Method, system and device for constructing equivalent circuit model of lithium ion battery and medium

Abstract

The application relates to a lithium ion battery equivalent circuit model construction method, system, device and medium, and belongs to the lithium ion battery equivalent circuit model construction field. The method comprises the following steps: constructing an initial lithium ion battery equivalent circuit model of a lithium ion battery to be modeled; determining the structure of a second lithium ion battery equivalent circuit model by using the population chromosome coding of a genetic algorithm based on the initial lithium ion battery equivalent circuit model, so as to obtain a plurality of different second lithium ion battery equivalent circuit models; optimizing the second lithium ion battery equivalent circuit model by using a particle swarm algorithm, so as to determine the optimal model parameters of a third lithium ion battery equivalent circuit model; optimizing the structures of the plurality of third lithium ion battery equivalent circuit models by using a genetic algorithm, so as to determine the optimal structure of the lithium ion battery equivalent circuit model, and obtaining the lithium ion battery equivalent circuit model. The application improves the accuracy of model construction.

Description

Technical Field

[0001] This invention relates to the field of equivalent circuit model construction for lithium-ion batteries, and in particular to a method, system, device, and medium for constructing equivalent circuit models for lithium-ion batteries. Background Technology

[0002] Lithium-ion battery energy storage is currently the fastest-growing and most widely used form of electrochemical energy storage, finding applications in power grids, electric vehicles, and other fields. Due to the inherent risks of its electrochemical processes, scientific control and effective management are crucial for its further development. Mathematical modeling of lithium-ion batteries is fundamental for designing rational control and management strategies, and is of great significance to lithium-ion battery technology.

[0003] Currently, experts and scholars from various countries have made considerable research achievements in describing and mathematically modeling the electrical behavior characteristics of lithium-ion batteries. Existing research methods can be divided into three main categories: 1) Mechanism-based methods based on battery electrochemical processes, mainly including electrochemical models and electrochemical impedance models. These models describe changes in battery voltage, state of charge, and AC impedance based on the electrochemical reaction process. The advantage is that they can specifically reflect the electrochemical reaction process of lithium-ion batteries, which is beneficial for describing the microscopic processes of charging and discharging, and has a guiding role in understanding the operating mechanism of lithium-ion batteries. However, the disadvantage is that the models are highly complex and difficult to guide practical applications; it is a purely theoretical calculation modeling method. 2) Black-box models based on data mining. These models measure a large amount of external characteristic data of lithium batteries and use machine learning, artificial intelligence, and other technologies to construct a functional mapping relationship between battery measurement variables and output variables. Among these, neural networks are the most widely used. Their advantage is that they do not require analysis of the microscopic electrochemical processes of lithium-ion batteries, but rather provide a macroscopic view. The relationship between the output and input of lithium-ion batteries is obtained by characterizing physical quantities such as electrical and thermal parameters using data mining techniques. This method is relatively simple and straightforward, but it involves a large amount of computation and is greatly affected by data quality, which is a difficult aspect to control and estimate. 3) The equivalent circuit method based on equivalent lumped electrical parameters equates the battery to a two-port network and uses electrical components such as power supply, resistors, and capacitors to simulate battery characteristics. Classic equivalent circuit models include the Rint model, Thevenin first-order and second-order models, PNGV model, DP model, and GNL model, depending on the number of electrical components and the differences in series and parallel structures. The advantage of this method is that it uses lumped electrical components to simulate the electrochemical process of lithium-ion batteries. Combining the electrochemical principle of lithium-ion batteries, it uses data mining and system identification techniques to complete the calculation of electrical components, completing the lithium-ion battery modeling from the perspective of mechanism + data. At the same time, the model is represented as a circuit system, which is convenient for the development and design of controllers and management systems.

[0004] However, as the above studies show, researchers from various countries have established multiple equivalent models of lithium-ion batteries for different applications. The essence of these models lies in determining the order (number of resistors and capacitors) and parameters of the equivalent circuit of the lithium-ion battery. Higher-order systems offer high simulation accuracy but are susceptible to noise interference, making parameter tuning difficult. Conversely, lower-order systems have low simulation accuracy for lithium-ion batteries and struggle to reflect their complex electrochemical processes. Therefore, it is essential to conduct adaptive optimization that integrates the order and parameter tuning of the equivalent circuit of lithium-ion batteries, which will benefit the further development and application of lithium-ion batteries. Summary of the Invention

[0005] The purpose of this invention is to provide a method, system, device, and medium for constructing an equivalent circuit model of a lithium-ion battery, so as to solve the problems of difficult parameter tuning or low simulation accuracy in the existing methods for constructing equivalent circuit models of lithium-ion batteries.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] A method for constructing an equivalent circuit model of a lithium-ion battery includes:

[0008] Construct an initial equivalent circuit model of the lithium-ion battery to be modeled; the initial equivalent circuit model of the lithium-ion battery is an n-order electrical network consisting of a controlled voltage source and n pairs of RC parallel structures connected in series.

