Lithium battery parameter identification method and equipment

Abstract

The invention relates to a lithium battery parameter identification method and equipment. Battery parameters of a battery model are obtained by collecting battery electrical quantity information, constructing an OCV-SOC relation curve, constructing a battery model, obtaining a discretization state equation of the battery model, constructing an objective function according to the OCV-SOC relation curve and the discretization state equation, and solving the objective function. The problems that in the prior art, solid-phase diffusion of the battery cannot be rapidly and accurately described, and the efficiency and precision of identifying parameters of the lithium battery are insufficient are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of batteries, and in particular to a method and device for identifying parameters of a lithium battery. Background Art

[0002] Lithium-ion batteries, as highly efficient energy storage devices, are widely used in electric vehicles, portable electronic devices, and energy storage systems. However, to achieve efficient battery management and safe operation, accurate and efficient lithium-ion battery parameter identification is necessary. Currently, lithium-ion battery modeling methods mainly include neural network models, electrochemical models, and equivalent circuit models.

[0003] In the existing technology, the neural network model is trained by a large number of input samples. Although it has a certain degree of adaptive ability, its training process requires a large amount of data and has a slow convergence speed, which makes it difficult to meet the real-time requirements of practical applications. The electrochemical model is based on the electrochemical reaction mechanism inside the battery. It can better explain the internal performance of the battery with high accuracy, but the model parameters are complex and the calculation amount is large, making it difficult to apply in scenarios with high real-time requirements. The RC equivalent circuit model is widely used because of its small calculation amount and high accuracy. In particular, the second-order RC circuit model has shown good performance in practical applications. However, the traditional second-order RC circuit model has shortcomings in describing the solid-phase diffusion characteristics of the battery and it is difficult to fully reflect the dynamic behavior of the battery.

[0004] In addition, traditional intelligent algorithms have limitations in lithium battery parameter identification. They are prone to falling into local optimal solutions and take a long time to solve, making it difficult to meet the needs of efficient and accurate parameter identification.

[0005] Therefore, how to improve the existing technology's inability to quickly and accurately identify lithium battery parameters, and the insufficient efficiency and accuracy of parameter identification, is a technical problem that needs to be solved urgently in this field. Summary of the Invention

[0006] Based on this, the purpose of this application is to provide a lithium battery parameter identification method and device to solve at least one of the technical problems mentioned in the above background technology.

[0007] In a first aspect, the present application provides a lithium battery parameter identification method, comprising:

[0008] Collect battery electrical information and construct an OCV-SOC relationship curve;

[0009] Build a battery model and obtain the discretized state equation of the battery model;

[0010] Construct the objective function based on the OCV-SOC relationship curve and the discretized state equation;

[0011] Solve the objective function to obtain the battery parameters of the battery model.

[0012] Furthermore, the steps of constructing the OCV-SOC relationship curve include:

[0013] Set battery experiment parameters;

[0014] According to the experimental parameters, the battery is subjected to a pulse discharge test to collect the battery electrical quantity information;

[0015] Polynomial fitting is performed based on the battery electrical quantity information to obtain the OCV-SOC relationship curve.

[0016] Furthermore, the steps of constructing a battery model and obtaining a discretized state equation of the battery model include:

[0017] Construct a second-order RC equivalent circuit model based on fractional differentials as a battery model;

[0018] Based on current excitation, the state space equation is constructed according to the battery model;

[0019] The state space equation is converted into discrete form to obtain the discretized state equation.

[0020] Furthermore, the steps of constructing the state space equation include:

[0021] Determine the state variables according to the specific structure of the battery model;

[0022] Obtain the differential equations of each state variable;

[0023] With external current excitation as input variable and terminal voltage of battery as output variable, the state space equation is constructed according to the differential equation of each state variable.

[0024] Furthermore, the steps of obtaining the discretized state equation include:

[0025] Define discretization parameters; discretization parameters include one or more of system sampling interval, number of memory steps, and equation order;

[0026] Construct the fractional-order GL definition based on the discretization parameters;

[0027] The state space equation is discretized according to the fractional-order GL definition to obtain the discretized state equation.

