Battery module life estimation method, device, apparatus, and readable storage medium
By constructing a battery life estimation model based on the Wechsler distribution, the problem of inconsistency between individual cells in battery module life estimation is solved, achieving more efficient and accurate life assessment, which is applicable to the life assessment of battery modules.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHANGHAI RUIPU ENERGY CO LTD
- Filing Date
- 2023-07-25
- Publication Date
- 2026-08-04
AI Technical Summary
Existing battery module life estimation methods cannot accurately account for the inconsistencies of individual cells, resulting in poor accuracy of the estimation results.
A battery life estimation model is constructed. The value range of the target model parameters is determined by using the Wechsler distribution and experimental electrical performance data. Multiple life estimation models are formed by multiple random samplings. The models are then fitted by combining SOC, temperature and discharge capacity to generate a capacity decay curve to determine the life of the battery module.
It improves the accuracy of battery module life estimation, reduces estimation costs and time requirements, and is suitable for battery module life assessment in real-world complex environments.
Smart Images
Figure CN116953555B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of battery technology, and in particular to a method, apparatus, device and readable storage medium for estimating the lifespan of a battery module. Background Technology
[0002] As the core power component of electric vehicles, the power battery is formed by connecting multiple battery modules in series and parallel, while the battery module itself is formed by connecting multiple individual battery cells in the same series and parallel manner. Under certain environmental conditions, with repeated charging, discharging, and storage cycles, the lifespan of the battery module will decline, manifested in changes such as reduced capacity and increased internal resistance. Furthermore, due to the influence of manufacturing processes and actual usage environments, the degree of lifespan decline varies among individual cells within a battery module in practical applications. Therefore, it is typically difficult to directly calculate and evaluate the lifespan of a battery module formed by connecting multiple cells in series and parallel.
[0003] In related technologies, traditional mathematical models and equivalent circuit models for battery life estimation are only applicable to life estimation at the cell level. However, due to differences in physical environment and pressure, individual cells in a battery module often have inconsistencies with individual cells before assembly. Traditional battery life estimation models do not consider this inconsistency, resulting in poor accuracy of their estimated battery module life. Summary of the Invention
[0004] This application provides a method, apparatus, device, and readable storage medium for estimating the lifespan of a battery module, in order to solve the problem of inaccurate estimation of battery module lifespan in related technologies.
[0005] Firstly, a method for estimating the lifespan of a battery module is provided, including the following steps:
[0006] A battery life estimation model is constructed. Based on the Wechsler distribution and the experimental electrical performance data corresponding to the battery module under test, the range of values for the target model parameters in the battery life estimation model is determined. The experimental electrical performance data includes the state of charge (SOC), temperature, and discharge capacity.
[0007] Multiple random samples are taken from the value range to form multiple target battery life estimation models;
[0008] The battery module under test is estimated based on each target battery life estimation model and the Wechsler distribution to obtain multiple life estimation values;
[0009] The target lifetime estimate of the battery module under test is determined by using multiple lifetime estimates.
[0010] In some embodiments, the battery life estimation model is as follows:
[0011] Q(t) = c × e f(soc) ×d×e f(T) ×e k ×t z
[0012] In the formula, Q(t) represents the remaining battery capacity after decay over time t, c represents the first model parameter which is related to the state of charge, d represents the second model parameter which is related to temperature, k represents the third model parameter which is related to the discharge capacity, z represents the model parameter related to the battery chemistry system, f(SOC) represents the SOC Weyssee distribution probability value, and f(T) represents the temperature Weyssee distribution probability value.
[0013] In some embodiments, the target model parameters include a first model parameter c, a second model parameter d, and a third model parameter k.
[0014] In some embodiments, determining the range of values for the target model parameters in the battery lifetime estimation model based on the Wechsler distribution and experimental electrical performance data corresponding to the battery module under test includes:
[0015] Test the experimental battery module that is of the same type as the battery module under test to obtain the SOC, temperature and discharge capacity of each cell in the experimental battery module.
[0016] Based on the Weys distribution, the SOC, temperature and discharge capacity were fitted respectively to obtain the first Weys distribution function corresponding to SOC, the second Weys distribution function corresponding to temperature and the third Weys distribution function corresponding to discharge capacity.
[0017] The range of values for the first model parameter c is determined based on the first Weys distribution function and the mean SOC in the experimental battery module.