[0009] Based on the initial lithium-ion battery equivalent circuit model, the structure of the second lithium-ion battery equivalent circuit model is determined by using the population chromosome encoding of the genetic algorithm, resulting in multiple different second lithium-ion battery equivalent circuit models.

[0010] The particle swarm optimization algorithm is used to optimize each second lithium-ion battery equivalent circuit model, and the optimal model parameters of each third lithium-ion battery equivalent circuit model are determined to obtain multiple third lithium-ion battery equivalent circuit models; the optimal model parameters include the optimal resistance value and the optimal capacitance value.

[0011] The genetic algorithm is used to optimize the structure of multiple equivalent circuit models of the third lithium-ion battery to determine the optimal structure of the equivalent circuit model of the lithium-ion battery.

[0012] The equivalent circuit model of the lithium-ion battery is determined based on the optimal structure of the equivalent circuit model, so as to design the controller of the lithium-ion battery and formulate the lithium-ion battery management strategy.

[0013] Optionally, constructing the initial lithium-ion battery equivalent circuit model of the lithium-ion battery to be modeled specifically includes:

[0014] The current, output voltage, and open-circuit voltage of the lithium-ion battery are obtained, and the state of charge of the lithium-ion battery is calculated based on the ampere-hour integration method.

[0015] Establish an initial functional relationship between the open-circuit voltage and the state of charge;

[0016] The function coefficients of the initial functional relationship are calculated using the least squares method to obtain the initial functions of the open-circuit voltage and the state of charge.

[0017] The parameters of the controlled voltage source are determined based on the initial function, and the number of RC parallel structures is defined.

[0018] Based on the number of the controlled voltage sources and the RC parallel structures, an initial equivalent circuit model of the lithium-ion battery is established.

[0019] Optionally, the construction of the initial lithium-ion battery equivalent circuit model for the lithium-ion battery to be modeled further includes:

[0020] Using formula Calculate the output voltage of the initial lithium-ion battery equivalent circuit model; where, S represents the output voltage of the initial lithium-ion battery equivalent circuit model; SOC (t) represents the state of charge of the lithium-ion battery; U i(t) Represents the voltage value of the i-th pair of RC parallel structures; R i (t) represents the resistance value of the i-th pair of RC parallel structures; C i (t) represents the capacitance value of the i-th pair of RC parallel structures.

[0021] Optionally, based on the initial lithium-ion battery equivalent circuit model, the structure of the second lithium-ion battery equivalent circuit model is determined using population chromosome encoding via a genetic algorithm, resulting in multiple different second lithium-ion battery equivalent circuit models, specifically including:

[0022] The structure of the initial lithium-ion battery equivalent circuit model was binary-encoded using population chromosome coding, resulting in multiple coding results.

[0023] Based on the encoding results, electrical components are selected in the initial lithium-ion battery equivalent circuit model to determine the structure of the second lithium-ion battery equivalent circuit model, resulting in multiple different second lithium-ion battery equivalent circuit models.

[0024] Optionally, the step of using particle swarm optimization to optimize each of the second lithium-ion battery equivalent circuit models and determining the optimal model parameters for each third lithium-ion battery equivalent circuit model to obtain multiple third lithium-ion battery equivalent circuit models specifically includes:

[0025] Based on the output voltage of the lithium-ion battery and the output voltage of the initial lithium-ion battery equivalent circuit model, determine the objective function of the model parameters under the state of charge at a set time and determine the objective function value of the model parameters under the state of charge at the set time;

[0026] Sort the objective function values ​​of each model parameter and select the optimal model parameters;

[0027] Determine whether the maximum number of iterations has been reached, and obtain the first determination result;

[0028] If the first judgment result is that the maximum number of iterations has been reached, then update the state of charge at the next moment, initialize the particle swarm algorithm parameters, and calculate the objective function of the model parameters under the state of charge at the next set moment in order to obtain the optimal model parameters of the third lithium-ion battery equivalent circuit model.

[0029] If the first judgment result is that the maximum number of iterations has not been reached, then the position and velocity of the particles are updated according to the particle swarm algorithm to obtain the updated particle swarm, and the step of "determining the objective function of the model parameters under the charged state at a set time and determining the objective function value of the model parameters under the charged state at the set time based on the output voltage of the lithium-ion battery and the output voltage of the initial lithium-ion battery equivalent circuit model" is returned.

[0030] Optionally, it also includes:

[0031] Based on the objective function of the model parameters under the state of charge at all set times, using the formula Calculate the objective function of the model structure of the equivalent circuit model of the third lithium-ion battery in each group; where, J1(S) represents the objective function of the model structure of the equivalent circuit model of the third lithium-ion battery in the i-th group of the g-th generation; SOC (t) represents the objective function of the model parameters under the charged state at a given time t; T represents the total number of time points; M represents the length of the variable represented by the particle.