[0028] Furthermore, the state space equations include:

[0029]

[0030] U=U ocv -U1-U2-IR0 2-2

[0031] Among them, U OCV and R0 are the open circuit voltage and ohmic internal resistance respectively, C1, C2 are the polarization capacitances of the two RC circuits, R1, R2 are the polarization internal resistances of the two RC circuits, U1, U2 are the polarization voltages of the two RC circuits, I is the operating current, and U is the terminal voltage of the battery;

[0032] Fractional GL definition, including:

[0033]

[0034] Among them, α is the order of the equation, h is the system sampling interval, is the number of memory steps;

[0035] Discretized state equations, including:

[0036]

[0037] U k =C k X k +I k R0+U ocv 2-5

[0038]

[0039] C k =(-1 -1 0) 2-7

[0040]

[0041] Where η is the battery charge and discharge efficiency.

[0042] Furthermore, the steps of constructing the objective function include:

[0043] Initialize the battery model according to the OCV-SOC relationship curve;

[0044] Obtain the output voltage of the battery model according to the discretized state equation;

[0045] The experimental voltage is collected to construct the objective function so that the sum of square errors between the experimental voltage and the output voltage is minimized.

[0046] Furthermore, the objective function includes:

[0047]

[0048] Where J(i) is the sum of squared errors, Vs(j) is the experimental voltage, and Vm(j) is the model output voltage.

[0049] Furthermore, the step of obtaining battery parameters of the battery model includes:

[0050] S41: Initialize the whale population and the optimal whale position, and set the whale optimization algorithm parameters;

[0051] S42: Obtain the updated position of each whale in the whale population;

[0052] S43: Calculate the objective function value of each whale position to update the optimal whale position;

[0053] S44: Determine whether the iteration termination condition is met. If so, output the battery parameters of the current battery model. If not, return to step S42.

[0054] In a second aspect, the present application also provides a terminal device comprising a memory and a processor; the memory stores a program code that can be executed by the processor; the program code is used to execute the lithium battery parameter identification method described in any one of the first aspects.

[0055] The present invention provides a lithium battery parameter identification method and device. By collecting battery electrical quantity information, an OCV-SOC relationship curve is constructed. The relationship between the open circuit voltage and state of charge of the battery is accurately described through experimental data, providing high-precision static characteristics for the battery model. The battery model is then constructed to accurately describe the dynamic behavior of the battery, including voltage changes, polarization effects, and solid-phase diffusion of the battery during charging and discharging. The discretized state equation of the battery model is obtained, thereby improving the computational efficiency and real-time performance of the model. An objective function is then constructed based on the OCV-SOC relationship curve and the discretized state equation, thereby quantifying the error between the model output and the experimental data, providing a clear direction for adjusting the model parameters. Finally, the objective function is solved to obtain the battery parameters of the battery model. By solving the objective function, the optimal battery model parameters are found, thereby improving the accuracy and predictive ability of the model. This solves the problems of the existing technology's inability to quickly and accurately describe the solid-phase diffusion of the battery and the insufficient efficiency and accuracy in identifying lithium battery parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 Flowchart of a lithium battery parameter identification method according to an embodiment of the present invention;

[0057] Figure 2 Schematic diagram of the structure of a battery model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0059] It should be noted that if the embodiments of the present invention involve directional indications, such as up, down, left, right, front, back, etc., then the directional indications are only used to explain the relative position relationship, movement status, etc. between the components in a certain specific posture. If the specific posture changes, the directional indications will also change accordingly. In addition, if the embodiments of the present invention involve descriptions of "first, second", "S1, S2", "step one, step two", etc., then such descriptions are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features or indicating the execution order of the method, etc. Those skilled in the art can understand that anything that does not violate the main points of the invention under the technical concept of the invention should be included in the scope of protection of the present invention.

