Method for optimizing battery parameters, optimization device, battery and storage medium

Abstract

The application discloses a battery parameter optimization method, a battery parameter optimization device, a battery and a storage medium. The battery parameter optimization method comprises the following steps: determining a to-be-optimized battery parameter corresponding to an electrochemical simulation model; wherein the to-be-optimized battery parameter is an input parameter of the electrochemical simulation model; determining a value range and an initial value of the to-be-optimized battery parameter; and performing parameter optimization processing on the to-be-optimized battery parameter according to the value range and the initial value to obtain an optimized battery parameter. In the battery parameter optimization method, after the to-be-optimized battery parameter is determined, the value range and the initial value of the to-be-optimized battery parameter can be further determined, and the parameter optimization processing is performed on the to-be-optimized battery parameter on the basis of the value range and the initial value to obtain the optimized parameter, so that the optimized battery parameter can be used as the model input parameter to design the battery, the time required for parameter optimization can be reduced, the efficiency is improved, and the optimization effect is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of batteries, and in particular to a battery parameter optimization method, a battery parameter optimization device, a battery and a storage medium. BACKGROUND

[0002] In the related art, an electrochemical simulation model is generally used to model and analyze a battery. However, the electrochemical simulation model has many input parameters, including structural parameters of a battery cell, material performance parameters and a solid-liquid ratio. The material performance parameters are difficult to measure and often require large testing equipment. Moreover, the testing results are often not accurate enough, which brings difficulties to electrochemical modeling and makes it impossible to directly use experimental test data as model input parameters. The current battery parameters of the electrochemical simulation model are set by manual trial and error, which is time-consuming and ineffective. SUMMARY

[0003] The present application provides a battery parameter optimization method, a battery parameter optimization device, a battery and a storage medium.

[0004] The battery parameter optimization method of the present application includes:

[0005] determining a to-be-optimized battery parameter corresponding to an electrochemical simulation model; wherein the to-be-optimized battery parameter is an input parameter of the electrochemical simulation model;

[0006] determining a value range and an initial value of the to-be-optimized battery parameter;

[0007] performing parameter optimization processing on the to-be-optimized battery parameter according to the value range and the initial value to obtain an optimized battery parameter.

[0008] In the above battery parameter optimization method, after the to-be-optimized battery parameter is determined, the value range and the initial value of the to-be-optimized battery parameter can be further determined. The to-be-optimized battery parameter is then subjected to parameter optimization processing based on the value range and the initial value to obtain an optimized parameter. The optimized battery parameter can then be used as a model input parameter to design a battery, which helps to reduce the time required for parameter optimization, improve efficiency and optimize results.

[0009] In some embodiments, determining the value range and the initial value of the to-be-optimized battery parameter includes:

[0010] determining the value range of the to-be-optimized battery parameter;

[0011] determining the initial value of the to-be-optimized battery parameter according to the value range of the to-be-optimized battery parameter.

[0012] In this way, the time spent on optimization can be reduced.

[0013] In some embodiments, determining the initial value of the battery parameter to be optimized according to the value range of the battery parameter to be optimized comprises:

[0014] generating a plurality of groups of parameter values corresponding to the battery parameter to be optimized within the value range;

[0015] performing clustering processing on the plurality of groups of parameter values until a single group of parameter values is obtained;

[0016] determining the single group of parameter values as the initial value corresponding to the battery parameter to be optimized.

[0017] In this way, the initial value can be determined by clustering.

[0018] In some embodiments, in the case where there are a plurality of battery parameters to be optimized in the electrochemical simulation model, the parameter optimization processing on the battery parameter to be optimized according to the value range and the initial value to obtain the optimized battery parameter comprises:

[0019] determining a first battery analysis result error according to the initial value and the electrochemical simulation model;

[0020] determining a first battery parameter to be optimized according to the value range;

[0021] determining a second battery analysis result error according to the first battery parameter to be optimized and the electrochemical simulation model;

[0022] performing the parameter optimization processing according to the first battery analysis result error and the second battery analysis result error to obtain the optimized battery parameter.

[0023] In this way, the optimized battery parameter can be obtained.

[0024] In some embodiments, determining the first battery parameter to be optimized according to the value range comprises:

[0025] selecting a first initial battery parameter to be optimized from the plurality of battery parameters to be optimized; wherein the first initial battery parameter to be optimized is any one of the plurality of battery parameters to be optimized;

[0026] performing parameter adjustment on the first initial battery parameter to be optimized according to a preset multiple rule to obtain an adjusted parameter;

[0027] in the case where the adjusted parameter is within the value range, determining the adjusted parameter as the first battery parameter to be optimized;

[0028] In a case where the adjusted parameter exceeds the value range, the parameter adjustment is continuously performed according to the preset multiple ratio rule until the adjusted parameter is located in the value range, and the first battery parameter to be optimized is obtained.