[0032] Optionally, the step of using the genetic algorithm to optimize the structure of multiple third lithium-ion battery equivalent circuit models to determine the optimal structure of the lithium-ion battery equivalent circuit model specifically includes:

[0033] Based on the objective function of the model structure of multiple third lithium-ion battery equivalent circuit models, a genetic algorithm is used to select a preset number of chromosomes corresponding to the model structure of the third lithium-ion battery equivalent circuit model with the maximum objective function value.

[0034] Perform a crossover operation using a genetic algorithm on the selected chromosomes to obtain an equal number of new chromosomes;

[0035] The mutation operation of the genetic algorithm is used to update an equal number of the new chromosomes to obtain the updated chromosomes;

[0036] The new chromosome and the updated chromosome are combined to form a new generation of chromosomes;

[0037] Determine whether the number of iterations of the genetic algorithm has reached the maximum number of iterations. If so, determine the optimal structure based on the new generation of chromosomes.

[0038] If not, then calculate the objective function of all new generation chromosomes based on the new generation chromosomes, and return the step of "selecting a preset number of chromosomes corresponding to the model structure of the third lithium-ion battery equivalent circuit model with the maximum objective function value based on the objective function of the model structure of multiple third lithium-ion battery equivalent circuit models using the selection operation of the genetic algorithm".

[0039] A lithium-ion battery equivalent circuit model construction system includes:

[0040] The initial model building module is used to build an initial equivalent circuit model of the lithium-ion battery to be modeled; the initial equivalent circuit model of the lithium-ion battery is an n-order electrical network consisting of a controlled voltage source and n pairs of RC parallel structures connected in series.

[0041] The model structure determination module is used to determine the structure of the second lithium-ion battery equivalent circuit model based on the initial lithium-ion battery equivalent circuit model and by using the population chromosome encoding of the genetic algorithm, thereby obtaining multiple different second lithium-ion battery equivalent circuit models.

[0042] The parameter optimization module is used to optimize each second lithium-ion battery equivalent circuit model using the particle swarm optimization algorithm, determine the optimal model parameters of each third lithium-ion battery equivalent circuit model, and obtain multiple third lithium-ion battery equivalent circuit models; the optimal model parameters include the optimal open-circuit voltage, the optimal resistance value, and the optimal capacitance value;

[0043] The structure optimization module is used to optimize the structure of multiple third lithium-ion battery equivalent circuit models using the genetic algorithm, and determine the optimal structure of the lithium-ion battery equivalent circuit model.

[0044] The lithium-ion battery equivalent circuit model construction module is used to determine the lithium-ion battery equivalent circuit model based on the optimal structure of the lithium-ion battery equivalent circuit model, so as to design the lithium-ion battery controller and formulate the lithium-ion battery management strategy.

[0045] An electronic device includes: a memory and a processor, wherein the memory stores a computer program, and the processor runs the computer program to cause the electronic device to perform the above-described method for constructing an equivalent circuit model of a lithium-ion battery.

[0046] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for constructing an equivalent circuit model of a lithium-ion battery.

[0047] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0048] The present invention provides a method for constructing an equivalent circuit model of a lithium-ion battery. This method involves building an initial equivalent circuit model of the lithium-ion battery to be modeled, and then using a two-layer optimization approach combining genetic algorithms and particle swarm optimization to select the electrical structure and optimize the model parameters. Finally, based on the optimized electrical structure and parameter values ​​of the equivalent circuit model, a functional relationship between the state of charge and the model parameters is fitted, completing the construction of the equivalent lithium-ion battery model. This facilitates the design of lithium-ion battery controllers and the formulation of lithium-ion battery management strategies. The fully intelligent optimization process of this invention reduces the bias of human assumptions, making parameter tuning easier. Furthermore, it allows for the customization of the objective function according to the model application requirements, achieving customized adaptive optimization. This invention improves the accuracy and applicability of lithium-ion battery modeling, contributing to the subsequent design of controllers and the formulation of management strategies for lithium-ion batteries, and promoting the wider application of lithium-ion batteries in fields such as power grids and electric vehicles. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 Flowchart of the method for constructing the equivalent circuit model of a lithium-ion battery provided by the present invention;

[0051] Figure 2 The flowchart shows the lithium-ion battery equivalent circuit model construction method of the present invention in practical application.

[0052] Figure 3 The initial equivalent circuit model structure diagram of the lithium-ion battery provided by this invention;

[0053] Figure 4 The system structure diagram for constructing the equivalent circuit model of the lithium-ion battery provided by this invention is shown. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] The purpose of this invention is to provide a method, system, device, and medium for constructing an equivalent circuit model of a lithium-ion battery, so as to solve the problems of difficult parameter tuning or low simulation accuracy in the existing methods for constructing equivalent circuit models of lithium-ion batteries.

