CN116359735A

CN116359735A - Battery parameter identification method, device, equipment and storage medium

Info

Publication number: CN116359735A
Application number: CN202111630376.7A
Authority: CN
Inventors: 马春山; 薛剑; 纪铮; 刘凯峰; 蒙越; 宁昀鹏
Original assignee: Beijing Co Wheels Technology Co Ltd
Current assignee: Beijing Co Wheels Technology Co Ltd
Priority date: 2021-12-28
Filing date: 2021-12-28
Publication date: 2023-06-30

Abstract

The embodiment of the application provides a battery parameter identification method, device, equipment and storage medium. In the battery parameter identification method, an initial predicted value of a parameter to be identified in a thermal management equivalent model of the battery can be calculated through a differential evolution algorithm, and the fitness of the initial predicted value is calculated through the thermal management equivalent model. And adopting a differential evolution algorithm, and carrying out iterative optimization on the initial predicted value according to the fitness of the initial predicted value to obtain an optimized parameter value of the parameter to be identified. By the implementation mode, the efficiency of battery parameter identification is improved, and the labor cost is saved.

Description

Battery parameter identification method, device, equipment and storage medium

Technical Field

The present invention relates to the field of power battery technologies, and in particular, to a method, an apparatus, a device, and a storage medium for identifying battery parameters.

Background

In recent years, with the large-scale expansion of the electric automobile industry, the safety problem of the electric automobile power battery has become a problem to be solved in the industry. In the process of charging the power battery, if the temperature of the battery is too low, the charging speed is reduced; if the battery temperature is too high, there is a risk of thermal runaway of the battery, etc. Therefore, it is necessary to control the temperature of the battery through a thermal management equivalent model of the battery. In the prior art, a temperature sensor is generally used to monitor the temperature of a battery, and parameters of a thermal management equivalent model of the battery are identified in an experimental manner. However, this method is less efficient through experiments and consumes more manpower cost. Therefore, a solution is needed.

Disclosure of Invention

The embodiment of the application provides a battery parameter identification method, device, equipment and storage medium, which are used for identifying parameters of a battery more efficiently.

The embodiment of the application provides a battery parameter identification method, which comprises the following steps: calculating an initial predicted value of a parameter to be identified in a thermal management equivalent model of the battery through a differential evolution algorithm; calculating the adaptability of the initial predicted value through the thermal management equivalent model; and adopting the differential evolution algorithm, and carrying out iterative optimization on the initial predicted value according to the adaptability of the initial predicted value to obtain the optimized parameter value of the parameter to be identified.

Further alternatively, calculating an initial predicted value of a parameter to be identified in a thermal management equivalent model of the battery by a differential evolution algorithm includes: determining an optimizing vector corresponding to the parameter to be identified; the differential evolution algorithm is adopted, and a solution population corresponding to the optimizing vector is randomly generated to serve as the initial predicted value according to preset boundary conditions and/or preset constraint conditions; wherein the boundary condition includes: upper and lower boundaries of the parameter to be identified; the constraint condition is a constraint condition of the parameter to be identified on a physical attribute.

Further optionally, calculating, by the thermal management equivalent model, the fitness of the initial predicted value includes: carrying out temperature prediction according to actually detected actually measured input data of the battery and the initial predicted value through the thermal management equivalent model to obtain a first predicted temperature of the battery; the measured input data includes: at least one of an actually detected ambient temperature of the battery, a charging current of the battery, a rotation speed of a compressor, an opening degree of a water pump, and PTC power; and calculating the fitness of the parameter to be identified according to the error between the first predicted temperature and the actually detected measured temperature of the battery.

Further optionally, the differential evolution algorithm is adopted, and according to the fitness of the initial predicted value, iterative optimization is performed on the initial predicted value to obtain an optimized parameter value of the parameter to be identified, including: taking the initial predicted value as an original predicted value; judging whether the adaptability of the original predicted value meets a preset adaptability condition or not; if yes, the original predicted value is used as the optimized parameter value; if not, iteratively performing the following operations to update the original predicted value: performing differential mutation operation on the original predicted value through a preset differential operator to obtain a differential vector of the original predicted value; performing cross mutation operation on the original predicted value by adopting a difference vector of the original predicted value to obtain a mutation predicted value of the original predicted value; calculating the adaptability of the variation predicted value through the thermal management equivalent model; and if the adaptability of the variation predicted value is better than that of the original predicted value, taking the variation predicted value as the updated original predicted value, and repeating the judging step.

Further optionally, the parameter to be identified includes a time-varying parameter. After performing iterative optimization on the initial predicted value to obtain the optimized parameter value of the parameter to be identified, the method further comprises the following steps: optimizing the time change proportion of the time-varying parameter by adopting the differential evolution algorithm to obtain an optimized value of the time change proportion of the time-varying parameter; acquiring an optimized parameter value of the time-varying parameters in the optimized parameter values as a reference parameter value of the time-varying parameters; and calculating the final optimized value of the time-varying parameter according to the reference parameter value of the time-varying parameter and the optimized value of the time-varying proportion.

Further optionally, optimizing the time variation ratio of the time-varying parameter by 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 change proportion of the time-varying parameters through a differential evolution algorithm; obtaining an initial time-varying value of the time-varying parameter according to the initial time-varying proportion and the reference parameter value; calculating the adaptability of the initial time-varying value according to the thermal management equivalent model; and adopting the differential evolution algorithm, and carrying out iterative optimization on the initial time change proportion according to the adaptability of the initial time change value to obtain an optimized value of the time change proportion of the time-varying parameter.

Further 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-variant value of the time-variant parameter and the actually measured input data into the thermal management equivalent model; calculating the actually measured input data according to the optimized parameter value of the time-invariant parameter and the initial time-variant value of the time-variant parameter through the thermal management equivalent model to obtain a second predicted temperature of the battery; and calculating the fitness of the initial time-varying parameter according to the error between the second predicted temperature and the measured temperature of the battery.

