A battery pack life evaluation method and computer device

Abstract

The application relates to the technical field of battery life evaluation, and discloses a battery pack life evaluation method and computer equipment, which acquires state parameters of each battery monomer in a battery pack, including temperature values and capacity values, then calculates a life range value of the battery pack based on the maximum temperature value and the minimum capacity value, and compares the life range value with a preset value, so that whether the battery pack meets the life requirement can be accurately judged. The method not only considers the inconsistency inside the battery pack, avoids the evaluation error caused by simply regarding the battery pack as a whole, improves the evaluation accuracy, but also avoids the high cost and resource consumption caused by directly performing cycle and storage tests on the battery pack, significantly reduces the test cost and complexity, and has important practical significance for battery research and application.

Description

Technical Field

[0001] This invention relates to the field of battery life assessment technology, and in particular to a battery pack life assessment method and computer equipment. Background Technology

[0002] Lithium-ion batteries are attracting increasing attention due to their superior performance. While safety and applicability continue to improve, lifespan remains a core focus of research and practice, and is a top priority in current battery development. Specifically, the long lifespan of batteries is often accompanied by a performance feedback lag, which can last for months or even years. To address this challenge, the current mainstream approach is to conduct in-depth analysis of cycle and storage test data for individual batteries to construct empirical models for assessing the lifespan of individual batteries, thereby achieving effective evaluation of their lifespan.

[0003] However, it is worth noting that as a battery system composed of multiple cells, a battery pack inevitably exhibits inconsistencies in the performance of its individual cells. Furthermore, temperature conditions vary significantly across different locations within the battery pack. Ignoring these complexities and simply treating the battery pack as a single unit, directly applying empirical models for individual cell lifespan assessments to evaluate the entire battery pack's lifespan, will inevitably lead to significant errors in the assessment results.

[0004] Furthermore, battery packs are expensive to manufacture, and the corresponding testing equipment is also costly. In addition, battery packs consume a significant amount of electrical energy during charging and discharging. If a strategy of directly conducting cycle and storage tests on the battery packs is adopted to obtain actual cycle and storage test data to build an empirical model for predicting battery pack lifespan and thus assessing its lifespan, the cost would be very high, which is detrimental to cost control.

[0005] Therefore, it is particularly important to provide a high-precision, low-cost method for evaluating battery pack life. Summary of the Invention

[0006] This invention provides a battery pack life assessment method and computer equipment to improve the accuracy of battery pack life assessment while reducing assessment costs.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] In a first aspect, the present invention provides a method for evaluating the lifespan of a battery pack, the method comprising:

[0009] Obtain the state parameters of each individual battery cell in the battery pack; the state parameters include at least temperature and capacity values.

[0010] Based on the maximum temperature value, minimum capacity value, and life prediction model, the life range of the battery pack is calculated and determined. The maximum temperature value is the maximum value among the obtained temperature values, and the minimum capacity value is the minimum value among the obtained capacity values.

[0011] If 1- If the value is greater than or equal to the first preset value, then the lifespan requirement is met.

[0012] Furthermore, the battery pack life assessment method further includes:

[0013] Based on the minimum temperature value, maximum capacity value, and the lifespan prediction model, the optimal lifespan of the battery pack is calculated and determined. The minimum temperature value is the minimum value among the obtained temperature values, and the maximum capacity value is the maximum value among the obtained capacity values.

[0014] like - If the second preset value is not met, the battery pack does not meet the lifespan requirements and there is a safety risk.

[0015] Furthermore, the battery pack life assessment method further includes:

[0016] If 1- <First preset value, and 1- If the value is greater than or equal to the first preset value, then the battery pack meets the lifespan requirement, but there is a probability of failure.

[0017] If 1- If the value is less than the first preset value, then the battery pack does not meet the lifespan requirement and the performance of the battery pack is poor.

[0018] Furthermore, the battery pack life assessment method further includes:

[0019] Cycle life and calendar life tests are conducted using individual battery cells in the battery pack as target battery cells to establish cycle life and calendar life assessment models corresponding to the target battery cells.

[0020] Based on the cycle life assessment model and the calendar life assessment model, the life prediction model of the battery pack is calculated and determined.

[0021] Furthermore, in the battery pack life assessment method, the life range of the battery pack is calculated and determined based on the maximum temperature value, the minimum capacity value, and the life prediction model. The steps include:

[0022] The cycle life assessment model is corrected by adjusting the maximum value of the cycle decay coefficient to obtain the corrected cycle life assessment model.

[0023] The calendar lifetime assessment model is corrected by adjusting the maximum value of the calendar decay coefficient to obtain the corrected calendar lifetime assessment model.

[0024] Based on the modified cycle life assessment model and the modified calendar life assessment model, the modified life prediction model of the battery pack is calculated and determined.

[0025] Based on the maximum temperature value, minimum capacity value, and the modified life prediction model, the life range of the battery pack is calculated and determined. .

[0026] Furthermore, in the battery pack life assessment method, the optimal lifespan of the battery pack is calculated and determined based on the minimum temperature value, the maximum capacity value, and the lifespan prediction model. The steps include:

[0027] The cycle life assessment model is corrected by minimizing the cycle decay coefficient to obtain the corrected cycle life assessment model.

[0028] The calendar lifetime assessment model is corrected by minimizing the calendar decay coefficient to obtain the corrected calendar lifetime assessment model.

[0029] Based on the modified cycle life assessment model and the modified calendar life assessment model, the modified life prediction model of the battery pack is calculated and determined.

[0030] Based on the minimum temperature value, maximum capacity value, and the modified life prediction model, the optimal life value of the battery pack is calculated and determined. .

[0031] Furthermore, the battery pack life assessment method further includes:

[0032] The maximum and minimum values ​​of the cyclic decay coefficient are obtained as follows: and the maximum / minimum value of the storage decay coefficient :

[0033] Using individual battery cells in a battery pack as target battery cells, and conducting cycle life and calendar life tests on multiple target battery cells under the same operating conditions, multiple cycle life evaluation models and multiple calendar life evaluation models are established for the multiple target battery cells, thereby determining multiple cycle degradation coefficients for the multiple target battery cells. and multiple storage decay coefficients ;

[0034] Based on the multiple cyclic decay coefficients and multiple storage decay coefficients Determine the maximum or minimum value of the cyclic decay coefficient. and the maximum / minimum value of the storage decay coefficient .

[0035] Furthermore, in the battery pack life assessment method, the step of basing the assessment on multiple cycle degradation coefficients... and multiple storage decay coefficients Calculate and determine the maximum and minimum values ​​of the cyclic decay coefficient. and the maximum / minimum value of the storage decay coefficient The steps include:

[0036] Based on the multiple cyclic decay coefficients Calculate and determine multiple cyclic decay coefficients. mean and standard deviation ;

[0037] The extreme value of the cyclic decay coefficient is calculated using the following formula. :

[0038] ;in, These are multiple cyclic decay coefficients. The mean, For multiple cyclic decay coefficients The standard deviation of , where d is a constant, and d takes values ​​of 2.58-3;

[0039] Based on multiple storage attenuation coefficients Calculate and determine multiple storage attenuation coefficients mean and standard deviation ;

[0040] The maximum or minimum value of the storage decay coefficient is calculated using the following formula. :

[0041] ;in, These are multiple storage decay coefficients. The mean, For multiple storage decay coefficients standard deviation It is a constant. The value ranges from 2.58 to 3.

