A method, apparatus, device, and storage medium for battery parameter identification.

Abstract

This application provides a battery parameter identification method, apparatus, device, and storage medium. In the battery parameter identification method, an initial predicted value of the parameter to be identified in the thermal management equivalent model of the battery can be calculated using a differential evolution algorithm, and the fitness of the initial predicted value can be calculated using the thermal management equivalent model. Using the differential evolution algorithm, the initial predicted value is iteratively optimized based on its fitness to obtain the optimized parameter value of the parameter to be identified. This implementation method improves the efficiency of battery parameter identification and saves labor costs.

Description

Technical Field

[0001] This invention relates to the field of power battery technology, and in particular to a battery parameter identification method, device, equipment, and storage medium. Background Technology

[0002] In recent years, with the large-scale expansion of the electric vehicle industry, the safety of electric vehicle power batteries has become a pressing issue. During battery charging, if the battery temperature is too low, the charging speed will slow down; if the battery temperature is too high, there is a risk of thermal runaway. Therefore, it is necessary to control the battery temperature using an equivalent thermal management model. Current technologies typically use temperature sensors to monitor battery temperature and experimentally identify the parameters of the equivalent thermal management model. However, this experimental approach is inefficient and consumes significant manpower. Therefore, a solution is urgently needed. Summary of the Invention

[0003] This application provides a battery parameter identification method, apparatus, device, and storage medium for more efficient identification of battery parameters.

[0004] This application provides a battery parameter identification method, comprising: calculating an initial predicted value of a parameter to be identified in a thermal management equivalent model of a battery using a differential evolution algorithm; calculating the fitness of the initial predicted value using the thermal management equivalent model; and iteratively optimizing the initial predicted value using the differential evolution algorithm based on the fitness of the initial predicted value to obtain an optimized parameter value of the parameter to be identified.

[0005] Optionally, the initial predicted value of the parameter to be identified in the equivalent model of battery thermal management is calculated using a differential evolution algorithm, including: determining the optimization vector corresponding to the parameter to be identified; using the differential evolution algorithm, randomly generating a population of solutions corresponding to the optimization vector according to pre-set boundary conditions and / or set constraint conditions, as the initial predicted value; wherein the boundary conditions include: the upper and lower boundaries of the parameter to be identified; and the constraint conditions are the physical property constraints of the parameter to be identified.

[0006] Optionally, the fitness of the initial predicted value is calculated using the thermal management equivalent model, including: using the thermal management equivalent model, predicting the temperature based on the measured input data of the battery and the initial predicted value to obtain a first predicted temperature of the battery; the measured input data includes at least one of the actual detected ambient temperature of the battery, the charging current of the battery, the compressor speed, the water pump opening degree, and the PTC power; and calculating the fitness of the parameter to be identified based on the error between the first predicted temperature and the actual detected temperature of the battery.

[0007] Further optionally, the differential evolution algorithm is used to iteratively optimize the initial predicted value based on its fitness to obtain the optimized parameter value of the parameter to be identified, including: using the initial predicted value as the original predicted value; determining whether the fitness of the original predicted value meets a preset fitness condition; if yes, using the original predicted value as the optimized parameter value; if no, iteratively performing the following operations to update the original predicted value: performing a differential mutation operation on the original predicted value using a preset differential operator to obtain a differential vector of the original predicted value; performing a crossover mutation operation on the original predicted value using the differential vector of the original predicted value to obtain a mutated predicted value of the original predicted value; calculating the fitness of the mutated predicted value using the thermal management equivalent model; if the fitness of the mutated predicted value is better than the fitness of the original predicted value, then using the mutated predicted value as the updated original predicted value, and repeating the determination step.

[0008] Further optionally, the parameter to be identified includes a time-varying parameter. After iteratively optimizing the initial predicted value to obtain the optimized parameter value of the parameter to be identified, the method further includes: using the differential evolution algorithm to optimize the time variation ratio of the time-varying parameter to obtain an optimized value of the time variation ratio of the time-varying parameter; obtaining the optimized parameter value of the time-varying parameter from the optimized parameter values ​​as the baseline parameter value of the time-varying parameter; and calculating the final optimized value of the time-varying parameter based on the baseline parameter value of the time-varying parameter and the optimized value of the time variation ratio.

[0009] Optionally, the time variation ratio of the time-varying parameter is optimized using the differential evolution algorithm to obtain an optimized value of the time variation ratio of the time-varying parameter, including: calculating the initial time variation ratio of the time-varying parameter using the differential evolution algorithm; obtaining the initial time-varying value of the time-varying parameter based on the initial time variation ratio and the baseline parameter value; calculating the fitness of the initial time-varying value based on the thermal management equivalent model; and iteratively optimizing the initial time variation ratio using the differential evolution algorithm based on the fitness of the initial time-varying value to obtain the optimized value of the time variation ratio of the time-varying parameter.

[0010] Optionally, calculating the fitness of the initial time-varying value according to the thermal management equivalent model includes: inputting the optimized parameter value of the time-invariant parameter in the parameters to be identified, the initial time-varying value of the time-varying parameter, and the measured input data into the thermal management equivalent model; calculating the measured input data using the thermal management equivalent model based on the optimized parameter value of the time-invariant parameter and the initial time-varying value of the time-varying parameter to obtain the second predicted temperature of the battery; and calculating the fitness of the initial time-varying parameter based on the error between the second predicted temperature and the measured temperature of the battery.

[0011] This application embodiment also provides a battery parameter identification device, including: a prediction value calculation module, used to: calculate the initial predicted value of the parameter to be identified in the thermal management equivalent model of the battery using a differential evolution algorithm; a fitness calculation module, used to: calculate the fitness of the initial predicted value using the thermal management equivalent model; and an iterative optimization module, used to: use the differential evolution algorithm to iteratively optimize the initial predicted value according to the fitness of the initial predicted value to obtain the optimized parameter value of the parameter to be identified.