[0029] In this way, the first battery parameter to be optimized is located in the value range.

[0030] In some embodiments, the parameter optimization processing according to the first battery analysis result error and the second battery analysis result error comprises:

[0031] In a case where the second battery analysis result error is reduced relative to the first battery analysis result error, the parameter adjustment is accepted.

[0032] The parameter adjustment is continuously performed on the first battery parameter to be optimized according to the preset multiple ratio rule until an adjusted parameter corresponds to a battery analysis result error less than a first preset threshold value or a parameter adjustment frequency is greater than a second preset threshold value, and the optimized battery parameter is obtained.

[0033] In this way, the optimized battery parameter can be obtained in a case where the second battery analysis result error is reduced relative to the first battery analysis result error.

[0034] In some embodiments, the parameter optimization processing according to the first battery analysis result error and the second battery analysis result error comprises:

[0035] In a case where the second battery analysis result error is increased relative to the first battery analysis result error, an acceptance probability of the parameter adjustment is calculated according to a difference between the second battery analysis result error and the first battery analysis result error.

[0036] In a case where the acceptance probability of the parameter adjustment is greater than a probability threshold value, the parameter adjustment is accepted and the first battery parameter to be optimized is retained.

[0037] The parameter adjustment is continuously performed on the first battery parameter to be optimized according to the preset multiple ratio rule until an adjusted parameter corresponds to a battery analysis result error less than a first preset threshold value or a parameter adjustment frequency is greater than a second preset threshold value, and the optimized battery parameter is obtained.

[0038] In this way, the optimized battery parameter can be obtained in a case where the second battery analysis result error is increased relative to the first battery analysis result error and the acceptance probability is greater than the probability threshold value.

[0039] In some embodiments, the parameter optimization processing according to the first battery analysis result error and the second battery analysis result error comprises:

[0040] In the case that the second battery analysis result error is higher than the first battery analysis result error, the acceptance probability of the parameter adjustment is calculated according to the difference between the second battery analysis result error and the first battery analysis result error.

[0041] In the case that the acceptance probability of the parameter adjustment is less than or equal to the probability threshold value, the parameter adjustment is not accepted and the first to-be-optimized battery parameter is not reserved.

[0042] The second initial to-be-optimized battery parameter is continuously selected from the plurality of to-be-optimized battery parameters, and the parameter adjustment and the determination and comparison processing of the battery analysis result error are continuously performed until the battery analysis result error corresponding to the adjusted parameter is less than the first preset threshold value or the number of parameter adjustments is greater than the second preset threshold value, so as to obtain the optimized battery parameter.

[0043] In this way, the optimized battery parameter can be obtained in the case that the second battery analysis result error is higher than the first battery analysis result error and the acceptance probability is less than or equal to the probability threshold value.

[0044] In some embodiments, the battery analysis result error is obtained by the following steps:

[0045] The to-be-optimized battery parameter is input into the electrochemical simulation model to obtain a battery simulation analysis result.

[0046] The battery experimental analysis result corresponding to the to-be-optimized battery parameter is obtained.

[0047] The battery analysis result error is determined according to the battery simulation analysis result and the battery experimental analysis result.

[0048] In this way, the battery analysis result error can be determined.

[0049] In some embodiments, the acceptance probability is related to a temperature parameter, and the determination method of the acceptance probability comprises:

[0050] The acceptance probability corresponding to the first to-be-optimized battery parameter is calculated for the first time by using a preset temperature value.

[0051] With the increase of the number of parameter adjustments, the corresponding number of parameter adjustments is subjected to a cyclic change processing of gradually decreasing from the preset temperature parameter value to 0 degrees, directly jumping to the preset temperature value, and then gradually decreasing from the preset temperature value to 0 degrees when calculating the acceptance probability.

[0052] In this way, the probability of finding a global optimal solution can be increased.

[0053] The optimization device for battery parameters in the embodiment of the present application comprises a processor and a memory, the memory stores a computer program, and the computer program realizes the steps of the optimization method for battery parameters in any of the above embodiments when executed by the processor.

[0054] The computer readable storage medium in the embodiment of the present application stores a computer program, and the computer program realizes the steps of the optimization method for battery parameters in any of the above embodiments when executed by the processor.

[0055] In the optimization device for battery parameters and the storage medium, after determining the battery parameter to be optimized, the value range and the initial value of the battery parameter to be optimized can be further determined, and the parameter optimization processing is performed on the battery parameter to be optimized based on the value range and the initial value to obtain the optimized parameter, and then the optimized battery parameter is used as the model input parameter to design the battery, which is beneficial to reduce the time required for parameter optimization, improve the efficiency, and improve the optimization effect.