[0042] Furthermore, in the battery pack life assessment method, the cycle life assessment model is:

[0043] Among them, A cis the cycle degradation coefficient, m is a function related to the SOC range, temperature T, and rate C of the cycle, N is the number of cycles for the target battery cell, and n is the exponential constant of the number of cycles.

[0044] The calendar lifetime assessment model is as follows:

[0045] Among them, A s denoted as the storage decay coefficient, a is a function related to the state of charge (SOC) and temperature T, t is the storage time of the target battery cell, and b is an exponential constant of the storage time.

[0046] The battery pack lifespan model is as follows:

[0047] ;

[0048] The modified cycle life assessment model is as follows:

[0049] ;in, The maximum or minimum value of the cycle decay coefficient is given, where m is a function related to the SOC range, temperature T, and rate C of the cycle, N is the number of cycles for the target battery cell, and n is an exponential constant for the number of cycles.

[0050] The revised calendar lifetime assessment model is as follows:

[0051] ;in, , where a is the maximum or minimum value of the storage decay coefficient, a is a function related to the state of charge (SOC) and temperature T, t is the storage time of the target battery cell, and b is an exponential constant of the storage time.

[0052] The corrected battery pack life model is as follows:

[0053] .

[0054] In a second aspect, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the battery pack life assessment method provided in the first aspect above.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] This invention provides a battery pack life assessment method and computer device, which acquires the state parameters of each battery cell in the battery pack, including temperature and capacity values, and then calculates the battery pack life range based on the maximum temperature value and the minimum capacity value. The results are compared with preset values ​​to accurately determine whether the battery pack meets the lifespan requirements. This method not only considers the inconsistencies within the battery pack, avoiding the evaluation errors caused by simply treating the battery pack as a whole and improving the accuracy of the evaluation, but also avoids the high costs and resource consumption of directly conducting cycle and storage tests on the battery pack, significantly reducing testing costs and complexity. This has important practical significance for battery research and development and applications. Attached Figure Description

[0057] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 This is one of the flowcharts of a battery pack life assessment method provided in Embodiment 1 of the present invention;

[0059] Figure 2 This is a second schematic flowchart of a battery pack life assessment method provided in Embodiment 1 of the present invention;

[0060] Figure 3 This is the third flowchart of a battery pack life assessment method provided in Embodiment 1 of the present invention;

[0061] Figure 4 This is the fourth flowchart of a battery pack life assessment method provided in Embodiment 1 of the present invention;

[0062] Figure 5 This is provided in Embodiment 1 of the present invention. Figure 1 A further detailed flowchart of S102 in China;

[0063] Figure 6 This is provided in Embodiment 1 of the present invention. Figure 2 A further detailed flowchart of S104 in China;

[0064] Figure 7 This is the fifth flowchart of a battery pack life assessment method provided in Embodiment 1 of the present invention;

[0065] Figure 8 This is a schematic diagram of the structure of a computer device provided in Embodiment 2 of the present invention. Detailed Implementation

[0066] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0067] Example 1

[0068] Currently, the life assessment models for individual batteries are relatively mature and diverse, including cycle life assessment models and calendar life assessment models.

[0069] The calendar life of a single battery is related to factors such as the state of charge (SOC) stored in the battery, the storage temperature (T), and the storage time (t). The calendar life assessment model is as follows:

[0070] ;

[0071] in, , where is the capacity decay rate during storage, SOC is the ratio of the remaining capacity stored in the battery to the rated capacity, i.e., the state of charge, T is the battery storage temperature, a is a coefficient related to SOC and T, t is the battery storage time, and b is the exponential coefficient of storage time.

[0072] The cycle life of a single battery is related to factors such as the battery's state of charge (SOC) range, actual cycle temperature (T), cycle rate (C), and number of cycles (N). The cycle life assessment model is as follows:

[0073] ;

[0074] in, The capacity decay rate during the cycle is denoted by SOC, the SOC range of the battery cycle is denoted by T, the battery cycle temperature is denoted by N, the number of battery cycles is denoted by m, a coefficient related to the SOC range, T, and C, and n is the exponential coefficient of the number of cycles.

[0075] In actual use, the battery pack alternates between cycling and storage. For example, a day in a vehicle consists of a period of driving or charging, and a period of parking and resting. The driving and charging process is considered to be the battery pack cycling in the vehicle, while the parking and resting process is considered to be the battery pack storing in the vehicle.

[0076] Battery pack lifespan degradation This is a result of the coupling between cycling and storage. Since a battery pack consists of several individual cells, differences between cells lead to inconsistent degradation among them. Simply evaluating the battery pack's lifespan based on the coupling result of individual cell cycling and storage would be insufficient.

[0077] ;

[0078] Therefore, the lifespan of the battery pack will have a large error.

[0079] However, if we consider the impact of the differences in various influencing factors within the battery pack on lifespan, and evaluate the corresponding lifespan of individual cells under extremely poor and excellent conditions respectively, then the lifespan of each cell in the battery pack should be distributed within these two extreme cases. Moreover, according to the weakest link effect, the lifespan of a cell under extremely poor conditions is closer to the lifespan of the battery pack.

[0080] Therefore, embodiments of the present invention provide a battery pack life assessment method, which can be applied to various battery management systems, and is particularly suitable for electric vehicles, large-scale energy storage systems, and other applications requiring high reliability and long lifespan battery packs. This method allows for more effective monitoring and management of the battery pack's health status and prediction of its remaining lifespan, thereby providing a scientific basis for battery pack maintenance and replacement.

[0081] Please refer to Figure 1 The method specifically includes the following steps:

[0082] S101. Obtain the state parameters of each battery cell in the battery pack; the state parameters include at least temperature and capacity values.

[0083] It should be noted that this step is fundamental to the entire evaluation methodology. To accurately assess the lifespan of the battery pack, it is necessary to obtain the state parameters of each individual battery cell within the pack, including at least temperature and capacity values.

[0084] Temperature reflects the thermal state of a single battery cell under different operating conditions and has a significant impact on battery performance and lifespan.

[0085] Capacity directly reflects the ability of a single battery cell to store electrical energy and is one of the key indicators for assessing battery life. By obtaining these parameters, we can understand the actual operating status of each battery cell in the battery pack.

[0086] S102. Based on the maximum temperature value, minimum capacity value, and life prediction model, calculate and determine the life range of the battery pack. The maximum temperature value is the maximum value among the obtained temperature values, and the minimum capacity value is the minimum value among the obtained capacity values.

[0087] It should be noted that in this step, the maximum temperature value is first identified from the acquired temperature values, and the minimum capacity value is identified from the acquired capacity values. Then, using a pre-established lifespan prediction model, combined with the maximum temperature value and the minimum capacity value, calculations are performed to determine the battery pack's lifespan range. This lifespan range It reflects the range of lifespan fluctuations of the battery pack considering internal cell differences, and can more accurately reflect the overall lifespan of the battery pack.

[0088] The battery life prediction model is established based on extensive experimental data and theoretical analysis, comprehensively considering the impact of multiple factors such as temperature and capacity on battery life. During the calculation process, the maximum temperature and minimum capacity values ​​are substituted into the model, and calculations are performed using specific algorithms and formulas to obtain the final result. The value of .

[0089] S103, If 1- If the value is greater than or equal to the first preset value, then the lifespan requirement is met.