[0012] This application also provides an electronic device, including: a memory and a processor; wherein, the memory is used to: store one or more computer instructions; the processor is used to execute the one or more computer instructions to: perform the steps in the above-described battery parameter identification method.

[0013] This application also provides a computer-readable storage medium storing a computer program, which, when executed, can implement the steps in the battery parameter identification method described above.

[0014] This application provides a method, apparatus, device, and storage medium for battery parameter identification. The method involves calculating initial predicted values ​​of the parameters to be identified in a thermal management equivalent model of the battery using a differential evolution algorithm, and then calculating the fitness of the initial predicted values ​​using the thermal management equivalent model. The differential evolution algorithm iteratively optimizes the initial predicted values ​​based on their fitness to obtain optimized parameter values ​​for the parameters to be identified. This implementation method improves the efficiency of battery parameter identification and saves labor costs. Attached Figure Description

[0015] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0016] Figure 1A schematic flowchart of a battery parameter identification method provided for an exemplary embodiment of this application;

[0017] Figure 2 A schematic diagram of an equivalent circuit provided for an exemplary embodiment of this application;

[0018] Figure 3 A flowchart of iterative optimization provided for an exemplary embodiment of this application;

[0019] Figure 4 A schematic diagram illustrating a practical application provided for an exemplary embodiment of this application;

[0020] Figure 5 A schematic diagram of a battery parameter identification device provided in an exemplary embodiment of this application;

[0021] Figure 6 A schematic diagram of the structure of an electronic device provided for an exemplary embodiment of this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] In existing technologies, temperature sensors are typically used to monitor battery temperature, and thermal management parameters are identified experimentally. This method is inefficient and requires significant manpower. To address this issue, this application provides a battery parameter identification method in some embodiments.

[0024] In this battery parameter identification method, the initial predicted values ​​of the parameters to be identified in the thermal management equivalent model of the battery can be calculated using a differential evolution algorithm, and the fitness of the initial predicted values ​​can be calculated using the thermal management equivalent model. Using the differential evolution algorithm, the initial predicted values ​​are iteratively optimized based on their fitness to obtain the optimized parameter values ​​of the parameters to be identified. The technical solutions provided by various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0025] Figure 1 A flowchart illustrating a battery parameter identification method provided in an exemplary embodiment of this application is shown below. Figure 1 As shown, the method includes:

[0026] Step 11: Calculate the initial predicted values ​​of the parameters to be identified in the equivalent model of battery thermal management using the differential evolution algorithm.

[0027] Step 12: Calculate the fitness of the initial predicted value using the thermal management equivalent model.

[0028] Step 13: Using the differential evolution algorithm, the initial predicted value is iteratively optimized based on its fitness to obtain the optimized parameter value of the parameter to be identified.

[0029] This embodiment can be executed by a terminal device, which can run a computer program for identifying battery parameters based on a thermal management equivalent model.

[0030] In this embodiment, the terminal device can obtain the thermal management equivalent model of the battery. The thermal equivalent model refers to a mathematical model pre-established based on the characteristics and principles of the battery.

[0031] After obtaining the thermal management equivalent model, the initial predicted values ​​of the parameters to be identified in the thermal management equivalent model can be calculated using the Differential Evolution Algorithm (DE). The Differential Evolution Algorithm (DE) is an efficient global optimization algorithm and a population-based heuristic search algorithm. The parameters to be identified refer to the parameters in the thermal management equivalent model used to represent the characteristics of the battery itself, and may include, but are not limited to: the battery's lumped internal resistance r, the battery's lumped thermal resistance R, the battery's lumped heat capacity C, and the compressor's heat conversion coefficient k. Cmp and PTC power conversion coefficient k pTC The initial predicted value refers to the solution of a set of parameters calculated using the differential evolution algorithm.

[0032] It should be noted that the battery parameter identification method provided in this application embodiment is applicable to various different thermal management equivalent models. When the implementation of the thermal management equivalent model is different, the parameters to be identified may also be different. When the parameters to be identified included in the thermal management equivalent model are different from the parameters to be identified listed above, identification can still be performed based on the method provided in this embodiment, which will not be elaborated further.

[0033] After obtaining the initial predicted value, its fitness can be calculated using a thermal management equivalent model. For example, a preset fitness function can be used to calculate the fitness. This fitness function may include Mean Absolute Deviation (MAE), Root Mean Squared Error (RMSE), or the difference of squares function, etc., and this embodiment is not limited to any particular function. The fitness serves as the basis for evaluating the initial predicted value, reflecting its quality. The better the fitness of the initial predicted value meets the preset conditions, the better the initial predicted value. If the fitness function is implemented as a RMSE or the difference of squares function, then for any predicted value, the smaller its fitness, the better the predicted value. For example, fitness is calculated using the root mean square error function. The initial predicted values ​​are four sets of parametric solutions: A1, A2, A3, and A4. The calculated fitness values ​​for A1-A4 are 1, 2, 3, and 4, respectively. Therefore, the four sets of parametric solutions, ordered from largest to smallest fitness, are A1, A2, A3, and A4. A smaller root mean square error means that the initial predicted value of the parameter to be identified is closer to the optimal value.

[0034] Next, a differential evolution algorithm can be used to iteratively optimize the initial predicted values ​​based on their fitness, thereby obtaining optimized parameter values ​​for the parameters to be identified. The iterative optimization in the differential evolution algorithm aims to continuously evolve, retaining superior parameter solutions while eliminating inferior ones, guiding the parameter solutions towards the optimal solution. The optimized parameter values ​​refer to the parameter values ​​of the parameters to be identified after iterative optimization.