[0060] like Figure 1 As shown, the present invention provides a lithium battery parameter identification method:

[0061] S1: Collect battery electrical information and construct an OCV-SOC relationship curve;

[0062] Specifically, it is optional but not limited to setting the temperature and discharge rate to test the charge and discharge characteristics of the battery according to the set parameters, and collecting battery electrical quantity information such as battery open circuit voltage (OCV) and battery state of charge (SOC) at set intervals during the experiment. According to the corresponding relationship between the battery open circuit voltage (OCV) and the battery state of charge (SOC) at each moment, the OCV-SOC (open circuit voltage-state of charge) relationship curve is fitted.

[0063] Preferably, the step of collecting battery electrical quantity information and constructing an OCV-SOC relationship curve may optionally include:

[0064] S11: Setting battery experiment parameters;

[0065] S12: Perform a pulse discharge test on the battery according to the experimental parameters and collect battery electrical quantity information;

[0066] Specifically, since lithium batteries have voltage hysteresis characteristics, the OCV-SOC relationship curves corresponding to the charging state and the discharging state are also different. The battery can be optionally subjected to charging and discharging HPPC characteristic tests respectively to obtain the battery electrical quantity information of the battery in the charging state and the battery electrical quantity information of the battery in the discharging state.

[0067] For example, you can set the normal temperature to 25°C and the discharge rate to 1C, and then perform the HPPC characteristic test on the battery respectively. During the experiment, the battery is subjected to a small current constant current discharge test from 100% SOC to 0% SOC. After each pulse, the battery is placed in a static state until internal equilibrium is reached to avoid the influence of factors such as polarization and self-discharge. Then, the battery open circuit voltage (OCV) is collected, and the actual battery SOC value is calculated using the ampere-hour integration method to obtain the battery electrical quantity information.

[0068] S13: Perform polynomial fitting based on the battery electrical quantity information to obtain an OCV-SOC relationship curve.

[0069] Specifically, the battery electrical quantity information such as the battery open circuit voltage (OCV) and the battery state of charge (SOC) collected in step S12 can be optionally used to obtain the OCV-SOC relationship curve of the battery (under charging or discharging state) through 8th-order polynomial fitting, so that in the voltage estimation stage, the OCV value under different current states can be selected according to the current state (charging or discharging) when the battery is working, thereby improving the model voltage estimation accuracy.

[0070] For example, the OCV-SOC relationship curve in the charging state and the OCV-SOC relationship curve in the discharging state obtained by fitting an 8th-order polynomial can be shown in Formulas 1-1 and 1-2 respectively:

[0071] U OCV =p0+p1·SOC+p2·SOC 2 +p3·SOC 3 +p4·SOC 4 +p5·SOC 5

[0072] +p6·SOC 6 +p7·SOC 7 +p8·SOC 8 1-1

[0073] U' OCV =p'0+p1'·SOC1+p'2·SOC1 2 +p'3·SOC1 3 +p'4·SOC1 4 +p'5·SOC1 5 +p'6·SOC1 6 +p'7·SOC1 7 +p'8·SOC1 8 1-2

[0074] Among them, U OCVis the open circuit voltage of the battery during charging, SOC is the state of charge of the battery during charging, p0, p1, p2, p3, p4, p5, p6, p7, p8 are polynomial coefficients; U' OCV is the open circuit voltage of the battery during discharge, SOC' is the state of charge of the battery during discharge, and p'0, p'1, p'2, p'3, p'4, p'5, p'6, p'7, p'8 are polynomial coefficients.

[0075] S2: Build a battery model and obtain the discretized state equation of the battery model;

[0076] Specifically, since fractional-order operators have memory characteristics, unlike integer-order calculus, they can more accurately describe the solid-phase diffusion of the battery and describe the battery model with fewer parameters and higher accuracy. Therefore, it is optional but not limited to using the existing second-order RC model as the battery model, and then replacing the standard capacitor in the second-order RC model with a fractional-order capacitor to obtain a second-order RC equivalent circuit model with fractional-order differentials, which is the updated battery model, and further obtaining the discretized state equation of the battery model.