[0090] It should be noted that this step determines whether the battery pack meets the lifespan requirements. This is achieved by calculating 1- Compare with the first preset value; if 1- If the value is greater than or equal to the first preset value, it means that the battery pack meets the lifespan requirements; otherwise, it does not.

[0091] The first preset value is set in advance based on the actual application requirements and design standards of the battery pack. It reflects the expected lifespan of the battery pack in a specific application scenario.

[0092] This assessment method provides a clear basis for battery pack maintenance and replacement. If the battery pack meets the lifespan requirements, it can continue to be used normally; if not, timely maintenance measures should be taken or battery pack replacement should be considered to ensure stable system operation.

[0093] Understandably, based on this assessment, battery pack manufacturers or users can better plan their battery pack maintenance and replacement schedules. This helps avoid potential risks and costs resulting from sudden battery pack failure.

[0094] Please refer to Figure 2 In one embodiment of this example, in Figure 1 Building upon the existing methods, this approach further expands upon them by introducing new steps to more comprehensively evaluate the battery pack's lifespan and safety status. By comprehensively considering the battery pack's performance under different combinations of extreme parameters, it provides richer evidence for the management and maintenance of the battery pack. The method further includes the following steps:

[0095] S104. Based on the minimum temperature value, maximum capacity value, and the lifespan prediction model, calculate and determine the optimal lifespan value of the battery pack. The minimum temperature value is the minimum value among the obtained temperature values, and the maximum capacity value is the maximum value among the obtained capacity values.

[0096] It should be noted that the core of this step is to calculate and determine the optimal lifespan of the battery pack based on the minimum temperature value, the maximum capacity value, and a pre-set lifespan prediction model. The minimum temperature value is the smallest value selected from the temperature values ​​of each individual battery cell in the battery pack, and the maximum capacity value is the largest value selected from the capacity values ​​of each individual battery cell.

[0097] Excellent lifespan This represents the potential lifespan of the battery pack when the individual cells are at relatively optimal temperature and capacity. This is an ideal lifespan assessment, differing from previously calculated lifespan ranges. This contrasts together to outline the range of fluctuations in battery pack lifespan.

[0098] Similarly, the life prediction model is based on a large amount of experimental data and theoretical analysis, and it comprehensively considers the impact of multiple factors such as temperature and capacity on battery life. When the minimum temperature value and maximum capacity value are substituted into the model, the model will simulate the life performance of the battery pack under these relatively optimal conditions.

[0099] calculate The significance lies in the fact that it can serve as a reference upper limit, helping us understand the lifespan potential of a battery pack under ideal conditions. Meanwhile, with... By comparing them, we can more clearly see the impact of differences in individual battery cells within the battery pack on the overall lifespan. For example, if... and The significant difference indicates that the performance of individual battery cells within the battery pack varies considerably, which may lead to unstable lifespan of the battery pack during actual use.

[0100] S105, if - If the second preset value is not met, the battery pack does not meet the lifespan requirements and there is a safety risk.

[0101] It should be noted that the judgment condition for this step is if - If the second preset value is reached, it is determined that the battery pack does not meet the lifespan requirements and poses a safety risk.

[0102] The second preset value is a threshold that is pre-set based on factors such as the actual application scenario, safety standards, and design requirements of the battery pack.

[0103] when and When the difference exceeds this second preset value, it means that the performance differences between the individual battery cells in the battery pack are too large, causing the battery pack's lifespan to fluctuate beyond the acceptable range under extreme conditions (best and worst). In this case, the battery pack may experience some unpredictable problems during actual use, thus affecting its lifespan and potentially even posing safety risks.

[0104] Logically speaking, - This reflects the "elasticity" or "fluctuation range" of the battery pack's lifespan. If this difference is too large, it indicates that some individual battery cells within the battery pack have excessively high performance, while others have excessively low performance. This imbalance may lead to problems such as localized overheating, overcharging, or over-discharging during the charging and discharging process, thereby affecting the safety and lifespan of the entire battery pack.

[0105] For example, in electric vehicle applications, if there are significant performance differences in battery packs, the lower-performing battery cells may reach their lifespan limit first during high-speed driving or fast charging, resulting in a sharp drop in capacity or internal short circuits. This not only affects the vehicle's driving range but may also trigger serious safety issues such as battery thermal runaway. Therefore, when meeting the requirements... - When the second preset value condition is met, it is reasonable and necessary to determine that the battery pack does not meet the lifespan requirements and there is a safety risk, so that timely measures can be taken, such as equalization maintenance of the battery pack, replacement of some battery cells, or optimization of the battery management system, to ensure the safe and reliable operation of the battery pack.

[0106] In summary, this embodiment... Figure 1 These two additional steps, building upon the previous ones, further refine the battery pack life assessment method. This is achieved by calculating the optimal lifespan value. and the lifespan range By making comparisons, a more comprehensive and accurate assessment of the battery pack's lifespan and safety risks can be achieved. This method not only considers the differences between individual battery cells within the battery pack but also takes an extreme case perspective, providing a more scientific and reliable basis for the maintenance, replacement, and safe use of the battery pack. This helps improve the overall performance and safety of the battery pack and has significant practical implications for the stable operation of batteries in various application scenarios.

[0107] Please refer to Figure 3 In one embodiment of this example, in Figure 2Building upon this foundation, the method goes a step further by introducing new judgment criteria to more precisely classify the state of the battery pack, providing a more accurate basis for decision-making in battery pack management and maintenance. The method further includes the following steps:

[0108] S106, If 1- <First preset value, and 1- If the value is greater than or equal to the first preset value, then the battery pack meets the lifespan requirement, but there is a probability of failure.

[0109] It should be noted that the judgment condition for this step is if 1- <First preset value, and 1- If the value is greater than or equal to a first preset value, the battery pack is determined to meet the lifespan requirements, but there is a probability of failure. This first preset value is a key threshold pre-set based on factors such as the battery pack's application scenario, performance requirements, and industry experience.

[0110] 1- This reflects the battery pack's performance under poor operating conditions (corresponding to) The degree of deviation from the ideal lifespan. When 1- When the value is less than the first preset value, it indicates that under the worst-case scenario, the difference between the battery pack's lifespan and the ideal lifespan is within an acceptable small range. And 1- This reflects the battery pack's optimal operating condition (corresponding to) The degree of closeness to the ideal lifespan state. When 1- When the value is greater than or equal to the first preset value, it indicates that the battery pack is approaching its ideal lifespan under optimal conditions. Considering both conditions, the battery pack is deemed to meet the lifespan requirements. However, due to factors such as potential differences between individual battery cells within the pack, a certain probability of failure still exists.

[0111] Logically speaking, 1- Small size means that even under relatively poor operating conditions, the lifespan of the battery pack will decrease within a controllable range, and there will be no drastic reduction in lifespan. And 1- A large value indicates that the battery pack has a good lifespan under ideal conditions. However, since a battery pack is composed of multiple battery cells, there may be slight differences in performance between the cells. These differences may gradually accumulate during long-term use, causing some battery cells to fail prematurely, which in turn affects the performance of the entire battery pack. Therefore, there is a probability of failure.

[0112] For example, in a battery pack composed of multiple lithium-ion battery cells, although the overall lifespan of the pack meets the requirements under various operating conditions, individual battery cells may have slightly lower performance than others due to manufacturing processes, minor damage during use, or other reasons. During long-term charge-discharge cycles, these slightly less performing cells may experience capacity decay first, increasing the likelihood of pack failure. This assessment method helps in practical applications to more meticulously monitor and manage battery packs that meet basic lifespan requirements, preventing potential failures in advance.