The embodiment of the application also provides a battery parameter identification device, which comprises: the predicted value calculation module is used for: calculating an initial predicted value of a parameter to be identified in a thermal management equivalent model of the battery through a differential evolution algorithm; the fitness calculation module is used for: calculating the adaptability of the initial predicted value through the thermal management equivalent model; an iteration optimization module, which is used for: and adopting the differential evolution algorithm, and carrying out iterative optimization on the initial predicted value according to the adaptability of the initial predicted value to obtain the optimized parameter value of the parameter to be identified.

The embodiment of the application also provides electronic equipment, which comprises: a memory and a processor; wherein the memory is for: store one or more computer instructions; the processor is configured to execute the one or more computer instructions to: executing the steps in the battery parameter identification method.

The embodiment of the application also provides a computer readable storage medium storing a computer program, and the computer program can realize the steps in the battery parameter identification method when being executed.

In the method, the device, the equipment and the storage medium for identifying the battery parameters, provided by the embodiment of the application, the initial predicted value of the parameter to be identified in the thermal management equivalent model of the battery can be calculated through the differential evolution algorithm, and the fitness of the initial predicted value is calculated through the thermal management equivalent model. And adopting a differential evolution algorithm, and carrying out iterative optimization on the initial predicted value according to the fitness of the initial predicted value to obtain an optimized parameter value of the parameter to be identified. By the implementation mode, the efficiency of battery parameter identification is improved, and the labor cost is saved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:

Fig. 1 is a flowchart of a battery parameter identification method according to an exemplary embodiment of the present application;

FIG. 2 is a schematic diagram of an equivalent circuit provided in an exemplary embodiment of the present application;

FIG. 3 is a flow chart of iterative optimization provided by an exemplary embodiment of the present application;

FIG. 4 is a schematic diagram of a practical application provided in an exemplary embodiment of the present application;

FIG. 5 is a schematic diagram of a battery parameter identification device according to an exemplary embodiment of the present application;

fig. 6 is a schematic structural diagram of an electronic device according to an exemplary embodiment of the present application.

Detailed Description

For the purposes, technical solutions and advantages of the present application, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.

In the prior art, a temperature sensor is generally adopted to monitor the temperature of the battery, and the thermal management parameters of the battery are identified in an experimental mode, so that the efficiency is low, and more labor cost can be consumed. In view of this technical problem, in some embodiments of the present application, a battery parameter identification method is provided.

In the battery parameter identification method, an initial predicted value of a parameter to be identified in a thermal management equivalent model of the battery can be calculated through a differential evolution algorithm, and the fitness of the initial predicted value is calculated through the thermal management equivalent model. And adopting a differential evolution algorithm, and carrying out iterative optimization on the initial predicted value according to the fitness of the initial predicted value to obtain an optimized parameter value of the parameter to be identified. The following describes in detail the technical solutions provided by the embodiments of the present application with reference to the accompanying drawings.

Fig. 1 is a flowchart of a battery parameter identification method according to an exemplary embodiment of the present application, as shown in fig. 1, the method includes:

and 11, calculating an initial predicted value of the parameter to be identified in the thermal management equivalent model of the battery through a differential evolution algorithm.

And step 12, calculating the adaptability of the initial predicted value through the thermal management equivalent model.

And step 13, adopting the differential evolution algorithm, and carrying out iterative optimization on the initial predicted value according to the adaptability of the initial predicted value to obtain the optimized parameter value of the parameter to be identified.

The present embodiment may be performed by a terminal device on which a computer program for battery parameter identification according to a thermal management equivalent model may be run.

In this embodiment, the terminal device may acquire a thermal management equivalent model of the battery. Wherein, the thermal equivalent model refers to a mathematical model which is pre-established according to the characteristics and principles of the battery.

After the thermal management equivalent model is obtained, an initial predicted value of the parameter to be identified in the thermal management equivalent model can be calculated through a differential evolution algorithm. Among these, the differential evolution algorithm (Differential Evolution Algorithm, DE) is an efficient global optimization algorithm and a population-based heuristic search algorithm. Wherein, the parameters to be identified refer to parameters used for representing the characteristics of the battery in the thermal management equivalent model, and the parameters can include but are not limited to: battery lumped internal resistance R, battery lumped thermal resistance R, battery lumped heat capacity C, compressor heat conversion coefficient k _Cmp PTC power conversion coefficient k _pTC . Wherein the initial predictor refers to a solution of a set of parameters calculated by a differential evolution algorithm.

It should be noted that the battery parameter identification method provided by the embodiment of the present application may be applicable to a plurality of different thermal management equivalent models. When the implementation forms of the thermal management equivalent models are different, 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 above listed parameters to be identified, the identification may also be performed based on the method provided in this embodiment, which is not described again.

After the initial predicted value is calculated, the fitness of the initial predicted value can be calculated through a thermal management equivalent model. For example, fitness may be calculated using a preset fitness function, which may include: average absolute error (Mean Absolute Deviation, MAE), root mean square error function (root mean squared error, RMSE), or square difference function, etc., the present embodiment is not limited. The fitness can be used as a basis for evaluating the initial predicted value, and the degree of the initial predicted value can be reflected according to the fitness. The more the fitness of the initial predicted value meets the preset condition, the more excellent the initial predicted value. If the fitness function is implemented as a root mean square error function or a square difference function, the smaller the fitness of any predicted value, the more excellent the predicted value. For example, the fitness is obtained by calculating a root mean square error function, the initial predicted value is a parameter solution A1, a parameter solution A2, a parameter solution A3 and a parameter solution A4, and the calculated fitness of A1-A4 is 1,2,3 and 4 respectively, so that the four groups of parameter solutions A1-A4 are A1, A2, A3 and A4 in sequence according to the order of the fitness from large to small. The smaller the root mean square error, the closer the initial predicted value and the optimal value of the parameter to be identified are.