[0056] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0057] Example 1

[0058] Figure 1 The flowchart illustrates the method for constructing an equivalent circuit model of a lithium-ion battery provided by this invention. Figure 2 This is a flowchart illustrating the practical application of the lithium-ion battery equivalent circuit model construction method of the present invention, as shown below. Figure 1 and Figure 2 As shown, the method for constructing the equivalent circuit model of a lithium-ion battery includes:

[0059] Step 101: Construct an initial equivalent circuit model of the lithium-ion battery to be modeled; the initial equivalent circuit model is an nth-order electrical network consisting of a controlled voltage source and n pairs of RC parallel structures connected in series, such as... Figure 3 As shown.

[0060] Further, step 101 specifically includes:

[0061] Step 1.1: Obtain the current, output voltage, and open-circuit voltage of the lithium-ion battery, and calculate the state of charge (SOC) of the lithium-ion battery based on the ampere-hour integration method. In practical applications, based on a hybrid power pulse experiment, measure the current, output voltage, and open-circuit voltage of the lithium-ion battery, and the current I(t) and output voltage U(t) of the lithium-ion battery at time t. o (t), Open-circuit voltage U ocv (t), and the state of charge S of the lithium-ion battery at time t is calculated based on the ampere-hour integration method. SOC (t), where t = 1, 2, ..., T.

[0062] Wherein, γ, η c η d Q and Q represent the self-discharge rate, charging efficiency, discharging efficiency, and rated capacity of a lithium-ion battery, respectively.

[0063] Step 1.2: Construct the initial functional relationship between the open-circuit voltage and the state of charge.

[0064] Step 1.3: Calculate the function coefficients of the initial function relationship using the least squares method to obtain the initial functions of the open-circuit voltage and the state of charge.

[0065] In practical applications, the open-circuit voltage U of a lithium-ion battery is defined. ocv With charge state S SOC Initial function relation U ocv (t)=f(S SOC (t)), such as f(·), can be expressed as a cubic function. The initial function coefficients are calculated using optimization algorithms such as the least squares method, and the parameters of the open circuit voltage and state of charge of the lithium-ion battery are tuned.

[0066] Step 1.4: Determine the parameters of the controlled voltage source and the number of RC parallel structures based on the initial function. In practical applications, the initial function is used to determine the open-circuit voltage under a set charging state, and the parameters of the controlled voltage source are determined based on the open-circuit voltage; that is, the voltage value of the controlled voltage source is determined by the open-circuit voltage.

[0067] Step 1.5: Establish an initial equivalent circuit model of the lithium-ion battery based on the number of the controlled voltage sources and the RC parallel structures.

[0068] In practical applications, the equivalent circuit model of the lithium-ion battery of this invention is based on the open-circuit voltage U. ocv The controlled voltage source determined by (t) and the n-order electrical network consisting of n pairs of resistors and capacitors in parallel (RC parallel structure) connected in series. Each pair of resistors and capacitors can be represented as the lithium-ion battery S. SOC The function, where the i-th pair of resistors R i (t)=g i (S SOC (t)), capacitance C i (t)=h i (S SOC (t)), then the output voltage of the initial lithium-ion battery equivalent circuit model It can be represented as:

[0069] in, S represents the output voltage of the initial lithium-ion battery equivalent circuit model; SOC (t) represents the state of charge of the lithium-ion battery; U i(t) Represents the voltage value of the i-th pair of RC parallel structures; R i (t) represents the resistance value of the i-th pair of RC parallel structures; C i(t) represents the capacitance value of the i-th pair of RC parallel structures.

[0070] Step 102: Based on the initial lithium-ion battery equivalent circuit model, the structure of the second lithium-ion battery equivalent circuit model is determined by using the population chromosome encoding of the genetic algorithm, resulting in multiple different second lithium-ion battery equivalent circuit models.

[0071] Further, step 102 specifically includes:

[0072] Step 2.1: Use population chromosome encoding to perform binary encoding on the structure of the initial lithium-ion battery equivalent circuit model to obtain multiple encoding results.

[0073] In practical applications, the binary encoding length of chromosomes in the genetic algorithm is set to the number of RC components in the lithium-ion battery equivalent circuit model in step 1.4, i.e., 2^n. Genetic algorithm parameters are initialized, such as the number of chromosomes N in the population and the selection rate P. s Crossover rate P c Variation rate P m Given a maximum number of iterations G, and setting the iteration count g = 0, a genetic optimization algorithm with objective function J is designed to evaluate the modeling effect of the equivalent circuit of a lithium-ion battery. Based on the number of chromosomes in the population and the chromosome encoding length, K binary codes of length 2n are randomly initialized. If the j-th encoding bit is 0, it indicates that this electrical component (resistor or capacitor, where j is odd for resistance and j is even for capacitance) is not selected and does not exist in the equivalent circuit model; if the j-th encoding bit is 1, it indicates that this electrical component (resistor or capacitor, where j is odd for resistance and j is even for capacitance) is selected and exists in the equivalent circuit model.