[0035] Assume that the fitness is calculated using the root mean square error function, and the fitness is the root mean square error. The expected condition is that the fitness is less than 0.5. The fitnesss of the initial predicted solutions A1, A2, A3, and A4 are 1, 2, 3, and 4, respectively. Solutions A1-A4 do not meet the expected condition of a fitness less than 0.5. Through iterative optimization of the above solutions, four sets of solutions A100-A104 are obtained, with fitness values ​​of 0.8, 0.7, 0.6, and 0.4, respectively. Therefore, A104 meets the expected requirement; that is, A104 is the optimized parameter value.

[0036] In this embodiment, the initial predicted values ​​of the parameters to be identified in the battery's thermal management equivalent model can be calculated using a differential evolution algorithm, and the fitness of the initial predicted values ​​can be calculated using the thermal management equivalent model. Using the differential evolution algorithm, the initial predicted values ​​are iteratively optimized based on their fitness to obtain optimized parameter values ​​for the parameters to be identified. This implementation method improves the efficiency of battery parameter identification and saves labor costs.

[0037] Alternatively, the thermal management equivalent model can be established through the following derivation process.

[0038] The equivalent model of thermal management during battery charging is a nonlinear, time-varying model involving processes such as battery self-heating, battery temperature rise and heat absorption, heat exchange between the battery and the external environment, compressor cooling of the battery, and PTC (Positive Temperature Coefficient) heating of the battery.

[0039] To establish an equivalent thermal management model, it is necessary to consider the balance between heat generation and heat dissipation during the charging process. Heat generation may include: the heat generated by the charging current flowing through the battery's internal resistance due to Joule's law and the heat brought by the vehicle's heat source (PTC, etc.) to heat the battery; heat dissipation includes the heat carried away by natural heat dissipation caused by the temperature difference between the battery and the external environment and the heat carried away by the vehicle's cold source (compressor, etc.) from the battery's forced heat exchanger.

[0040] The above process can be simplified by using an equivalent circuit to create a lumped parameter model, such as... Figure 2 As shown, the heat in the thermal management circuit is equivalent to current. The heating process of the charging current flowing through the battery's internal resistance is equivalent to a current source (Batt_heat). The heating effect of the PTC on the battery is equivalent to a current source (PTC_heat). The battery's thermal capacity is equivalent to capacitance C1. The heat exchange between the battery and the environment is equivalent to resistance R1. The cooling effect of the compressor and water pump on the battery is equivalent to a shunt resistor for forced heat exchange. Figure 2 k in cmp ·pmp·cmp.

[0041] Figure 2 The model in the equation can be described by Equation 1.

[0042]

[0043] In Formula 1, r is the total internal resistance of the battery, and T batt R is the battery temperature, C is the battery's total thermal resistance, and k is the battery's total thermal capacity. cmp k is the heat conversion coefficient of the compressor. PTC T is the power conversion factor of the PTC. air Here, I represents the ambient temperature, cmp represents the battery charging current, pmp represents the compressor speed, pmp represents the water pump opening, and PTC represents the PTC power. The heat transferred due to the temperature difference between the battery and the environment. The heat absorbed to compensate for the increase in battery temperature.

[0044] Since Equation 1 is continuous, it cannot be directly used to identify battery parameters. Therefore, a sampling interval of 1 second can be selected to discretize Equation 1, resulting in Equation 2, which is the equivalent model for thermal management.

[0045]

[0046] Among them, T batt(k+1) T represents the current temperature of the battery. batt(k) Let be the temperature of the battery at the previous moment. Formula 2 gives the relationship between the current temperature of the battery and the temperature at the previous moment, as well as other quantities. This formula can be used for parameter identification.

[0047] In some optional embodiments, the initial predicted values ​​of the parameters to be identified in the thermal management equivalent model are calculated using a differential evolution algorithm, which can be achieved based on the following steps:

[0048] Determine the optimization vector corresponding to the parameter to be identified. Continuing with the previous example, the parameter to be identified could be, for example, the battery's total internal resistance r, battery's total thermal resistance R, battery's total heat capacity C, or the compressor's heat conversion coefficient k. Cmp and PTC power conversion coefficient k pTC These are used to form row vectors or column vectors, which serve as optimization vectors.

[0049] After determining the optimization vector, a differential evolution algorithm can be used to randomly generate a population of solutions corresponding to the optimization vector, based on the set boundary conditions and / or set constraints, as the initial prediction values. The boundary conditions include the upper and lower boundaries of the parameters to be identified. The constraints are the physical property constraints on the parameters to be identified. The constraints and boundary conditions can be set according to the battery's material characteristics and actual design needs, etc. For example, the compressor heat conversion coefficient k in the parameters can be used as an example. cmp and PTC power conversion coefficient k pTC For example, the constraint condition can be k. Cmp <0.9, the boundary condition can be k pTC The maximum value is 0.8, and the minimum value is 0.1. The solution population consists of a series of parameter solutions corresponding to a randomly generated optimization vector.

[0050] After calculating the initial predicted value using the above steps, the fitness of the initial predicted value can be calculated based on the following steps.

[0051] Using a thermal management equivalent model, the battery's temperature is predicted based on the actual measured input data and initial predicted values. The first predicted temperature is then obtained. The measured input data refers to the actual values ​​of input quantities obtained by the user through experiments. The measured input data includes at least one of the following: the actual measured ambient temperature of the battery, the battery charging current, the compressor speed, the water pump opening degree, and the PTC power. In the aforementioned Formula 2, the ambient temperature T... air The input parameters are battery charging current I, compressor speed (cmp), water pump opening (pmp), and PTC power (Positive Temperature Coefficient, PTC). For example, the measured input data could be: ambient temperature 39°C, cmp 5000 r / min, water pump opening (pmp) 0.5, and PTC power (PTC) 100W. The term "first" is used to define the predicted temperature, distinguishing it only from the temperature predicted by the thermal management equivalent model and not limiting the predicted temperature value. Substituting the measured input data (input parameters) and the initial predicted values ​​(parameter values) into the thermal management equivalent model outputs the first predicted temperature of the battery.

