CN-120745395-B - EIS-based lithium ion battery electric-thermal-aging coupling model construction method

CN120745395BCN 120745395 BCN120745395 BCN 120745395BCN-120745395-B

Abstract

The invention discloses an EIS-based lithium ion battery electric-thermal-aging coupling model construction method, which comprises the steps of firstly determining the number of elements of a fractional equivalent circuit model based on measured electrochemical impedance spectrum data of a battery, utilizing relaxation time distribution to complete parameter identification through a particle swarm optimization algorithm, improving model precision, secondly constructing a uniform thermal model by combining geometric parameters and thermophysical data of the battery, calculating heat generating, heat transferring and heat dissipating processes of each unit through grid division to generate a three-dimensional temperature profile, and further introducing a semi-empirical aging model to establish a dynamic correlation equation of a health state and a heat transferring coefficient to realize real-time updating of aging parameters, and dynamically adjusting the electrical parameters, the temperature distribution and the aging state through a closed-loop coupling mechanism driven by a time step, thereby solving the problem that the traditional single model cannot adapt to the limitation of internal and external environment change.

Inventors

CAO FENGJIN
YANG YUKANG
CHEN NING
LIU DONG
DENG YU
SHEN YONG
ZHOU YU
HE YAOTING
CAO KE
CHEN KEN

Assignees

长沙矿山研究院有限责任公司
中南大学

Dates

Publication Date: 20260505
Application Date: 20250624

Claims (9)

