Power battery parameter identification method and system based on adaptive crested porcupine algorithm

Abstract

The present invention relates to the technical field of automotive power batteries, and provides a power battery parameter identification method and system based on an adaptive crown porcupine algorithm, including: constructing a battery electrochemical model and identification parameters; optimizing the identification parameters using an adaptive crown porcupine algorithm until the difference between a voltage simulation value and a test voltage value meets a condition, thereby obtaining the best identification parameters, wherein the voltage simulation value is obtained by simulating the battery electrochemical model based on the identification parameters; wherein the adaptive crown porcupine algorithm uses an adaptive mutation operator to replace the random numbers in the first defense strategy and the second defense strategy of the crown porcupine algorithm, and the adaptive mutation operator is negatively correlated with the number of iterations and positively correlated with the fitness function value. The battery model parameters can be quickly and accurately identified in practical applications, thereby improving the speed and accuracy of three-dimensional simulation of power batteries.

Description

Technical Field

[0001] The present invention belongs to the technical field of automotive power batteries, and in particular relates to a power battery parameter identification method and system based on an adaptive crested porcupine algorithm. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] The accuracy of the battery model depends on whether the battery parameters are accurately obtained. By building a high-precision model, the voltage and power of the battery can be effectively managed, and the heat generation behavior of the battery can be predicted, which is helpful to develop an effective battery thermal management system. Current batteries are mainly divided into equivalent circuit models, electrochemical models, and microscopic electrochemical models. The accuracy of the battery model is directly proportional to the required battery parameters and the computational cost. Among them, the electrochemical model represented by pseudo-two-dimensional has high accuracy and can accurately describe the internal reactions of the battery.

[0004] Due to the characteristics of lithium-ion battery electrochemical models such as large parameter sets, high nonlinearity, no analytical solutions, and the need for iterative solutions, traditional parameter estimation methods are difficult to apply directly. If you want to quickly obtain the internal parameters of finished batteries without destroying the batteries, a feasible solution is to use the external characteristics of the battery and the electrochemical model to reverse the value of the internal parameters. Common online parameter identification methods, such as recursive least squares, have the advantages of small computational complexity and easy implementation on embedded chips. However, their identification accuracy is limited and often cannot meet the requirements of electrochemical models. Summary of the invention

[0005] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a power battery parameter identification method and system based on an adaptive crowned porcupine algorithm. The crowned porcupine algorithm is used to identify the power battery parameters. Moreover, by introducing an adaptive mutation scaling operator, the diversity of the population can be guaranteed, the algorithm can be prevented from falling into the local optimum, and the convergence speed of the algorithm can be accelerated. In practical applications, the battery model parameters can be quickly and accurately identified, and the speed and accuracy of the three-dimensional simulation of the power battery can be improved.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] The first aspect of the present invention provides a power battery parameter identification method based on an adaptive crested porcupine algorithm, which comprises:

[0008] Construct battery electrochemical models and identify parameters;

[0009] The identification parameters are optimized by using an adaptive crested porcupine algorithm until the difference between the voltage simulation value and the test voltage value satisfies a condition, thereby obtaining an optimal identification parameter, wherein the voltage simulation value is obtained by simulating the battery electrochemical model based on the identification parameters;

[0010] Among them, the adaptive crown porcupine algorithm adopts an adaptive mutation operator to replace the random numbers in the first defense strategy and the second defense strategy of the crown porcupine algorithm. The adaptive mutation operator is negatively correlated with the number of iterations and positively correlated with the fitness function value.

[0011] Furthermore, the adaptive mutation operator is:

[0012]

[0013] Among them, F t represents the adaptive mutation operator, t is the current iteration number, t max is the maximum number of iterations, represents the fitness function value of the i-th individual at the t-th iteration, f min is the minimum value of the fitness function, f max is the maximum value of the fitness function.

[0014] Furthermore, the first defense strategy of the adaptive crested porcupine algorithm is:

[0015]

[0016] In the formula, represents the position of the i-th individual at the t-th iteration, F t is an adaptive mutation operator, τ2 is a random value in the interval [0, 1], is the best solution evaluated by the fitness function at the tth iteration, r is an integer between [1, N′], and N′ is the population size.