[0113] S107, If 1- If the value is less than the first preset value, then the battery pack does not meet the lifespan requirement and the performance of the battery pack is poor.

[0114] It should be noted that the judgment condition for this step is if 1- If the value is less than the first preset value, it is determined that the battery pack does not meet the lifespan requirement and that the performance of the battery pack is poor. When 1- When the value is less than the first preset value, it means that even under optimal operating conditions, the battery pack's lifespan is significantly different from the ideal state and cannot meet the expected lifespan requirements.

[0115] This indicates that the overall performance of the battery pack is poor, and there may be a variety of problems, such as quality issues with the individual battery cells, design flaws in the battery pack, or improper operation during use, which prevent the battery pack from achieving its expected lifespan under ideal conditions.

[0116] Logically speaking, 1- This reflects the battery pack's lifespan potential under optimal conditions. If this value is too low, it indicates that the battery pack fundamentally lacks the ability to achieve its expected lifespan. For example, if the battery cells used in the battery pack are of poor quality, their internal chemicals are unstable, or the heat dissipation system is inadequately designed, the performance of the individual cells will rapidly decline even under the most suitable temperature and charge / discharge conditions, resulting in the overall battery pack's lifespan being far below the ideal level.

[0117] In practical applications, such poor-performing battery packs can cause numerous inconveniences for users. Taking electric vehicles as an example, if the battery pack does not meet lifespan requirements and has poor performance, the vehicle's driving range will be significantly shortened, requiring frequent charging. Moreover, the battery pack may experience severe capacity degradation after a short period of use, affecting the normal operation of the vehicle and even necessitating premature replacement, increasing operating costs. Therefore, timely identification of such poor-performing battery packs is of significant guiding importance for taking corresponding improvement measures, such as replacing individual battery cells, optimizing battery pack design, or adjusting usage strategies.

[0118] In summary, this embodiment...Figure 2 These two additional steps, building upon the previous ones, further refine the evaluation criteria for battery pack life and performance. Through 1- and 1- Comparison with the first preset value can not only accurately determine whether the battery pack meets the lifespan requirements, but also distinguish between situations where the requirements are met but there is a probability of failure, and situations where the requirements are not met at all and the performance is poor. This more detailed evaluation method helps to take differentiated management and maintenance measures for battery packs in different states, improve the efficiency and safety of battery pack use, extend their service life, and has important practical value for the reliable operation of batteries in various complex application scenarios.

[0119] Please refer to Figure 4 In one embodiment of this method, starting from a more fundamental level, the focus is on the individual battery cells within the battery pack. By conducting cycle life and calendar life tests on these cells, an evaluation model is constructed, thereby establishing a battery pack life prediction model. This provides a more scientific and comprehensive basis for more accurate prediction of battery pack life. The method also includes the following steps:

[0120] S201. Using individual battery cells in the battery pack as target battery cells, perform cycle life tests and calendar life tests to establish cycle life evaluation models and calendar life evaluation models corresponding to the target battery cells.

[0121] It's important to note that the core of this step is to use individual battery cells within the battery pack as the target cells for cycle life and calendar life testing. Cycle life testing primarily simulates the repeated charging and discharging of individual battery cells during actual use. By setting different charging and discharging currents, charging and discharging cutoff voltages, and the number of charge and discharge cycles, performance indicators such as capacity decay and internal resistance changes of individual battery cells are recorded at different cycle counts. For example, in electric vehicle applications, the battery charging and discharging process is simulated due to frequent start-stop, acceleration, and deceleration during daily driving, and the performance changes of individual battery cells after multiple cycles are observed.

[0122] Calendar life testing simulates the aging process of individual battery cells during storage. The battery cells are placed under specific environmental conditions, such as different temperatures and humidity levels, and the changes in performance indicators such as capacity retention and self-discharge rate over time are recorded. For example, it simulates the performance degradation of batteries when stored in a warehouse or parked in a vehicle for extended periods. Through these two types of testing, a large amount of data is collected on how the performance of the target battery cell changes over time and with the number of cycle cycles.

[0123] Based on these test data, cycle life assessment models and calendar life assessment models for the corresponding target battery cells are established. The cycle life assessment model can be a mathematical function or algorithm, taking parameters such as the number of charge-discharge cycles and charge-discharge conditions as inputs, and outputting predicted values ​​of performance indicators such as the remaining capacity and internal resistance of the battery cell. The calendar life assessment model is also a mathematical expression, taking parameters such as storage time and storage environment conditions as inputs, and outputting predicted values ​​of the performance changes of the battery cell during storage.

[0124] Cycle life testing and calendar life testing are fundamental to understanding the performance degradation patterns of individual battery cells. Different types of battery cells (such as lithium-ion batteries and lead-acid batteries) exhibit different performance degradation patterns under different usage scenarios and environmental conditions. These two tests allow for accurate assessment of the performance changes of a target battery cell during actual use and storage.

[0125] The significance of establishing cycle life assessment models and calendar life assessment models lies in their ability to predict the future performance of individual battery cells based on specific charge / discharge conditions and usage environments. This is crucial for predicting the overall lifespan of the battery pack, as a battery pack is composed of multiple battery cells, and changes in the performance of each cell will affect the performance of the entire battery pack. For example, if a particular battery cell has a short cycle life, it may fail first during the use of the battery pack, leading to a decline in the overall performance of the battery pack. These two assessment models allow for the early detection of potential problems, providing a basis for the management and maintenance of the battery pack.

[0126] S202. Based on the cycle life assessment model and the calendar life assessment model, calculate and determine the life prediction model of the battery pack.

[0127] It should be noted that this step involves calculating and determining the battery pack's lifespan prediction model based on the established cycle life assessment model and calendar life assessment model. Specifically, the calculation method involves adding the two assessment models together; that is, simply adding the battery's capacity degradation rate during cycling to its capacity degradation rate during storage to obtain the battery's total degradation rate.

[0128] Developing a battery pack lifespan prediction model has several important implications. From a practical application perspective, it helps users accurately understand the battery pack's lifespan, rationally schedule battery pack replacements, and avoid production interruptions or safety accidents caused by sudden battery pack failures. For example, in energy storage power stations, accurately predicting battery pack lifespan allows for advance planning of battery replacement and upgrades, ensuring the stable operation of the power station.

[0129] From a battery manufacturer's perspective, life prediction models can be used to optimize battery pack design and manufacturing processes. By analyzing the impact of various parameters in the model on battery pack life, manufacturers can adjust the selection of individual battery cells, the structural design of the battery pack, and the manufacturing process to improve the overall life and reliability of the battery pack and enhance the product's market competitiveness.

[0130] Furthermore, battery pack life prediction models can provide crucial decision-making support for battery management systems (BMS). Based on the results of these models, the BMS can adjust the battery pack's charging and discharging strategies in real time, such as limiting charging and discharging current and adjusting charging and discharging cutoff voltages, to extend the battery pack's lifespan and improve its safety and performance.