[0018] The range of values for the second model parameter d is determined based on the second Weys distribution function and the average temperature in the experimental battery module.
[0019] The range of values for the third model parameter k is determined based on the third Weys distribution function and the average discharge capacity of the experimental battery module.
[0020] In some embodiments, the battery module under test is estimated based on each target battery lifetime estimation model to obtain multiple lifetime estimates, including:
[0021] The battery module under test is tested to obtain the SOC test value and temperature test value of each cell in the battery module under test.
[0022] The SOC test value and temperature test value were fitted based on the Wechsler distribution to obtain the Wechsler distribution probability value of SOC and the Wechsler distribution probability value of temperature.
[0023] For each target battery life estimation model, the SOC Weyseau distribution probability value and the temperature Weyseau distribution probability value are input into the target battery life estimation model to obtain the capacity decay curve, and the life estimation value is determined based on the capacity decay curve and the preset capacity decay threshold.
[0024] Secondly, a battery module life estimation device is provided, comprising:
[0025] The first processing unit is used to construct a battery life estimation model. Based on the Wechsler distribution and the experimental electrical performance data corresponding to the battery module under test, it determines the value range of the target model parameters in the battery life estimation model. The experimental electrical performance data includes the state of charge (SOC), temperature, and discharge capacity.
[0026] The second processing unit is used to perform multiple random samplings from the value range to form multiple target battery life estimation models.
[0027] The lifespan estimation unit is used to estimate the lifespan of the battery module under test based on each target battery lifespan estimation model and the Wechsler distribution to obtain multiple lifespan estimates; and to determine the target lifespan estimate of the battery module under test using the multiple lifespan estimates.
[0028] In some embodiments, the battery life estimation model is as follows:
[0029] Q(t) = c × e f(soc) ×d×e f(T) ×e k ×t z
[0030] In the formula, Q(t) represents the remaining battery capacity after decay over time t, c represents the first model parameter which is related to the state of charge, d represents the second model parameter which is related to temperature, k represents the third model parameter which is related to the discharge capacity, z represents the model parameter related to the battery chemistry system, f(SOC) represents the SOC Weyssee distribution probability value, and f(T) represents the temperature Weyssee distribution probability value.
[0031] In some embodiments, the target model parameters include a first model parameter c, a second model parameter d, and a third model parameter k.
[0032] In some embodiments, the first processing unit is specifically used for:
[0033] Test the experimental battery module that is of the same type as the battery module under test to obtain the SOC, temperature and discharge capacity of each cell in the experimental battery module.
[0034] Based on the Weys distribution, the SOC, temperature and discharge capacity were fitted respectively to obtain the first Weys distribution function corresponding to SOC, the second Weys distribution function corresponding to temperature and the third Weys distribution function corresponding to discharge capacity.
[0035] The range of values for the first model parameter c is determined based on the first Weys distribution function and the mean SOC in the experimental battery module.
[0036] The range of values for the second model parameter d is determined based on the second Weys distribution function and the average temperature in the experimental battery module.
[0037] The range of values for the third model parameter k is determined based on the third Weys distribution function and the average discharge capacity of the experimental battery module.
[0038] In some embodiments, the lifetime estimation unit is specifically used for:
[0039] The battery module under test is tested to obtain the SOC test value and temperature test value of each cell in the battery module under test.
[0040] The SOC test value and temperature test value were fitted based on the Wechsler distribution to obtain the Wechsler distribution probability value of SOC and the Wechsler distribution probability value of temperature.
[0041] For each target battery life estimation model, the SOC Weyseau distribution probability value and the temperature Weyseau distribution probability value are input into the target battery life estimation model to obtain the capacity decay curve, and the life estimation value is determined based on the capacity decay curve and the preset capacity decay threshold.
[0042] Thirdly, a battery module life estimation device is provided, comprising: a memory and a processor, wherein the memory stores at least one instruction, and the at least one instruction is loaded and executed by the processor to implement the aforementioned battery module life estimation method.
[0043] Fourthly, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned battery module life estimation method.