1. The EIS-based lithium ion battery electric-thermal-aging coupling model construction method is characterized by comprising the following steps of: S100, acquiring battery basic data, wherein the battery basic data comprise actual capacity and OCV-SOC curves of a battery and EIS data of lithium ion batteries with different charge states under different temperature conditions; S200, constructing a fractional equivalent circuit model of the lithium ion battery, namely acquiring DRT of the battery according to the EIS data, determining the number of CPE elements in the equivalent circuit model by calculating the number of DRT peaks, and carrying out wide area search on each element in the model by using a particle swarm optimization algorithm to realize parameter identification of the fractional equivalent circuit model; S300, constructing a lithium ion battery uniform thermal model and a battery semi-empirical aging model, namely acquiring basic thermal parameters of the battery, meshing a battery structure, calculating the total heat generation and heat transfer of each unit of the battery and the total heat dissipation quantity of the surface of the battery to obtain a thermal model, simplifying partial differential calculation of a heat conduction equation through a differential equation, acquiring the temperatures of each grid unit of the battery at different moments, generating a three-dimensional temperature profile of the battery, recording battery working condition data in the operation process of the lithium ion battery, calculating the capacity loss of the battery at the time, updating SOH of the battery, and constructing an aging model; S400, constructing a coupling model of an electric model, a thermal model and an aging model, namely configuring initial values and termination conditions of basic data of the battery model, calculating SOH of a current period of the battery according to a historical cycle period of the battery, calculating heat transfer coefficients of the battery in different directions of x, y and z under the SOH period, realizing the coupling of the electric model, the thermal model and the aging model on a battery cycle number level, updating the temperature distribution and the state of charge SOC of the battery, determining the sizes of the battery resistive and capacitive elements at the moment, realizing the dynamic updating of element parameters, judging whether the configured termination conditions are met, and if the configured termination conditions are not met, automatically increasing the running time by one step length, and continuously executing the step S430 until the termination conditions are met, wherein the step S400 comprises: S410, configuring initial parameters and termination conditions of a battery model, wherein the initial parameters comprise the maximum full charge capacity of the battery Initial SOC, number of cycles n, constant current operating current I, simulated time step And the basic thermal parameters of the battery, wherein the termination condition is that the total running time exceeds the preset running time or the battery SOC is less than 0.1; S420, calculating SOH of the current cycle of the battery according to the historical cycle of the battery, determining the aging state of the battery, and calculating heat transfer coefficients of the battery in different directions of x, y and z under the SOH of the current cycle, so as to realize the coupling of an electric model, a thermal model and an aging model on the battery cycle number level; S430, calculating the electric heating data of the battery in the running process, namely calculating the open-circuit voltage and the heat generation of the battery by taking the time step as a unit, calculating the temperature of different grid units of the battery according to the heat generation of the battery, updating the temperature distribution and the state of charge (SOC) of the battery, updating the SOC of the battery at the moment according to the ampere-hour integration method, and calculating the node temperature of each grid of the battery at different moments And use The temperature of the grid is taken as the central temperature of the battery A, b and c respectively represent the length, width and height of the battery; s440, determining the sizes of the resistive and capacitive elements of the battery at the moment according to the highest temperature, the SOC and the cycle period in the battery, designing an updating equation to dynamically update the element parameters of the fractional equivalent circuit model, and introducing an aging correction equation of the element parameters as shown in a formula (29): (29) Wherein X is the size of the element parameter after ageing correction, As a factor of the ageing of the component, The parameter of the element updated by the parameter updating equation is the parameter size of the element updated by the parameter updating equation; S450, judging whether the termination condition is met, if yes, terminating the model operation, and recording all data obtained in the battery model operation process, otherwise, automatically increasing the operation time by one time step, and continuing to execute S430 until the termination condition is met.
2. The EIS-based lithium ion battery electric-thermal-aging coupling model construction method according to claim 1, wherein the S100 comprises the steps of obtaining an actual capacity and OCV-SOC curve of a battery through battery capacity test, placing the battery to different charge states through constant current discharge, placing the battery in a constant temperature box, and measuring electrochemical impedance spectrum EIS data of the lithium ion battery with different charge states under different temperature conditions by using a lithium ion battery electrochemical impedance spectrum measuring instrument.
3. The EIS-based lithium ion battery electricity-heat-aging coupling model construction method according to claim 1, wherein S200 comprises: S210, acquiring relaxation time distribution DRT of a battery according to the electrochemical impedance spectroscopy EIS data and a battery relaxation time distribution conversion equation, and determining the number of CPE elements in an equivalent circuit model by calculating the number of DRT peaks; the battery relaxation time distribution conversion equation is as follows: (1) Wherein, the Representing the magnitude of the impedance of the battery under excitation, Is the ohmic internal resistance of the equivalent circuit model, F represents the frequency corresponding to the electrochemical impedance spectrum data, In order to be a relaxation time constant, I represents an imaginary unit for a distribution density function of the polarization resistance in a time constant domain; the solving method for the number of CPE elements comprises the following steps: The frequency point size is determined by using a frequency multiplication acquisition mode during measurement, and the distribution density function is discretized by using a logarithmic mode, so that the formulas (2) - (4) are obtained: (2) (3) (4) Wherein x n represents a discretized point, Representing a radial basis function with a shape parameter u; substituting formula (4) into formula (1) results in an impedance polarization expression of the battery as shown in formula (5): (5) When the number of interpolation points is sufficiently large, Regarding as a constant, formula (6) and formula (7) are obtained: (6) (7) And (3) substituting the formulas (6) and (7) into the formula (3) to obtain a matrix expression form of the polarization impedance of the lithium ion battery, wherein the matrix expression form is shown in formulas (8) - (10): (8) (9) (10) Wherein, the For the DRT function vector to be solved, As a matrix of weights for the real part of the polarization impedance, For the polarization impedance imaginary part weight matrix, m is the interpolation point number, N is the test frequency point number, f m is the interpolation frequency point, The time constant is the test frequency point; Calculating an error between the estimated value and the measured value of the polarization impedance to obtain an error expression (11): (11) wherein Re represents the real part of the complex number, im represents the imaginary part of the complex number, and Z p represents the impedance magnitude; Therefore, the DRT solving problem is converted into an optimization problem with