[0052] After obtaining the first predicted temperature, the fitness of the parameter to be identified is calculated based on the error between the first predicted temperature and the actual measured temperature of the battery. The actual measured temperature of the battery refers to the actual tested battery temperature corresponding to the measured input data. The error can be the square root error, the root mean square error, etc., and this embodiment does not impose any limitations. Continuing with the previous example, when the measured input data is: ambient temperature 39°C, cmp 5000 r / min, water pump opening pmp 0.5, and PTC power PTC 100W, the measured battery temperature is 50°C. The calculated root mean square error between the measured temperature and the first predicted temperature is the fitness of the initial predicted value.

[0053] In some optional embodiments, after calculating the fitness of the initial predicted values, optimized parameter values ​​for the parameters to be identified can be obtained based on the following steps. The following will combine... Figure 3 Further explanation.

[0054] Step 131: Use the initial predicted value as the original predicted value.

[0055] The original predicted value can be the initial predicted value or the predicted value output from the previous iteration. In the next iteration of optimization, the original predicted value can serve as the basis for generating new predicted values, and in some cases, it can be updated by the newly generated predicted values.

[0056] Step 132: Determine whether the fitness of the original predicted value meets the preset fitness conditions.

[0057] The fitness condition can be set according to actual needs. For example, when using root mean square error to calculate fitness, the fitness condition can be: fitness less than 0.3, or less than 0.25, etc. This embodiment does not impose any restrictions. If the fitness of the original predicted value meets the fitness condition, then the original predicted value is used as the optimization parameter value. If the fitness of the original predicted value does not meet the fitness condition, then the following operations are performed iteratively to update the original predicted value:

[0058] Step 133: Perform a difference mutation operation on the original predicted value using a preset difference operator to obtain the difference vector of the original predicted value.

[0059] Among them, the difference operator refers to the change of a discrete function at discrete nodes. Difference operators can include: forward difference operator, backward difference operator, and central difference operator, etc. Users can select and preset difference operators according to their actual needs.

[0060] Step 134: Perform a crossover and mutation operation on the original predicted values ​​using the difference vector of the original predicted values ​​to obtain the mutated predicted values. Here, the mutated predicted value refers to the predicted value after the mutation operation.

[0061] Step 135: Calculate the fitness of the predicted variation values ​​using the thermal management equivalent model.

[0062] The method for calculating the fitness of the variability predictions is the same as the method for calculating the fitness of the initial predictions, and will not be elaborated here.

[0063] Step 136: Determine whether the fitness of the mutated predicted value is better than the fitness of the original predicted value. If the fitness of the mutated predicted value is better than the fitness of the original predicted value, then use the mutated predicted value as the updated original predicted value and repeat step 132. If the fitness of the mutated predicted value is worse than the fitness of the original predicted value, then step 133 can be executed to continue mutating the original predicted value, thereby generating a new predicted value.

[0064] For example, in some embodiments, when calculating fitness using the root mean square error function, if the root mean square error of the mutated predicted value is less than the root mean square error of the original predicted value, then the mutated predicted value is used as the updated original predicted value, and step 132 is repeated. If the root mean square error of the mutated predicted value is greater than or equal to the root mean square error of the original predicted value, then step 133 can be executed to continue mutating the original predicted value, thereby generating a new predicted value.

[0065] In some embodiments, when performing step 132, if it is determined that the original predicted value meets the preset fitness condition, the iteration can be stopped and the original predicted value can be used as the optimization parameter value.

[0066] In other embodiments, such as Figure 3 As shown, the number of iterations can be used to determine whether the above iterative process should stop. For example, if the preset condition is that the number of iterations is <100, the above iterative process can be stopped after the 99th iteration to obtain the predicted value of the mutation, and the optimized parameter value of the parameter to be identified can be obtained.

[0067] Considering that time-varying parameters may exist among the parameters to be identified, the time-varying characteristics of these parameters may affect the accuracy of battery parameter identification. In some optional embodiments, after iteratively optimizing the initial predicted values ​​to obtain optimized parameter values ​​for the parameters to be identified, the time-varying parameters among the parameters to be identified can be identified. This process will be further explained below.

[0068] Since the values ​​of time-varying parameters change over time, a differential evolution algorithm can be used to optimize the time-varying parameter's variation ratio among the parameters to be identified, yielding an optimized value for the time-varying parameter's variation ratio. The time-varying parameters may include parameters such as the battery's total internal resistance. The optimized value refers to the optimal solution for the time-varying ratio.

[0069] The aforementioned embodiments do not consider the time-varying characteristics of time-varying parameters. They perform iterative optimization and other similar operations on both the time-varying and time-invariant parameters in the parameters to be identified, ultimately obtaining the optimal solution, i.e., the optimized parameter value, for the parameter to be identified. From this optimized parameter value, the optimized parameter value of the time-varying parameter is obtained as the baseline parameter value for the time-varying parameter. For example, the baseline parameter value might be: the total internal resistance of the battery is 50Ω.

[0070] Based on the baseline parameter value of the time-varying parameter and the optimized value of the time-varying ratio, the final optimized value of the time-varying parameter is calculated. For example, based on the baseline parameter value of 50Ω for the total internal resistance of the battery and the optimized value of 95% for the time-varying ratio of the total internal resistance of the battery, the final optimized value is calculated to be 50 × 95% = 47.5Ω.

[0071] Further optionally, the "optimization of the time variation ratio of the time-varying parameter by using the differential evolution algorithm to obtain the optimized value of the time variation ratio of the time-varying parameter" described in the foregoing embodiments can be implemented based on the following steps.

[0072] The differential evolution algorithm is used to calculate the initial time variation ratio of the time-varying parameters, and the initial time-varying value of the time-varying parameters is obtained based on the initial time variation ratio and the baseline parameter value. For example, based on the baseline parameter value of 50Ω for the battery's total internal resistance and the optimized value of the initial time variation ratio of the battery's total internal resistance being 95%, the calculated initial time-varying value of the time-varying parameters is 50 × 95% = 47.5Ω.