[0077] Preferably, the steps of constructing a battery model and obtaining a discretized state equation of the battery model may optionally include but are not limited to:

[0078] S21: Construct a second-order RC equivalent circuit model based on fractional differentials for the battery model;

[0079] Specifically, in the prior art, a second-order RC model consisting of a voltage source, an internal resistor, and two sets of RC networks is generally used to represent the battery model. The structure of the model is intuitive, easy to understand and implement, and the number of parameters is small, which facilitates parameter identification and optimization. However, the traditional integer-order capacitor cannot accurately describe the solid-phase diffusion of the battery. At the same time, under the nonlinear working conditions of the battery (such as high-rate charge and discharge or extreme temperature), the accuracy of the traditional model may decrease. Therefore, the standard capacitor in the second-order RC model can be replaced with a fractional-order capacitor to obtain a second-order RC equivalent circuit model based on the fractional-order theory, that is, the second-order RC equivalent circuit model of fractional-order differential is as follows: Figure 2 The figure shows a battery model to better simulate the polarization phenomenon and dynamic behavior of the battery.

[0080] S22: Based on current excitation, construct the state space equation according to the battery model;

[0081] Specifically, since current is the main factor affecting the dynamic behavior of the battery in the battery model, it directly determines the battery's charging and discharging process and the rate of internal electrochemical reactions. Therefore, current can be selected as the input variable to make the input and output relationship of the model clear, facilitate parameter identification and optimization through experimental data, and thus construct the state space equation according to the changes of each state variable.

[0082] Preferably, the step of constructing a state space equation according to a battery model based on current excitation may optionally include:

[0083] S221: Determine state variables according to the specific structure of the battery model;

[0084] S222: Obtain differential equations of each state variable;

[0085] S223: Using the external current excitation as the input variable and the terminal voltage of the battery as the output variable, a state space equation is constructed based on the differential equations of each state variable.

[0086] Specifically, the known battery model, that is, the second-order RC equivalent circuit model based on fractional-order theory, consists of a voltage source (U ocv ), an internal resistor (R0) and two fractional-order capacitors CPE1 and CPE2. The fractional-order capacitors and the parallel resistors form an RC loop. The terminal voltage of the battery and the polarization voltages of the two RC loops can be selected as state variables. According to Kirchhoff's law and fractional-order differential equations, the differential equation of each state variable is derived. Then, with external current excitation as the input variable and the terminal voltage of the battery as the output variable, the state space equation can be constructed according to the differential equations of each state variable.

[0087] Preferably, the state space equation may be as shown in Equations 2-1 and 2-2:

[0088]

[0089] U=U ocv -U1-U2-IR0 2-2

[0090] Among them, U OCV and R0 are the open circuit voltage and ohmic internal resistance respectively, C1, C2 are the polarization capacitances of the two RC circuits, R1, R2 are the polarization internal resistances of the two RC circuits, U1, U2 are the polarization voltages of the two RC circuits, I is the operating current, and U is the terminal voltage of the battery.

[0091] S23: Convert the state space equation into a discrete form to obtain a discretized state equation.

[0092] Specifically, since many models are more accurately described by fractional-order differential equations, and the discretized state equations can maintain the characteristics of fractional-order differential equations, so that the dynamic behavior of these systems can be accurately simulated in the discrete time domain, it is optional but not limited to using the GL fractional-order differential definition to discretize the state space equations to obtain the discretized state equations of the battery model, thereby more accurately describing the complex dynamic behavior of the battery, such as polarization effects and diffusion processes. Compared with traditional integer-order models, the discretized state equations have better fitting capabilities for the dynamic response of the battery, thereby more accurately estimating the dynamic characteristics of the battery under different working conditions.

[0093] Preferably, the step of converting the state space equation into a discrete form to obtain a discretized state equation may optionally include:

[0094] S231: Define discretization parameters; discretization parameters include system sampling interval, number of memory steps, equation order, etc.

