Parameter identification method and device for electrochemical-thermal-aging coupled battery model, medium and equipment

Abstract

The application provides a parameter identification method and device for an electrochemical-thermal-aging coupled battery model, a medium and equipment. The method comprises the following steps: constructing an electrochemical-thermal-aging coupled battery simulation model based on the actual structure of a battery; obtaining experimental data of the battery under different rates; the experimental data comprises charge-discharge voltage data, temperature data and aging data; identifying each target parameter of the electrochemical-thermal-aging coupled battery model according to the experimental data and a genetic algorithm, and obtaining the optimal value of each target parameter. Thus, the application considers the coupling effect among the electrochemical field, the thermal field and the aging reaction, so that the electrochemical reaction, the ion and electron transmission process in the battery can be accurately simulated, and the performance state change of the battery can be accurately reflected. When the genetic algorithm is used to identify the parameters of the coupled battery model, the global optimization ability of the genetic algorithm is strong, so that the accuracy of the optimal solution can be ensured, the identification accuracy of the battery model parameters can be ensured, and the battery quality can be ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery model simulation, and in particular to a parameter identification method and device for an electrochemical-thermal-aging coupled battery model, a medium and equipment. BACKGROUND

[0002] With the wide popularity of electric vehicles, the demand for power batteries in the new energy industry is increasing day by day. In order to better design the battery and ensure the quality of the battery, a battery model needs to be established for simulation to describe the internal reaction process and external corresponding characteristics, and then the simulation results are used to guide the optimization design of the battery.

[0003] However, the existing battery model cannot accurately reflect the performance state change of the battery, so the identification accuracy of the parameters of the lithium battery model cannot be ensured when simulating the battery, which affects the quality of the battery. SUMMARY

[0004] To solve or partially solve the technical problem that the identification accuracy of the parameters of the battery model cannot be ensured in the prior art, thereby affecting the quality of the battery, the embodiments of the present application provide a parameter identification method and device for an electrochemical-thermal-aging coupled battery model, a medium and equipment.

[0005] In a first aspect of the present application, a parameter identification method for an electrochemical-thermal-aging coupled battery model is provided, which comprises:

[0006] An electrochemical-thermal-aging coupled battery simulation model is constructed based on the actual structure of the battery;

[0007] Experimental data of the battery under different rates are obtained; the experimental data include charge-discharge voltage data, temperature data and aging data;

[0008] Each target parameter of the electrochemical-thermal-aging coupled battery model is identified according to the experimental data and a genetic algorithm, to obtain the optimal value of each target parameter.

[0009] In the above scheme, the construction of the electrochemical-thermal-aging coupled battery simulation model comprises:

[0010] A three-dimensional electrochemical model of a single battery unit under mesoscale is constructed, and a corresponding first control equation is set for the three-dimensional electrochemical model;

[0011] construct a solid electrolyte interface film growth mechanism model of a battery negative electrode on the basis of the three-dimensional electrochemical model, and set a corresponding second control equation for the solid electrolyte interface film growth mechanism model;

[0012] construct a three-dimensional thermal model of the battery at a macroscopic scale, and set a corresponding third control equation for the three-dimensional thermal model;

[0013] couple the electrochemical reaction heat output by the first control equation to the third control equation, couple the average temperature output by the third control equation to the first control equation, couple the local current density on the particle surface output by the first control equation to the second control equation, and couple the parasitic reaction current output by the second control equation to the first control equation, to obtain the electrochemical-thermal-aging coupled battery simulation model.