[0073] After obtaining the initial time-varying values, the fitness of the initial time-varying values ​​can be calculated based on the thermal management equivalent model.

[0074] Based on the above steps, a differential evolution algorithm can be used to iteratively optimize the initial time variation ratio according to the fitness of the initial time-varying value, thereby obtaining the optimized value of the time variation ratio of the time-varying parameter. This will be explained in detail below.

[0075] The initial time-varying value is used as the original proportional value, and it is determined whether the original proportional value meets the preset fitness condition. The fitness condition can be set according to actual needs; for example, it can be less than 0.3, less than 0.25, etc., and this embodiment does not impose any restrictions. If the fitness of the original proportional value meets the fitness condition, then the original proportional value is used as the optimized value of the time-varying parameter's time change ratio. If the fitness of the original proportional value does not meet the fitness condition, the following operations are performed iteratively to update the original proportional value:

[0076] By using preset difference operators, the original proportional value is subjected to a difference mutation operation to obtain the difference vector of the original proportional value. Here, the difference operator refers to the change of the discrete function at discrete nodes. Difference operators can include: forward difference operator, backward difference operator, and center difference operator, etc., and users can select and preset difference operators according to their actual needs.

[0077] After obtaining the difference vector, the original proportional value can be cross-mutated using the difference vector to obtain the mutated proportional value. The mutated proportional value refers to the proportional value after the mutation operation.

[0078] The fitness of the variation ratio is calculated using the thermal management equivalent model. If the fitness of the variation ratio is better than that of the original ratio, the variation ratio is used as the updated original ratio, and the judgment step is repeated.

[0079] Alternatively, the iteration process can be stopped based on the number of iterations. For example, if the preset condition is that the number of iterations is <100, the iteration process can be stopped after the 99th iteration to obtain the variation ratio value, thus obtaining the optimized value of the time-varying parameter's time change ratio.

[0080] In some alternative embodiments, the "calculation of the fitness of the initial time-varying value based on the thermal management equivalent model" described in the foregoing embodiments can be implemented based on the following steps.

[0081] The optimized parameter values ​​of the time-invariant parameters, the initial time-varying values ​​of the time-varying parameters, and the measured input data are input into the thermal management equivalent model.

[0082] By using the thermal management equivalent model, and based on the optimized parameter values ​​of the time-invariant parameters and the initial time-varying values ​​of the time-varying parameters, the measured input data is calculated to obtain the second predicted temperature of the battery. The term "second" is used to define the predicted temperature only to distinguish it from the temperature predicted by the thermal management equivalent model, and does not impose any limitation on the value of the temperature predicted by the thermal management equivalent model.

[0083] The fitness of the initial time-varying parameters is calculated based on the error between the second predicted temperature and the measured battery temperature. The measured battery temperature refers to the actual tested battery temperature corresponding to the measured input data. The error can be the square root error, the root mean square error, etc., and this embodiment does not impose any limitations. Continuing with the previous example, when the measured input data is: ambient temperature 39°C, cmp 5000 r / min, water pump opening pmp 0.5, and PTC power PTC 100W, the measured battery temperature is 50°C. The calculated root mean square error between the measured temperature and the second predicted temperature is the fitness of the initial time-varying parameters.

[0084] The following will combine Figure 4 The battery parameter identification method provided in this application embodiment will be further explained in terms of practical application scenarios.

[0085] Battery identification methods such as Figure 4 As shown, the process can be divided into two steps. The first step involves disregarding the time-varying characteristics of some parameters to be identified and treating all parameters as fixed single parameters (i.e., instantaneously invariant parameters) for identification. Researchers can consult literature to obtain the approximate order-of-magnitude range of the parameters to be identified, and then perform calculations using dimensionality to further reduce the order-of-magnitude range of the parameters, thereby obtaining the boundary conditions of the parameters (i.e.,...). Figure 4 (within the empirical range). In addition, considering the potential constraints between parameters, these can be organized into quantified constraints. From the experimental test data, the battery's highest, lowest, and average temperatures (i.e., the measured temperatures) can be obtained. These three temperatures can be identified to obtain three sets of parameters. Taking the highest temperature as an example, the initial predicted value can be obtained through the DE (Differential Evolution) algorithm, and then the thermal management equivalent model (i.e.,...) can be used... Figure 4 A simplified model for battery charging thermal management (in the context of battery charging) combined with measured battery input data (i.e., Figure 4 Using limited experimental test data, a first predicted temperature is calculated. The root mean square error (RMSE) between this first predicted temperature and the measured temperature is used as the fitness value for this set of parameters. Then, the DE algorithm iteratively optimizes the parameters by minimizing the RMS error to obtain the optimized parameter values ​​(i.e., ...). Figure 4 (The single-variable parameter value in the text).

[0086] The result of the first step of identification is to obtain a set of optimized parameter values ​​that make the first predicted temperature of the simplified model as close as possible to the measured temperature.

[0087] After completing the first step of the identification process, considering the time-varying characteristics of some parameters, such as the change in battery internal resistance with temperature and battery SOC, and the compressor conversion coefficient k... cmp Due to changes in the pressure difference between the battery temperature and the ambient temperature, a second step of fine identification of time-varying parameters is required.

[0088] The second step involves using the optimized parameter values ​​identified in the first step as baseline parameters to design a lookup table for time-varying parameters, determining the range of possible time variation ratios for these parameters under different lookup table inputs. Then, the DE algorithm can be used to calculate the initial time variation ratio. Based on the above steps, the baseline parameter value is multiplied by the time variation ratio to obtain the initial time-varying value. According to the simplified battery charging thermal management model and experimental test data, the second predicted temperature during the battery charging process can be calculated.