And then, adopting a differential evolution algorithm, and carrying out iterative optimization on the initial predicted value according to the fitness of the initial predicted value to obtain an optimized parameter value of the parameter to be identified. The iterative optimization in the differential evolution algorithm aims at maintaining excellent parameter solutions through continuous evolution, eliminating inferior parameter solutions and guiding the parameter solutions to approach to optimal solutions. The optimized parameter value refers to a parameter value of the parameter to be identified after iterative optimization.

The fitness is assumed to be calculated by adopting a root mean square error function, the fitness is a root mean square error, and the expected condition is that the fitness is less than 0.5. The adaptation degree of the parameter solution A1, the parameter solution A2, the parameter solution A3 and the parameter solution A4 in the initial predicted value is 1,2,3 and 4 respectively, and the parameter solutions A1-A4 do not conform to the expected condition that the adaptation degree is smaller than 0.5. By continuously and iteratively optimizing the above parameter solutions, four groups of parameter solutions A100-A104 are obtained, and the fitness values of A100-A104 are respectively 0.8, 0.7, 0.6 and 0.4, so that A104 meets the expected requirement, namely, A104 is the optimized parameter value.

In this embodiment, the initial predicted value of the parameter to be identified in the thermal management equivalent model of the battery may be calculated by the differential evolution algorithm, and the fitness of the initial predicted value may be calculated by the thermal management equivalent model. And adopting a differential evolution algorithm, and carrying out iterative optimization on the initial predicted value according to the fitness of the initial predicted value to obtain an optimized parameter value of the parameter to be identified. By the implementation mode, the efficiency of battery parameter identification is improved, and the labor cost is saved.

Further alternatively, the thermal management equivalent model may be established by the following derivation process.

The thermal management equivalent model of the battery charging process is a nonlinear and parameter time-varying model, and relates to the processes of self-heating of the battery, self-temperature rising of the battery to absorb heat, heat exchange between the battery and the external environment, cooling of the battery by a compressor, heating of the battery by PTC (Positive Temperature Coefficient, positive temperature coefficient thermistor) and the like.

Establishing a thermal management equivalent model, wherein the balance of heat generation and heat dissipation in the charging process needs to be considered, and the heat generation can comprise: the charging current flows through the heat generated by Joule's law and the heat brought by the automobile heat source (PTC, etc.) to heat the battery; the heat dissipation comprises heat taken away by natural heat dissipation caused by temperature difference between the battery temperature and the external environment temperature and heat taken away by forced heat exchange of an automobile cold source (a compressor and the like) from the battery.

The above process can use an equivalent circuit to perform simplified modeling of a lumped parameter model, as shown in fig. 2, the heat in the thermal management loop is equivalent to a current, the heating process of the charging current flowing through the internal resistance of the battery is equivalent to a current source (battheat), the heating effect of the PTC on the battery is equivalent to a current source (PTC heat), the heat capacity of the battery is equivalent to a capacitor C1, the heat exchange between the battery and the environment is equivalent to a resistor R1, the cooling effect of the compressor and the water pump on the battery is equivalent to a shunt resistor for forced heat exchange, namely k in fig. 2 _cmp ·pmp·cmp。

The model in fig. 2 can be described by equation 1.

In formula 1, r is the total internal resistance of the battery, T _batt R is the lumped thermal resistance of the battery, C is the lumped heat capacity of the battery, k _cmp K is the heat conversion coefficient of the compressor _PTC For PTC power conversion coefficient, T _air Is the ambient temperature, I is the battery charging current, cmp is the compressor speed, pmp is the water pump opening, PTC is PTC power,

heat transferred for the temperature difference between the battery and the environment, < >>

Heat absorbed for the temperature rise of the battery.

Since equation 1 is continuous, it cannot be used directly to identify battery parameters. Therefore, the sampling interval can be selected to be 1s, and the formula 1 is discretized to obtain the formula 2, namely the thermal management equivalent model.

Wherein T is _batt(k+1) T is the temperature of the battery at the current moment _batt(k) Is the temperature at the last moment of the battery. Equation 2, which gives the relationship between the current temperature of the battery and the temperature and other quantities at the previous time, can be used for parameter identification.

In some alternative embodiments, the calculation of the initial predicted value of the parameter to be identified in the thermal management equivalent model by the differential evolution algorithm may be implemented based on the following steps:

and determining an optimizing vector corresponding to the parameter to be identified. Continuing with the previous example, parameters to be identified, such as the total internal resistance R, the total thermal resistance R, the total heat capacity C, and the heat transfer coefficient k of the compressor, can be exemplified _Cmp PTC power conversion coefficient k _pTC And the like, a row vector or a column vector is formed as an optimizing vector.

After the optimizing vector is determined, a differential evolution algorithm can be adopted, and a solution population corresponding to the optimizing vector is randomly generated according to a set boundary condition and/or a set constraint condition to serve as an initial predicted value. Wherein the boundary conditions include: the upper and lower boundaries of the parameter to be identified. The constraint condition is the constraint condition of the parameters to be identified on the physical attribute. Wherein the constraint conditions and boundary conditions may be set according to material characteristics of the battery, actual design requirements, and the like. For example, by compressor heat transfer coefficient k in parameters _cmp And PTC workRate conversion coefficient k _pTC For example, the constraint may be k _Cmp <0.9, boundary condition may be k _pTC The maximum value is 0.8, and the minimum value is 0.1. The solution population is a series of parameter solutions corresponding to the randomly generated optimizing vectors.

After the initial predicted value is calculated through the above steps, the fitness of the initial predicted value may be calculated based on the following steps.