[0074] Step 2.2: Based on the encoding results, select electrical components in the initial lithium-ion battery equivalent circuit model, determine the structure of the second lithium-ion battery equivalent circuit model, and obtain multiple different second lithium-ion battery equivalent circuit models.

[0075] In practical applications, based on the chromosome coding situation (coding results) in the population, the electrical components of the equivalent circuit of the lithium-ion battery are selected, and the equivalent circuit structure is determined. For example, in the g-th generation, the i-th chromosome is encoded... The j-th encoded bit like If j is an odd number, then the j-th resistor is not selected in the equivalent circuit model of a lithium-ion battery; if If j is even, then the j-th capacitor is not selected in the lithium-ion battery equivalent circuit model; if If j is an odd number, then the j-th resistor is selected in the equivalent circuit model of a lithium-ion battery; if If j is an even number, then the j-th capacitor is selected in the lithium-ion battery equivalent circuit model. Furthermore, based on the chromosome binary encoding, the structure of the second lithium-ion battery equivalent circuit model and the parameters that need to be optimized can be determined.

[0076] Step 103: Optimize each second lithium-ion battery equivalent circuit model using the particle swarm optimization algorithm to determine the optimal model parameters of each third lithium-ion battery equivalent circuit model, thereby obtaining multiple third lithium-ion battery equivalent circuit models; the optimal model parameters include the optimal resistance value and the optimal capacitance value.

[0077] In practical applications, based on the lithium-ion battery equivalent circuit model structure determined by the chromosome encoding in step 2.2, swarm intelligence optimization algorithms are used to optimize the model parameters. Taking particle swarm optimization as an example, one example corresponds to a set of model parameters. The number of particles K and the maximum number of iterations R in the particle swarm optimization algorithm are set. The length M of the variable represented by the particle is determined according to the chromosome encoding, which is equal to the number of 1 bits in the chromosome encoding. The number of iterations r is initialized to 0, and the initial positions and velocities of each particle in the particle swarm are also determined. The position of the k-th particle in the r-th generation is... speed is

[0078] Further, step 103 specifically includes:

[0079] Step 3.1: Based on the output voltage of the lithium-ion battery and the output voltage of the initial lithium-ion battery equivalent circuit model, determine the objective function of the model parameters under the state of charge at a set time and determine the objective function value of the model parameters under the state of charge at the set time.

[0080] In practical applications, the lithium-ion battery output voltage U measured at different times according to step 1.1 is used as a reference. o (t) and the calculated S soc (t), the output voltage combined with the initial lithium-ion battery equivalent circuit model Calculate a specific S soc The objective function J1(S) is obtained by determining the positions of each particle in the particle swarm under (t). soc The formula for calculating (t) is as follows:

[0081]

[0082] Step 3.2: Sort the objective function values ​​of each model parameter and select the optimal model parameter.

[0083] Step 3.3: Determine if the maximum number of iterations has been reached. If yes, proceed to step 3.4; otherwise, proceed to step 3.5.

[0084] Step 3.4: Update the state of charge at the next time step, initialize the particle swarm optimization algorithm parameters, and calculate the objective function of the model parameters under the state of charge at the next set time step to obtain the optimal model parameters of the third lithium-ion battery equivalent circuit model.

[0085] Step 3.5: Update the position and velocity of the particles according to the particle swarm algorithm, obtain the updated particle swarm, and return to "Step 3.1".

[0086] In practical applications, the optimal particle (representing the optimal model parameters) under the objective function J1(Ssoc(t)) is selected, and it is determined whether the iteration number r is greater than the maximum iteration number R of the particle swarm optimization algorithm. If so, another S is calculated. soc If the objective function is not specified (t), proceed to step 3.4; otherwise, proceed to step 3.5 to update the particle position and velocity.

[0087] The formula for updating particle position and velocity according to the particle swarm optimization algorithm is as follows. After obtaining the particle with the new position and velocity, the algorithm continues to iterate and optimize.

[0088] Where ω, c1, and c2 represent the inertia parameter and learning factor (constant) of the particle swarm optimization algorithm, respectively, and rand represents a random number that follows a uniform distribution. and X gbest The historical best position of the k-th particle and the historical global best position of all particles are given respectively.

[0089] Based on the objective function of the model parameters under the state of charge at all set times, using the formula Calculate the objective function of the model structure of the equivalent circuit model of the third lithium-ion battery in each group; where, J1(S) represents the objective function of the model structure of the equivalent circuit model of the third lithium-ion battery in the i-th group of the g-th generation; SOC (t) represents the objective function of the model parameters under the charged state at a given time t; T represents the total number of time points; M represents the length of the variable represented by the particle.

[0090] Step 104: Optimize the structure of multiple equivalent circuit models of the third lithium-ion battery using the genetic algorithm to determine the optimal structure of the lithium-ion battery equivalent circuit model. In practical applications, the selection, crossover, and mutation operations of the genetic algorithm are used to update chromosome information, obtain a new generation of chromosomes, achieve iterative optimization, and obtain the optimal chromosome (the optimal chromosome represents the optimal structure).