[0089] The root mean square error (RMSE) between the predicted and measured temperatures is calculated. Using the minimum RMSE as the direction, the DE algorithm is employed to identify the time-varying parameter's time variation ratio. Finally, the time variation ratio identified in the second step is multiplied by the baseline parameter value to obtain the specific value of the time-varying parameter.

[0090] It should be noted that the execution subject of each step of the method provided in the above embodiments can be the same device, or the method can be executed by different devices. For example, the execution subject of steps 11 to 13 can be device A; or the execution subject of steps 11 and 12 can be device A, and the execution subject of step 13 can be device B; and so on.

[0091] Furthermore, some processes described in the above embodiments and accompanying drawings include multiple operations that appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or they may be executed in parallel. The operation numbers, such as 11, 12, etc., are merely used to distinguish different operations and do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel.

[0092] It should be noted that the terms "first" and "second" in this article are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0093] This application provides a battery parameter identification device, such as... Figure 5 As shown, the device includes: a prediction value calculation module 501, a fitness calculation module 502, and an iterative optimization module 503. The prediction value calculation module 501 is used to: calculate the initial predicted values ​​of the parameters to be identified in the thermal management equivalent model of the battery using a differential evolution algorithm; the fitness calculation module 502 is used to: calculate the fitness of the initial predicted values ​​using the thermal management equivalent model; and the iterative optimization module 503 is used to: use the differential evolution algorithm to iteratively optimize the initial predicted values ​​based on their fitness, thereby obtaining optimized parameter values ​​for the parameters to be identified.

[0094] Further optionally, when the prediction value calculation module 501 calculates the initial prediction value of the parameter to be identified in the equivalent model of battery thermal management using the differential evolution algorithm, it is specifically used to: determine the optimization vector corresponding to the parameter to be identified; and use the differential evolution algorithm to randomly generate the solution population corresponding to the optimization vector according to the pre-set constraints and / or set boundary conditions, as the initial prediction value; wherein, the boundary conditions include: the upper and lower boundaries of the parameter to be identified; and the constraints are the constraints on the physical properties of the parameter to be identified.

[0095] Optionally, when the fitness calculation module 502 calculates the fitness of the initial predicted value using the thermal management equivalent model, it specifically performs the following: using the thermal management equivalent model, it performs temperature prediction based on the actual detected measured input data of the battery and the initial predicted value to obtain a first predicted temperature of the battery; the measured input data includes at least one of the actual detected ambient temperature of the battery, the charging current of the battery, the compressor speed, the water pump opening degree, and the PTC power; and calculates the fitness of the parameter to be identified based on the error between the first predicted temperature and the actual detected measured temperature of the battery.

[0096] Further optionally, when the iterative optimization module 503 uses the differential evolution algorithm to iteratively optimize the initial predicted value based on the fitness of the initial predicted value to obtain the optimized parameter value of the parameter to be identified, it specifically performs the following steps: using the initial predicted value as the original predicted value; determining whether the fitness of the original predicted value meets a preset fitness condition; if yes, using the original predicted value as the optimized parameter value; if no, iteratively performing the following operations to update the original predicted value: performing a differential mutation operation on the original predicted value using a preset differential operator to obtain a differential vector of the original predicted value; performing a crossover mutation operation on the original predicted value using the differential vector of the original predicted value to obtain a mutated predicted value of the original predicted value; calculating the fitness of the mutated predicted value using the thermal management equivalent model; if the fitness of the mutated predicted value is better than the fitness of the original predicted value, using the mutated predicted value as the updated original predicted value, and repeating the determination step.

[0097] Further optionally, the parameter to be identified includes a time-varying parameter. After iteratively optimizing the initial predicted value to obtain the optimized parameter value of the parameter to be identified, the iterative optimization module 503 is further configured to: optimize the time variation ratio of the time-varying parameter using the differential evolution algorithm to obtain an optimized value of the time variation ratio of the time-varying parameter; obtain the optimized parameter value of the time-varying parameter from the optimized parameter value as the baseline parameter value of the time-varying parameter; and calculate the final optimized value of the time-varying parameter based on the baseline parameter value of the time-varying parameter and the optimized value of the time variation ratio.

[0098] Further optionally, when the iterative optimization module 503 optimizes the time variation ratio of the time-varying parameter using the differential evolution algorithm to obtain the optimized value of the time variation ratio of the time-varying parameter, it is specifically used to: calculate the initial time variation ratio of the time-varying parameter using the differential evolution algorithm; obtain the initial time-varying value of the time-varying parameter based on the initial time variation ratio and the baseline parameter value; calculate the fitness of the initial time-varying value based on the thermal management equivalent model; and iteratively optimize the initial time variation ratio using the differential evolution algorithm based on the fitness of the initial time-varying value to obtain the optimized value of the time variation ratio of the time-varying parameter.

[0099] When the fitness calculation module 502 calculates the fitness of the initial time-varying value according to the thermal management equivalent model, it is specifically used to: input the optimized parameter value of the time-invariant parameter in the parameters to be identified, the initial time-varying value of the time-varying parameter, and the measured input data into the thermal management equivalent model; calculate the measured input data using the thermal management equivalent model based on the optimized parameter value of the time-invariant parameter and the initial time-varying value of the time-varying parameter to obtain the second predicted temperature of the battery; and calculate the fitness of the initial time-varying parameter based on the error between the second predicted temperature and the measured temperature of the battery.

[0100] In this embodiment, the initial predicted values ​​of the parameters to be identified in the battery's thermal management equivalent model can be calculated using a differential evolution algorithm, and the fitness of the initial predicted values ​​can be calculated using the thermal management equivalent model. Using the differential evolution algorithm, the initial predicted values ​​are iteratively optimized based on their fitness to obtain optimized parameter values ​​for the parameters to be identified. This implementation method improves the efficiency of battery parameter identification and saves labor costs.

[0101] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an exemplary embodiment of this application, such as... Figure 6 As shown, the electronic device includes a memory 601 and a processor 602.

