the information entropy of each indicator and then calculates its corresponding weight based on the information entropy. It changes the traditional approach of relying heavily on subjective expert experience for weight assignment in comprehensive evaluation, achieving complete objectivity and data-driven weight allocation. The underlying principle is that information entropy is an indicator in information theory that measures the degree of disorder or information content of a system. In this method, for each evaluation indicator, its information entropy is calculated across all test sample data. If an indicator exhibits significant numerical differences across different samples (i.e., high dispersion), its information entropy is low, meaning it contains a large amount of discriminative information and contributes significantly to the performance of differentiating samples; therefore, it should be assigned a higher weight. Conversely, if an indicator's values ​​converge across all samples, its information entropy is high, and its weight is reduced. The entire process is calculated based entirely on the objective distribution of the test data, requiring no human intervention. The beneficial effects of this mechanism are that the evaluation results are no longer influenced by the evaluator's subjective experience, and the weights can adapt to the data characteristics of different batches and types of batteries, making the evaluation model more objective, consistent, and widely applicable.

[0019] The raw test data from multiple dimensions are transformed into a single, quantifiable comprehensive evaluation value through an objective standardization and weighted aggregation process. This value intuitively represents the overall performance level of the battery, enabling direct and quantitative comparisons between different batteries, and making complex test data conclusions extremely clear and easy to understand. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the power battery performance evaluation method of this application. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0023] Existing battery performance evaluation methods suffer from problems such as a single evaluation dimension, insufficient consideration of factors such as dynamic performance and environmental adaptability, and a lack of standardized multi-index comprehensive models.

[0024] To address the aforementioned problems, in a first aspect, embodiments of this application provide a method for evaluating the performance of a power battery, comprising: Step 100: Obtain multiple test sample data; each test sample data includes the cycle life degradation rate, charge and discharge efficiency, rate performance retention rate, and low temperature capacity retention rate of the power battery under test; Step 200: Perform dimensional processing on each indicator of each test sample data, and then perform standardization processing to obtain a dataset with unified standards; calculate the information entropy of each indicator of each test sample data in the dataset, and calculate the corresponding weight based on the information entropy of each indicator. Step 300: Calculate the weighted sum of each indicator value and its corresponding weight for each test sample in the dataset to obtain the comprehensive evaluation value; Step 400: Based on the comprehensive evaluation value, determine the performance level of the power battery according to the preset performance level classification rules.

[0025] In step 100, Obtain the battery cycle life degradation rate ( The specific steps are as follows: 50% of the samples (e.g., sample number = m) from each battery batch are randomly selected as test subjects. Cyclic tests are performed at a constant temperature of 25°C using a standard charge-discharge cycle (charged to rated voltage, discharged at 1C rate to cutoff voltage). The initial battery capacity C0 (first cycle discharge capacity) and the capacity Cn after the nth cycle (n≥500, simulating long-term degradation in actual use, n=1000 recommended) are recorded, calculated using the following formula. .

[0026] Obtain battery charge and discharge efficiency ( The specific steps are as follows: A full charge-discharge cycle was performed on a fully charged battery at a constant temperature of 25°C: the total energy input during the charging process was recorded. (Calculated by voltage-current-time integration), the total output energy is recorded during the discharge process. Calculate using the following formula ; Repeat the test 3 times and take the average value as the final result to reduce random errors.

[0027] Obtain battery rate performance retention rate ( The specific steps are as follows: At a constant temperature of 25°C, two discharge rate tests were conducted on the fully charged battery: Standard rate discharge: Discharge at a rate of 0.5C to the cutoff voltage and record the capacity C0.5C; High-rate discharge: Discharge at a rate of 5C to the cutoff voltage and record the capacity C5c; According to the formula To calculate the battery rate performance retention rate; Repeat the test 3 times and take the average value as the final result to reduce random errors.

[0028] Obtain battery low-temperature capacity retention rate ( The specific steps are as follows: Two temperature environments were set to conduct discharge tests on a fully charged battery. At room temperature: After standing for 2 hours at 25℃, discharge at a rate of 1C to the cutoff voltage and record the capacity C25; Low temperature environment: After standing at -20℃ for 2 hours, discharge at 1C rate to the cutoff voltage and record the capacity C-20; According to the formula Calculate the battery's low-temperature capacity retention rate; The test was repeated three times, and the average value was taken as the final result to reduce random errors. Data for multiple test samples was obtained in this manner.