[0091] Step 4.1: Based on the objective functions of the model structures of the multiple third lithium-ion battery equivalent circuit models, a genetic algorithm is used to select a predetermined number of chromosomes corresponding to the model structures of the third lithium-ion battery equivalent circuit models with the largest objective function values. In practical applications, this is based on the objective function sequence (objective function sequence of the model structure) obtained above under the N chromosomes in the g-th generation. Sort and select the largest. indivual Values ​​and corresponding chromosomes This indicates the floor function.

[0092] Step 4.2: Perform a genetic algorithm crossover operation on the selected chromosomes to obtain an equal number of new chromosomes. In practical applications, randomly pairwise combinations are used to select chromosomes from step 5.1. Chromosomes Perform the standard crossover operation in the genetic algorithm to form... A new chromosome

[0093] Step 4.3: Using the mutation operation of a genetic algorithm, update the same number of the new chromosomes to obtain the updated chromosomes. In practical applications, the chromosomes obtained after crossover in Step 4.2 are... Chromosomes Each encoded bit undergoes the standard mutation operation found in genetic algorithms to update... Chromosomes

[0094] Step 4.4: Combine the new chromosome and the updated chromosome to form a new generation of chromosomes. In practical applications, the chromosomes obtained from the crossover in step 4.2 and the mutation in step 4.3 are used... The chromosomes and those retained in step 4.1 1 chromosome, to form N new chromosomes. That is, the optimal chromosome.

[0095] Step 4.5: Determine whether the number of iterations of the genetic algorithm is greater than the maximum number of iterations G; if yes, determine the optimal structure based on the new generation chromosomes; if no, calculate the objective function of all new generation chromosomes based on the new generation chromosomes, g = g + 1, and proceed to step 4.1.

[0096] Step 105: Based on the optimal structure of the lithium-ion battery equivalent circuit model, determine the lithium-ion battery equivalent circuit model in order to design the lithium-ion battery controller and formulate the lithium-ion battery management strategy.

[0097] In practical applications, based on the optimal chromosome obtained through iteration of the genetic algorithm in step 104, the optimal structure of the equivalent circuit model of the lithium-ion battery is determined. Furthermore, based on the optimal chromosome, the optimal model parameters for the state of charge (SOC) at different times are optimized. A fitting method is used to obtain the functional relationship between the model parameters and the SOC, thus completing the construction of the equivalent lithium-ion battery model. Based on the equivalent circuit model of the lithium-ion battery, Kalman state estimation can be used to calculate the SOC of the lithium-ion battery, which is a very mature application and will not be elaborated upon here.

[0098] This invention proposes a method for establishing a mathematical model of a lithium-ion equivalent battery under a two-layer optimization structure (method for constructing an equivalent circuit model of a lithium-ion battery). First, based on hybrid power pulse experiments, the open-circuit voltage and output voltage of the lithium-ion battery under different states of charge are obtained, and a functional relationship between the open-circuit voltage and the state of charge is constructed. Then, an n-order equivalent circuit model of the lithium-ion battery is defined. Combining the output voltage measured by the lithium-ion battery under different states of charge and the voltage expression of the equivalent circuit model, a two-layer optimization method using genetic algorithm and swarm intelligence optimization algorithm is used to complete the selection of the electrical structure of the equivalent circuit and the optimization of model parameters. Finally, based on the optimized electrical structure and parameter optimization values ​​of the equivalent circuit model, the functional relationship between the state of charge and the model parameters is fitted to complete the construction of the equivalent model of the lithium-ion battery.

[0099] The key feature of this invention lies in its application of an outer-layer genetic algorithm to optimize the location of the equivalent battery model structure through a custom objective function. Based on this, an inner-layer optimization algorithm is then used to optimize the fitting error, achieving accurate identification of model parameters. Compared to existing lithium-ion battery equivalent circuit modeling methods, which define the equivalent model structure by defining electrochemical processes or subjective assumptions, and then calculate model parameter values ​​using specific changes in measured data such as voltage and current, or use a single-layer optimization algorithm to tune model parameters based on errors, this invention considers a two-layer nested optimization that integrates the equivalent circuit model structure and model parameters. The outer layer determines the model structure and defined parameter variables, while the inner-layer optimization objective function is fed back as the output to the comprehensive objective function defined by the outer layer, completing the overall modeling process. The advantage of this method is that the fully intelligent optimization reduces the bias of human assumptions. Furthermore, it allows for customized outer-layer model objective functions based on application requirements, achieving customized adaptive optimization. This solution will improve the accuracy and applicability of lithium-ion battery modeling, contributing to the subsequent design of controllers and the formulation of management strategies for lithium-ion batteries, and promoting the wider application of lithium-ion batteries in fields such as power grids and electric vehicles.