[0102] Memory 601 is used to store computer programs and can be configured to store various other data to support operation on the terminal device. Examples of this data include instructions for any application or method used to operate on the terminal device, contact data, phone book data, messages, pictures, videos, etc.

[0103] The memory 601 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0104] The processor 602, coupled to the memory 601, is used to execute a computer program in the memory 601 for: calculating initial predicted values ​​of the parameters to be identified in the thermal management equivalent model of the battery using a differential evolution algorithm; calculating the fitness of the initial predicted values ​​using the thermal management equivalent model; and iteratively optimizing the initial predicted values ​​using the differential evolution algorithm based on the fitness of the initial predicted values ​​to obtain optimized parameter values ​​of the parameters to be identified.

[0105] Further optionally, when the processor 602 calculates the initial predicted value of the parameter to be identified in the equivalent model of battery thermal management using the differential evolution algorithm, it is specifically used to: determine the optimization vector corresponding to the parameter to be identified; and use the differential evolution algorithm to randomly generate a population of solutions corresponding to the optimization vector according to the pre-set constraints and / or set boundary conditions, as the initial predicted value; wherein the boundary conditions include: the upper and lower boundaries of the parameter to be identified; and the constraints are the constraints on the physical properties of the parameter to be identified.

[0106] Further optionally, when the processor 602 calculates the fitness of the initial predicted value using the thermal management equivalent model, it specifically performs the following: using the thermal management equivalent model, it predicts the temperature based on the measured input data of the battery and the initial predicted value to obtain a first predicted temperature of the battery; the measured input data includes at least one of the actual detected ambient temperature of the battery, the charging current of the battery, the compressor speed, the water pump opening degree, and the PTC power; and calculates the fitness of the parameter to be identified based on the error between the first predicted temperature and the measured temperature of the battery.

[0107] Further optionally, when the processor 602 uses the differential evolution algorithm to iteratively optimize the initial predicted value based on the fitness of the initial predicted value to obtain the optimized parameter value of the parameter to be identified, it specifically performs the following steps: using the initial predicted value as the original predicted value; determining whether the fitness of the original predicted value meets a preset fitness condition; if yes, using the original predicted value as the optimized parameter value; if no, iteratively performing the following operations to update the original predicted value: performing a differential mutation operation on the original predicted value using a preset differential operator to obtain a differential vector of the original predicted value; performing a crossover mutation operation on the original predicted value using the differential vector of the original predicted value to obtain a mutated predicted value of the original predicted value; calculating the fitness of the mutated predicted value using the thermal management equivalent model; if the fitness of the mutated predicted value is better than the fitness of the original predicted value, using the mutated predicted value as the updated original predicted value, and repeating the determination step.

[0108] Further optionally, the parameter to be identified includes a time-varying parameter. After iteratively optimizing the initial predicted value to obtain the optimized parameter value of the parameter to be identified, the processor 602 is further configured to: optimize the time variation ratio of the time-varying parameter using the differential evolution algorithm to obtain an optimized value of the time variation ratio of the time-varying parameter; obtain the optimized parameter value of the time-varying parameter from the optimized parameter value as a baseline parameter value of the time-varying parameter; and calculate the final optimized value of the time-varying parameter based on the baseline parameter value of the time-varying parameter and the optimized value of the time variation ratio.

[0109] Further optionally, when the processor 602 optimizes the time variation ratio of the time-varying parameter using the differential evolution algorithm to obtain an optimized value of the time variation ratio of the time-varying parameter, it specifically performs the following steps: calculating the initial time variation ratio of the time-varying parameter using the differential evolution algorithm; obtaining the initial time-varying value of the time-varying parameter based on the initial time variation ratio and the baseline parameter value; calculating the fitness of the initial time-varying value based on the thermal management equivalent model; and iteratively optimizing the initial time variation ratio using the differential evolution algorithm based on the fitness of the initial time-varying value to obtain an optimized value of the time variation ratio of the time-varying parameter.

[0110] Further optionally, when the processor 602 calculates the fitness of the initial time-varying value according to the thermal management equivalent model, it specifically performs the following steps: inputting the optimized parameter value of the time-invariant parameter, the initial time-varying value of the time-varying parameter, and the measured input data into the thermal management equivalent model; calculating the measured input data using the thermal management equivalent model based on the optimized parameter value of the time-invariant parameter and the initial time-varying value of the time-varying parameter to obtain the second predicted temperature of the battery; and calculating the fitness of the initial time-varying parameter based on the error between the second predicted temperature and the measured temperature of the battery.

[0111] The above Figure 6 The memory in the memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0112] The above Figure 6 The display 603 includes a screen, which may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touchscreen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors can sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation.

[0113] Furthermore, such as Figure 6 As shown, the electronic device also includes other components such as a communication component 604 and a power supply component 605. Figure 6 The diagram only shows some components and does not mean that the electronic device includes only these components. Figure 6 The components shown.

[0114] The above Figure 6 The communication component 604 is configured to facilitate wired or wireless communication between the device containing the communication component and other devices. The device containing the communication component can access wireless networks based on communication standards, such as WiFi, 2G, 3G, 4G, or 5G, or combinations thereof. In one exemplary embodiment, the communication component receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication component may be implemented based on Near Field Communication (NFC), Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra Wideband (UWB), Bluetooth (BT), and other technologies.

[0115] The power supply component 605 provides power to various components of the device in which it resides. The power supply component may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device in which it resides.

[0116] In this embodiment, the initial predicted values ​​of the parameters to be identified in the battery's thermal management equivalent model can be calculated using a differential evolution algorithm, and the fitness of the initial predicted values ​​can be calculated using the thermal management equivalent model. Using the differential evolution algorithm, the initial predicted values ​​are iteratively optimized based on their fitness to obtain optimized parameter values ​​for the parameters to be identified. This implementation method improves the efficiency of battery parameter identification and saves labor costs.