[0014] In the above scheme, the first control equation includes: a lithium ion concentration equation of a solid-phase electrode particle, a current density equation of the solid-phase electrode particle, a current density equation of an electrolyte liquid phase, and a local current density equation on the surface of an electrode particle; wherein,

[0015] The lithium ion concentration equation of the solid-phase electrode particle is:

[0016] The current density equation of the solid-phase electrode particle is: s s s ;

[0017] The current density equation of the electrolyte liquid phase is:

[0018] The local current density equation on the surface of the electrode particle is: wherein,

[0019] The c s is a lithium ion concentration in a solid-phase electrode particle, the r is a radius of the solid-phase electrode particle, the D s is a solid-phase diffusion coefficient, the i s is a current density of the solid-phase electrode particle, the σ s is an electrical conductivity of the solid-phase electrode particle, the ▽ is a gradient operator, the φ s is a solid-phase potential, the i l is a current density of the electrolyte liquid phase, the σ1 is a liquid-phase electrical conductivity, the φ1 is a liquid-phase potential, the R is an ideal gas constant, the T is a temperature of the electrochemical-thermal-aging coupled battery model, the F is a Faraday constant, the t0 is a lithium ion transference number, the f ± is an average molar activity coefficient of an electrolyte, and the j​​n is the local current density on the surface of the solid phase electrode particle, j0 is the reference exchange current density, a a is the transfer coefficient of the cathode, a c is the transfer coefficient of the anode, η is the interface overpotential generated by the electrochemical reaction, and exp is the exponential function.

[0020] In the scheme, the electrochemical-thermal-aging coupled battery model includes an electrochemical model, a thermal model, and an aging model; and the identification of each target parameter of the electrochemical-thermal-aging coupled battery model according to the experimental data and the genetic algorithm includes:

[0021] The target parameters of the electrochemical model are encoded by using a preset encoding mode to generate a first initial population, the first initial population contains a plurality of first individuals, and each first individual represents a set of solutions of the target parameters of the electrochemical model; in each iteration process, a first objective function is constructed according to experimental data of the electrochemical model and simulation data of the electrochemical model; the fitness value of each first individual is calculated by using the first objective function; the parent generation is selected based on the fitness value of each first individual, the parent generation is subjected to a cross operation to generate new offspring, and a new population is obtained; the above iteration process is repeated until the iteration condition is met, and an optimal first individual is output, the optimal first individual is the optimal solution of each target parameter of the electrochemical model;

[0022] The optimal solution of each target parameter of the electrochemical model is substituted into the electrochemical model, the target parameters of the thermal model are encoded by using a preset encoding mode to generate a second initial population, the second initial population contains a plurality of second individuals, and each second individual represents a set of solutions of the target parameters of the thermal model; in each iteration process, a second objective function is constructed according to experimental data of the thermal model and simulation data of the thermal model; the fitness value of each second individual is calculated by using the second objective function; the parent generation is selected based on the fitness value of each second individual, the parent generation is subjected to a cross operation to generate new offspring, and a new population is obtained; the above iteration process is repeated until the iteration condition is met, and an optimal second individual is output, the optimal second individual is the optimal solution of each target parameter of the thermal model;

[0023] The optimal solution of each target parameter of the thermal model is substituted into the thermal model, the target parameters of the aging model are encoded by using a preset encoding mode, to generate a third initial population, the third initial population contains a plurality of third individuals, and each third individual represents a set of solutions of the target parameters of the aging model; in each iteration process, a third objective function is constructed according to the experimental data of the aging model and the simulation data of the aging model; the fitness value of each third individual is calculated by using the third objective function; the parent generation is selected based on the fitness value of each third individual, the parent generation is subjected to a crossover operation to generate new offspring, and a new population is obtained; the above iteration process is repeated until the iteration condition is met, and an optimal third individual is output, and the optimal third individual is the optimal solution of each target parameter of the aging model.

[0024] In the scheme, the target parameters of the electrochemical model include first-type parameters, second-type parameters and third-type parameters; the first objective function is constructed according to the experimental data of the electrochemical model and the simulation data of the electrochemical model, including:

[0025] According to the formula the first objective function corresponding to the first-type parameters is constructed;

[0026] According to the formula the first objective function corresponding to the second-type parameters is constructed;

[0027] According to the formula the first objective function corresponding to the third-type parameters is constructed; wherein,