[0131] In summary, this embodiment first conducts cycle life and calendar life tests on individual battery cells to establish corresponding evaluation models, and then calculates and determines the battery pack's life prediction model based on these models. This provides a more scientific, accurate, and comprehensive method for predicting battery pack life. This method not only considers the performance changes of individual battery cells during actual use and storage, but also comprehensively considers the impact of the overall battery pack structure and operating conditions on life. Compared with previous methods that only assessed life based on partial state parameters, this method can more accurately predict the battery pack's life, providing stronger support for battery pack management, maintenance, and optimization. It helps improve the efficiency and safety of battery packs and promotes the widespread application of battery technology in various fields.

[0132] Please refer to Figure 5 In one embodiment of this example, Figure 1 Step S102 can be further refined to include the following steps: The corresponding evaluation model is corrected by introducing a cycle degradation coefficient and a calendar degradation coefficient to improve the accuracy and reliability of battery pack life prediction.

[0133] S1021. The cycle life assessment model is corrected by the maximum value of the cycle decay coefficient to obtain the corrected cycle life assessment model.

[0134] It's important to note that the core of this step is to correct the cycle life assessment model by maximizing the cycle decay coefficient, thus obtaining the corrected cycle life assessment model. The cycle decay coefficient is a key parameter that comprehensively considers various factors that lead to capacity loss during battery charge-discharge cycles. These include the loss of electrochemical active materials; during repeated charge-discharge cycles, the active materials in the positive and negative electrode materials gradually decrease, affecting the battery's energy storage capacity; electrolyte decomposition; the electrolyte may decompose during electrochemical reactions, producing gases or other byproducts, altering the electrolyte's properties, and thus affecting battery performance; and SEI film growth; the SEI film is a passivation film formed during the first charge-discharge cycle, and it thickens continuously with increasing cycle count, increasing the battery's internal resistance and leading to capacity decay.

[0135] The maximum value of the cycle degradation coefficient reflects the scenario with the greatest impact on battery cycle life degradation under all possible cyclic conditions. By refining the cycle life assessment model using this maximum value, the model can more accurately reflect the most unfavorable life degradation scenarios in actual battery use. For example, for a single battery, its cycle degradation coefficient will vary under different charge / discharge currents, charge / discharge cut-off voltages, and other cyclic conditions. Identifying the largest cycle degradation coefficient and applying it to the cycle life assessment model adjusts the relationship between capacity decay and the number of cycles, allowing the revised model to more accurately predict battery life under complex cyclic conditions.

[0136] Revising the cycle life assessment model is of great significance. In practical applications, battery cycle conditions are often complex and varied, and a simple, unrevised model may not accurately predict battery life. By incorporating the maximum value of the cycle degradation coefficient for revision, the model can be made closer to reality, improving the accuracy of battery cycle life prediction. For example, in the field of electric vehicles, batteries need to undergo frequent charge-discharge cycles under different driving modes, including high-power charge-discharge situations such as rapid acceleration and hard braking. Using a revised cycle life assessment model, the battery's lifespan under these complex operating conditions can be predicted more accurately, providing a more reliable basis for vehicle use and maintenance.

[0137] S1022. The calendar lifetime assessment model is corrected by the maximum value of the calendar decay coefficient to obtain the corrected calendar lifetime assessment model.

[0138] It should be noted that this step involves correcting the calendar lifetime assessment model using the maximum value of the calendar decay coefficient, resulting in the corrected calendar lifetime assessment model. The calendar decay coefficient is primarily used to correct for capacity degradation during storage, taking into account the capacity loss caused by various factors during battery storage. These factors include self-discharge, which occurs even without charging or discharging, leading to a gradual decrease in capacity; and environmental temperature changes, which significantly impact battery storage performance. High temperatures accelerate internal chemical reactions, leading to faster capacity degradation, while low temperatures may reduce battery activity and hinder performance recovery.

[0139] The maximum value of the calendar degradation factor represents the scenario with the greatest impact on battery calendar life degradation under the most unfavorable storage conditions. For example, the calendar degradation factor of a battery will vary under different storage temperatures and humidity environments. The maximum calendar degradation factor is identified, and this value is used to correct the calendar life assessment model, adjusting the relationship between capacity degradation and storage time in the model. This corrected model can more accurately predict battery life under harsh storage conditions.

[0140] Revising calendar lifetime assessment models is also of significant value. During storage, battery performance gradually changes over time, and different storage environments significantly impact these changes. Using an unrevised model for prediction may lead to inaccurate estimates of battery storage life. By incorporating the maximum value of the calendar degradation coefficient for revision, the model can better reflect the actual battery life degradation during storage. For example, in energy storage power plants, batteries may be stored for extended periods under specific environmental conditions. Using a revised calendar lifetime assessment model allows for more accurate planning of battery storage time and replacement cycles, improving the reliability and economics of the energy storage system.

[0141] S1023. Based on the modified cycle life assessment model and the modified calendar life assessment model, calculate and determine the modified life prediction model of the battery pack.

[0142] It should be noted that this step calculates and determines the modified life prediction model for the battery pack based on the modified cycle life assessment model and the modified calendar life assessment model. By fully considering the interaction of different aging processes, the modified life prediction model can more accurately assess the battery pack life, improving the accuracy and reliability of the assessment results.

[0143] S1024. Based on the maximum temperature value, minimum capacity value, and the modified life prediction model, calculate and determine the life range of the battery pack. .

[0144] It should be noted that this step calculates and determines the battery pack's lifespan range based on the maximum temperature value, minimum capacity value, and the corrected lifespan prediction model. The maximum temperature value reflects the highest temperature reached by a single battery cell within the battery pack during use. Temperature significantly impacts battery performance and lifespan; high temperatures accelerate internal chemical reactions, leading to faster capacity decay and increased internal resistance. The minimum capacity value represents the lowest capacity of a single battery cell within the battery pack; excessively low capacity will affect the normal operation of the battery pack.

[0145] Calculating lifetime range At this time, the maximum temperature and minimum capacity values ​​are input as important parameters into the corrected life prediction model. The model, based on these parameters and previous corrections to cycle and calendar life, comprehensively considers the impact of temperature and capacity on battery pack life, and calculates the life difference of the battery pack under different conditions, i.e., the life range. For example, when the battery pack is at its maximum temperature and a single battery cell reaches its minimum capacity, the model predicts the difference between the battery pack's lifespan at this point and its lifespan under ideal conditions. This difference is the lifespan range. .

[0146] In summary, this embodiment modifies the cycle life assessment model and the calendar life assessment model by introducing a cycle decay coefficient and a calendar decay coefficient in step S102, thereby obtaining a modified battery pack life prediction model, and calculating and determining the life range value. This method offers significant advantages. It fully considers the different aging factors of batteries during actual use and storage, as well as their interactions. Compared to the method of simply adding capacity degradation rates, it can more accurately assess the lifespan degradation of battery packs under different operating conditions. By correcting the model and calculating the lifespan range, it provides more accurate data support for the management, maintenance, and optimization of battery packs, helping to improve the efficiency and safety of battery packs and promoting the widespread application of battery technology in various fields.

[0147] Please refer to Figure 6 In one embodiment of this example, Figure 2 Step S104 can be further refined to include the following steps: by introducing a cycle decay coefficient and a calendar decay coefficient to correct the corresponding evaluation model, the aim is to significantly improve the accuracy and reliability of battery pack life prediction, and provide a solid basis for the scientific management and rational use of battery packs:

[0148] S1041. The cycle life assessment model is corrected by the minimum value of the cycle decay coefficient to obtain the corrected cycle life assessment model.

[0149] It should be noted that the core of this step is to use the minimum value of the cycle decay coefficient to correct the cycle life assessment model, thereby obtaining the corrected cycle life assessment model.