[0044] This application provides a method, apparatus, device, and readable storage medium for estimating battery module lifespan. The method includes constructing a battery lifespan estimation model; determining the value range of target model parameters in the battery lifespan estimation model based on a Wechsler distribution and experimental electrical performance data corresponding to the battery module under test, wherein the experimental electrical performance data includes state of charge (SOC), temperature, and discharge capacity; performing multiple random samplings from the value range to form multiple target battery lifespan estimation models; estimating the battery module under test based on each target battery lifespan estimation model and the Wechsler distribution to obtain multiple lifespan estimates; and using the multiple lifespan estimates to determine the target lifespan estimate of the battery module under test. This application establishes a specific distribution of data based on the Wechsler distribution to characterize the impact of cell inconsistency on battery module performance, thereby effectively improving the accuracy of battery module lifespan estimation. Attached Figure Description
[0045] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0046] Figure 1 A flowchart illustrating a battery module life estimation method provided in this application embodiment;
[0047] Figure 2 A schematic diagram of the probability density function of the discharge capacity corresponding to the embodiment of this application;
[0048] Figure 3 This is a schematic diagram of the probability density function of the Wechsler distribution corresponding to temperature provided in an embodiment of this application;
[0049] Figure 4 This is a schematic diagram of the Wechsler probability density function corresponding to the SOC provided in the embodiments of this application;
[0050] Figure 5 A schematic diagram of the capacity decay curve of a battery module estimated based on the Monte Carlo method, provided for an embodiment of this application;
[0051] Figure 6 This is a schematic diagram of a battery module life estimation device provided in an embodiment of this application. Detailed Implementation
[0052] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0053] Traditional mathematical models and equivalent circuit models for battery life estimation are only applicable to cell-level life estimation, not to battery modules. Therefore, methods combining big data and neural network models (i.e., data-driven models) have been used to estimate the life of battery modules or battery systems. However, this requires acquiring a large amount of measured data, which is time-consuming and costly. This embodiment provides a method for estimating battery module life without requiring a data-driven model, effectively improving estimation efficiency and reducing costs.
[0054] This application provides a method, apparatus, device, and readable storage medium for estimating the lifespan of a battery module, which can solve the problem of inaccurate estimation of battery module lifespan in related technologies.
[0055] Figure 1 This application provides a method for estimating the lifespan of a battery module, comprising the following steps:
[0056] Step S10: Construct a battery life estimation model. Based on the Wechsler distribution and the experimental electrical performance data corresponding to the battery module under test, determine the value range of the target model parameters in the battery life estimation model. The experimental electrical performance data includes the state of charge (SOC), temperature, and discharge capacity. The battery life estimation model is as follows:
[0057] Q(t) = c × e f(soc) ×d×e f(T) ×e k ×t z
[0058] In the formula, Q(t) represents the remaining battery capacity after decay over time t, c represents the first model parameter related to the state of charge, d represents the second model parameter related to temperature, k represents the third model parameter related to the discharge capacity, z represents the model parameter related to the battery chemistry system, f(SOC) represents the SOC Weygd probability value, and f(T) represents the temperature Weygd probability value. The target model parameters include the first model parameter c, the second model parameter d, and the third model parameter k.
[0059] Specifically, this embodiment will first construct a model (i.e., a battery life estimation model) that can be used to estimate the lifespan of battery modules, battery systems, etc. The specific calculation formula of this model is as follows:
[0060] Q(t) = c × e f(soc) ×d×e f(T) ×e k ×t z
[0061] In the formula, Q(t) represents the remaining battery capacity after decay over time t; c represents the first model parameter, which is related to the state of charge; d represents the second model parameter, which is related to temperature; k represents the third model parameter, which is related to the discharge capacity; z represents the model parameter related to the battery chemistry system. The specific value of z can be determined according to the actual product characteristics. For example, the z value of the lithium iron phosphate / graphite system can usually be set to 0.4 to 0.5, and the z value of the ternary / graphite system can usually be set to 0.6 to 0.8; f(SOC) represents the SOC Weyspeare probability value, and f(T) represents the temperature Weyspeare probability value. Time t can be determined by t = N × 60 / C2 (unit: min), where N represents the number of cell cycles and C2 represents the discharge rate.
[0062] It should be noted that the first model parameter c, the second model parameter d, and the third model parameter k can be determined by the Wechsler distribution, so that there are only two independent variables f(SOC) and f(T) in the battery life estimation model. These two independent variables can be obtained by testing the battery module under test, and then Q(t) can be calculated.