[0056] The battery in the embodiment of the present application has the parameter determined by the optimization method for battery parameters in any of the above embodiments.

[0057] The parameter of the battery is determined by the optimization method for battery parameters in the embodiment of the present application, which can improve the manufacturing efficiency of the battery and the quality of the finished battery.

[0058] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0059] The above and / or additional aspects and advantages of the present application will become apparent and be readily appreciated from the following description, including the appended drawings, wherein:

[0060] Figures 1 to 8 is a flowchart of the optimization method for battery parameters in the embodiment of the present application;

[0061] Figure 9 is an experimental result graph before optimization of battery parameters;

[0062] Figure 10 is an optimization result graph after optimization of battery parameters by the optimization method for battery parameters in the embodiment of the present application;

[0063] Figure 11 is a module schematic diagram of the optimization device for battery parameters in the embodiment of the present application.

[0064] Main reference signs: optimization device of battery parameters - 100, processor - 12, memory - 14. DETAILED DESCRIPTION

[0065] Embodiments of the present application are described in detail below with reference to the attached drawings, wherein the same or similar components have the same or similar designations throughout the several views. The embodiments described below are merely exemplary for the purpose of explanation and are not to be understood as limiting the present application.

[0066] In the description of the present application, it should be noted that unless specifically defined and limited otherwise, the terms "mounting", "connection", "connecting" should be interpreted broadly, for example, it can be fixed connection, or detachable connection, or integral connection. It can be mechanical connection, or electrical connection. It can be direct connection, or indirect connection through intermediate medium, or internal communication of two elements, or interaction relationship between two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0067] The disclosure herein provides many different embodiments or examples for implementing different structures of the present application. In order to simplify the disclosure of the present application, the components and settings of specific examples are described herein. Of course, they are only examples, and the purpose is not to limit the present application. In addition, the present application can repeatedly refer to numbers and / or letters in different examples, and such repetition is for the purpose of simplification and clarity, which itself does not indicate the relationship between the various embodiments and / or settings discussed. In addition, the present application provides examples of various specific processes and materials, but those skilled in the art can realize the application of other processes and / or the use of other materials.

[0068] Please refer to Figure 1 A battery parameter optimization method of an embodiment of the present application comprises:

[0069] Step 101, determining the to-be-optimized battery parameter corresponding to the electrochemical simulation model; wherein the to-be-optimized battery parameter is an input parameter of the electrochemical simulation model;

[0070] Step 103, determining the value range and initial value of the to-be-optimized battery parameter;

[0071] Step 105, performing parameter optimization processing on the to-be-optimized battery parameter according to the value range and initial value, to obtain the optimized battery parameter.

[0072] In the above method for optimizing the battery parameters, after the battery parameter to be optimized is determined, the value range and the initial value of the battery parameter to be optimized can be further determined, and the parameter optimization processing is performed on the battery parameter to be optimized based on the value range and the initial value, to obtain the optimized parameter. Then, the optimized battery parameter can be used as the model input parameter to design the battery, which is beneficial to reduce the time required for parameter optimization, improve the efficiency, and improve the optimization effect.

[0073] Specifically, the electrochemical simulation model can be built in advance according to the cell design parameters. In an embodiment, the electrochemical simulation model is a pseudo two-dimensional (P2D) model, which can simulate the entire battery structure, and model and analyze the battery under the macro-micro, time-space multi-scale conditions through three-layer simplification. It can be used to predict the charge-discharge curve under different rates, the DCR (Direct Current Resistance) curve under different temperatures, and the simulated electrochemical impedance spectrum, etc. It can be understood that in other embodiments, the electrochemical simulation model can also be other types of models, and is not limited to the pseudo two-dimensional model.

[0074] The electrochemical simulation model can include one or more battery parameters to be optimized. In step 101, the battery parameters that are difficult to measure or measure inaccurately, such as the solid-phase diffusion coefficient, the exchange current density, the tortuosity of electrode coating, the effective active particle proportion, etc., can be determined as the battery parameters to be optimized. The battery parameters to be optimized are slightly different when the battery parameters of the built electrochemical simulation model are set differently. The type of the battery parameter to be optimized can be determined according to actual needs, and is not specifically limited herein.

[0075] In some embodiments, referring to Figure 2 , step 103 includes:

[0076] Step 1031, determining the value range of the battery parameter to be optimized.

[0077] Step 1033, determining the initial value of the battery parameter to be optimized according to the value range of the battery parameter to be optimized.

[0078] In this way, the time spent on optimization can be reduced.