[0095] S232: Construct the fractional-order GL definition based on the discretization parameters;

[0096] Specifically, it is optional but not limited to determine the equation order α based on existing references or experimental data, and those skilled in the art can arbitrarily set the system sampling interval h and the number of memory steps. The definition of fractional-order GL is constructed based on the discretization parameters as shown in Equation 2-3:

[0097]

[0098] S233: Discretize the state space equation according to the fractional-order GL definition to obtain a discretized state equation.

[0099] Specifically, the state space equation can be discretized according to the fractional-order GL definition formula 2-3 constructed in step S222 to obtain a discretized state equation, so that the output voltage of the model in subsequent steps can be directly compared with the experimental voltage at each sampling moment, thereby improving the model accuracy by comparing the error between the model output voltage and the experimental voltage and adjusting the model parameters using an optimization algorithm.

[0100] For example, the state space equation can be discretized according to the fractional-order GL definition formula 2-3 to obtain the discretized state equations of the battery model as shown in formulas 2-4, 2-5, 2-6, 2-7, and 2-8:

[0101]

[0102] U k =C k X k +I k R0+U ocv2-5

[0103]

[0104] C k =(-1 -1 0) 2-7

[0105]

[0106] Wherein, η is the battery charge and discharge efficiency, which can be selected as 0.95.

[0107] S3: Construct the objective function based on the OCV-SOC relationship curve and the discretized state equation;

[0108] Specifically, it is optional but not limited to obtaining the output voltage of the battery model according to the discretized state equation and collecting the experimental voltage in the actual scenario to construct an objective function that minimizes the sum of squares of errors between the experimental voltage and the output voltage.

[0109] Preferably, the step of constructing the objective function according to the discretized state equation optionally includes:

[0110] S31: Initialize the battery model according to the OCV-SOC relationship curve;

[0111] Specifically, it is optional but not limited to initializing the model parameters of the battery model according to the technical manual or the initial values pre-set by technical personnel in this field, then collecting the initial open circuit voltage of the battery, and obtaining the initial value of the corresponding SOC according to the OCV-SOC relationship curve, and initializing the polarization voltage to 0 to complete the initialization of the battery model.

[0112] S32: Obtaining the output voltage of the battery model according to the discretized state equation;

[0113] S33: Collect the experimental voltage to construct an objective function so as to minimize the sum of squares of the errors between the experimental voltage and the output voltage.

[0114] Specifically, it is optional but not limited to obtaining the open-circuit voltage based on the current SOC value to further calculate the battery terminal voltage, that is, the output voltage of the battery model, and then in the HPPC characteristic test of step S12, according to the set experimental operating conditions (such as different charge and discharge currents, temperatures, etc.), the terminal voltage data of the battery is collected as the experimental voltage, so as to construct an objective function that minimizes the sum of the squares of the errors between the output voltage and the experimental voltage based on the error between the output voltage and the experimental voltage.

[0115] For example, the objective function can be as shown in Formula 3-1:

[0116]

[0117] Where J(i) is the sum of squared errors, Vs(j) is the experimental voltage, and Vm(j) is the model output voltage.

[0118] S4: Solve the objective function to obtain the battery parameters of the battery model.

[0119] Specifically, since the whale optimization algorithm (WOA) is a new swarm intelligence optimization algorithm with simple operation, few parameters and strong ability to escape local optimality, the whale optimization algorithm can be optionally used to solve the objective function to obtain the battery parameters of the battery model.

[0120] Preferably, the step of solving the objective function to obtain battery parameters of the battery model may optionally include:

[0121] S41: Initialize the whale population and the optimal whale position, and set the whale optimization algorithm parameters;

[0122] Specifically, it is optional but not limited to initializing the whale population. Each whale in the population represents a parameter vector, which contains all the parameters of the battery model (such as the ohmic internal resistance, polarization internal resistance, polarization capacitance, fractional order, etc. in the lithium-ion battery model), and provides initial parameters for subsequent optimization, thereby providing an initial search starting point for the optimization algorithm, ensuring that the algorithm can cover a wide area of the solution space, and accelerate the convergence speed through a reasonable initial parameter range to avoid the algorithm falling into the local optimum too early, and randomly select the position of any individual in the population as the best whale position to provide an initial optimal solution for subsequent comparison and update, and then set the whale optimization algorithm parameters to control the behavior and convergence characteristics of the algorithm, balance the global search and local search capabilities, and at the same time, by adjusting the random numbers and constants, the algorithm can flexibly explore the solution space.