minimized error, and the DRT image of the battery is obtained by solving the optimization problem, so that the number of CPE elements contained in the fractional equivalent model of the battery is determined; S220, carrying out parameter identification on the fractional equivalent circuit model, namely determining the total quantity of parameters to be detected of the fractional equivalent circuit model, carrying out wide area search on each element in the model by using a particle swarm optimization algorithm for battery models with different temperatures and charge states, firstly identifying the order of CPE elements and the inductance value of inductance elements, and then identifying the capacitance and the resistance; the final expression of the particle swarm optimization algorithm is shown in the formula (14): (14) Wherein, the i=1, 2. Once again, the total number of the components, N is the total number of particles, For the velocity of the particles, , In order for the learning factor to be a function of, And (3) with For the upper and lower extreme values of the particle position, Is an inertial factor; The loss function in the parameter identification process is jointly affected by the real part and the imaginary part of the model impedance errors, as shown in the formula (15): (15) where Re represents the real impedance measurement, im represents the imaginary impedance measurement, Representing the real part impedance of the model, Representing the imaginary impedance of the model.
4. The EIS-based lithium ion battery electricity-heat-aging coupling model construction method of claim 3, wherein S200 further comprises: S230, checking the identified fractional equivalent circuit model, namely checking the accuracy of the model by comparing the electrochemical impedance spectrum of the identified fractional equivalent circuit model with the difference of the electrochemical impedance spectrum measured value of the test battery; (16) (17) Where m represents the number of CPE and resistor parallel circuits, m=3, Representing the impedance of the ith CPE and resistor parallel loop, Representing the resistance of the corresponding loop, i=1, ·, m is the number of the m, Representing the capacitance value of the CPE element, Representing the order of the CPE element, f represents the frequency of the calculated frequency point, L represents the inductance value, R represents the resistance in the R// CPE loop, and R 0 represents the ohmic internal resistance in the whole equivalent circuit model.
5. The EIS-based lithium ion battery electricity-heat-aging coupling model construction method of claim 4, wherein S300 comprises: s310, obtaining basic thermal parameters of the battery, including length a, width b, height c, volume V and density of the battery Heat transfer coefficient in the xyz direction of specific heat C A surface convection heat dissipation coefficient h, a battery surface area S; S320, carrying out grid division on the battery structure, calculating the total heat generation and heat transfer of each unit of the battery and the total heat dissipation of the surface of the battery to obtain a battery uniform thermal model, simplifying partial differential calculation of a heat conduction equation through a differential equation, obtaining the temperature of each grid unit of the battery at different moments, and generating a three-dimensional temperature profile of the battery; the total heat generation rate equation of the battery is shown in formula (18): (18) Wherein I represents current, R 0 、R 1 、R 2 、R 3 represents resistance; the battery surface heat dissipation equation is shown in formula (19): (19) Wherein q loss represents the heat dissipation quantity of the battery surface, h is the heat dissipation coefficient of the battery surface, S is the surface area of the battery grid unit, For the temperature of the battery grid cell, Is ambient temperature; the heat conduction equation of the battery is shown in formula (20): (20) the boundary condition of the thermal conduction equation of the battery is as shown in formula (21): (21) Wherein, the Representing the rate of change of the temperature of the grid cells, Representing the rate of change of temperature in different directions of x, y and z, Representing the total heat rate of the battery, A, b and c respectively represent the length, width and height of the battery; When calculating the temperature of different grid cells, the temperature of the grid cells of the battery is updated firstly through a total heat generation rate equation (18) and a surface heat dissipation equation (19) of the battery, wherein the temperature update equation is shown as a formula (22): (22) then, the battery temperature is updated from inside to outside by using the heat conduction equation (20), and the final calculation equation of each grid temperature at the next moment is obtained as shown in the formula (23): (23) Wherein x, y and z respectively represent the positions of the temperature points in the battery temperature grid, dx, dy and dz respectively represent the size of grid division; S330, recording the cycle period, working current and environmental temperature of the battery in the operation process of the lithium ion battery, calculating the capacity loss of the battery in the current operation and updating the SOH of the battery, and constructing a semi-empirical aging model of the battery; Calculating SOH of the battery in different cycle periods according to an update equation of the SOH, wherein the update equation of the SOH is shown as a formula (24): (24) Wherein, the Is the percentage of the capacity loss that is present, For activation energy, R is the gas constant, Is an exponential factor, wherein c is the charge-discharge multiplying power, Is an index parameter of the device, which is a parameter of the index, Is the center temperature at which the battery operates, Is the throughput of the device and, N is the number of battery cycles, Is the depth of discharge of the battery, Is the initial capacity of the battery.
6. The EIS-based lithium ion battery electricity-heat-aging coupling model construction method according to claim 5, wherein in S420, an update equation of heat transfer coefficients of the battery in different directions of x, y and z is shown as formula (25): (25) Wherein, the Representing initial values of heat transfer coefficients of the battery in different directions, Is a regulating factor for intervening in the relationship between SOH and the heat transfer coefficient of the battery; in S430, the calculation expression of the open circuit voltage of the battery is shown in the formula (26): (26) Wherein, the Representing the SOH-corrected state of charge of the battery, which is equal to the product of the current state of charge, SOC, of the battery and the state of health, SOH, β i representing the coefficients of the SOC polynomial, N representing the power of the maximum polynomial used to fit the OCV-SOC curve; the equation for updating the SOC of the battery at this time according to the ampere-hour integration method is shown as formula (27): (27) Wherein I (k) represents the current corresponding to the step length k; in S440, an update equation of the dynamic update of the element parameters of the fractional equivalent circuit model is shown in formula (28): (28) Wherein, the Representing the updated result of the capacitive or resistive element at this point, Representing the inverse of the distance from the nearest reference node to X, The parameter value representing the corresponding reference node is at the minimum temperature, T min is at the maximum temperature, SOC min is at the minimum SOC, and SOC max is at the maximum SOC.
7. The EIS-based lithium ion battery electricity-heat-aging coupling model construction method of claim 1, further comprising: s500, performing result inspection on simulation operation data of the battery generated by simulation of the battery model.
8. An EIS-based lithium ion battery electricity-heat-aging coupling model construction system, comprising: and at least one memory communicatively coupled to the processor, wherein: The memory stores program instructions executable by the processor, the processor invoking the program instructions to perform the method of any of claims 1-7.
9. A non-transitory computer readable storage medium storing computer instructions that cause the computer to perform the method of any one of claims 1 to 7.