[0079] Specifically, first, the value range of the battery parameter to be optimized needs to be determined, which can be determined according to historical experimental data or empirical values or other ways. If it is not determined, the value range can be set to be relatively large, but correspondingly, the time spent on optimization will also increase. Next, the initial value is determined, which can be an empirical value or an experimental measurement value (in the case that the experiment cannot measure the accurate value or the repeatability is poor), which can be determined from the minimum value of the value range, or the middle value or the maximum value, or the initial value is determined by using steps 1035, 1037 and 1039 of the following embodiments, and then the initial value of the battery parameter to be optimized can be obtained. First, the value range is determined, and then the initial value is determined in the value range, which can reduce the time spent on optimization.

[0080] In some embodiments, referring to Figure 3 , step 1033 comprises:

[0081] Step 1035, generating a plurality of groups of parameter values corresponding to the battery parameter to be optimized within the value range;

[0082] Step 1037, clustering the plurality of groups of parameter values until a single group of parameter values is obtained;

[0083] Step 1039, determining the single group of parameter values as the initial value corresponding to the battery parameter to be optimized.

[0084] In this way, the initial value can be determined by clustering.

[0085] Specifically, if the initial value cannot be determined by empirical values or actual measurement values, a plurality of groups of parameter values corresponding to the battery parameter to be optimized within the value range can be generated using the value range. First, a large number of random numbers are generated within the value range, and the number of groups of parameter values depends on the value range. In one example, 10,000 groups of parameter values can be generated. If the value range is large, a larger number of groups of parameter values can be set. The 10,000 groups of parameter values can be clustered using the Kmeans clustering method, for example, which can be clustered into 50 classes. The number of classes will affect the accuracy of the final parameter and the optimization speed. The more classes, the slower the optimization, but the more accurate the initial value generated.

[0086] The parameters of each class center are substituted into the electrochemical simulation model to calculate the error between the simulation analysis result and the experimental analysis result. The class with the smallest error is clustered again, and the iteration is continued until a single group of parameter values is obtained. The single group of parameter values is determined as the initial value corresponding to the battery parameter to be optimized. The experimental analysis result can be obtained from the experiment.

[0087] In some embodiments, referring to Figure 4In the case that there are multiple battery parameters to be optimized in the electrochemical simulation model, step 105 comprises:

[0088] Step 1051, determining a first battery analysis result error according to the initial value and the electrochemical simulation model;

[0089] Step 1053, determining a first battery parameter to be optimized according to the value range;

[0090] Step 1055, determining a second battery analysis result error according to the first battery parameter to be optimized and the electrochemical simulation model;

[0091] Step 1057, performing parameter optimization processing according to the first battery analysis result error and the second battery analysis result error to obtain an optimized battery parameter.

[0092] In this way, the optimized battery parameter can be obtained.

[0093] Specifically, the initial value is first substituted into the electrochemical simulation model to calculate the first battery analysis result error.

[0094] In an embodiment, the first battery parameter to be optimized can be a battery parameter to be optimized obtained by multiplying a randomly selected battery parameter to be optimized in the multiple battery parameters to be optimized by a random number (for example, the random number can be selected from 80% to 130%). The first battery parameter to be optimized is located in the value range, the obtained first battery parameter to be optimized is substituted into the electrochemical simulation model to calculate the second battery analysis result error, and then the parameter optimization processing can be performed according to the first battery analysis result error and the second battery analysis result error to obtain the optimized battery parameter.

[0095] In some embodiments, please refer to Figure 5 Step 1053 comprises:

[0096] Step 1059, selecting a first initial battery parameter to be optimized in the multiple battery parameters to be optimized; wherein the first initial battery parameter to be optimized is any one of the multiple battery parameters to be optimized;

[0097] Step 1061, adjusting the first initial battery parameter to be optimized according to a preset scaling rule to obtain an adjusted parameter;

[0098] Step 1063, in the case that the adjusted parameter is located in the value range, determining the adjusted parameter as the first battery parameter to be optimized;

[0099] Step 1065, in the case that the adjusted parameter exceeds the value range, continuously adjusting the parameter according to the preset scaling rule until the adjusted parameter is located in the value range to obtain the first battery parameter to be optimized.

[0100] Thus, the first to-be-optimized battery parameter can be located in the value range.

[0101] Specifically, one to-be-optimized battery parameter can be randomly selected from the plurality of to-be-optimized battery parameters as the first initial to-be-optimized battery parameter, or one to-be-optimized battery parameter can be selected from the plurality of to-be-optimized battery parameters in ascending order, or in descending order, or, from the middle to large or small.

[0102] The preset multiplier can be preset and stored, and in an embodiment, the preset multiplier can be one randomly selected from a preset multiplier range, or the preset multiplier can be one selected from the preset multiplier range in descending order or in ascending order, or, from the middle to large or small. In an example, the preset multiplier range is [80%, 130%].