[0150] The minimum cycle degradation coefficient represents the condition that minimizes the impact on battery cycle life degradation under all possible cycling conditions. Using this minimum value to refine the cycle life assessment model adjusts the relationship between capacity decay and cycle count, allowing the model to more accurately reflect battery life degradation under relatively mild cycling conditions. For example, the cycle degradation coefficient of a battery will vary under different charge / discharge currents and charge / discharge cutoff voltages. Identifying the minimum cycle degradation coefficient and applying it to the cycle life assessment model can optimize the model's prediction of battery life under ideal cycling conditions.

[0151] S1042. The calendar lifetime assessment model is corrected by using the minimum value of the calendar decay coefficient to obtain the corrected calendar lifetime assessment model.

[0152] It should be noted that this step involves correcting the calendar lifetime assessment model by finding the minimum value of the calendar decay coefficient, resulting in the corrected calendar lifetime assessment model.

[0153] The minimum calendar degradation factor represents the condition under ideal storage conditions where the impact on battery calendar life degradation is minimized. For example, the calendar degradation factor of a battery will vary under different storage temperatures and humidity environments. Finding the minimum calendar degradation factor allows us to refine the calendar life assessment model, adjusting the relationship between capacity degradation and storage time. This refinement enables the model to more accurately predict battery life under optimal storage conditions.

[0154] S1043. Based on the modified cycle life assessment model and the modified calendar life assessment model, calculate and determine the modified life prediction model of the battery pack.

[0155] It should be noted that this step calculates and determines the modified life prediction model for the battery pack based on the modified cycle life assessment model and the modified calendar life assessment model. By fully considering the interaction of different aging processes, the modified life prediction model can more accurately assess the battery pack life, improving the accuracy and reliability of the assessment results.

[0156] S1044. Based on the minimum temperature value, maximum capacity value, and the modified life prediction model, calculate and determine the optimal life value of the battery pack. .

[0157] It should be noted that this step calculates and determines the optimal lifespan of the battery pack based on the minimum temperature value, maximum capacity value, and a modified lifespan prediction model. The minimum temperature value reflects the lowest temperature at which individual battery cells exist during battery pack use. Battery activity decreases at low temperatures, but the damage to the battery may be relatively minor. The maximum capacity value represents the highest capacity of the individual battery cells in the battery pack; a higher capacity indicates better battery performance.

[0158] When calculating the optimal lifespan, the minimum temperature and maximum capacity values ​​are input as important parameters into the corrected lifespan prediction model. The model, based on these parameters and previous corrections to cycle and calendar lifespans, comprehensively considers the beneficial effects of temperature and capacity on battery pack lifespan, and calculates the battery pack's lifespan under ideal conditions, i.e., the optimal lifespan. For example, when the battery pack is at its minimum temperature and all individual cells have reached their maximum capacity, the model predicts the battery pack's lifespan at this point; this lifespan value is the optimal lifespan. .

[0159] In summary, this embodiment modifies the cycle life assessment model and calendar life assessment model in step S104 by introducing the minimum values ​​of the cycle decay coefficient and calendar decay coefficient, thereby obtaining a modified battery pack life prediction model and calculating and determining the optimal life value, which has significant advantages. This method fully considers the ideal cycling and storage conditions that batteries may encounter in actual use, as well as the beneficial effects of temperature and capacity on battery pack life. Compared with traditional methods, it can more comprehensively and accurately assess the life of the battery pack under ideal conditions. By modifying the model and calculating the optimal life value, it provides more accurate data support for the design, production, use, and management of battery packs, which helps to improve the overall performance and utilization efficiency of battery packs and promotes the further development of battery technology in various fields.

[0160] In the field of battery pack life prediction, accurately obtaining the maximum and minimum values ​​of the cycle degradation coefficient and the storage degradation coefficient is crucial for improving prediction accuracy. Please refer to... Figure 7 In one embodiment of this invention, the method further includes the following steps, which aim to accurately obtain the maximum and minimum values ​​of the cycle degradation coefficient and the storage degradation coefficient through specific steps, providing key parameter support for the subsequent correction of the battery pack life assessment model and more accurate life prediction, thereby improving the scientificity and effectiveness of battery pack management:

[0161] S301. The maximum and minimum values ​​of the cyclic decay coefficient are obtained by the following method. and the maximum / minimum value of the storage decay coefficient :

[0162] Using individual battery cells in a battery pack as target battery cells, and conducting cycle life and calendar life tests on multiple target battery cells under the same operating conditions, multiple cycle life evaluation models and multiple calendar life evaluation models are established for the multiple target battery cells, thereby determining multiple cycle degradation coefficients for the multiple target battery cells. and multiple storage decay coefficients The number of target battery cells can be 10, 20, 30, etc., and can be configured as needed; the number of target battery cells in cycle life test and calendar life test can be the same or different, and can also be configured as needed.

[0163] It should be noted that the target battery cell is the individual cell within the battery pack. This is because a battery pack is composed of numerous battery cells, and its overall performance and lifespan depend on the performance of each individual cell. The characteristics of a single battery cell can reflect the microscopic state of the battery pack. By studying the target battery cell, we can gain a deeper understanding of the internal working mechanism and aging process of the battery pack.

[0164] For example, a battery pack for an electric vehicle contains hundreds of lithium-ion battery cells. Performance differences among these cells can lead to imbalances in the overall performance of the battery pack. Selecting a target battery cell for study can reveal potential problems during cycling and storage, such as capacity decay and increased internal resistance, providing fundamental data for subsequent analysis.

[0165] Multiple target battery cells are subjected to cycle life and calendar life tests under identical operating conditions. Using the same conditions eliminates the interference of different environmental conditions and usage patterns on the test results, ensuring the comparability and accuracy of the test data.

[0166] In cycle life testing, the same parameters are set for charge / discharge current, charge / discharge cutoff voltage, and number of cycles. For example, for a single battery cell, the charge / discharge current is set to 1C (i.e., 1-hour rate current), and the charge / discharge cutoff voltages are 4.2V and 2.75V, respectively, for 500 cycle tests. This method allows observation of the capacity decay of the battery cell during repeated charge / discharge processes, providing data for establishing a cycle life assessment model.

[0167] Calendar life test: The calendar life test simulates the performance changes of individual battery cells during storage. Identical storage conditions such as temperature, humidity, and storage time are set. For example, battery cells are stored in an environment of 25°C and 50% relative humidity for 2-3 weeks. Indicators such as capacity retention and self-discharge rate of the battery cells during storage are recorded to establish a calendar life assessment model.

[0168] Based on the established cycle life assessment model and calendar life assessment model, multiple cycle degradation coefficients and multiple storage degradation coefficients are determined for multiple target battery cells. The cycle degradation coefficient can be determined by the relationship between the number of cycles and capacity decay in the model. The storage degradation coefficient can be determined by the relationship between storage time and capacity decay in the calendar life assessment model.

[0169] To further improve accuracy and ensure the randomness and representativeness of the samples, the target battery cells are randomly selected from the battery cells in the battery pack. This ensures that the samples can reflect the overall degradation characteristics of the batch of cells and avoids statistical distortion caused by "selecting cells with performance deviations". Specifically, the samples can be randomly selected from the same batch of battery cells in the battery pack.