[0029] It should be understood in connection with this application that: The evaluation method explicitly requires the acquisition of four key performance indicators: cycle life degradation rate, charge / discharge efficiency, rate performance retention rate, and low-temperature capacity retention rate. This overcomes the limitations of existing technologies that often rely on a single or a few static parameters (such as initial capacity and internal resistance) for evaluation. These four indicators characterize the core performance of the battery from different dimensions: cycle life degradation rate reflects the capacity degradation characteristics of the battery during long-term use (lifetime dimension); charge / discharge efficiency reflects the energy loss of the battery during energy storage and release (efficiency dimension); rate performance retention rate reflects the battery's ability to perform high-power charge and discharge in a short period of time (power dimension); and low-temperature capacity retention rate reflects the battery's usable capacity in cold environments (environmental adaptability dimension). By comprehensively examining these four dimensions, the overall performance of the power battery under actual complex operating conditions can be reflected more comprehensively and realistically, significantly improving the completeness and scientific nature of the evaluation, and providing a more reliable basis for battery selection, application, and retirement judgment.

[0030] This method calculates the information entropy of each indicator and then calculates its corresponding weight based on the information entropy. It changes the traditional approach of relying heavily on subjective expert experience for weight assignment in comprehensive evaluation, achieving complete objectivity and data-driven weight allocation. The underlying principle is that information entropy is an indicator in information theory that measures the degree of disorder or information content of a system. In this method, for each evaluation indicator, its information entropy is calculated across all test sample data. If an indicator exhibits significant numerical differences across different samples (i.e., high dispersion), its information entropy is low, meaning it contains a large amount of discriminative information and contributes significantly to the performance of differentiating samples; therefore, it should be assigned a higher weight. Conversely, if an indicator's values ​​converge across all samples, its information entropy is high, and its weight is reduced. The entire process is calculated based entirely on the objective distribution of the test data, requiring no human intervention. The beneficial effects of this mechanism are that the evaluation results are no longer influenced by the evaluator's subjective experience, and the weights can adapt to the data characteristics of different batches and types of batteries, making the evaluation model more objective, consistent, and widely applicable.

[0031] The raw test data from multiple dimensions is transformed into a single, quantifiable comprehensive evaluation value through an objective standardization and weighted aggregation process. Then, performance levels are assigned according to preset rules, making the evaluation results intuitive and easy to understand. This not only facilitates quality grading and management in battery production, use, and recycling, but also provides a quantitative basis for battery system integration and operation and maintenance strategy formulation, demonstrating strong engineering practicality and promotional value.

[0032] In some embodiments, each indicator of each test sample data is processed to have a unified dimension, and then standardized to obtain a standardized dataset. This specifically includes the following steps: For each indicator of each test sample data, a unified dimension processing is performed to ensure that the value of each indicator of each test sample data is within the standard range; the standard range is [0,1]. For each test sample data after uniform dimension processing, the charge / discharge efficiency, rate performance retention rate and low temperature capacity retention rate are all standardized according to Formula 1. The cycle life decay rate of each test sample data after uniform dimension processing is standardized according to Formula 2. Each metric of each test sample data after standardization is used as a dataset.

[0033] Formula 1 is: ; in, For the j-th indicator of the i-th test sample data, , These are the maximum and minimum values ​​selected from all test sample data for the j-th indicator, respectively. This is the standardized value of the j-th indicator for the i-th test sample. The formula uses range normalization, subtracting the minimum value and dividing by the range, to preserve the relative differences between data while eliminating the influence of absolute numerical values. This approach is particularly suitable for evaluation scenarios with multiple samples and multiple indicators, effectively identifying the distribution characteristics of each indicator across different samples, laying the foundation for subsequent weight calculations based on information entropy. Furthermore, the normalized data is numerically more stable, helping to avoid numerical overflow or precision loss issues caused by excessively large or small values ​​in entropy calculations.

[0034] Formula 2 is as follows:

[0035] in, For the j-th indicator of the i-th test sample data, , These are the maximum and minimum values ​​selected from all test sample data for the j-th indicator, respectively. This is the standardized value of the j-th indicator for the i-th test sample data. Since the cycle life decay rate is a negative indicator where "the smaller the better," directly using the same standardization formula as for positive indicators would result in a value direction opposite to the evaluation target. This claim transforms the negative indicator into a positive form where "the larger the value, the better the performance" by reversing the process (subtracting the original value from the maximum value and then dividing by the range), thus aligning it with other indicators in terms of evaluation direction.