[0063] It should be understood that battery modules of the same type often share similar characteristics, meaning their lifespans are typically the same. Therefore, in this embodiment, when it is determined that a battery module to be tested needs to have its lifespan estimated, multiple battery modules of the same type as the battery module to be tested will be acquired for experimental testing to obtain experimental electrical performance data, including SOC, temperature, and discharge capacity. The experimental electrical performance data will be processed according to normalization and regularization principles (such as uniformly converting the data to units like ampere-hours and percentages), and a distribution histogram of the electrical performance data can be plotted for intuitive visualization. Then, the experimental electrical performance data will be fitted and analyzed using the Weibull distribution (which is the theoretical basis for reliability analysis and lifespan verification, through which the distribution of wear-accumulated failure can be inferred), thereby obtaining the value range of the target model parameters, including the first model parameter c, the second model parameter d, and the third model parameter k, in the battery lifespan estimation model.
[0064] Furthermore, the determination of the value range of the target model parameters in the battery life estimation model based on the Wechsler distribution and the experimental electrical performance data corresponding to the battery module under test includes:
[0065] Test the experimental battery module that is of the same type as the battery module under test to obtain the SOC, temperature and discharge capacity of each cell in the experimental battery module.
[0066] Based on the Weys distribution, the SOC, temperature and discharge capacity were fitted respectively to obtain the first Weys distribution function corresponding to SOC, the second Weys distribution function corresponding to temperature and the third Weys distribution function corresponding to discharge capacity.
[0067] The range of values for the first model parameter c is determined based on the first Weys distribution function and the mean SOC in the experimental battery module.
[0068] The range of values for the second model parameter d is determined based on the second Weys distribution function and the average temperature in the experimental battery module.
[0069] The range of values for the third model parameter k is determined based on the third Weys distribution function and the average discharge capacity of the experimental battery module.
[0070] As an example, in this embodiment, multiple experimental battery modules of the same type as the battery module under test are placed in a testing machine, and tested under standard operating conditions or vehicle operating conditions to obtain the discharge capacity Q of each cell in each experimental battery module. real The experimental electrical performance data were obtained by calculating the discharge capacity dataset, temperature dataset, and SOC dataset based on the temperature T and the corresponding SOC at different times.
[0071] It should be noted that the standard operating conditions set in this embodiment can refer to "GB / T31484-2015 Requirements and Test Methods for Cycle Life of Power Batteries for Electric Vehicles". For example, under room temperature of 25℃±2℃, the experimental battery module is allowed to stand until the temperature of each cell is basically stable, and then discharged at a constant current of 1 / 3C (C represents the rated capacity of the battery) to the discharge cutoff voltage of the experimental battery module, and left to stand for 30 minutes; then charged at a constant current of 1 / 3C to the charging cutoff voltage of the experimental battery module, left to stand for 30 minutes, and then discharged at a constant current of 1 / 3C again. This cycle test is carried out until the usable discharge capacity of the experimental battery module decays to 80% of the initial capacity. The initial capacity of the experimental battery module is the average value of the discharge capacity from the 2nd to the 4th cycle. The vehicle operating condition test is generally derived by each battery manufacturer according to the requirements of the vehicle manufacturer or the NEDC / CLTC standard operating conditions, and will not be described in detail here. It can be selected according to the actual test requirements.
[0072] It should be understood that the USABC (United States Advanced Battery Association) defines the state of charge (SOC) of a battery as the ratio of its remaining capacity to its rated capacity under the same conditions at a given discharge rate.
[0073] SOC = (Q m -Q in ) / Q m
[0074] Q in =t∫(In)dt
[0075] In the formula, Q m Q represents the maximum discharge capacity of a battery when discharged at a constant current of 1 / 3C. in This represents the discharge capacity released by the battery under a constant current of 1 / 3C within time t; therefore, the discharge capacity Q of each cell obtained above can be used as a basis for calculation. real The SOC of the cell at the corresponding time can be calculated, and the OCV-SOC curve can be plotted for subsequent estimation and verification.