[0100] Example 2

[0101] To implement the method corresponding to Embodiment 1 above and achieve the corresponding functions and technical effects, a lithium-ion battery equivalent circuit model construction system is provided below, such as... Figure 4 As shown, the system includes:

[0102] The initial model construction module 401 is used to construct an initial lithium-ion battery equivalent circuit model of the lithium-ion battery to be modeled; the initial lithium-ion battery equivalent circuit model is a 2n-order electrical network consisting of a controlled voltage source and n pairs of RC parallel structures connected in series.

[0103] The model structure determination module 402 is used to determine the structure of the second lithium-ion battery equivalent circuit model based on the initial lithium-ion battery equivalent circuit model and by using the population chromosome encoding of a genetic algorithm, thereby obtaining multiple different second lithium-ion battery equivalent circuit models.

[0104] The parameter optimization module 403 is used to optimize each of the second lithium-ion battery equivalent circuit models using the particle swarm optimization algorithm, determine the optimal model parameters of each third lithium-ion battery equivalent circuit model, and obtain multiple third lithium-ion battery equivalent circuit models; the optimal model parameters include the optimal open-circuit voltage, the optimal resistance value, and the optimal capacitance value.

[0105] The structure optimization module 404 is used to optimize the structure of multiple third lithium-ion battery equivalent circuit models using the genetic algorithm, and determine the optimal structure of the lithium-ion battery equivalent circuit model.

[0106] The lithium-ion battery equivalent circuit model construction module 405 is used to determine the lithium-ion battery equivalent circuit model based on the optimal structure of the lithium-ion battery equivalent circuit model, so as to design the lithium-ion battery controller and formulate the lithium-ion battery management strategy.

[0107] Example 3

[0108] The present invention also provides an electronic device, including: a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the lithium-ion battery equivalent circuit model construction method of Embodiment 1.

[0109] Example 4

[0110] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for constructing an equivalent circuit model of a lithium-ion battery according to Embodiment 1.

[0111] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0112] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for constructing an equivalent circuit model of a lithium-ion battery, characterized in that, include: Construct an initial equivalent circuit model of the lithium-ion battery to be modeled; the initial equivalent circuit model of the lithium-ion battery is an n-order electrical network consisting of a controlled voltage source and n pairs of RC parallel structures connected in series. Based on the initial lithium-ion battery equivalent circuit model, the structure of the second lithium-ion battery equivalent circuit model is determined by using the population chromosome encoding of the genetic algorithm, resulting in multiple different second lithium-ion battery equivalent circuit models. The particle swarm optimization algorithm is used to optimize each second lithium-ion battery equivalent circuit model to determine the optimal model parameters of each third lithium-ion battery equivalent circuit model, resulting in multiple third lithium-ion battery equivalent circuit models; the optimal model parameters include the optimal resistance value and the optimal capacitance value. The genetic algorithm is used to optimize the structure of multiple equivalent circuit models of the third lithium-ion battery to determine the optimal structure of the equivalent circuit model of the lithium-ion battery. Based on the optimal structure of the lithium-ion battery equivalent circuit model, the lithium-ion battery equivalent circuit model is determined in order to design the lithium-ion battery controller and formulate the lithium-ion battery management strategy. The construction of the initial lithium-ion battery equivalent circuit model to be modeled specifically includes: The current, output voltage, and open-circuit voltage of the lithium-ion battery are obtained, and the state of charge of the lithium-ion battery is calculated based on the ampere-hour integration method. Establish an initial functional relationship between the open-circuit voltage and the state of charge; The function coefficients of the initial functional relationship are calculated using the least squares method to obtain the initial functions of the open-circuit voltage and the state of charge. The parameters of the controlled voltage source are determined based on the initial function, and the number of RC parallel structures is defined. Based on the number of the controlled voltage sources and the RC parallel structures, an initial equivalent circuit model of the lithium-ion battery is established. Using formula Calculate the output voltage of the initial lithium-ion battery equivalent circuit model; where, This represents the output voltage of the initial equivalent circuit model of the lithium-ion battery. S SOC ( t () indicates the state of charge of a lithium-ion battery; U i(t) This represents the voltage value of the i-th pair of RC parallel structures; R i ( t ) represents the resistance value of the i-th pair of RC parallel structures; C i ( t ) represents the capacitance value of the i-th pair of RC parallel structures.

2. The method for constructing an equivalent circuit model of a lithium-ion battery according to claim 1, characterized in that, Based on the initial lithium-ion battery equivalent circuit model, the structure of the second lithium-ion battery equivalent circuit model is determined using population chromosome encoding via a genetic algorithm, resulting in multiple different second lithium-ion battery equivalent circuit models, specifically including: The structure of the initial lithium-ion battery equivalent circuit model was binary-encoded using population chromosome coding, resulting in multiple coding results. Based on the encoding results, electrical components are selected in the initial lithium-ion battery equivalent circuit model to determine the structure of the second lithium-ion battery equivalent circuit model, resulting in multiple different second lithium-ion battery equivalent circuit models.