[0036] This approach not only aligns with evaluation logic but also ensures that all indicators contribute in a consistent direction when weighted and summed, preventing distortion of the overall evaluation due to inconsistent indicator directions and further enhancing the rationality and interpretability of the evaluation model. In this embodiment, firstly, by unifying the dimensions, indicators with different physical meanings and magnitudes are converted to a unified numerical range, eliminating evaluation bias caused by different dimensions and ensuring the fairness of each indicator in the weighted calculation.

[0037] Secondly, different standardization formulas are used for positive indicators (such as charge / discharge efficiency, rate performance retention, and low-temperature capacity retention) and negative indicators (such as cycle life decay rate), reflecting the impact of the differences in indicator properties on the evaluation results. Higher values ​​for positive indicators are better, while lower values ​​for negative indicators are better. This differentiated approach ensures that the standardized data are consistent in direction, facilitating subsequent weighted aggregation and comparison.

[0038] Finally, a standardized dataset was constructed through standardization, providing a structured input for subsequent information entropy calculation and weight allocation. This ensured the numerical stability and convergence of the entropy method during the calculation process, thereby improving the robustness and accuracy of the entire evaluation model.

[0039] In some embodiments, the information entropy of each indicator for each test sample in the dataset is calculated, and the corresponding weight is calculated based on the information entropy of each indicator. Specifically, this includes the following steps: Calculate the information entropy of the j-th indicator for each test sample in the dataset according to Formula 3. Formula 3 is: ; Calculate information entropy according to formula four. Corresponding difference coefficient Formula four is: ; The weight corresponding to the j-th indicator of each test sample data is calculated according to Formula 5. Formula 5 is: , Let be the weight of the j-th indicator, and .

[0040] In this embodiment, firstly, the information content or uncertainty of each indicator in the sample is quantified by calculating information entropy. The smaller the information entropy, the smaller the difference of the indicator among different samples, and the lower its contribution to distinguishing samples; conversely, the larger the information entropy, the higher the indicator's distinguishing ability.

[0041] Secondly, the information entropy is converted into a weighting criterion through the difference coefficient. The larger the difference coefficient, the higher the weight of the indicator in the comprehensive evaluation. This weighting method based on data distribution characteristics is entirely driven by sample data and requires no human intervention, significantly improving the objectivity and scientific rigor of the evaluation.

[0042] Finally, the automatic calculation of weights makes this method highly adaptable and scalable, applicable to the evaluation of different types and batches of batteries, without the need to reset the weights for each type of battery, thus possessing strong versatility and engineering practicality.

[0043] In some embodiments, the index values ​​of each test sample data in the dataset are weighted and summed with their corresponding weights to obtain a comprehensive evaluation value. This specifically includes the following steps: Calculate the comprehensive evaluation value according to Formula Six; Formula six is: ; This is the comprehensive evaluation value.

[0044] By weighting and summing the standardized indicator values ​​with their corresponding weights, the performance of the four dimensions is integrated into a single value, namely the comprehensive evaluation value. This value not only reflects the battery's overall performance across all indicators but also reflects the differences in importance of each indicator through its weights.

[0045] This comprehensive quantitative approach allows for direct comparison between different batteries, providing a concise and powerful decision-making basis for applications such as battery selection, quality grading, and lifespan prediction. At the same time, the comprehensive evaluation value facilitates benchmarking against historical data or industry standards, promoting the standardization process of battery performance evaluation.

[0046] In some embodiments, the performance level of the power battery is determined based on a comprehensive evaluation value and according to a preset performance level classification rule, specifically including the following steps: If the overall evaluation value is less than the first preset value, the performance level of the power battery is judged to be excellent. If the comprehensive evaluation value is greater than or equal to the first preset value and less than the second preset value, the performance level of the power battery is judged to be good. If the comprehensive evaluation value is greater than or equal to the second preset value and less than the third preset value, the performance level of the power battery is determined to be general. If the comprehensive evaluation value is greater than or equal to the third preset value and less than the fourth preset value, the performance level of the power battery is judged to be poor. If the comprehensive evaluation value is greater than the fourth preset value, the performance level of the power battery is determined to be very poor; where the first preset value is... The second preset value is The third preset value is The fourth preset value is .