[0028] The χ1 is the first-type parameter, the θ max,p is the maximum lithium intercalation of the battery positive electrode, the θ max,n is the maximum lithium intercalation of the battery positive electrode, the θ min,p is the minimum lithium intercalation of the battery positive electrode, the θ min,n is the minimum lithium intercalation of the battery negative electrode, the N is the number of data points, the s is the data point number, the U ch,full,ref (x full ) is the reference voltage corresponding to the 0.01 rate charging working condition, the U ch,sim,ref (x full ) is the simulation voltage corresponding to the 0.01 rate charging working condition, the U dis,full,ref (x full ) is the reference voltage corresponding to the 0.01 rate discharging working condition, the U dis,sim,ref (x full ) is the simulation voltage corresponding to the 0.01 rate discharging working condition, the χ2 is the second-type parameter, the ε p is the volume fraction of the battery positive electrode, the ε nVolume fraction of the battery cathode, the cs max_p Maximum lithium ion concentration of the battery cathode, the cs max_n Maximum lithium ion concentration of the battery anode, U 0.05C,ch,ref (t) is the corresponding reference voltage under 0.05C rate charging condition, U 0.05C,ch,sim (t) is the corresponding simulation voltage under 0.05C rate charging condition, U 0.05C,diss,ref (t) is the corresponding reference voltage under 0.05C rate discharging condition, U 0.05C,diss,sim (t) is the corresponding simulation voltage under 0.05C rate discharging condition, the χ3 is the third type of parameter, the σ e Electrolyte conductivity, the D e Diffusion coefficient, the i ref,p Exchange current density of the battery cathode, the i ref,n Exchange current density of the battery anode, U 0.5C,ch,ref (x full ) is the corresponding reference voltage under 0.5C rate charging condition, U 0.5C,ch,sim (x full ) is the corresponding simulation voltage under 0.5C rate charging condition, is the corresponding reference voltage under 0.5C rate discharging condition, is the corresponding simulation voltage under 0.5C rate discharging condition, U 2C,ch,ref (x full ) is the corresponding reference voltage under 2C rate charging condition, U 2C,diss,ref (x full ) is the corresponding reference voltage under 2C rate discharging condition, U 2C,ch,sim (x full ) is the corresponding simulation voltage under 2C rate charging condition, U 2C,diss,sim (x full ) is the corresponding simulation voltage under 2C rate discharging condition, full is full battery mode, x full is any coordinate point in the battery model of the full battery mode.

[0029] In the above scheme, the target parameters of the thermal model include the overall entropy thermal coefficient and the convective heat transfer coefficient; the second objective function is constructed according to the experimental data of the thermal model and the simulation data of the thermal model, including:

[0030] The second objective function is constructed according to the formula ; wherein,

[0031] The χ4 is the target parameter of the thermal model, the χ4 is the overall entropy thermal coefficient, the h total is the convective heat transfer coefficient, the N is the number of data points, and the s is the data point number, T0.5C,ch,ref (x full ) is the reference temperature corresponding to the 0.5C charging condition, T 0.5C,ch,sim (x full ) is the simulation temperature corresponding to the 0.5C charging condition, T 2C,ch,ref (x full ) is the reference temperature corresponding to the 2C charging condition, T 2C,ch,sim (x full ) is the reference temperature corresponding to the 2C charging condition, T 0.5C,diss,ref (x full ) is the reference temperature corresponding to the 0.5C discharging condition, T 0.5C,diss,sim (x full ) is the simulation temperature corresponding to the 0.5C discharging condition, T 2C,diss,ref (x full ) is the reference temperature corresponding to the 2C discharging condition, T 2C,diss,sim (x full ) is the simulation temperature corresponding to the 2C discharging condition.

[0032] In the above scheme, the target parameters of the aging model include the overall entropy heat coefficient and the convection heat transfer coefficient; the third objective function is constructed according to the experimental data of the aging model and the simulation data of the aging model, including:

[0033] The third objective function is constructed according to the formula ; wherein