Description

EIS-based lithium ion battery electric-thermal-aging coupling model construction method Technical Field The invention relates to the technical field of battery management, in particular to an EIS-based lithium ion battery electric-thermal-aging coupling model construction method. Background With the gradual exhaustion of fossil energy sources such as coal and petroleum, the global energy consumption structure is being changed from taking these resources as main sources to a diversified energy structure, the development of renewable energy sources is actively promoted, the construction of a green low-carbon economic system is promoted, and the production and life style is promoted to change to green low-carbon. The lithium ion battery has the advantages of high power density and energy density, long cycle life, low self-discharge rate, moderate price and the like, is favored by consumers, and is widely applied to various scenes. In practical application of the lithium ion battery, multiple variables such as dynamic load current, ambient temperature fluctuation, battery aging degree and the like can generate obvious coupling influence on the battery charging and discharging process, so that the internal state of the battery is difficult to control, and the risk of battery runaway is unclear. The lithium ion battery physical model reveals the internal working mechanism, dynamic characteristics and fault evolution rule of the battery through a mathematical modeling means, and has remarkable advantages in coping with research bottleneck problems such as fuzzy internal short circuit fault mechanism, weak early characteristics, scarcity of fault samples and the like. However, it is difficult to fully characterize multi-factor interactions such as temperature, SOC, and aging state with a single-dimensional model. Therefore, a physical simulation model capable of processing electric-thermal-aging multi-physical field coupling is constructed, cross-model feature fusion is realized through parameter transmission, and the regulation and control of people on the internal state in the operation process of the lithium ion battery are met, so that the method has become urgent demands of people. Disclosure of Invention First, the technical problem to be solved Based on the problems, the invention provides an EIS-based lithium ion battery electric-thermal-aging coupling model construction method, which solves the problems that the traditional single model is difficult to deal with multi-physical field coupling analysis of a lithium ion battery and model adjustment on internal and external environment changes of the lithium ion battery cannot be realized. (II) technical scheme Based on the technical problems, the invention provides an EIS-based lithium ion battery electric-thermal-aging coupling model construction method, which comprises the following steps: S100, acquiring battery basic data, wherein the battery basic data comprise actual capacity and OCV-SOC curves of a battery and EIS data of lithium ion batteries with different charge states under different temperature conditions; S200, constructing a fractional equivalent circuit model of the lithium ion battery, namely acquiring DRT of the battery according to the EIS data, determining the number of CPE elements in the equivalent circuit model by calculating the number of DRT peaks, and carrying out wide area search on each element in the model by using a particle swarm optimization algorithm to realize parameter identification of the fractional equivalent circuit model; S300, constructing a lithium ion battery uniform thermal model and a battery semi-empirical aging model, namely acquiring basic thermal parameters of the battery, meshing a battery structure, calculating total heat generation and heat transfer of each unit of the battery and heat dissipation amount of the surface of the battery, simplifying partial differential calculation of a heat conduction equation through a differential equation, acquiring temperatures of each grid unit of the battery at different moments, generating a three-dimensional temperature profile of the battery, recording cycle periods, working currents and environmental temperatures of the battery in the operation process of the lithium ion battery, calculating capacity loss of the battery in the current operation and updating SOH of the battery; S400, constructing a coupling model of an electric model, a thermal model and an aging model, namely configuring initial values and termination conditions of basic data of the battery model, calculating SOH of the current period of the battery according to the historical cycle period of the battery, calculating heat transfer coefficients of the battery in different directions of x, y and z under the SOH period, realizing the coupling of the electric model, the thermal model and the aging model on a battery cycle number level, updating the temperature distribution and the state of charge SOC of t