[0170] S302, Based on the plurality of said cyclic attenuation coefficients and multiple storage decay coefficients Determine the maximum or minimum value of the cyclic decay coefficient. and the maximum / minimum value of the storage decay coefficient .

[0171] It's important to note that the extreme values ​​of the cycle degradation coefficient reflect the extreme conditions of capacity decay in a single battery cell during cycle use. The maximum value represents the condition with the most severe cycle degradation, while the minimum value represents the condition with the least cycle degradation. Understanding these extreme values ​​helps in a more comprehensive assessment of the battery pack's lifespan under different cycle conditions.

[0172] The significance of the maximum and minimum values ​​of the storage degradation coefficient: The maximum and minimum values ​​of the storage degradation coefficient reflect the extreme conditions of capacity degradation of a single battery cell during storage. The maximum value represents the environmental condition with the most severe storage degradation, and the minimum value represents the environmental condition with the least storage degradation. This is very important for battery storage and management.

[0173] In summary, this embodiment, through steps S301 and S302, takes the individual battery cells in the battery pack as the research object, conducts cycle life tests and calendar life tests under the same operating conditions, establishes an evaluation model, and determines the degradation coefficient, thereby obtaining the maximum and minimum values ​​of the cycle degradation coefficient and the storage degradation coefficient. This method has significant advantages. On the one hand, testing under the same operating conditions ensures the accuracy and comparability of the data, and can truly reflect the performance changes of individual battery cells under different usage scenarios. On the other hand, by establishing multiple evaluation models and determining multiple degradation coefficients, the individual differences of individual battery cells are fully considered, and the obtained maximum and minimum values ​​of the degradation coefficients are more representative and reliable. These maximum and minimum values ​​provide accurate parameters for the subsequent correction of the battery pack life prediction model, which helps to improve the accuracy of battery pack life prediction and provides strong support for the scientific management and rational use of battery packs.

[0174] In one embodiment of this example, step S302 can be further refined to include the following steps:

[0175] S3021, Based on the multiple described cyclic attenuation coefficients Calculate and determine multiple cyclic decay coefficients. mean and standard deviation .

[0176] It should be noted that the average cycle degradation coefficient of multiple target battery cells is calculated. This allows us to obtain the average degradation level of the entire battery pack under cyclic operating conditions, reflecting the central tendency of group performance; standard deviation This quantifies the dispersion of the decay coefficient between individuals, revealing the consistency differences in the performance of individual battery cells.

[0177] S3022. Calculate and determine the maximum or minimum value of the cyclic decay coefficient according to the following formula. :

[0178] ;in, These are multiple cyclic decay coefficients. The mean, For multiple cyclic decay coefficients The standard deviation is given by d, which is a constant and takes values ​​of 2.58-3.

[0179] S3023, Based on the plurality of said storage attenuation coefficients Calculate and determine multiple storage attenuation coefficients mean and standard deviation .

[0180] It should be noted that the average storage degradation coefficient of multiple target battery cells is calculated. This allows us to obtain the average degradation level of the entire battery pack under storage conditions, reflecting the central tendency of group performance; standard deviation This quantifies the dispersion of the decay coefficient between individuals, revealing the consistency differences in the performance of individual battery cells.

[0181] S3024. Calculate and determine the maximum and minimum values ​​of the storage attenuation coefficient according to the following formula. :

[0182] ;in, These are multiple storage decay coefficients. The mean, For multiple storage decay coefficients standard deviation It is a constant. The value ranges from 2.58 to 3.

[0183] In this regard, considering that the number of samples meets the statistical significance, the number of target battery cells can be greater than or equal to 20 to ensure that the calculation results of the mean and standard deviation are reliable, and thus the derived maximum and minimum values ​​of the attenuation coefficient can truly cover the extreme cases of the cells in the battery pack.

[0184] It should be noted that a battery pack is composed of multiple individual battery cells, and even within the same batch, the degradation coefficients of individual cells can vary. Following the steps described above, by calculating the maximum and minimum values ​​of the degradation coefficients of multiple individual cells, we can cover as many extreme degradation scenarios as possible, avoiding evaluation bias caused by using average degradation coefficients and making the results more closely reflect the actual lifespan of the battery pack. Simultaneously, by testing individual cells and solving for the maximum and minimum values ​​of the degradation coefficients, the battery pack lifespan can be deduced based on individual cell data, significantly reducing the number and time required for overall pack testing, substantially lowering R&D and evaluation costs. Lifespan degradation issues can be identified in advance during the design phase, avoiding the problem of discovering substandard lifespan after the entire pack has been manufactured, leading to design rework, delays, and reduced prototype quantities, saving overall testing time and costs.

[0185] Furthermore, by statistically quantifying the extreme values ​​(including maximum and minimum values) of the attenuation coefficient, in the process of predicting the overall lifespan of the battery pack by testing the lifespan of individual battery cells, not only can the overall pattern be derived using a small number of individual battery cell test data, which can significantly reduce the number of individual battery cell tests, avoid comprehensive coverage testing, greatly reduce the test sample size and number of tests, and improve testing efficiency, but also ensure the accuracy of the input parameters of the battery pack lifespan prediction model, ultimately improving the accuracy and reliability of lifespan assessment and supporting the scientific management and optimized design of the battery pack.

[0186] In one embodiment of this invention, the cycle life assessment model is:

[0187] Among them, A cLet be the cycle degradation coefficient, m be a function related to the SOC range, temperature T, and rate capability C, N be the number of cycles for the target battery cell, and n be an exponential constant of the number of cycles. The SOC range for each cycle is the maximum value within that range, ensuring the model better reflects real-world testing scenarios. That is, even if the cycle range is the same in each test cycle, the maximum SOC value within that range may differ. Incorporating the maximum SOC value into the model better reflects actual testing conditions, addresses the discrepancy between different upper limits within the same range, and improves model accuracy. For example, if the cycle range for test 1 is 5%-95%, and the cycle range for test 2 is 10%-100%, then 95% is used as the parameter value in test 1, and 100% is used as the parameter value in test 2.

[0188] The calendar lifetime assessment model is as follows:

[0189] Among them, A s denoted as the storage decay coefficient, where a is a function related to the state of charge (SOC) and temperature T, t is the storage time of the target battery cell, and b is an exponential constant of the storage time.

[0190] The battery pack lifespan model is as follows:

[0191] ;

[0192] The modified cycle life assessment model is as follows:

[0193] ;in, The maximum or minimum value of the cycle decay coefficient is given, where m is a function related to the SOC range, temperature T, and rate C of the cycle, N is the number of cycles for the target battery cell, and n is an exponential constant for the number of cycles.

[0194] The revised calendar lifetime assessment model is as follows:

[0195] ;in, , where a is the maximum or minimum value of the storage decay coefficient, a is a function related to the state of charge (SOC) and temperature T, t is the storage time of the target battery cell, and b is an exponential constant of the storage time.

[0196] The corrected battery pack life model is as follows:

[0197] .

[0198] Although this application uses terms such as battery pack, lifespan, and model frequently, the possibility of using other terms is not excluded. These terms are used merely for the convenience of describing and explaining the essence of the invention; interpreting them as any additional limitation would contradict the spirit of the invention.