[0076] Then, the probability density function of the Weibull distribution was established using the Matlab platform. The experimental electrical performance data of the experimental battery modules were used as input, and the corresponding function expression was calculated using the embedded algorithms and toolboxes. Specifically, the Weibull distribution was used to calculate the probability density function for all discharge capacities Q corresponding to all the aforementioned experimental battery modules. real The parameters, temperature T, and SOC (i.e., the discharge capacity dataset, temperature dataset, and SOC dataset) are fitted separately to obtain the Weibull distribution function expressions corresponding to each parameter. The probability density function corresponding to the Weibull distribution is:
[0077]
[0078] In the formula, f represents the Wechsler probability distribution, x represents the random variable, a represents the size parameter, and b represents the shape parameter; in this embodiment, the discharge capacity Q in the discharge capacity dataset is respectively... real Substituting the SOC from the SOC dataset and the temperature T from the temperature dataset as x into the above function for fitting, we can obtain... Figures 2 to 4 The corresponding function curves allow us to obtain the size and shape parameters of the corresponding SOC, temperature, and discharge capacity, denoted as a1, b1, a2, b2, a3, and b3, respectively, thus yielding the Weibull distribution function expression. Specifically, the first Weibull distribution function expression corresponding to SOC is:
[0079]
[0080] The expression for the second Wechsler distribution function corresponding to temperature is:
[0081]
[0082] The expression for the third Weyslow distribution function corresponding to the discharge capacity is:
[0083]
[0084] Since c is a model parameter related to the state of charge, d is a model parameter related to temperature, and k is a model parameter related to discharge capacity, the SOC Weyghurst distribution probability value can be used as the c value, the discharge capacity Weyghurst distribution probability value as the k value, and the temperature Weyghurst distribution probability value as the d value. Therefore, in this embodiment, the specific values of the first model parameter c, the second model parameter d, and the third model parameter k, determined using the above Weyghurst distribution function, will be used. It should be noted that the values of c, d, and k can be determined based on the average value of experimental electrical performance data and using the above Weyghurst distribution function. Alternatively, the values of c, d, and k can be determined based on the mode, median, or other methods of the experimental electrical performance data. The specific implementation method can be determined according to actual needs and is not limited here.
[0085] Taking the determination of c, d, and k values using the mean method as an example: The mean SOC is obtained from the SOC dataset, the mean discharge capacity from the discharge capacity dataset, and the mean temperature from the temperature dataset. Then, the mean SOC is substituted into the first Wechsler distribution function expression to obtain f(SOC), which is used as the c value. Similarly, f(T) can be used as the d value, and f(Q) as the k value. real Let f(Q) be the value of k; then, based on the preset range, the ranges of c, d, and k are determined. For example, if the preset range is ±5, then the range of c is [f(SOC)-5, f(SOC)+5], similarly, the range of d is [f(T)-5, f(T)+5], and the range of k is [f(Q)-5, f(T)+5]. real )-5, f(Q real )+5).
[0086] Step S20: Perform multiple random samplings from the value range to form multiple target battery life estimation models.
[0087] Exemplary and understandable, the Monte Carlo method (also known as statistical simulation or statistical experiment) is a numerical simulation method that takes probabilistic phenomena as its research object. In this embodiment, the Monte Carlo method can be used to perform multiple random samplings on various value intervals to form multiple target battery life estimation models. For example, by performing three random samplings on each value interval to obtain c1, c2, c3, d1, d2, d3, k1, k2, and k3, a target battery life estimation model can be formed based on c1, d1, and k1. Similarly, a target battery life estimation model can be formed based on c2, d2, and k2, and a target battery life estimation model can be formed based on c3, d3, and k3.
[0088] It should be noted that, besides the Monte Carlo method, other random simulation methods with similar or identical properties can also be used to achieve random sampling. The specific method should be determined based on actual needs and is not limited here. Specifically, methods such as direct sampling and rejection sampling within the Monte Carlo method can be used to achieve random sampling across different value ranges.
[0089] Step S30: Estimate the battery module under test based on each target battery life estimation model and the Wechsler distribution to obtain multiple life estimation values.
[0090] As an example, in this embodiment, the battery module under test is also tested under set standard operating conditions or vehicle operating conditions to obtain the electrical performance data corresponding to the battery module under test. Since the specific testing methods and principles of the battery module under test are the same as those of the aforementioned experimental battery module, they will not be repeated here for the sake of simplicity. Then, the electrical performance data is fitted using a Wechsler distribution, and the fitting results are used as input to each target battery life estimation model to generate a set of capacity decay curves corresponding to the battery module under test. Then, based on each capacity decay curve, the life estimation value predicted by each target battery life estimation model can be obtained. It should be noted that this embodiment can use the plot function to visualize each capacity decay curve, so that users can intuitively understand the decay of the battery module under test.
[0091] Furthermore, the battery module under test is estimated based on each target battery lifetime estimation model to obtain multiple lifetime estimates, including:
[0092] The battery module under test is tested to obtain the SOC test value and temperature test value of each cell in the battery module under test.