And carrying out temperature prediction according to actually detected actually measured input data of the battery and an initial predicted value through a thermal management equivalent model to obtain a first predicted temperature of the battery. The actually measured input data refers to an actually measured value of the input quantity obtained by the user through experiments. The actual measurement input data includes: at least one of an ambient temperature of the battery, a charging current of the battery, a rotation speed of the compressor, an opening degree of the water pump, and PTC power, which are actually detected. In the foregoing equation 2, the ambient temperature T _air Battery charge current I, compressor speed (cmp), pump opening (pmp), and PTC power (Positive Temperature Coefficient PTC) are input amounts. Illustratively, the measured input data may be: the ambient temperature was 39 °, cmp was 5000r/min, the pump opening pmp was 0.5, and the PTC power PTC was 100w. The method is characterized in that the first is adopted to limit the predicted temperature, and the method is only used for distinguishing the temperature predicted by the thermal management equivalent model, and does not limit the value of the temperature predicted by the thermal management equivalent model. The measured input data (input quantity) and the initial predicted value (parameter value) are substituted into the thermal management equivalent model, and the first predicted temperature of the battery can be output.

After the first predicted temperature is obtained, the fitness of the parameter to be identified is calculated according to the error between the first predicted temperature and the actually detected measured temperature of the battery. The measured temperature of the battery refers to the actual measured battery temperature corresponding to the measured input data. The error may be a square root error, a root mean square error, or the like, which is not limited in this embodiment. Continuing with the previous example, when the measured input data is: when the ambient temperature was 39 °, cmp was 5000r/min, the pump opening pmp was 0.5, and PTC power PTC was 100w, the measured temperature of the battery was 50 °. And the root mean square error of the calculated measured temperature and the first predicted temperature is the fitness of the initial predicted value.

In some alternative embodiments, after calculating the fitness of the initial predicted value, the optimized parameter value of the parameter to be identified may be obtained based on the following steps. As will be further described below in connection with fig. 3.

Step 131, taking the initial predicted value as the original predicted value.

The original predicted value may be an initialized predicted value or a predicted value output in the previous iteration. In the next iteration of the optimization process, the original predictor may be used as a basis for generating a new predictor, and in some cases, may be updated by the new predictor.

And 132, judging whether the adaptability of the original predicted value meets the preset adaptability condition.

The fitness condition may be set according to actual requirements, for example, when the fitness is calculated by adopting a root mean square error, the fitness condition may be: the fitness is less than 0.3, may be less than 0.25, etc., and the embodiment is not limited. And if the adaptability of the original predicted value meets the adaptability condition, taking the original predicted value as an optimized parameter value. If the fitness of the original predicted value does not meet the fitness condition, the following operations are iteratively executed to update the original predicted value:

and 133, performing differential mutation operation on the original predicted value through a preset differential operator to obtain a differential vector of the original predicted value.

Wherein, the difference operator refers to the change amount of the discrete function on the discrete node. The difference operator may include: the forward difference operator, the backward difference operator, the center difference operator and the like, and a user can select and preset the difference operator according to actual requirements.

And step 134, performing cross mutation operation on the original predicted value by adopting a difference vector of the original predicted value to obtain a mutation predicted value of the original predicted value. The mutation predicted value refers to a predicted value after mutation operation.

And 135, calculating the fitness of the variation predicted value through a thermal management equivalent model.

The method for calculating the fitness of the variation predicted value is the same as the method for calculating the fitness of the initial predicted value, and will not be described here.

136, judging whether the fitness of the variation predicted value is better than that of the original predicted value; if the fitness of the variance prediction value is better than that of the original prediction value, the variance prediction value is used as the updated original prediction value, and step 132 is repeated. If the fitness of the mutated predictor is inferior to that of the original predictor, step 133 may be performed to continue to mutate the original predictor to generate a new predictor.

For example, in some embodiments, when the fitness is calculated using the root mean square error function, if the root mean square error value of the variance prediction value is smaller than the root mean square error value of the original prediction value, the variance prediction value is used as the updated original prediction value, and step 132 is repeated. If the rms error value of the mutated predictor is greater than or equal to the rms error value of the original predictor, step 133 may be performed to continue mutating the original predictor to generate a new predictor.

In some embodiments, when step 132 is performed, if it is determined that the original predicted value meets the preset fitness condition, the iteration may be stopped, and the original predicted value is used as the optimized parameter value.

In other embodiments, as shown in fig. 3, it may be determined whether the above iterative process is stopped according to the number of iterations. For example, the preset condition is that the iteration number is less than 100, and after the variation predicted value is obtained through the 99 th iteration process, the iteration process can be stopped to obtain the optimized parameter value of the parameter to be identified.

Considering that there may be a time-varying parameter in the parameters to be identified, the time-varying characteristics of the parameter may have an influence on the accuracy of the battery parameter identification. In some alternative embodiments, after the initial predicted value is iteratively optimized to obtain the optimized parameter value of the parameter to be identified, the time-varying parameter in the parameter to be identified may be identified. This process will be further described below.

Because the parameter value of the time-varying parameter can change along with the change of time, the time-varying proportion of the time-varying parameter can be optimized by adopting a differential evolution algorithm aiming at the time-varying parameter in the parameters to be identified, and the optimized value of the time-varying proportion of the time-varying parameter is obtained. Wherein the time-varying parameters may include: and the battery lumped internal resistance and other parameters. The optimal value refers to the optimal solution of the time variation ratio.

The foregoing embodiment does not consider the time-varying characteristics of the time-varying parameters, and performs the same operations such as iterative optimization on the time-varying parameters and the time-invariant parameters in the parameters to be identified, and finally obtains the optimal solution of the parameters to be identified, i.e., the optimal parameter values. And obtaining the optimized parameter value of the time-varying parameter from the optimized parameter value of the parameter to be identified, and taking the optimized parameter value as the reference parameter value of the time-varying parameter. For example, the reference parameter values are: the total internal resistance of the battery is 50Ω.