[0199] This invention provides a method for evaluating the lifespan of a battery pack. This method obtains the state parameters of each individual battery cell in the battery pack, including temperature and capacity values, and then calculates the lifespan range of the battery pack based on the maximum temperature and minimum capacity values. The results are compared with preset values ​​to accurately determine whether the battery pack meets the lifespan requirements. This method not only considers the inconsistencies within the battery pack, avoiding the evaluation errors caused by simply treating the battery pack as a whole and improving the accuracy of the evaluation, but also avoids the high costs and resource consumption of directly conducting cycle and storage tests on the battery pack, significantly reducing testing costs and complexity. This has important practical significance for battery research and development and applications.

[0200] Example 2

[0201] Figure 8 This is a schematic diagram of the structure of a computer device provided in Embodiment 2 of the present invention. Figure 8 A block diagram of an exemplary computer device 12 suitable for implementing embodiments of the present invention is shown. Figure 8 The computer device 12 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0202] like Figure 8 As shown, the computer device 12 is represented in the form of a general-purpose computing device. The components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0203] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0204] Computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0205] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 8 Not shown; usually referred to as a "hard drive"). Although Figure 8 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0206] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of the present invention.

[0207] Computer device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with the computer device 12, and / or with any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 22. Furthermore, computer device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with other modules of computer device 12 via bus 18. It should be understood that, although... Figure 8 As not shown, it can be used in conjunction with computer device 12 with other hardware and / or software modules, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0208] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing the battery pack life assessment method provided in the embodiments of the present invention.

[0209] Finally, it should be noted that although the above embodiments have been described in the text and drawings of this application, this should not limit the scope of patent protection of this application. Any technical solutions that are based on the essential concept of this application and utilize the content described in the text and drawings of this application, resulting in equivalent structural or procedural substitutions or modifications, as well as the direct or indirect application of the technical solutions of the above embodiments to other related technical fields, are all included within the scope of patent protection of this application.

Claims

1. A method for evaluating the lifespan of a battery pack, characterized in that, The method includes: Obtain the state parameters of each individual battery cell in the battery pack; the state parameters include at least temperature and capacity values. Based on the maximum temperature value, minimum capacity value, and life prediction model, the life range of the battery pack is calculated and determined. The maximum temperature value is the maximum value among the obtained temperature values, and the minimum capacity value is the minimum value among the obtained capacity values. If 1- If the value is greater than or equal to the first preset value, then the lifespan requirement is met; The method further includes: Cycle life and calendar life tests are conducted using individual battery cells in the battery pack as target battery cells to establish cycle life and calendar life assessment models corresponding to the target battery cells. Based on the cycle life assessment model and the calendar life assessment model, the life prediction model of the battery pack is calculated and determined; The lifespan range of the battery pack is calculated and determined based on the maximum temperature value, minimum capacity value, and lifespan prediction model. The steps include: The cycle life assessment model is corrected by adjusting the maximum value of the cycle decay coefficient to obtain the corrected cycle life assessment model. The calendar lifetime assessment model is corrected by adjusting the maximum value of the calendar decay coefficient to obtain the corrected calendar lifetime assessment model. Based on the modified cycle life assessment model and the modified calendar life assessment model, the modified life prediction model of the battery pack is calculated and determined. Based on the maximum temperature value, minimum capacity value, and the modified life prediction model, the life range of the battery pack is calculated and determined. .

2. The battery pack life assessment method according to claim 1, characterized in that, The method further includes: Based on the minimum temperature value, maximum capacity value, and the lifespan prediction model, the optimal lifespan of the battery pack is calculated and determined. The minimum temperature value is the minimum value among the obtained temperature values, and the maximum capacity value is the maximum value among the obtained capacity values. like - If the second preset value is not met, the battery pack does not meet the lifespan requirements and there is a safety risk.

3. The battery pack life assessment method according to claim 2, characterized in that, The method further includes: If 1- <First preset value, and 1- If the value is greater than or equal to the first preset value, then the battery pack meets the lifespan requirement, but there is a probability of failure. If 1- If the value is less than the first preset value, then the battery pack does not meet the lifespan requirement and the performance of the battery pack is poor.

4. The battery pack life assessment method according to claim 2, characterized in that, The optimal lifespan of the battery pack is calculated and determined based on the minimum temperature value, the maximum capacity value, and the lifespan prediction model. The steps include: The cycle life assessment model is corrected by minimizing the cycle decay coefficient to obtain the corrected cycle life assessment model. The calendar lifetime assessment model is corrected by minimizing the calendar decay coefficient to obtain the corrected calendar lifetime assessment model. Based on the modified cycle life assessment model and the modified calendar life assessment model, the modified life prediction model of the battery pack is calculated and determined. Based on the minimum temperature value, maximum capacity value, and the modified life prediction model, the optimal life value of the battery pack is calculated and determined. .

5. The battery pack life assessment method according to claim 1 or 4, characterized in that, The method further includes: The maximum and minimum values ​​of the cyclic decay coefficient are obtained as follows: and the maximum and minimum values ​​of storage decay coefficient : Using individual battery cells in a battery pack as target battery cells, and conducting cycle life and calendar life tests on multiple target battery cells under the same operating conditions, multiple cycle life evaluation models and multiple calendar life evaluation models are established for the multiple target battery cells, thereby determining multiple cycle degradation coefficients for the multiple target battery cells. and multiple storage decay coefficients ; Based on the multiple cyclic decay coefficients and multiple storage decay coefficients Determine the maximum or minimum value of the cyclic decay coefficient. and the maximum / minimum value of the storage decay coefficient .

6. The battery pack life assessment method according to claim 5, characterized in that, The basis of the plurality of said cyclic decay coefficients and multiple storage decay coefficients Calculate and determine the maximum and minimum values ​​of the cyclic decay coefficient. and the maximum / minimum value of the storage decay coefficient The steps include: Based on the multiple cyclic decay coefficients Calculate and determine multiple cyclic decay coefficients. mean and standard deviation ; The extreme value of the cyclic decay coefficient is calculated using the following formula. : ;in, These are multiple cyclic decay coefficients. The mean, For multiple cyclic decay coefficients The standard deviation of , where d is a constant, and d takes values ​​of 2.58-3; Based on multiple storage attenuation coefficients Calculate and determine multiple storage attenuation coefficients mean and standard deviation ; The maximum or minimum value of the storage decay coefficient is calculated using the following formula. : ;in, These are multiple storage decay coefficients. The mean, For multiple storage decay coefficients standard deviation It is a constant. The value ranges from 2.58 to 3.

7. The battery pack life assessment method according to claim 5, characterized in that, The cycle life assessment model is as follows: Among them, A c is the cycle degradation coefficient, m is a function related to the SOC range, temperature T, and rate C of the cycle, N is the number of cycles for the target battery cell, and n is the exponential constant of the number of cycles. The calendar lifetime assessment model is as follows: Among them, A s denoted as the storage decay coefficient, a is a function related to the state of charge (SOC) and temperature T, t is the storage time of the target battery cell, and b is an exponential constant of the storage time. The battery pack lifespan model is as follows: ； The modified cycle life assessment model is as follows: ;in, The maximum or minimum value of the cycle decay coefficient is given, where m is a function related to the SOC range, temperature T, and rate C of the cycle, N is the number of cycles for the target battery cell, and n is an exponential constant for the number of cycles. The revised calendar lifetime assessment model is as follows: ;in, , where a is the maximum or minimum value of the storage decay coefficient, a is a function related to the state of charge (SOC) and temperature T, t is the storage time of the target battery cell, and b is an exponential constant of the storage time. The corrected battery pack life model is as follows: 。 8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the battery pack life assessment method as described in any one of claims 1-7.

Images