[0017] Furthermore, the second defense strategy of the adaptive crested porcupine algorithm is:

[0018]

[0019] In the formula, represents the position of the i-th individual at the t-th iteration, is a randomly generated binary vector of 0 and 1, r1 and r2 are two random integers between [1, N′], and F t is the adaptive mutation operator, Indicates the location of the predator.

[0020] Furthermore, the difference between the voltage simulation value and the test voltage value satisfies the condition that: in, t is the current iteration number, x is the identification parameter vector, U exp is the test voltage value, The voltage simulation value obtained by substituting the identification parameter vector obtained by the adaptive crested porcupine algorithm at the tth iteration into the electrochemical model.

[0021] Furthermore, the identification parameters include electrode lithium insertion at 0 volts for the positive electrode, electrode lithium insertion at 100 volts for the positive electrode, electrode lithium insertion at 0 volts for the negative electrode, electrode lithium insertion at 100 volts for the negative electrode and reaction current density.

[0022] Furthermore, the battery electrochemical model adopts a pseudo two-dimensional model of a lithium-ion battery.

[0023] A second aspect of the present invention provides a power battery parameter identification system based on an adaptive crested porcupine algorithm, comprising:

[0024] A data acquisition module configured to: construct a battery electrochemical model and identify parameters;

[0025] A parameter identification module is configured to: optimize the identification parameters by using an adaptive crested porcupine algorithm until the difference between the voltage simulation value and the test voltage value satisfies a condition, thereby obtaining an optimal identification parameter, wherein the voltage simulation value is obtained by simulating the battery electrochemical model based on the identification parameter;

[0026] Among them, the adaptive crown porcupine algorithm adopts an adaptive mutation operator to replace the random numbers in the first defense strategy and the second defense strategy of the crown porcupine algorithm. The adaptive mutation operator is negatively correlated with the number of iterations and positively correlated with the fitness function value.

[0027] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the power battery parameter identification method based on the adaptive crested porcupine algorithm as described above.

[0028] The fourth aspect of the present invention provides a computer device, including a computer-readable storage medium, a processor, and a computer program stored on the computer-readable storage medium and executable on the processor, wherein when the processor executes the program, the steps in the power battery parameter identification method based on the adaptive crested porcupine algorithm as described above are implemented.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] The present invention proposes an adaptive crested porcupine algorithm, which is an intelligent algorithm for high-precision identification of sensitive parameters of a pseudo-two-dimensional model. The intelligent algorithm can quickly and accurately identify battery model parameters in practical applications, and can improve the speed and accuracy of three-dimensional simulation of power batteries.

[0031] The present invention improves the crested porcupine algorithm by using an adaptive mutation operator to obtain an adaptive crested porcupine algorithm. By introducing an adaptive mutation scaling operator, the algorithm can be kept at a relatively large value at the beginning of iteration of the adaptive crested porcupine algorithm to ensure the diversity of the population and prevent the adaptive crested porcupine algorithm from falling into a local optimum. In the later stage of iteration, the algorithm can be kept at a relatively small value to accelerate the convergence speed of the adaptive crested porcupine algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0033] Figure 1 is a flow chart of a power battery parameter identification method based on an adaptive crested porcupine algorithm according to a first embodiment of the present invention;

[0034] Figure 2 It is a structural diagram of a computer device according to a fourth embodiment of the present invention. DETAILED DESCRIPTION

[0035] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0036] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0037] Embodiment 1

[0038] This embodiment provides a power battery parameter identification method based on an adaptive crested porcupine algorithm.

[0039] The power battery parameter identification method based on the adaptive crested porcupine algorithm provided in this embodiment can accurately identify the battery electrochemical model parameters and improve the three-dimensional simulation accuracy of the power battery.

[0040] The power battery parameter identification method based on the adaptive crested porcupine algorithm provided in this embodiment is as follows: Figure 1 As shown, the following steps are included:

[0041] Step 1, constructing a battery electrochemical model (pseudo two-dimensional model of lithium-ion battery) and identifying parameters (battery electrochemical model parameters);

[0042] Step 2, using the adaptive mutation operator to improve the crested porcupine algorithm, and obtaining an adaptive crested porcupine algorithm;

[0043] Step 3: Initialize the battery electrochemical model parameters, and use the adaptive crown porcupine algorithm to iteratively optimize the battery electrochemical model parameters until the difference between the voltage simulation value obtained based on the model parameter simulation and the test voltage value meets the conditions, and the optimal battery electrochemical model parameters are obtained, which can be used for three-dimensional simulation of power batteries and other tasks.