[0123] For example, the population size N=40, the maximum number of iterations tmax=400, the convergence factor a (initial value is 2, which decreases linearly with the number of iterations), the random numbers rand1, rand2∈[0,1], the logarithmic spiral shape constant b=1, and the random number l∈[-1,1].

[0124] Preferably, it is optional to select the convergence factor a and the maximum number of iterations t max Construct the convergence factor update formula 4-1:

[0125]

[0126] S42: Obtain the updated position of each whale in the whale population;

[0127] Specifically, it is optional to iterate according to the whale position update formula to obtain the updated positions of each whale in the whale population, and retain the optimal solution after each iteration to avoid losing the optimal parameter combination found. The optimal solution is continuously updated through the algorithm, gradually approaching the global optimal solution.

[0128] Preferably, the whale position update formula can be optionally expressed as formula 4-2, 4-3, 4-4:

[0129]

[0130] A=2a·rand1-a 4-3

[0131] C=2·rand2 4-4

[0132] S44: Calculate the objective function value of each whale position to update the optimal whale position;

[0133] Specifically, the objective function value corresponding to each whale position can be optionally calculated, so as to update the optimal whale position according to the objective function value of each whale position, retain the current optimal solution, and thus evaluate the pros and cons of the current model parameters through the objective function value, provide guidance for the optimization algorithm, and enable the algorithm to effectively adjust the parameters.

[0134] S45: Determine whether the iteration termination condition is met, if so, output the current model parameters, if not, return to step S43.

[0135] Specifically, it is optional but not limited to judging whether the iteration termination condition is met. If so, it means that the current model parameters are accurate enough and the current model parameters can be output. If not, return to step S43 to continue iterating until the iteration termination condition is met.

[0136] Preferably, the iteration termination condition can be set to the maximum number of iterations t max .

[0137] Preferably, the model parameters include variables such as the ohmic internal resistance R0, the polarization internal resistance and polarization capacitance R1, R2, C1, C2, and the fractional order of polarization capacitance α, β.

[0138] In this embodiment, a lithium battery parameter identification method of the present invention is provided. By collecting battery electrical quantity information, an OCV-SOC relationship curve is constructed. The relationship between the open circuit voltage and the state of charge of the battery is accurately described through experimental data, providing a high-precision static characteristic for the battery model. Then, a battery model is constructed to accurately describe the dynamic behavior of the battery, including voltage changes, polarization effects, and solid-phase diffusion of the battery during the charging and discharging process, and a discretized state equation of the battery model is obtained, thereby improving the computational efficiency and real-time performance of the model. Then, an objective function is constructed based on the OCV-SOC relationship curve and the discretized state equation, thereby quantifying the error between the model output and the experimental data, providing a clear direction for adjusting the model parameters. Finally, the objective function is solved to obtain the battery parameters of the battery model. By solving the objective function, the optimal battery model parameters are found, thereby improving the accuracy and predictive ability of the model. This solves the problems of the existing technology being unable to quickly and accurately describe the solid-phase diffusion of the battery and the insufficient efficiency and accuracy in identifying lithium battery parameters.

[0139] On the other hand, the present invention further provides a terminal device, comprising a memory and a processor; the memory stores program code executable by the processor; the program code is used to execute any of the above-mentioned lithium battery parameter identification methods.

[0140] Exemplarily, the program code may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the program code in the terminal device.

[0141] The terminal device may be a computing device such as a desktop computer, laptop, PDA, or cloud server. The terminal device may include, but is not limited to, a processor and memory. Those skilled in the art will appreciate that the terminal device may also include input / output devices, network access devices, buses, and the like.