[0117] Accordingly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to implement the steps in the battery parameter identification method.

[0118] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0119] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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, generate instructions 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.

[0120] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0121] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0122] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0123] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0124] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0125] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0126] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of this application should be included within the scope of the claims of this application.

Claims

1. A method for identifying battery parameters, characterized in that, include: The initial predicted values ​​of the parameters to be identified in the equivalent model of battery thermal management are calculated using the differential evolution algorithm. The fitness of the initial predicted value is calculated using the thermal management equivalent model. Using the differential evolution algorithm, the initial predicted value is iteratively optimized based on its fitness to obtain the optimized parameter value of the parameter to be identified. The parameters to be identified include time-varying parameters; After iteratively optimizing the initial predicted value to obtain the optimized parameter value of the parameter to be identified, the method further includes: The time variation ratio of the time-varying parameter is optimized using the differential evolution algorithm to obtain the optimized value of the time variation ratio of the time-varying parameter; Obtain the optimized parameter value of the time-varying parameter from the optimized parameter values, and use it as the baseline parameter value of the time-varying parameter; The final optimized value of the time-varying parameter is calculated by multiplying the baseline parameter value of the time-varying parameter by the optimized value of the time change ratio.

2. The method according to claim 1, characterized in that, The differential evolution algorithm is used to calculate the initial predicted values ​​of the parameters to be identified in the equivalent model of battery thermal management, including: Determine the optimization vector corresponding to the parameter to be identified; Using the differential evolution algorithm, a population of solutions corresponding to the optimization vector is randomly generated based on pre-set constraints and / or set boundary conditions, which serves as the initial prediction value. The boundary conditions include: the upper and lower boundaries of the parameter to be identified; the constraint conditions are the physical property constraints of the parameter to be identified.

3. The method according to claim 1, characterized in that, The fitness of the initial predicted values ​​is calculated using the thermal management equivalent model, including: The thermal management equivalent model is used to predict the temperature of the battery based on the measured input data of the battery and the initial prediction value, so as to obtain the first predicted temperature of the battery. The measured input data includes at least one of the following: the actual detected ambient temperature of the battery, the charging current of the battery, the compressor speed, the water pump opening degree, and the PTC power. The fitness of the parameter to be identified is calculated based on the error between the first predicted temperature and the actual measured temperature detected by the battery.

4. The method according to claim 1, characterized in that, Using the differential evolution algorithm, the initial predicted value is iteratively optimized based on its fitness to obtain optimized parameter values ​​for the parameter to be identified, including: Use the initial predicted value as the original predicted value; Determine whether the fitness of the original predicted value meets the preset fitness condition; If so, the original predicted value is used as the optimized parameter value; If not, then iteratively perform the following operations to update the original predicted value: By using a preset difference operator, the original predicted value is subjected to a difference mutation operation to obtain the difference vector of the original predicted value; The original predicted value is subjected to a crossover mutation operation using the difference vector of the original predicted value to obtain the mutated predicted value of the original predicted value; The fitness of the predicted variation values ​​is calculated using the thermal management equivalent model. If the fitness of the predicted mutation value is better than that of the original predicted value, then the predicted mutation value is used as the updated original predicted value, and the judgment step is repeated.

5. The method according to claim 3, characterized in that, The differential evolution algorithm is used to optimize the time variation ratio of the time-varying parameter to obtain the optimized value of the time variation ratio of the time-varying parameter, including: The initial time variation ratio of the time-varying parameter is calculated using the differential evolution algorithm; The initial time-varying value of the time-varying parameter is obtained based on the initial time change ratio and the reference parameter value. The fitness of the initial time-varying value is calculated based on the thermal management equivalent model. Using the differential evolution algorithm, the initial time change ratio is iteratively optimized based on the fitness of the initial time-varying value to obtain the optimized value of the time change ratio of the time-varying parameter.

6. The method according to claim 5, characterized in that, Based on the aforementioned thermal management equivalent model, the fitness of the initial time-varying value is calculated, including: The optimized parameter values ​​of the time-invariant parameters, the initial time-varying values ​​of the time-varying parameters, and the measured input data are input into the thermal management equivalent model. Using the thermal management equivalent model, the measured input data is calculated based on the optimized parameter values ​​of the time-invariant parameters and the initial time-varying values ​​of the time-varying parameters to obtain the second predicted temperature of the battery. The fitness of the initial time-varying parameters is calculated based on the error between the second predicted temperature and the measured temperature of the battery.

7. A battery parameter identification device, characterized in that, include: The prediction value calculation module is used to: calculate the initial predicted values ​​of the parameters to be identified in the equivalent model of battery thermal management using the differential evolution algorithm; The fitness calculation module is used to: calculate the fitness of the initial predicted value using the thermal management equivalent model; The iterative optimization module is used to: employ the differential evolution algorithm to iteratively optimize the initial predicted value based on the fitness of the initial predicted value, thereby obtaining the optimized parameter value of the parameter to be identified; The parameters to be identified include time-varying parameters; After iteratively optimizing the initial predicted value to obtain the optimized parameter value of the parameter to be identified, the iterative optimization module is used to: The time variation ratio of the time-varying parameter is optimized using the differential evolution algorithm to obtain the optimized value of the time variation ratio of the time-varying parameter; Obtain the optimized parameter value of the time-varying parameter from the optimized parameter values, and use it as the baseline parameter value of the time-varying parameter; The final optimized value of the time-varying parameter is calculated by multiplying the baseline parameter value of the time-varying parameter by the optimized value of the time change ratio.

8. An electronic device, characterized in that, include: Memory and processor; The memory is used to store one or more computer instructions; The processor is configured to execute one or more computer instructions for performing the steps of the method according to any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, characterized in that, When a computer program is executed by a processor, it causes the processor to perform the steps of the method according to any one of claims 1-6.

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