[0103] The parameter adjustment is performed according to the preset multiplier rule, and in an embodiment, the first initial to-be-optimized battery parameter can be iteratively adjusted by using a Monte Carlo method, and specifically, a product of the first initial to-be-optimized battery parameter and the preset multiplier can be used as an adjusted parameter, and it is determined whether the adjusted parameter is located in the value range. If yes, the adjusted parameter is determined as the first to-be-optimized battery parameter. If no, the parameter adjustment is continuously performed according to the preset multiplier rule until the adjusted parameter is located in the value range, and the first to-be-optimized battery parameter is obtained.

[0104] In some embodiments, please refer to Figure 6 Step 1057 includes:

[0105] Step 1067, in the case that the second battery analysis result error is reduced relative to the first battery analysis result error, the parameter adjustment is accepted.

[0106] Step 1069, the parameter adjustment of the first to-be-optimized battery parameter is continuously performed according to the preset multiplier rule until the adjusted parameter corresponds to a battery analysis result error less than a first preset threshold or the number of parameter adjustments is greater than a second preset threshold, and an optimized battery parameter is obtained.

[0107] Thus, in the case that the second battery analysis result error is reduced relative to the first battery analysis result error, the optimized battery parameter can be obtained.

[0108] Specifically, since the adjusted parameter makes the second battery analysis result error lower than the first battery analysis result error, the adjusted parameter is close to the optimized battery parameter, so the parameter adjustment is accepted, and on this basis, the parameter adjustment of the first to-be-optimized battery parameter is continued according to the preset ratio rule. Preferably, when the parameter adjustment is performed again, the same preset ratio as the last parameter adjustment can be used to continue the parameter adjustment until the battery analysis result error corresponding to the adjusted parameter is less than the first preset threshold or the number of parameter adjustments is greater than the second preset threshold, and the optimized battery parameter is obtained. The specific size of the first preset threshold and the second preset threshold can be determined according to actual needs.

[0109] In some embodiments, please refer to Figure 7 Step 1057 includes:

[0110] Step 1071, in the case that the second battery analysis result error is higher than the first battery analysis result error, the acceptance probability of the parameter adjustment is calculated according to the difference between the second battery analysis result error and the first battery analysis result error.

[0111] Step 1073, in the case that the acceptance probability of the parameter adjustment is greater than the probability threshold, the parameter adjustment is accepted and the first to-be-optimized battery parameter is retained.

[0112] Step 1075, the parameter adjustment of the first to-be-optimized battery parameter is continued according to the preset ratio rule until the battery analysis result error corresponding to the adjusted parameter is less than the first preset threshold or the number of parameter adjustments is greater than the second preset threshold, and the optimized battery parameter is obtained.

[0113] In this way, in the case that the second battery analysis result error is higher than the first battery analysis result error and the acceptance probability is greater than the probability threshold, the optimized battery parameter can be obtained.

[0114] Specifically, in the case that the second battery analysis result error is higher than the first battery analysis result error, the acceptance probability of the parameter adjustment can be calculated according to the difference between the second battery analysis result error and the first battery analysis result error.

[0115] In one embodiment, the acceptance probability P is calculated by using formula 1,

[0116]

[0117] wherein ΔE represents the difference between the second battery analysis result error and the first battery analysis result error, k B represents the Boltzmann constant, T represents the temperature, and in one example, T = 298 K (room temperature).

[0118] In a case where the acceptance probability P is greater than a probability threshold (in one example, the probability threshold can be a randomly generated number between 0 and 1), the parameter adjustment of the current to-be-optimized battery parameter is accepted, and the parameter adjustment of the first to-be-optimized battery parameter is continued according to the preset ratio rule until the error of the battery analysis result corresponding to the adjusted parameter is less than the first preset threshold or the number of parameter adjustments is greater than the second preset threshold, to obtain the optimized battery parameter. The parameter adjustment of the first to-be-optimized battery parameter is continued according to the preset ratio rule, and the same preset ratio as the last parameter adjustment can be used.

[0119] In some embodiments, please refer to Figure 8 Step 1057 includes:

[0120] Step 1077, in a case where the second battery analysis result error is higher than the first battery analysis result error, the acceptance probability of the parameter adjustment is calculated according to the difference between the second battery analysis result error and the first battery analysis result error.

[0121] Step 1079, in a case where the acceptance probability of the parameter adjustment is less than or equal to the probability threshold, the parameter adjustment is not accepted and the first to-be-optimized battery parameter is not retained.