[0044] Among them, the calculation method of the fitness function of the adaptive crested porcupine algorithm is: introduce the battery electrochemical model parameters into the pseudo two-dimensional model of the lithium-ion battery to obtain the simulation result of the voltage; measure the corresponding test voltage value under the condition of small rate discharge, and use the minimum error value between the simulated voltage and the experimental voltage as the fitness function.

[0045] In the pseudo-two-dimensional model of lithium-ion batteries in step 1, the equilibrium potential of the positive and negative electrodes of lithium-ion batteries is mainly determined by the materials. The positive electrode is ternary lithium, and the negative electrode material is graphite. The negative electrode open circuit voltage can be calculated by the following formula:

[0046]

[0047] The positive open circuit voltage can be calculated by the following formula:

[0048]

[0049] Wherein, θ is the lithium insertion state of the electrode, specifically, θ p is the positive electrode lithium insertion state, θ n It is the negative electrode lithium insertion state, and its definition is shown as follows:

[0050]

[0051] Among them, C s is the surface concentration of solid particles, C s,max is the maximum concentration of solid particles.

[0052] Among them, C s It can be obtained by the following formula:

[0053]

[0054] Where F is the Faraday constant; D s is the ion diffusion coefficient; R s is the electrode particle diameter; j Li(x, s) is the reaction current density, which is also a parameter to be identified; x is the variable along the electrode thickness direction, r is the particle radius, and s is a symbol that exists in the transfer function.

[0055] Among them, C s,max It can be obtained by the following formula:

[0056]

[0057] Among them, Q is the battery capacity, unit is C; ε is the porosity of the electrolyte; L is the electrode thickness; A is the actual cross-sectional area of ​​the electrode; ΔSOL is the difference in electrode lithium insertion at 0 volts and 100 volts.

[0058] At the positive electrode, ΔSOL=SOL p0 -SOL p100 ; At the negative electrode, ΔSOL=SOL n100 -SOL n0 Among them, SOL p0 When the positive electrode is 0V, the electrode is lithium-intercalated, SOL p100 The positive electrode is lithium-intercalated at 100 volts, SOL n0 When the negative electrode is 0 volts, the electrode is lithium-intercalated, SOL n100 The negative electrode is lithium-intercalated at 100 volts.

[0059] In this embodiment, the corresponding variables in the formula (including the electrode lithium insertion range and other identification parameters of the battery electrochemical model) are parameters identified by the battery, specifically: the electrode lithium insertion range includes: SOL p0 is [0.6, 0.99], SOL p100 is [0.2, 0.6], SOL n0 is [0.001, 0.3], SOL n100 is [0.6, 0.99]; other identification parameters of the battery electrochemical model include: electrode thickness (positive electrode thickness L p , the thickness of the diaphragm L s , negative electrode thickness L n ), positive electrode particle radius R sp , the radius of the negative electrode particle R sn , liquid phase diffusion coefficient D e , liquid phase conductivity κ, porosity ε, positive electrode solid phase diffusion coefficient Dsp, negative electrode solid phase diffusion coefficient Dsn.

[0060] The crested porcupine algorithm in step 2 uses a mechanism of exploration and exploitation. The visual and auditory strategies allow crested porcupines to scare predators away from them. Using these defense mechanisms, the response of predators released after crested porcupines implement defensive behaviors can be simulated to explore the search space to reach promising areas. The strategies based on scent and physical attack exploit promising areas and approach the optimal solution.

[0061] Initialization phase:

[0062] First, randomly initialize their dimensions within the search space of each dimension using the following formula:

[0063]

[0064] Among them, N′ represents the number of individuals (population size), is a candidate solution in the search space, and are the lower and upper bounds of the search range, respectively, and is a vector randomly initialized between 0 and 1.

[0065] The first defense strategy:

[0066] When the crested porcupine becomes aware of the predator, it starts to raise and fan its quills to give the impression that it is larger. Therefore, the predator has two choices, either move towards it or away from it. The first option encourages exploration of the area between the predator and the crested porcupine in an attempt to speed up convergence. In contrast, the second option encourages exploration of distant areas to identify unvisited areas that may involve the desired solution. These options are mathematically simulated using a normal distribution to generate random values. These random values ​​encourage the crested porcupine to move closer to the crested porcupine if it is less than 1 or greater than -1. Otherwise, the predator will move away from the crested porcupine.