[0047] In this embodiment, the quantitative evaluation results are transformed into intuitive performance levels: By setting multiple threshold ranges (such as 10%, 30%, 50%, and 70%), continuous comprehensive evaluation values ​​are divided into five levels: "Excellent," "Good," "Average," "Poor," and "Very Poor," making the evaluation results easier for users to understand and apply. This grading not only facilitates the classification and management of batteries in production and operation but also provides clear quality judgment criteria for battery recycling and secondary use. Furthermore, the thresholds can be flexibly adjusted according to actual application scenarios or industry standards, enhancing the applicability and scalability of the method.

[0048] In summary, this application offers a more objective evaluation, with weights determined by the inherent dispersion of the indicator data, avoiding the subjectivity of manually setting weights. This makes the evaluation results more closely reflect the actual distribution characteristics of the data. Furthermore, the weight values ​​can be automatically adjusted based on the data, eliminating the need for manual modification of the weight coefficients and enhancing the model's versatility. This application can identify indicators that have a more significant impact on battery performance, enabling the evaluation results to more accurately reflect the core performance differences of batteries.

[0049] Secondly, a power battery performance evaluation system is provided, which includes: The first module is used to acquire multiple test sample data; each test sample data includes the cycle life decay rate, charge and discharge efficiency, rate performance retention rate and low temperature capacity retention rate of the power battery under test; The second module is used to process the dimensions of each indicator of each test sample data, and then perform standardization to obtain a dataset with unified standards; calculate the information entropy of each indicator of each test sample data in the dataset, and calculate the corresponding weight based on the information entropy of each indicator. The third module is used to sum the index values ​​of each test sample data in the dataset with their corresponding weights to obtain a comprehensive evaluation value. The fourth module is used to determine the performance level of the power battery based on the comprehensive evaluation value and according to the preset performance level classification rules.

[0050] Thirdly, embodiments of this application provide a power battery performance evaluation device, which can be a personal computer (PC), laptop computer, server, or other device with data processing capabilities.

[0051] In this embodiment of the application, the power battery performance evaluation device may include a processor, a memory, a communication interface, and a communication bus.

[0052] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0053] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting devices within the power battery performance evaluation equipment, as well as interfaces used for interconnecting the power battery performance evaluation equipment with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0054] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0055] The processor can be a general-purpose processor, which can call the power battery performance evaluation program stored in the memory and execute the power battery performance evaluation method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the power battery performance evaluation program is called can be referred to in the various embodiments of the power battery performance evaluation method of this application, and will not be repeated here.

[0056] Fourthly, embodiments of this application also provide a computer-readable storage medium.

[0057] The present application provides a computer-readable storage medium storing a power battery performance evaluation program, wherein when the power battery performance evaluation program is executed by a processor, it implements the steps of the power battery performance evaluation method described above.

[0058] The method implemented when the power battery performance evaluation procedure is executed can be referred to in various embodiments of the power battery performance evaluation method of this application, and will not be repeated here.

[0059] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0060] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0061] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0062] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0063] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0064] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0065] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for evaluating the performance of a power battery, characterized in that, It includes: Obtain multiple test sample data; Each test sample data includes the cycle life degradation rate, charge / discharge efficiency, rate performance retention rate, and low-temperature capacity retention rate of the power battery under test; Each indicator of each test sample data is processed for dimensionality, and then standardized to obtain a dataset with unified standards. Calculate the information entropy of each indicator for each test sample in the dataset, and calculate the corresponding weight based on the information entropy of each indicator; The comprehensive evaluation value is obtained by weighting and summing the index values ​​of each test sample data in the dataset with their corresponding weights. The performance level of the power battery is determined based on the comprehensive evaluation value and according to the preset performance level classification rules.

2. The power battery performance evaluation method as described in claim 1, characterized in that, For each indicator in each test sample data, a unified dimension processing is performed, followed by standardization processing to obtain a standardized dataset. This process includes the following steps: For each indicator of each test sample data, a unified dimension processing is performed to ensure that the value of each indicator of each test sample data is within the standard range; the standard range is [0,1]. For each test sample data after uniform dimension processing, the charge / discharge efficiency, rate performance retention rate and low temperature capacity retention rate are all standardized according to Formula 1. The cycle life decay rate of each test sample data after uniform dimension processing is standardized according to Formula 2. Each metric of each test sample data after standardization is used as a dataset.

3. The power battery performance evaluation method as described in claim 2, characterized in that: Formula 1 is: ; in, For the j-th indicator of the i-th test sample data, , These are the maximum and minimum values ​​selected from all test sample data for the j-th indicator, respectively. The value of the j-th indicator of the i-th test sample data after standardization.