And calculating the final optimized value of the time-varying parameter according to the reference parameter value of the time-varying parameter and the optimized value of the time-varying proportion. For example, according to the reference parameter value of the battery lumped internal resistance being 50Ω and the optimized value of the time-varying proportion of the battery lumped internal resistance being 95%, the calculated final optimized value is 50x95% =47.5Ω.

Further alternatively, the "optimizing the time variation ratio of the time-varying parameter by using the differential evolution algorithm" described in the foregoing embodiment, to obtain the optimized value of the time variation ratio of the time-varying parameter may be implemented based on the following steps.

And calculating the initial time change proportion of the time-varying parameter by a differential evolution algorithm, and obtaining the initial time-varying value of the time-varying parameter according to the initial time change proportion and the reference parameter value. For example, according to the reference parameter value of the battery lumped internal resistance being 50Ω and the optimized value of the initial time-varying proportion of the battery lumped internal resistance being 95%, the calculated initial time-varying value of the time-varying parameter is 50x95% =47.5Ω.

After the initial time-varying value is obtained, the fitness of the initial time-varying value can be calculated according to the thermal management equivalent model.

Based on the steps, a differential evolution algorithm can be adopted, and the initial time change proportion is subjected to iterative optimization according to the fitness of the initial time change value, so that an optimized value of the time change proportion of the time-varying parameter is obtained. The following will explain in detail.

And taking the initial time-varying value as an original proportion value, and judging whether the original proportion value meets a preset fitness condition. The fitness condition may be set according to actual requirements, for example, the fitness condition may be less than 0.3, or less than 0.25, or the like, which is not limited in this embodiment. And if the fitness of the original proportion value meets the fitness condition, taking the original proportion value as an optimized value of the time-varying proportion of the time-varying parameter. If the fitness of the original proportion value does not meet the fitness condition, the following operations are iteratively executed to update the original proportion value:

And carrying out differential mutation operation on the original proportion value through a preset differential operator to obtain a differential vector of the original proportion value. Wherein, the difference operator refers to the change amount of the discrete function on the discrete node. The difference operator may include: the forward difference operator, the backward difference operator, the center difference operator and the like, and a user can select and preset the difference operator according to actual requirements.

After the differential vector is obtained, the differential vector of the original proportion value can be adopted to carry out cross mutation operation on the original proportion value, so as to obtain the mutation proportion value of the original proportion value. Wherein, the variation ratio value refers to the ratio value after the variation operation.

And calculating the fitness of the variation proportion value through a thermal management equivalent model. And if the fitness of the variation ratio value is better than that of the original ratio value, taking the variation ratio value as the updated original ratio value, and repeating the judging step.

Further alternatively, whether the above iterative process is stopped may be determined according to the number of iterations. For example, the preset condition is that the iteration number is less than 100, and after the variation ratio value is obtained through the 99 th iteration process, the iteration process can be stopped to obtain the optimized value of the time variation ratio of the time-varying parameter.

In some alternative embodiments, the "calculate fitness of initial time-varying values according to thermal management equivalent model" described in the previous embodiments may be implemented based on the following steps.

And inputting the optimized parameter value of the time-invariant parameter in the parameters to be identified, the initial time-variant value of the time-variant parameter and the actually measured input data into a thermal management equivalent model.

And calculating the actually measured input data according to the optimized parameter value of the time-invariant parameter and the initial time-variant value of the time-variant parameter through a thermal management equivalent model, so as to obtain a second predicted temperature of the battery. The "second" is used to limit the predicted temperature, and is only used to distinguish the temperature predicted by the thermal management equivalent model, and does not limit the value of the temperature predicted by the thermal management equivalent model.

And calculating the fitness of the initial time-varying parameter according to the error between the second predicted temperature and the measured temperature of the battery. The measured temperature of the battery refers to the actual measured battery temperature corresponding to the measured input data. The error may be a square root error, a root mean square error, or the like, which is not limited in this embodiment. Continuing with the previous example, when the measured input data is: when the ambient temperature was 39 °, cmp was 5000r/min, the pump opening pmp was 0.5, and PTC power PTC was 100w, the measured temperature of the battery was 50 °. The root mean square error of the measured temperature and the second predicted temperature is obtained through calculation, and the root mean square error is the fitness of the initial time-varying parameter.

The battery parameter identification method provided in the embodiment of the present application will be further described with reference to fig. 4 and a practical application scenario.

The battery identification method is shown in fig. 4 and can be divided into two steps. In the first step, all parameters to be identified are considered as fixed single parameters (i.e. constant parameters) for identification without considering the time-varying characteristics of part of the parameters to be identified. The developer may consult the literature to obtain an approximate magnitude range for the parameter to be identified, and then calculate in combination with the dimensions to further reduce the magnitude range of the parameter to obtain the boundary conditions (i.e., the empirical range in fig. 4) for the parameter. In addition, considering that there may be constraint relationships between parameters, they may be sorted into quantized constraints. From the experimental test data, the highest temperature, the lowest temperature and the average temperature (namely the measured temperature) of the battery can be obtained, and three temperatures can be respectively identified to obtain three groups of parameters. Taking the highest temperature as an example, an initial predicted value can be obtained through a DE (Differential Evolution ) algorithm, and then a thermal management equivalent model (i.e. a simplified battery charging thermal management model in fig. 4) is used to combine measured input data (i.e. a small amount of experimental test data in fig. 4) of the battery to calculate a first predicted temperature, and the first predicted temperature and the measured temperature calculate a root mean square error and serve as an adaptive value of the set of parameters. The DE algorithm then performs a continuous iterative optimization with the root mean square error minimum as a direction to obtain the optimized parameter values (i.e., the univariate parameter values in fig. 4).

The result of the first step of identification is to obtain a set of optimized parameter values such that the values of the first predicted temperature and the measured temperature of the reduced model are closest.