[0093] The SOC test value and temperature test value were fitted based on the Wechsler distribution to obtain the Wechsler distribution probability value of SOC and the Wechsler distribution probability value of temperature.
[0094] For each target battery life estimation model, the SOC Weyseau distribution probability value and the temperature Weyseau distribution probability value are input into the target battery life estimation model to obtain the capacity decay curve, and the life estimation value is determined based on the capacity decay curve and the preset capacity decay threshold.
[0095] In this exemplary embodiment, after completing the testing of the battery module under test, corresponding SOC test value datasets and temperature test datasets are obtained. By fitting the SOC data in the SOC test value dataset and the temperature data in the temperature test dataset using a Weyssee distribution, the Weyssee distribution probability values f′(SOC) and f′(T) of the SOC and temperature corresponding to the battery module under test can be obtained. Then, f′(SOC) and f′(T) are substituted into each target battery life estimation model to generate... Figure 5 The diagram shows multiple capacity decay curves. In this embodiment, the capacity decay of the battery module under test is used as the indicator for determining the battery module's lifespan. Therefore, for each capacity decay curve, the preset capacity decay threshold can be set to 80% of the initial discharge capacity of the battery module under test. The capacity decay curve is calculated using a matrix algorithm. That is, when the available discharge capacity of the battery module under test decays to 80% of the initial discharge capacity of the battery module under test, the corresponding decay time is taken as the lifespan estimate value predicted by the target battery lifespan estimation model. Similarly, multiple lifespan estimates can be obtained through this method.
[0096] Step S40: Determine the target lifetime estimate of the battery module under test using multiple lifetime estimates.
[0097] In this exemplary embodiment, when multiple lifetime estimates are obtained from numerous lifetime calculations of the battery modules under test, the lifetime estimate with the highest frequency can be directly used as the target lifetime estimate for the battery module under test. Alternatively, the lifetime estimate at the median can be used, or the lifetime estimate at the mode can also be used. It should be noted that the specific method of using the lifetime estimate corresponding to the statistical value can be determined according to actual needs and is not limited here. Furthermore, this embodiment can visualize the multiple lifetime estimates using a cumulative probability distribution chart, allowing users to intuitively understand the lifetime distribution of the battery modules under test. Therefore, this embodiment establishes a specific data distribution based on the Wechsler distribution to characterize the impact of cell inconsistency on battery module performance, thereby effectively improving the accuracy of battery module lifetime estimation.
[0098] In summary, the battery module lifespan estimation method in this embodiment can not only quantitatively describe the impact of cell inconsistency on battery module lifespan, but also further reflect the different manifestations of this impact through discharge capacity, temperature, and state of charge parameters. Without increasing testing requirements, this lifespan estimation method establishes an intrinsic link between cell lifespan and battery module lifespan. It not only achieves a quantitative assessment of cell inconsistency but also improves the accuracy of battery module lifespan estimation, contributing to better practical applications of battery modules and making it more suitable for estimating battery module lifespan in complex real-world environments.
[0099] Furthermore, it is understood that battery modules of the same type often have the same characteristics, that is, their lifespan is usually the same. Therefore, in this embodiment, when estimating the lifespan of the battery module under test, only a small number of battery modules of the same type as the battery module under test will be tested. Then, based on the experimental test data and the constructed battery life estimation model, the lifespan can be estimated by performing Wechsler distribution processing, without the need to obtain a large amount of measured data, thereby effectively improving the estimation efficiency and reducing the cost.
[0100] This application also provides a battery module life estimation device, including:
[0101] The first processing unit is used to construct a battery life estimation model. Based on the Wechsler distribution and the experimental electrical performance data corresponding to the battery module under test, it determines the value range of the target model parameters in the battery life estimation model. The experimental electrical performance data includes the state of charge (SOC), temperature, and discharge capacity.
[0102] The second processing unit is used to perform multiple random samplings from the value range to form multiple target battery life estimation models.
[0103] The lifespan estimation unit is used to estimate the lifespan of the battery module under test based on each target battery lifespan estimation model and the Wechsler distribution to obtain multiple lifespan estimates; and to determine the target lifespan estimate of the battery module under test using the multiple lifespan estimates.