[0122] Step 1081, continue to select a second initial to-be-optimized battery parameter from the plurality of to-be-optimized battery parameters, and continue the parameter adjustment and the determination and comparison of the battery analysis result error until the error of the battery analysis result corresponding to the adjusted parameter is less than the first preset threshold or the number of parameter adjustments is greater than the second preset threshold, to obtain the optimized battery parameter.

[0123] In this way, the optimized battery parameter can be obtained in a case where the second battery analysis result error is higher than the first battery analysis result error and the acceptance probability is less than or equal to the probability threshold.

[0124] Specifically, in a case where the acceptance probability of the parameter adjustment is less than or equal to the probability threshold, the current parameter adjustment is not accepted and the first initial to-be-optimized battery parameter before the parameter adjustment is retained, that is, the parameter adjustment is not accepted and the first initial to-be-optimized battery parameter before the parameter adjustment is retained, and a second initial to-be-optimized battery parameter is selected from the plurality of to-be-optimized battery parameters, and the parameter adjustment and the determination and comparison of the battery analysis result error are continued until the error of the battery analysis result corresponding to the adjusted parameter is less than the first preset threshold or the number of parameter adjustments is greater than the second preset threshold, to obtain the optimized battery parameter. In one embodiment, the acceptance probability can be calculated by the above formula 1.

[0125] In some embodiments, the battery analysis result error is obtained by the following steps:

[0126] input the to-be-optimized battery parameter into the electrochemical simulation model to obtain a battery simulation analysis result;

[0127] obtain a battery experimental analysis result corresponding to the to-be-optimized battery parameter;

[0128] determine a battery analysis result error according to the battery simulation analysis result and the battery experimental analysis result.

[0129] In this way, the battery analysis result error can be determined.

[0130] Specifically, the battery analysis result error can include a first battery analysis result error and a second battery analysis result error.

[0131] For the first battery analysis result error, the to-be-optimized battery parameter input into the electrochemical simulation model is an initial value of the to-be-optimized battery parameter, a first battery simulation analysis result is obtained, and a first battery experimental analysis result is obtained by experimentally analyzing the battery using the initial value. The first battery analysis result error is determined according to the first battery simulation analysis result and the first battery experimental analysis result.

[0132] For the second battery analysis result error, the to-be-optimized battery parameter input into the electrochemical simulation model is a value of the first to-be-optimized battery parameter, a second battery simulation analysis result is obtained, and a second battery experimental analysis result is obtained by experimentally analyzing the battery using the value of the first to-be-optimized battery parameter. The second battery analysis result error is determined according to the second battery simulation analysis result and the second battery experimental analysis result.

[0133] In some embodiments, the acceptance probability is related to a temperature parameter, and the method for determining the acceptance probability includes:

[0134] The acceptance probability corresponding to the first to-be-optimized battery parameter is calculated for the first time using a preset temperature value.

[0135] As the number of parameter adjustments increases, the corresponding number of parameter adjustments is gradually decreased from the preset temperature parameter value to 0 degrees, then directly jumps to the preset temperature value, and then gradually decreases from the preset temperature value to 0 degrees in the cycle change processing when calculating the acceptance probability.

[0136] In this way, the probability of finding a global optimal solution can be increased.

[0137] Specifically, in one embodiment, the acceptance probability can be calculated by the above formula 1, where T represents the temperature parameter. In the above embodiment, T = 298 K, which is equivalent to using a constant room temperature. In this embodiment, a greedy algorithm can be used to find the optimal solution in a simulated annealing manner. The preset temperature value can be a temperature value greater than 0 degrees, and in one example, the preset temperature value is 1000 K.

[0138] In one embodiment, a fixed step size can be preset, and the temperature parameter is gradually decreased from the preset temperature value to 0 degrees with the increase of the number of parameter adjustments to calculate the acceptance probability. In other embodiments, a variable step size can also be used to adjust the temperature parameter.

[0139] The acceptance probability is first calculated using a temperature parameter with a large preset parameter value, and at this time, the acceptance probability is increased, and it is easier to jump out of the local minimum value. With the increase of the number of parameter adjustments (iteration times), the value of the temperature parameter is gradually decreased, and when the value of the temperature parameter is decreased to 0 degrees, it is directly jumped to the maximum temperature value (preset temperature value), and then gradually decreased from the preset temperature value to 0 degrees, and the cycle is repeated for several times, and the probability of finding a global optimal solution is increased.

[0140] In summary, the battery parameter optimization method of the embodiment of the application can automatically obtain a better parameter combination of the electrochemical simulation model in a short time without relying on manpower.