3. The method for constructing an equivalent circuit model of a lithium-ion battery according to claim 1, characterized in that, The process of optimizing each second lithium-ion battery equivalent circuit model using particle swarm optimization to determine the optimal model parameters for each third lithium-ion battery equivalent circuit model, resulting in multiple third lithium-ion battery equivalent circuit models, specifically includes: Based on the output voltage of the lithium-ion battery and the output voltage of the initial lithium-ion battery equivalent circuit model, determine the objective function of the model parameters under the state of charge at a set time and determine the objective function value of the model parameters under the state of charge at the set time; Sort the objective function values ​​of each model parameter and select the optimal model parameters; Determine whether the maximum number of iterations has been reached, and obtain the first determination result; If the first judgment result is that the maximum number of iterations has been reached, then update the state of charge at the next moment, initialize the particle swarm algorithm parameters, and calculate the objective function of the model parameters under the state of charge at the next set moment in order to obtain the optimal model parameters of the third lithium-ion battery equivalent circuit model. If the first judgment result is that the maximum number of iterations has not been reached, then the position and velocity of the particles are updated according to the particle swarm algorithm to obtain the updated particle swarm, and the step of "determining the objective function of the model parameters under the charged state at a set time and determining the objective function value of the model parameters under the charged state at the set time based on the output voltage of the lithium-ion battery and the output voltage of the initial lithium-ion battery equivalent circuit model" is returned.

4. The method for constructing an equivalent circuit model of a lithium-ion battery according to claim 3, characterized in that, Also includes: Based on the objective function of the model parameters under the state of charge at all set times, using the formula Calculate the objective function of the model structure of the equivalent circuit model of the third lithium-ion battery in each group; where, The objective function represents the model structure of the equivalent circuit model of the third lithium-ion battery in the i-th group of the g-th generation; The objective function represents the model parameters under the charged state at a given time t; T represents the total number of time points; and M represents the length of the variable represented by the particle.

5. The method for constructing an equivalent circuit model of a lithium-ion battery according to claim 1, characterized in that, The optimization of the structure of multiple third lithium-ion battery equivalent circuit models using the genetic algorithm to determine the optimal structure of the lithium-ion battery equivalent circuit model specifically includes: Based on the objective function of the model structure of multiple third lithium-ion battery equivalent circuit models, a genetic algorithm is used to select a preset number of chromosomes corresponding to the model structure of the third lithium-ion battery equivalent circuit model with the maximum objective function value. Perform a crossover operation using a genetic algorithm on the selected chromosomes to obtain an equal number of new chromosomes; The mutation operation of the genetic algorithm is used to update an equal number of the new chromosomes to obtain the updated chromosomes; The new chromosome and the updated chromosome are combined to form a new generation of chromosomes; Determine whether the number of iterations of the genetic algorithm has reached the maximum number of iterations. If so, determine the optimal structure based on the new generation of chromosomes. If not, then based on the new generation chromosomes, calculate the objective function of all new generation chromosomes and return the step of "selecting a preset number of chromosomes corresponding to the model structure of the third lithium-ion battery equivalent circuit model with the largest objective function based on the objective function of the model structure of multiple third lithium-ion battery equivalent circuit models using the selection operation of the genetic algorithm".

6. A system for constructing an equivalent circuit model of a lithium-ion battery, characterized in that, The lithium-ion battery equivalent circuit model construction system is used to implement the lithium-ion battery equivalent circuit model construction method according to any one of claims 1-5, and the lithium-ion battery equivalent circuit model construction system includes: The initial model building module is used to build an initial equivalent circuit model of the lithium-ion battery to be modeled; the initial equivalent circuit model of the lithium-ion battery is an n-order electrical network consisting of a controlled voltage source and n pairs of RC parallel structures connected in series. The model structure determination module is used to determine the structure of the second lithium-ion battery equivalent circuit model based on the initial lithium-ion battery equivalent circuit model and by using the population chromosome encoding of the genetic algorithm, thereby obtaining multiple different second lithium-ion battery equivalent circuit models. The parameter optimization module is used to optimize each second lithium-ion battery equivalent circuit model using the particle swarm optimization algorithm, determine the optimal model parameters of each third lithium-ion battery equivalent circuit model, and obtain multiple third lithium-ion battery equivalent circuit models; the optimal model parameters include the optimal resistance value and the optimal capacitance value; The structure optimization module is used to optimize the structure of multiple third lithium-ion battery equivalent circuit models using the genetic algorithm, and determine the optimal structure of the lithium-ion battery equivalent circuit model. The lithium-ion battery equivalent circuit model construction module is used to determine the lithium-ion battery equivalent circuit model based on the optimal structure of the lithium-ion battery equivalent circuit model, so as to design the lithium-ion battery controller and formulate the lithium-ion battery management strategy.

7. An electronic device, characterized in that, include: A memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to cause the electronic device to perform the lithium-ion battery equivalent circuit model construction method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the lithium-ion battery equivalent circuit model construction method according to any one of claims 1-5.

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