After the identification process of the first step is completed, considering time-varying characteristics of partial parameters, such as the change of internal resistance of the battery along with temperature and SOC of the battery, the conversion coefficient k of the compressor _cmp Along with the change of the pressure difference between the battery temperature and the ambient temperature, the fine identification of the time-varying parameters in the second step is needed.

And secondly, designing a table look-up table of the time-varying parameters by taking the optimized parameter value identified in the first step as a reference parameter value, and determining the range of possible time variation proportion of the time-varying parameters under different table look-up inputs. The initial time-varying proportion can then be calculated using the DE algorithm. Based on the above steps, the reference parameter value is multiplied by the time change proportion to obtain an initial time-varying value. And according to the battery charging thermal management simplified model and experimental test data, a second predicted temperature of the battery charging process can be calculated.

And calculating the root mean square error of the second predicted temperature and the actually measured temperature, and identifying the time variation proportion of the time-varying parameter by using the DE algorithm with the minimum root mean square error as the direction. And finally, multiplying the time change proportion obtained by the second step by the reference parameter value to obtain a specific value of the time change parameter.

It should be noted that, the execution subjects of each step of the method provided in the above embodiment may be the same device, or the method may also be executed by different devices. For example, the execution subject of steps 11 to 13 may be the device a; for another example, the execution subject of

steps

11 and 12 may be device a, and the execution subject of step 13 may be device B; etc.

In addition, in some of the flows described in the above embodiments and the drawings, a plurality of operations appearing in a specific order are included, but it should be clearly understood that the operations may be performed out of the order in which they appear herein or performed in parallel, the sequence numbers of the operations such as 11, 12, etc. are merely used to distinguish between the various operations, and the sequence numbers themselves do not represent any order of execution. In addition, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel.

It should be noted that, the descriptions of "first" and "second" herein are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, and are not limited to the "first" and the "second" being different types.

An embodiment of the present application provides a battery parameter identification device, as shown in fig. 5, the device includes: a predictive value calculation module 501, an adaptation calculation module 502 and an iterative optimization module 503. Wherein, the predicted value calculation module 501 is configured to: calculating an initial predicted value of a parameter to be identified in a thermal management equivalent model of the battery through a differential evolution algorithm; the fitness calculation module 502 is configured to: calculating the adaptability of the initial predicted value through the thermal management equivalent model; an iterative optimization module 503, configured to: and adopting the differential evolution algorithm, and carrying out iterative optimization on the initial predicted value according to the adaptability of the initial predicted value to obtain the optimized parameter value of the parameter to be identified.

Further alternatively, the predicted value calculation module 501 is specifically configured to, when calculating, by using a differential evolution algorithm, an initial predicted value of a parameter to be identified in a thermal management equivalent model of a battery: determining an optimizing vector corresponding to the parameter to be identified; the differential evolution algorithm is adopted, and a solution population corresponding to the optimizing vector is randomly generated to serve as the initial predicted value according to preset constraint conditions and/or preset boundary conditions; wherein the boundary condition includes: upper and lower boundaries of the parameter to be identified; the constraint condition is the constraint condition of the parameters to be identified on the physical attribute.

Further optionally, the fitness calculating module 502 is specifically configured to, when calculating the fitness of the initial predicted value through the thermal management equivalent model: carrying out temperature prediction according to actually detected actually measured input data of the battery and the initial predicted value through the thermal management equivalent model to obtain a first predicted temperature of the battery; the measured input data includes: at least one of an actually detected ambient temperature of the battery, a charging current of the battery, a rotation speed of a compressor, an opening degree of a water pump, and PTC power; and calculating the fitness of the parameter to be identified according to the error between the first predicted temperature and the actually detected measured temperature of the battery.

Further optionally, when the iterative optimization module 503 performs iterative optimization on the initial predicted value according to the fitness of the initial predicted value by using the differential evolution algorithm, the iterative optimization module is specifically configured to: taking the initial predicted value as an original predicted value; judging whether the adaptability of the original predicted value meets a preset adaptability condition or not; if yes, the original predicted value is used as the optimized parameter value; if not, iteratively performing the following operations to update the original predicted value: performing differential mutation operation on the original predicted value through a preset differential operator to obtain a differential vector of the original predicted value; performing cross mutation operation on the original predicted value by adopting a difference vector of the original predicted value to obtain a mutation predicted value of the original predicted value; calculating the adaptability of the variation predicted value through the thermal management equivalent model; and if the adaptability of the variation predicted value is better than that of the original predicted value, taking the variation predicted value as the updated original predicted value, and repeating the judging step.

Further optionally, the parameter to be identified includes a time-varying parameter. The iterative optimization module 503 is further configured to, after performing iterative optimization on the initial predicted value to obtain an optimized parameter value of the parameter to be identified: optimizing the time change proportion of the time-varying parameter by adopting the differential evolution algorithm to obtain an optimized value of the time change proportion of the time-varying parameter; acquiring an optimized parameter value of the time-varying parameters in the optimized parameter values as a reference parameter value of the time-varying parameters; and calculating the final optimized value of the time-varying parameter according to the reference parameter value of the time-varying parameter and the optimized value of the time-varying proportion.

Further optionally, the iterative optimization module 503 is specifically configured to, when optimizing the time variation ratio of the time-varying parameter by using the differential evolution algorithm to obtain an optimized value of the time variation ratio of the time-varying parameter: calculating the initial time change proportion of the time-varying parameters through a differential evolution algorithm; obtaining an initial time-varying value of the time-varying parameter according to the initial time-varying proportion and the reference parameter value; calculating the adaptability of the initial time-varying value according to the thermal management equivalent model; and adopting the differential evolution algorithm, and carrying out iterative optimization on the initial time change proportion according to the adaptability of the initial time change value to obtain an optimized value of the time change proportion of the time-varying parameter.