4. The power battery performance evaluation method as described in claim 2, characterized in that: Formula 2 is as follows: in, For the j-th indicator of the i-th test sample data, , These are the maximum and minimum values ​​selected from all test sample data for the j-th indicator, respectively. The value of the j-th indicator of the i-th test sample data after standardization.

5. The power battery performance evaluation method as described in claim 3 or 4, characterized in that, Calculate the information entropy of each indicator for each test sample in the dataset, and calculate the corresponding weight based on the information entropy of each indicator. This includes the following steps: Calculate the first data point of each test sample in the dataset according to Formula 3. j Information entropy of the indicator Formula 3 is: ; Calculate information entropy according to formula four. Corresponding difference coefficient Formula four is: ; Calculate the first data point for each test sample according to Formula 5. j Weight of each indicator Formula 5 is: , Let be the weight of the j-th indicator, and .

6. The power battery performance evaluation method as described in claim 5, characterized in that, The comprehensive evaluation value is obtained by weighting and summing the index values ​​of each test sample in the dataset with their corresponding weights. This process includes the following steps: Calculate the comprehensive evaluation value according to Formula Six; Formula six is: ; This is the comprehensive evaluation value.

7. The power battery performance evaluation method as described in claim 1, characterized in that, Based on the comprehensive evaluation value, the performance level of the power battery is determined according to the preset performance level classification rules, specifically including the following steps: If the overall evaluation value is less than the first preset value, the performance level of the power battery is judged to be excellent. If the comprehensive evaluation value is greater than or equal to the first preset value and less than the second preset value, the performance level of the power battery is judged to be good. If the comprehensive evaluation value is greater than or equal to the second preset value and less than the third preset value, the performance level of the power battery is determined to be general. If the comprehensive evaluation value is greater than or equal to the third preset value and less than the fourth preset value, the performance level of the power battery is judged to be poor. If the comprehensive evaluation value is greater than the fourth preset value, the performance level of the power battery is determined to be very poor; where the first preset value is... The second preset value is The third preset value is The fourth preset value is .

8. The power battery performance evaluation method as described in claim 1, characterized in that, To obtain multiple test sample data, the specific steps include: After performing N standard charge-discharge cycles on the battery under constant temperature conditions, the cycle life decay rate is obtained by calculating the ratio of the difference between the initial discharge capacity and the discharge capacity of the Nth cycle to the initial capacity. By performing a complete charge-discharge cycle on a fully charged battery under constant temperature conditions, the total input energy and total output energy are obtained by integration. Then, the ratio of the total output energy to the total input energy is calculated to obtain the charge-discharge efficiency. The above steps are repeated to obtain multiple charge-discharge efficiencies, and the average of the multiple charge-discharge efficiencies is taken as the final charge-discharge efficiency. By discharging a fully charged battery at 0.5C and 5C rates under constant temperature conditions, two discharge capacities are obtained. The ratio of these two discharge capacities is calculated to obtain the rate performance retention rate. The above steps are repeated to obtain multiple rate performance retention rates, and the average of the multiple rate performance retention rates is taken as the final rate performance retention rate. By placing fully charged batteries separately in room temperature and low temperature environments and then discharging them to the cutoff voltage at the same rate, the discharge capacity of each battery is recorded. The ratio of the two discharge capacities is calculated to obtain the low temperature capacity retention rate. The above steps are repeated to obtain multiple low temperature capacity retention rates, and then the average of the multiple low temperature capacity retention rates is taken as the final low temperature capacity retention rate.

9. A power battery performance evaluation system, characterized in that, It includes: The first module is used to acquire multiple test sample data; each test sample data includes the cycle life decay rate, charge and discharge efficiency, rate performance retention rate and low temperature capacity retention rate of the power battery under test; The second module is used to perform dimensional processing on each indicator of each test sample data, and then perform standardization processing to obtain a dataset with unified standards. Calculate the information entropy of each indicator for each test sample in the dataset, and calculate the corresponding weight based on the information entropy of each indicator; The third module is used to sum the index values ​​of each test sample data in the dataset with their corresponding weights to obtain a comprehensive evaluation value. The fourth module is used to determine the performance level of the power battery based on the comprehensive evaluation value and according to the preset performance level classification rules.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a power battery performance evaluation program, wherein when the power battery performance evaluation program is executed by a processor, it implements the steps of the power battery performance evaluation method as described in any one of claims 1 to 8.

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