[0142] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0143] The memory may be an internal storage unit of the terminal device, such as a hard disk or memory. The memory may also be an external storage device of the terminal device, such as a plug-in hard disk, a SmartMedia Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the memory may include both an internal storage unit of the terminal device and an external storage device. The memory is used to store the program code and other programs and data required by the terminal device. The memory may also be used to temporarily store data that has been output or is about to be output.

[0144] The above-mentioned terminal device is created based on the above-mentioned lithium battery parameter identification method. Its technical functions and beneficial effects will not be repeated here. The various technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0145] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A lithium battery parameter identification method, characterized in that: include: Collect battery electrical quantity information and construct OCV-SOC relationship curve; Build a battery model and obtain the discretized state equation of the battery model; Construct the objective function based on the OCV-SOC relationship curve and the discretized state equation; Solve the objective function to obtain the battery parameters of the battery model.

2. The method according to claim 1, characterized in that The steps to construct the OCV-SOC relationship curve include: Set battery experiment parameters; According to the experimental parameters, the battery is subjected to a pulse discharge test to collect the battery electrical quantity information; Polynomial fitting is performed based on the battery electrical quantity information to obtain the OCV-SOC relationship curve.

3. The method according to claim 2, characterized in that The steps of building a battery model and obtaining the discretized state equation of the battery model include: Construct a second-order RC equivalent circuit model based on fractional differentials as a battery model; Based on the current excitation, the state space equation is constructed according to the battery model; The state space equation is converted into discrete form to obtain the discretized state equation.

4. The method according to claim 3, characterized in that The steps to construct the state-space equations include: Determine the state variables according to the specific structure of the battery model; Obtain the differential equations of each state variable; With external current excitation as input variable and battery terminal voltage as output variable, the state space equation is constructed according to the differential equations of each state variable.

5. The method according to claim 4, characterized in that The steps to obtain the discretized state equation include: Define discretization parameters; discretization parameters include one or more of system sampling interval, number of memory steps, and equation order; Construct the fractional-order GL definition based on the discretization parameters; The state space equation is discretized according to the fractional-order GL definition to obtain the discretized state equation.

6. The method according to claim 5, characterized in that State-space equations, including: U=U ocv -U1-U2-IR0 2-2 Among them, U OCV and R0 are the open circuit voltage and ohmic internal resistance respectively, C1, C2 are the polarization capacitances of the two RC circuits, R1, R2 are the polarization internal resistances of the two RC circuits, U1, U2 are the polarization voltages of the two RC circuits, I is the operating current, and U is the terminal voltage of the battery; Fractional GL definition, including: Among them, α is the order of the equation, h is the system sampling interval, is the number of memory steps; Discretized state equations, including: U k =C k X k +I k R0+U ocv 2-5 C k ＝(-1 -1 0) 2-7 Where η is the battery charge and discharge efficiency.

7. The method according to claim 1, characterized in that The steps to construct the objective function include: Initialize the battery model according to the OCV-SOC relationship curve; Obtain the output voltage of the battery model according to the discretized state equation; The experimental voltage is collected to construct the objective function so that the sum of square errors between the experimental voltage and the output voltage is minimized.

8. The method according to claim 7, characterized in that The objective function includes: Where J(i) is the sum of squared errors, Vs(j) is the experimental voltage, and Vm(j) is the model output voltage.

9. The method according to claims 1-8, characterized in that The steps for obtaining battery parameters of the battery model include: S41: Initialize the whale population and the best whale position, and set the whale optimization algorithm parameters; S42: Obtain the updated position of each whale in the whale population; S43: Calculate the objective function value of each whale position to update the optimal whale position; S44: Determine whether the iteration termination condition is met. If so, output the battery parameters of the current battery model. If not, return to step S42.

10. A terminal device, characterized in that: It comprises a memory and a processor; the memory stores a program code that can be executed by the processor; the program code is used to execute the lithium battery parameter identification method according to any one of claims 1 to 8.

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