[0067] The first defense strategy can be expressed as:

[0068]

[0069] in, represents the position of the i-th individual at the t-th iteration; is the best solution evaluated by the fitness function; τ1 is a random number based on normal distribution, and τ2 is a random value in the interval [0, 1]; Is in the current crested porcupine and crested porcupines randomly selected from the population The vector generated between is used to represent the position of the predator at iteration t, The mathematical formula is as follows:

[0070]

[0071] Wherein, r is an integer between [1, N′].

[0072] The second defense strategy:

[0073] In this strategy, crested porcupines use vocalizations to make noise and intimidate predators. When a predator approaches the porcupine, the porcupine's vocalizations become louder. To mathematically model this behavior, the following formula was proposed:

[0074]

[0075] where r1 and r2 are two random integers between [1, N′]; τ3 is a random value generated between 0 and 1; and a binary vector between 0 and 1 is generated using random to cover all possible probabilities; the vector Indicates the location of the predator.

[0076] The third defense strategy:

[0077] In this strategy, the crested porcupine secretes a foul odor that diffuses into the area around it to deter predators from approaching it. To mathematically model this behavior, the following formula was proposed:

[0078]

[0079] Where r3 is a random integer between [1, N′], is the position of the i-th individual at iteration t, τ3 is a random value in the interval [0, 1];

[0080] Among them, γ t is the defense factor:

[0081]

[0082] in, is a parameter used to control the search direction:

[0083]

[0084] in, is the odor diffusion factor, defined as follows:

[0085]

[0086] in, represents the objective function value (i.e., fitness function value) of the i-th individual at iteration t; Δ is a small value to avoid division by zero; is a vector containing a randomly generated value between 0 and 1; rand is a variable containing a randomly generated number between 0 and 1; t is the number of the current iteration, and t max is the maximum number of iterations.

[0087] in, Used to simulate three possible scenarios in this strategy:

[0088] (1) When If it is equal to 0, the crested porcupine will stop spreading its scent because the predator will stop moving due to fear of the crested porcupine, so the distance between the predator and the crested porcupine remains unchanged;

[0089] (2) When If it is equal to 1, the crested porcupine will obviously emit the scent because the predator is close at hand;

[0090] (3) Between 0 and 1, predators stay a safe distance from crested porcupines, so there's no need to release their scent widely.

[0091] in, Responsible for the odor diffusion rate during the optimization process, which ranges between 0.3 and 2.6.

[0092] The fourth defense strategy:

[0093] When the predator is very close, the crested porcupine will resort to physical attack, and the two objects will merge strongly, representing a one-dimensional inelastic collision. Its physical attack behavior is expressed mathematically as:

[0094]

[0095] in, is the best solution to obtain, is the position of the ith individual at iteration t, α is the convergence rate factor, and τ4 and τ5 are both random values ​​in the interval [0, 1]; is the average force of the crested porcupine affecting the i-th predator, which is provided by the law of inelastic collision as follows:

[0096]

[0097]

[0098] in, is the mass of the i-th individual (predator) at iteration t; represents the objective function; is the final velocity of the ith individual at the next iteration t+1 and is assigned according to the selection of random solutions from the current population; is the initial velocity of the ith individual at iteration t; t is the number of current iterations; is a vector containing random values ​​generated between 0 and 1.

[0099] In this embodiment, the first and second defense strategies of the crown porcupine algorithm use the adaptive mutation operator F t Improve its performance. The adaptive mutation operator is negatively correlated with the number of iterations and positively correlated with the fitness function value. Its expression is shown in the following formula:

[0100]

[0101] Where t is the number of current iterations, and t max is the maximum number of iterations, λ t The proportional transformation function f in the mutation process i t Decision, f i t The definition is as follows:

[0102]

[0103] And t Satisfy the following formula:

[0104]

[0105] Among them, f min is the minimum value of the fitness function, f max is the maximum value of the fitness function.