The fitness calculating module 502 is specifically configured to, when calculating the fitness of the initial time-varying value according to the thermal management equivalent model: inputting the optimized parameter value of the time-invariant parameter in the parameters to be identified, the initial time-variant value of the time-variant parameter and the actually measured input data into the thermal management equivalent model; calculating the actually measured input data according to the optimized parameter value of the time-invariant parameter and the initial time-variant value of the time-variant parameter through the thermal management equivalent model to obtain a second predicted temperature of the battery; and calculating the fitness of the initial time-varying parameter according to the error between the second predicted temperature and the measured temperature of the battery.

Fig. 6 is a schematic structural diagram of an electronic device according to an exemplary embodiment of the present application, as shown in fig. 6, including: a memory 601 and a processor 602.

The memory 601 is used for storing a computer program and may be configured to store other various data to support operations on the terminal device. Examples of such data include instructions for any application or method operating on the terminal device, contact data, phonebook data, messages, pictures, video, etc.

The memory 601 may be implemented by any type or combination of volatile or non-volatile memory devices, 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 memory, flash memory, magnetic or optical disk.

A processor 602 coupled to the memory 601 for executing the computer programs in the memory 601 for: calculating an initial predicted value of a parameter to be identified in a thermal management equivalent model of the battery through a differential evolution algorithm; calculating the adaptability of the initial predicted value through the thermal management equivalent model; and adopting the differential evolution algorithm, and carrying out iterative optimization on the initial predicted value according to the adaptability of the initial predicted value to obtain the optimized parameter value of the parameter to be identified.

Further optionally, the processor 602 is specifically configured to, when calculating the initial predicted value of the parameter to be identified in the thermal management equivalent model of the battery by using a differential evolution algorithm: determining an optimizing vector corresponding to the parameter to be identified; the differential evolution algorithm is adopted, and a solution population corresponding to the optimizing vector is randomly generated to serve as the initial predicted value according to preset constraint conditions and/or preset boundary conditions; wherein the boundary condition includes: upper and lower boundaries of the parameter to be identified; the constraint condition is a constraint condition of the parameter to be identified on a physical attribute.

Further optionally, the processor 602 is specifically configured to, when calculating the fitness of the initial predicted value by the thermal management equivalent model: carrying out temperature prediction according to actually detected actually measured input data of the battery and the initial predicted value through the thermal management equivalent model to obtain a first predicted temperature of the battery; the measured input data includes: at least one of an actually detected ambient temperature of the battery, a charging current of the battery, a rotation speed of a compressor, an opening degree of a water pump, and PTC power; and calculating the fitness of the parameter to be identified according to the error between the first predicted temperature and the actually detected measured temperature of the battery.

Further optionally, when the processor 602 performs iterative optimization on the initial predicted value according to the fitness of the initial predicted value by using the differential evolution algorithm to obtain an optimized parameter value of the parameter to be identified, the processor is specifically configured to: taking the initial predicted value as an original predicted value; judging whether the adaptability of the original predicted value meets a preset adaptability condition or not; if yes, the original predicted value is used as the optimized parameter value; if not, iteratively performing the following operations to update the original predicted value: performing differential mutation operation on the original predicted value through a preset differential operator to obtain a differential vector of the original predicted value; performing cross mutation operation on the original predicted value by adopting a difference vector of the original predicted value to obtain a mutation predicted value of the original predicted value; calculating the adaptability of the variation predicted value through the thermal management equivalent model; and if the adaptability of the variation predicted value is better than that of the original predicted value, taking the variation predicted value as the updated original predicted value, and repeating the judging step.

Further optionally, the parameter to be identified includes a time-varying parameter. The processor 602 is further configured to, after performing iterative optimization on the initial predicted value to obtain an optimized parameter value of the parameter to be identified: optimizing the time change proportion of the time-varying parameter by adopting the differential evolution algorithm to obtain an optimized value of the time change proportion of the time-varying parameter; acquiring an optimized parameter value of the time-varying parameters in the optimized parameter values as a reference parameter value of the time-varying parameters; and calculating the final optimized value of the time-varying parameter according to the reference parameter value of the time-varying parameter and the optimized value of the time-varying proportion.

Further optionally, the processor 602 is specifically configured to, when optimizing the time variation ratio of the time-varying parameter by using the differential evolution algorithm, obtain an optimized value of the time variation ratio of the time-varying parameter: calculating the initial time change proportion of the time-varying parameters through a differential evolution algorithm; obtaining an initial time-varying value of the time-varying parameter according to the initial time-varying proportion and the reference parameter value; calculating the adaptability of the initial time-varying value according to the thermal management equivalent model; and adopting the differential evolution algorithm, and carrying out iterative optimization on the initial time change proportion according to the adaptability of the initial time change value to obtain an optimized value of the time change proportion of the time-varying parameter.

Further optionally, the processor 602 is specifically configured to, when calculating the fitness of the initial time-varying value according to the thermal management equivalent model: inputting the optimized parameter value of the time-invariant parameter in the parameters to be identified, the initial time-variant value of the time-variant parameter and the actually measured input data into the thermal management equivalent model; calculating the actually measured input data according to the optimized parameter value of the time-invariant parameter and the initial time-variant value of the time-variant parameter through the thermal management equivalent model to obtain a second predicted temperature of the battery; and calculating the fitness of the initial time-varying parameter according to the error between the second predicted temperature and the measured temperature of the battery.

The memory of fig. 6 described above may be implemented by any type or combination of volatile or non-volatile memory devices, 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 memory, flash memory, magnetic or optical disk.

The display 603 in fig. 6 described above 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 may be implemented as a touch screen 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 sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation.

Further, as shown in fig. 6, the electronic device further includes: communication component 604, power supply component 605, and the like. Only some of the components are schematically shown in fig. 6, which does not mean that the electronic device only comprises the components shown in fig. 6.