[0104] Furthermore, the battery life estimation model is as follows:
[0105] Q(t) = c × e f(soc) ×d×e f(T) ×e k ×t z
[0106] In the formula, Q(t) represents the remaining battery capacity after decay over time t, c represents the first model parameter which is related to the state of charge, d represents the second model parameter which is related to temperature, k represents the third model parameter which is related to the discharge capacity, z represents the model parameter related to the battery chemistry system, f(SOC) represents the SOC Weyssee distribution probability value, and f(T) represents the temperature Weyssee distribution probability value.
[0107] Furthermore, the target model parameters include a first model parameter c, a second model parameter d, and a third model parameter k.
[0108] Furthermore, the first processing unit is specifically used for:
[0109] Test the experimental battery module that is of the same type as the battery module under test to obtain the SOC, temperature and discharge capacity of each cell in the experimental battery module.
[0110] Based on the Weys distribution, the SOC, temperature and discharge capacity were fitted respectively to obtain the first Weys distribution function corresponding to SOC, the second Weys distribution function corresponding to temperature and the third Weys distribution function corresponding to discharge capacity.
[0111] The range of values for the first model parameter c is determined based on the first Weys distribution function and the mean SOC in the experimental battery module.
[0112] The range of values for the second model parameter d is determined based on the second Weys distribution function and the average temperature in the experimental battery module.
[0113] The range of values for the third model parameter k is determined based on the third Weys distribution function and the average discharge capacity of the experimental battery module.
[0114] Furthermore, the lifetime estimation unit is specifically used for:
[0115] The battery module under test is tested to obtain the SOC test value and temperature test value of each cell in the battery module under test.
[0116] The SOC test value and temperature test value were fitted based on the Wechsler distribution to obtain the Wechsler distribution probability value of SOC and the Wechsler distribution probability value of temperature.
[0117] For each target battery life estimation model, the SOC Weyseau distribution probability value and the temperature Weyseau distribution probability value are input into the target battery life estimation model to obtain the capacity decay curve, and the life estimation value is determined based on the capacity decay curve and the preset capacity decay threshold.
[0118] It should be noted that the step numbers in the embodiments of this application do not limit the order of operations in the technical solution of this application.
[0119] It should be noted that those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the device and each unit described above can be referred to the corresponding process in the aforementioned battery module life estimation method embodiment, and will not be repeated here.
[0120] The apparatus provided in the above embodiments can be implemented as a computer program, which can be used in, for example... Figure 6 The battery module life estimation device shown is running on the machine.
[0121] This application also provides a battery module life estimation device, including: a memory, a processor, and a network interface connected via a system bus. The memory stores at least one instruction, which is loaded and executed by the processor to implement all or part of the steps of the aforementioned battery module life estimation method.
[0122] The network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0123] A processor can be a CPU, or other general-purpose processors, DSPs (Digital Signal Processors), ASICs (Application Specific Integrated Circuits), FPGAs (Field Programmable Gate Arrays), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor can be a microprocessor, or any conventional processor. The processor is the control center of a computer device, connecting all parts of the computer device through various interfaces and lines.
[0124] Memory can be used to store computer programs and / or modules. The processor implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. Memory can mainly include a program storage area and a data storage area. The program storage area can store the operating system, at least one application program required for a function (such as video playback, image playback, etc.), etc.; the data storage area can store data created based on the use of the mobile phone (such as video data, image data, etc.). Furthermore, memory can include high-speed random access memory (RAM), and can also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, SMC (SmartMediaCard), SD (Secure Digital) cards, flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.
[0125] This application also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements all or part of the steps of the aforementioned battery module life estimation method.
[0126] The embodiments of this application can implement all or part of the aforementioned processes, or they can be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various methods described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, ROM (Read-Only memory), RAM (Random Access memory), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added to or subtracted according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.
[0127] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, servers, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.
[0128] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0129] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.
[0130] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.
Claims
1. A method for estimating the lifespan of a battery module, characterized in that, Includes the following steps: A battery life estimation model is constructed. Based on the Wechsler distribution and the experimental electrical performance data corresponding to the battery module under test, the range of values for the target model parameters in the battery life estimation model is determined. The experimental electrical performance data includes the state of charge (SOC), temperature, and discharge capacity. Multiple random samples are taken from the value range to form multiple target battery life estimation models; The battery module under test is estimated based on each target battery life estimation model and the Wechsler distribution to obtain multiple life estimation values; The target lifetime estimate of the battery module under test is determined by using multiple lifetime estimates.