[0106] The first improved defense strategy is:

[0107]

[0108] In the formula, is the best solution evaluated by the fitness function at the tth iteration, is a vector generated between the current crested porcupine and a randomly selected crested porcupine from the population, used to represent the position of the predator at iteration t, F t is an adaptive mutation operator, τ2 is a random value in the interval [0, 1], and r is an integer between [1, N′].

[0109] The improved second defense strategy is:

[0110]

[0111] In the formula, r1 and r2 are two random integers between [1, N′], and F t is the adaptive mutation operator, Indicates the location of the predator.

[0112] Thus, the adaptive crested porcupine algorithm is obtained.

[0113] In this embodiment, the fitness function of the adaptive crested porcupine algorithm includes: identifying the identification parameters by the adaptive crested porcupine algorithm, putting the obtained identification parameters into the voltage calculation formula of the power battery to obtain the voltage simulation value of the power battery, discharging the power battery with a small rate current, and obtaining the voltage value measured by the test. Thus, the error between the voltage obtained by simulation calculation and the voltage measured by the test is used as the fitness function of the adaptive crested porcupine algorithm, and its expression is as follows:

[0114]

[0115] In the formula, x is the parameter vector to be identified, U exp is the voltage value measured in the test, The voltage value calculated by substituting the identification parameter vector obtained at the tth iteration into the electrochemical model.

[0116] When the fitness function continuously iterates n t times, for example n t When it is 20 times, the following formula is satisfied:

[0117]

[0118] The parameter identification is completed, thereby identifying the high-precision power battery parameters.

[0119] The power battery parameter identification method based on the adaptive crown porcupine algorithm provided in this embodiment proposes an adaptive crown porcupine algorithm, which is a high-precision identification intelligent algorithm for sensitive parameters of a pseudo-two-dimensional model. This intelligent algorithm can quickly and accurately identify battery model parameters in practical applications, and can improve the speed and accuracy of three-dimensional simulation of power batteries.

[0120] The power battery parameter identification method based on the adaptive crown porcupine algorithm provided in this embodiment uses an adaptive mutation operator to improve the crown porcupine algorithm to obtain an adaptive crown porcupine algorithm. By introducing an adaptive mutation scaling operator, the adaptive crown porcupine algorithm can be maintained at a larger value at the beginning of the iteration, ensuring the diversity of the population and preventing the adaptive crown porcupine algorithm from falling into a local optimum. In the later stage of the iteration, the algorithm can be kept at a smaller value, thereby accelerating the convergence speed of the adaptive crown porcupine algorithm.

[0121] Embodiment 2

[0122] This embodiment provides a power battery parameter identification system based on an adaptive crested porcupine algorithm, which specifically includes:

[0123] A data acquisition module configured to: construct a battery electrochemical model and identify parameters;

[0124] A parameter identification module is configured to: optimize the identification parameters by using an adaptive crested porcupine algorithm until the difference between the voltage simulation value and the test voltage value satisfies a condition, thereby obtaining an optimal identification parameter, wherein the voltage simulation value is obtained by simulating the battery electrochemical model based on the identification parameter;

[0125] Among them, the adaptive crown porcupine algorithm adopts an adaptive mutation operator to replace the random numbers in the first defense strategy and the second defense strategy of the crown porcupine algorithm. The adaptive mutation operator is negatively correlated with the number of iterations and positively correlated with the fitness function value.

[0126] It should be noted here that each module in this embodiment corresponds to each step in Example 1 one by one, and the specific implementation process is the same, which will not be repeated here.

[0127] Embodiment 3

[0128] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the power battery parameter identification method based on the adaptive crested porcupine algorithm as described in the first embodiment are implemented.

[0129] Embodiment 4

[0130] This embodiment provides a computer device, such as Figure 2 As shown, it includes a computer-readable storage medium 1003, a processor 1001, a communication interface 1002, and a computer program stored on the computer-readable storage medium 1003 and executable on the processor 1001, wherein the processor 1001, the communication interface 1002, and the computer-readable storage medium 1003 may be connected via a bus or other means. Among them, the communication interface 1002 is used to receive and send data, and when the processor 1001 executes the program, the steps in the power battery parameter identification method based on the adaptive crested porcupine algorithm as described in the first embodiment above are implemented.