The communication component 604 of fig. 6 described above is configured to facilitate communication between the device in which the communication component resides and other devices, either wired or wireless. The device in which the communication component is located may access a wireless network based on a communication standard, such as WiFi,2G, 3G, 4G, or 5G, or a combination thereof. In one exemplary embodiment, the communication component receives a broadcast signal 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) technology, radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.

Wherein the power supply module 605 provides power to the various components of the device in which the power supply module is located. The power components may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the devices in which the power components are located.

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

It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may 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, and the like) having computer-usable program code embodied therein.

The present 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 flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

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

The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer 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 technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.

The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.

Claims

1. A battery parameter identification method, comprising:

calculating an initial predicted value of a parameter to be identified in a thermal management equivalent model of the battery through a differential evolution algorithm;

calculating the adaptability of the initial predicted value through the thermal management equivalent model;

And adopting the differential evolution algorithm, and carrying out iterative optimization on the initial predicted value according to the adaptability of the initial predicted value to obtain the optimized parameter value of the parameter to be identified.

2. The method of claim 1, wherein calculating the initial predicted value of the parameter to be identified in the thermal management equivalent model of the battery by a differential evolution algorithm comprises:

determining an optimizing vector corresponding to the parameter to be identified;

the differential evolution algorithm is adopted, and a solution population corresponding to the optimizing vector is randomly generated to serve as the initial predicted value according to preset constraint conditions and/or preset boundary conditions;

wherein the boundary condition includes: upper and lower boundaries of the parameter to be identified; the constraint condition is a constraint condition of the parameter to be identified on a physical attribute.

3. The method of claim 1, wherein calculating the fitness of the initial predictor by the thermal management equivalent model comprises:

carrying out temperature prediction according to actually detected actually measured input data of the battery and the initial predicted value through the thermal management equivalent model to obtain a first predicted temperature of the battery; the measured input data includes: at least one of an actually detected ambient temperature of the battery, a charging current of the battery, a rotation speed of a compressor, an opening degree of a water pump, and PTC power;

And calculating the fitness of the parameter to be identified according to the error between the first predicted temperature and the actually detected measured temperature of the battery.

4. The method according to claim 1, wherein performing iterative optimization on the initial predicted value according to the fitness of the initial predicted value by using the differential evolution algorithm to obtain an optimized parameter value of the parameter to be identified comprises:

taking the initial predicted value as an original predicted value;

judging whether the adaptability of the original predicted value meets a preset adaptability condition or not;

if yes, the original predicted value is used as the optimized parameter value;

if not, iteratively performing the following operations to update the original predicted value:

performing differential mutation operation on the original predicted value through a preset differential operator to obtain a differential vector of the original predicted value;

performing cross mutation operation on the original predicted value by adopting a difference vector of the original predicted value to obtain a mutation predicted value of the original predicted value;

calculating the adaptability of the variation predicted value through the thermal management equivalent model;

and if the adaptability of the variation predicted value is better than that of the original predicted value, taking the variation predicted value as the updated original predicted value, and repeating the judging step.

5. The method according to any one of claims 1-4, wherein the parameter to be identified comprises a time-varying parameter;

after performing iterative optimization on the initial predicted value to obtain the optimized parameter value of the parameter to be identified, the method further comprises the following steps:

optimizing the time change proportion of the time-varying parameter by adopting the differential evolution algorithm to obtain an optimized value of the time change proportion of the time-varying parameter;

acquiring an optimized parameter value of the time-varying parameters in the optimized parameter values as a reference parameter value of the time-varying parameters;

and calculating the final optimized value of the time-varying parameter according to the reference parameter value of the time-varying parameter and the optimized value of the time-varying proportion.

6. The method of claim 5, wherein optimizing the time-varying proportion of the time-varying parameter using the differential evolution algorithm to obtain an optimized value of the time-varying proportion of the time-varying parameter comprises:

calculating the initial time change proportion of the time-varying parameters through a differential evolution algorithm;

obtaining an initial time-varying value of the time-varying parameter according to the initial time-varying proportion and the reference parameter value;

calculating the adaptability of the initial time-varying value according to the thermal management equivalent model;

And adopting the differential evolution algorithm, and carrying out iterative optimization on the initial time change proportion according to the adaptability of the initial time change value to obtain an optimized value of the time change proportion of the time-varying parameter.

7. The method of claim 6, wherein calculating the fitness of the initial time-varying value based on the thermal management equivalent model comprises:

inputting the optimized parameter value of the time-invariant parameter in the parameters to be identified, the initial time-variant value of the time-variant parameter and the actually measured input data into the thermal management equivalent model;

calculating the actually measured input data according to the optimized parameter value of the time-invariant parameter and the initial time-variant value of the time-variant parameter through the thermal management equivalent model to obtain a second predicted temperature of the battery;

and calculating the fitness of the initial time-varying parameter according to the error between the second predicted temperature and the measured temperature of the battery.

8. A battery parameter identification device, comprising:

the predicted value calculation module is used for: calculating an initial predicted value of a parameter to be identified in a thermal management equivalent model of the battery through a differential evolution algorithm;

The fitness calculation module is used for: calculating the adaptability of the initial predicted value through the thermal management equivalent model;

an iteration optimization module, which is used for: and adopting the differential evolution algorithm, and carrying out iterative optimization on the initial predicted value according to the adaptability of the initial predicted value to obtain the optimized parameter value of the parameter to be identified.

9. An electronic device, comprising: a memory and a processor;

wherein the memory is for: store one or more computer instructions;

the processor is configured to execute the one or more computer instructions to: performing the steps of the method of any one of claims 1-7.

10. A computer readable storage medium storing a computer program, characterized in that the computer program, when executed by a processor, causes the processor to carry out the steps of the method according to any one of claims 1-7.