[0141] In one example, please refer to Figure 9 and Figure 10 When the discharge rate prediction is performed using the electrochemical simulation model, the model parameter correction needs to be performed for the experimental data first. This discharge rate curve optimization is relatively complex, firstly, there are many parameters that can be adjusted in the electrochemical simulation model, and secondly, it is easy to have a trade-off when fitting multiple curves at the same time. Manual parameter adjustment based on experience often requires a lot of time, and the battery parameter optimization method of the embodiment of the application only uses 8 hours to obtain the optimization result in Figure 10 (restricted by the operation speed of the electrochemical simulation model), which greatly improves the efficiency compared with the time of several days or even months of manual optimization, and the simulation analysis result of the optimized parameters is well fitted with the experimental analysis result. The parameter optimization method of the embodiment of the application can quickly correct the battery parameters, and then the curves of other discharge rates can be predicted, the electrochemical modeling time is shortened, and the battery design efficiency is improved. In Figure 9 and Figure 10 , the horizontal coordinate represents the capacity, and the vertical coordinate represents the voltage. In the curve description, simulated represents the simulation result, 0.33C is the discharge rate, reference represents the experimental result, and the other parameters refer to the description.

[0142] Please refer to Figure 11 The battery parameter optimization device 100 of the embodiment of the application includes a processor 12 and a memory 14, and the memory 14 stores a computer program, and the computer program implements the steps of the battery parameter optimization method of any of the above embodiments when executed by the processor 12.

[0143] The embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program realizes the steps of the battery parameter optimization method of any of the above-mentioned embodiments when executed by the processor 12.

[0144] In the above-mentioned battery parameter optimization device 100 and storage medium, after the battery parameter to be optimized is determined, the value range and the initial value of the battery parameter to be optimized can be further determined, and the parameter optimization processing is performed on the battery parameter to be optimized based on the value range and the initial value to obtain the optimized parameter, and then the optimized battery parameter can be used as the model input parameter to design the battery, which is beneficial to reduce the time required for parameter optimization, improve the efficiency, and improve the optimization effect.

[0145] In one embodiment, the battery parameter optimization method realized by the computer program when executed by the processor 12 comprises:

[0146] Step 101, determining a battery parameter to be optimized corresponding to an electrochemical simulation model; wherein the battery parameter to be optimized is an input parameter of the electrochemical simulation model;

[0147] Step 103, determining a value range and an initial value of the battery parameter to be optimized;

[0148] Step 105, performing parameter optimization processing on the battery parameter to be optimized according to the value range and the initial value, to obtain an optimized battery parameter.

[0149] It should be noted that the above-mentioned explanations and advantages of the embodiments of the battery parameter optimization method are also applicable to the battery parameter optimization device and the storage medium of the present embodiment, and to avoid redundancy, they will not be described in detail here.

[0150] The embodiment of the present application provides a battery, and the parameter of the battery is determined by the battery parameter optimization method of any of the above-mentioned embodiments.

[0151] The parameter of the above-mentioned battery is determined by the battery parameter optimization method of the embodiment of the present application, which can improve the manufacturing efficiency of the battery and the quality of the finished battery.

[0152] Specifically, the battery parameter can include a cell parameter, and the cell parameter can be determined by the battery parameter optimization method of the embodiment of the present application. The battery can be applied to an energy storage device, or can be applied to a passenger car, which is not limited here.

[0153] It can be understood that the computer program includes computer program code. The computer program code can be in the form of source code, object code, executable files or some intermediate forms. The computer readable storage medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), software distribution medium, etc. The processor can be a central processing unit, and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), ready programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc.

[0154] In the description of the present specification, the description referring to the terms "one embodiment", "some embodiments", "exemplary embodiment", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the exemplary description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0155] Although the embodiments of the present application have been shown and described, those skilled in the art can understand that various changes, modifications, replacements and variations can be made to the embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the claims and their equivalents.

Claims

1. A method for optimizing battery parameters, characterized in that, include: Determine the battery parameters to be optimized corresponding to the electrochemical simulation model; wherein, the battery parameters to be optimized are the input parameters of the electrochemical simulation model; Determine the range and initial value of the battery parameters to be optimized; Based on the value range and the initial value, the battery parameters to be optimized are optimized by performing parameter optimization processing to obtain the optimized battery parameters. When the electrochemical simulation model contains multiple battery parameters to be optimized, the step of performing parameter optimization processing on the battery parameters to be optimized based on the value range and the initial value to obtain optimized battery parameters includes: The error of the first battery analysis result is determined based on the initial values ​​and the electrochemical simulation model. The first battery parameter to be optimized is determined based on the range of values. The error of the second battery analysis result is determined based on the first battery parameters to be optimized and the electrochemical simulation model. Based on the errors of the first battery analysis result and the second battery analysis result, the parameter optimization process is performed to obtain the optimized battery parameters; The step of determining the first battery parameter to be optimized based on the value range includes: Select a first initial battery parameter from the plurality of battery parameters to be optimized; wherein, the first initial battery parameter to be optimized is any one of the plurality of battery parameters to be optimized; The parameters of the first initial battery to be optimized are adjusted according to a preset rate rule to obtain the adjusted parameters. If the adjusted parameter is within the range of values, the adjusted parameter is determined as the first battery parameter to be optimized. If the adjusted parameter exceeds the value range, continue to adjust the parameter according to the preset multiplier rule until the adjusted parameter is within the value range, and obtain the first battery parameter to be optimized.