[0131] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0132] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0134] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A power battery parameter identification method based on an adaptive crown porcupine algorithm, characterized in that: include: Construct battery electrochemical models and identify parameters; The identification parameters are optimized by using an adaptive crested porcupine algorithm until the difference between the voltage simulation value and the test voltage value satisfies a condition, thereby obtaining an optimal identification parameter, wherein the voltage simulation value is obtained by simulating the battery electrochemical model based on the identification parameters; Among them, the adaptive crown porcupine algorithm adopts an adaptive mutation operator to replace the random numbers in the first defense strategy and the second defense strategy of the crown porcupine algorithm. The adaptive mutation operator is negatively correlated with the number of iterations and positively correlated with the fitness function value; The adaptive mutation operator is: Among them, F t represents the adaptive mutation operator, t is the current iteration number, t max is the maximum number of iterations, represents the fitness function value of the i-th individual at the t-th iteration, f min is the minimum value of the fitness function, f max is the maximum value of the fitness function; The first defense strategy of the adaptive crested porcupine algorithm is: In the formula, represents the position of the i-th individual at the t-th iteration, τ2 is a random value in the interval [0, 1], is the best solution evaluated by the fitness function at the tth iteration, r is an integer between [1, N′], and N′ is the population size; The second defense strategy of the adaptive crested porcupine algorithm is: In the formula, is a randomly generated binary vector of 0 and 1, r1 and r2 are two random integers between [1, N′], Indicates the location of the predator; The difference between the voltage simulation value and the test voltage value satisfies the condition that: in, x is the identification parameter vector, U exp is the test voltage value, The voltage simulation value obtained by substituting the identification parameter vector obtained by the adaptive crested porcupine algorithm at the tth iteration into the electrochemical model.

2. The power battery parameter identification method based on the adaptive crested porcupine algorithm according to claim 1, characterized in that: The identification parameters include electrode lithium insertion when the positive electrode is at 0 volts, electrode lithium insertion when the positive electrode is at 100 volts, electrode lithium insertion when the negative electrode is at 0 volts, electrode lithium insertion when the negative electrode is at 100 volts, and reaction current density.

3. The power battery parameter identification method based on the adaptive crested porcupine algorithm according to claim 1, characterized in that: The battery electrochemical model adopts a pseudo two-dimensional model of a lithium-ion battery.

4. The power battery parameter identification system based on the adaptive crown porcupine algorithm is characterized by: include: A data acquisition module configured to: construct a battery electrochemical model and identify parameters; A parameter identification module is configured to: optimize the identification parameters by using an adaptive crested porcupine algorithm until the difference between the voltage simulation value and the test voltage value satisfies a condition, thereby obtaining an optimal identification parameter, wherein the voltage simulation value is obtained by simulating the battery electrochemical model based on the identification parameter; Among them, the adaptive crown porcupine algorithm adopts an adaptive mutation operator to replace the random numbers in the first defense strategy and the second defense strategy of the crown porcupine algorithm. The adaptive mutation operator is negatively correlated with the number of iterations and positively correlated with the fitness function value; The adaptive mutation operator is: Among them, F t represents the adaptive mutation operator, t is the current iteration number, t max is the maximum number of iterations, represents the fitness function value of the i-th individual at the t-th iteration, f min is the minimum value of the fitness function, f max is the maximum value of the fitness function; The first defense strategy of the adaptive crested porcupine algorithm is: In the formula, represents the position of the i-th individual at the t-th iteration, τ2 is a random value in the interval [0, 1], is the best solution evaluated by the fitness function at the tth iteration, r is an integer between [1, N′], and N′ is the population size; The second defense strategy of the adaptive crested porcupine algorithm is: In the formula, is a randomly generated binary vector of 0 and 1, r1 and r2 are two random integers between [1, N′], Indicates the location of the predator; The difference between the voltage simulation value and the test voltage value satisfies the condition that: in, x is the identification parameter vector, U exp is the test voltage value, The voltage simulation value obtained by substituting the identification parameter vector obtained by the adaptive crested porcupine algorithm at the tth iteration into the electrochemical model.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the power battery parameter identification method based on the adaptive crested porcupine algorithm as described in any one of claims 1 to 3 are implemented.

6. A computer device comprising a computer-readable storage medium, a processor, and a computer program stored on the computer-readable storage medium and executable on the processor, characterized in that: When the processor executes the program, the steps in the power battery parameter identification method based on the adaptive crested porcupine algorithm as described in any one of claims 1 to 3 are implemented.

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