2. The battery module life estimation method as described in claim 1, characterized in that, The battery life estimation model is as follows: Q(t)=c×e f(soc) ×d×e f(T) ×e k ×t z In the formula, Q(t) represents the remaining battery capacity after decay over time t, c represents the first model parameter which is related to the state of charge, d represents the second model parameter which is related to temperature, k represents the third model parameter which is related to the discharge capacity, z represents the model parameter related to the battery chemistry system, f(SOC) represents the SOC Weyssee distribution probability value, and f(T) represents the temperature Weyssee distribution probability value.
3. The battery module life estimation method as described in claim 2, characterized in that: The target model parameters include a first model parameter c, a second model parameter d, and a third model parameter k.
4. The battery module life estimation method as described in claim 3, characterized in that, The value range of the target model parameters in the battery life estimation model is determined based on the Wechsler distribution and the experimental electrical performance data corresponding to the battery module under test, including: Test the experimental battery module that is of the same type as the battery module under test to obtain the SOC, temperature and discharge capacity of each cell in the experimental battery module. Based on the Weys distribution, the SOC, temperature and discharge capacity were fitted respectively to obtain the first Weys distribution function corresponding to SOC, the second Weys distribution function corresponding to temperature and the third Weys distribution function corresponding to discharge capacity. The range of values for the first model parameter c is determined based on the first Weys distribution function and the mean SOC in the experimental battery module. The range of values for the second model parameter d is determined based on the second Weys distribution function and the average temperature in the experimental battery module. The range of values for the third model parameter k is determined based on the third Weys distribution function and the average discharge capacity of the experimental battery module.
5. The battery module life estimation method as described in claim 1, characterized in that, The battery module under test is estimated based on each target battery life estimation model to obtain multiple life estimation values, including: The battery module under test is tested to obtain the SOC test value and temperature test value of each cell in the battery module under test. The SOC test value and temperature test value were fitted based on the Wechsler distribution to obtain the Wechsler distribution probability value of SOC and the Wechsler distribution probability value of temperature. For each target battery life estimation model, the SOC Weyseau distribution probability value and the temperature Weyseau distribution probability value are input into the target battery life estimation model to obtain the capacity decay curve, and the life estimation value is determined based on the capacity decay curve and the preset capacity decay threshold.
6. A battery module life estimation device, characterized in that, include: The first processing unit is used to construct a battery life estimation model. Based on the Wechsler distribution and the experimental electrical performance data corresponding to the battery module under test, it determines the value range of the target model parameters in the battery life estimation model. The experimental electrical performance data includes the state of charge (SOC), temperature, and discharge capacity. The second processing unit is used to perform multiple random samplings from the value range to form multiple target battery life estimation models. The lifespan estimation unit is used to estimate the lifespan of the battery module under test based on each target battery lifespan estimation model and the Wechsler distribution to obtain multiple lifespan estimates; and to determine the target lifespan estimate of the battery module under test using the multiple lifespan estimates.
7. The battery module life estimation device as described in claim 6, characterized in that, The battery life estimation model is as follows: Q(t)=c×e f(soc) ×d×e f(T) ×e k ×t z In the formula, Q(t) represents the remaining battery capacity after decay over time t, c represents the first model parameter which is related to the state of charge, d represents the second model parameter which is related to temperature, k represents the third model parameter which is related to the discharge capacity, z represents the model parameter related to the battery chemistry system, f(SOC) represents the SOC Weyssee distribution probability value, and f(T) represents the temperature Weyssee distribution probability value.
8. The battery module life estimation device as described in claim 6, characterized in that, The lifetime estimation unit is specifically used for: The battery module under test is tested to obtain the SOC test value and temperature test value of each cell in the battery module under test. The SOC test value and temperature test value were fitted based on the Wechsler distribution to obtain the Wechsler distribution probability value of SOC and the Wechsler distribution probability value of temperature. For each target battery life estimation model, the SOC Weyseau distribution probability value and the temperature Weyseau distribution probability value are input into the target battery life estimation model to obtain the capacity decay curve, and the life estimation value is determined based on the capacity decay curve and the preset capacity decay threshold.
9. A battery module life estimation device, characterized in that, include: A memory and a processor, wherein the memory stores at least one instruction, which is loaded and executed by the processor to implement the battery module life estimation method according to any one of claims 1 to 5.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program that, when executed by a processor, implements the battery module life estimation method according to any one of claims 1 to 5.