2. The method for optimizing battery parameters according to claim 1, characterized in that, Determining the range and initial values ​​of the battery parameters to be optimized includes: Determine the value range of the battery parameters to be optimized; The initial value of the battery parameter to be optimized is determined based on the range of values ​​of the battery parameter to be optimized.

3. The method for optimizing battery parameters according to claim 2, characterized in that, Determining the initial values ​​of the battery parameters to be optimized based on their range includes: Generate multiple sets of parameter values ​​corresponding to the battery parameters to be optimized that are within the range of values; Cluster the multiple sets of parameter values ​​until a single set of parameter values ​​is obtained; The single set of parameter values ​​is determined as the initial values ​​corresponding to the battery parameters to be optimized.

4. The method for optimizing battery parameters according to claim 1, characterized in that, The step of performing parameter optimization processing based on the errors of the first battery analysis result and the second battery analysis result to obtain optimized battery parameters includes: If the error of the second battery analysis result is lower than the error of the first battery analysis result, the parameter adjustment is accepted. The first battery parameter to be optimized is adjusted according to the preset rate rule until the error of the battery analysis result corresponding to the adjusted parameter is less than the first preset threshold or the number of parameter adjustments is greater than the second preset threshold, so as to obtain the optimized battery parameter.

5. The method for optimizing battery parameters according to claim 1, characterized in that, The step of performing parameter optimization processing based on the errors of the first battery analysis result and the second battery analysis result to obtain optimized battery parameters includes: If the error of the second battery analysis result is higher than the error of the first battery analysis result, the acceptance probability of the parameter adjustment is calculated based on the difference between the error of the second battery analysis result and the error of the first battery analysis result. If the probability of accepting the parameter adjustment is greater than the probability threshold, accept the parameter adjustment and retain the first battery parameter to be optimized; The first battery parameter to be optimized is adjusted according to the preset rate rule until the error of the battery analysis result corresponding to the adjusted parameter is less than the first preset threshold or the number of parameter adjustments is greater than the second preset threshold, so as to obtain the optimized battery parameter.

6. The method for optimizing battery parameters according to claim 1, characterized in that, The step of performing parameter optimization processing based on the errors of the first battery analysis result and the second battery analysis result to obtain optimized battery parameters includes: When the error of the second battery analysis result is higher than the error of the first battery analysis result, the acceptance probability of parameter adjustment is calculated based on the difference between the error of the second battery analysis result and the error of the first battery analysis result. If the probability of accepting the parameter adjustment is less than or equal to the probability threshold, the parameter adjustment will not be accepted and the first battery parameter to be optimized will not be retained. Continue to select the second initial battery parameter to be optimized from the plurality of battery parameters to be optimized, and continue to adjust the parameters and determine and compare the battery analysis result error until the battery analysis result error corresponding to the adjusted parameters is less than the first preset threshold or the number of parameter adjustments is greater than the second preset threshold, so as to obtain the optimized battery parameters.

7. The method for optimizing battery parameters according to claim 1, characterized in that, The battery analysis results error is obtained through the following steps: Input the battery parameters to be optimized into the electrochemical simulation model to obtain the battery simulation analysis results; Obtain the battery experimental analysis results corresponding to the battery parameters to be optimized; The error of the battery analysis results is determined based on the battery simulation analysis results and the battery experimental analysis results.

8. The method for optimizing battery parameters according to claim 5 or 6, characterized in that, The acceptance probability is related to a temperature parameter, and the method for determining the acceptance probability includes: The acceptance probability corresponding to the first battery parameter to be optimized is calculated for the first time using a preset temperature value; As the number of parameter adjustments increases, when calculating the acceptance probability, the corresponding parameter adjustment number is subjected to a cyclical change process: gradually decreasing from the preset temperature value to 0 degrees, then directly jumping back to the preset temperature value, and then gradually decreasing from the preset temperature value to 0 degrees again.

9. A device for optimizing battery parameters, characterized in that, include: Processor, and; A memory storing a computer program that, when executed by the processor, implements the steps of the battery parameter optimization method according to any one of claims 1-8.

10. A battery, characterized in that, The parameters of the battery are determined by the battery parameter optimization method according to any one of claims 1-8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the battery parameter optimization method according to any one of claims 1-8.

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