[0034] The χ5 is the target parameter of the aging model, and the δ film is the thickness of the solid electrolyte interface film, the j n is the local current density on the surface of the solid-phase electrode particles, the N is the number of data points, the s is the data point number, and R 0.5C,ch,ref,film (x full ) is the reference solid electrolyte interface film resistance corresponding to the 0.5C charging condition, R 0.5C,ch,sim,film (x full ) is the simulation solid electrolyte interface film resistance corresponding to the 0.5C charging condition, R 0.5C,diss,ref,film (x full ) is the reference solid electrolyte interface film resistance corresponding to the 0.5C discharging condition, R 0.5C,diss,sim,film (x full ) is the simulation solid electrolyte interface film resistance corresponding to the 0.5C discharging condition, R 2C,ch,ref,film (x full ) is the reference solid electrolyte interface film resistance corresponding to the 2C charging condition, R 2C,ch,sim,film (x full ) is the simulation solid electrolyte interface film resistance corresponding to the 2C charging condition, R2C,diss,ref,film (x full ) is the reference solid electrolyte interface film resistance corresponding to the 2C discharge condition, R 2C,diss,sim,film (x full ) is the simulation solid electrolyte interface film resistance corresponding to the 2C discharge condition.

[0035] In a second aspect, the application provides a parameter identification device for an electrochemical-thermal-aging coupled battery model, the device comprising:

[0036] a construction unit configured to construct an electrochemical-thermal-aging coupled battery simulation model based on the actual structure of the battery;

[0037] an acquisition unit configured to acquire experimental data of the battery under different rates, wherein the experimental data comprises charge-discharge voltage data, temperature data and aging data;

[0038] an identification unit configured to identify each target parameter of the electrochemical-thermal-aging coupled battery model according to the experimental data and a genetic algorithm, and obtain the optimal value of each target parameter.

[0039] In a third aspect, the application provides a computer readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the steps of the method of any one of the first aspect.

[0040] In a fourth aspect, the application provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any one of the first aspect when executing the program.

[0041] The application provides a parameter identification method, device, medium and equipment for an electrochemical-thermal-aging coupled battery model, the method comprising: constructing an electrochemical-thermal-aging coupled battery simulation model based on the actual structure of the battery; acquiring experimental data of the battery under different rates; the experimental data comprises charge-discharge voltage data, temperature data and aging data; identifying each target parameter of the electrochemical-thermal-aging coupled battery model according to the experimental data and a genetic algorithm, and obtaining the optimal value of each target parameter; thus, the application considers the coupling effect between the electrochemical field, the thermal field and the aging reaction, and therefore can accurately simulate the electrochemical reaction, the ion and electron transfer process in the battery, and further accurately reflect the performance state change of the battery; when the genetic algorithm is used to identify the parameters of the coupled battery model, the global optimization ability of the genetic algorithm is strong, and therefore the accuracy of the optimal solution can be ensured, and the identification accuracy of the battery model parameters can be ensured, and the quality of the battery can be ensured. BRIEF DESCRIPTION OF DRAWINGS

[0042] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments with reference made to the accompanying drawings. The drawings are for purposes of illustration only and are not intended to be limiting in

[0043] Figure 1 Fig. 1 shows a flowchart of a parameter identification method of an electrochemical-thermal-aging coupled battery model according to an embodiment of the present application;

[0044] Figure 2 Fig. 2 shows a schematic diagram of an open-circuit voltage (OCV) curve of a half-cell negative electrode under 0.01C condition according to an embodiment of the present application;

[0045] Figure 3 Fig. 3 shows a schematic diagram of an open-circuit voltage (OCV) curve of a half-cell positive electrode under 0.01C condition according to an embodiment of the present application;

[0046] Figure 4 Fig. 4 shows a schematic diagram of an open-circuit voltage (OCV) curve of a full-cell under 0.01C condition according to an embodiment of the present application;

[0047] Figure 5 Fig. 5 shows a reference voltage curve of a charging and discharging experiment process and a simulation voltage curve of a simulation process under 0.05C condition according to an embodiment of the present application;

[0048] Figure 6a Fig. 6 shows a schematic diagram of a simulation temperature curve and a reference temperature curve of a constant current charging and discharging under 0.5C condition according to an embodiment of the present application;

[0049] Figure 6b Fig. 7 shows a schematic diagram of a simulation temperature curve and a reference temperature curve of a constant current charging and discharging under 1C condition according to an embodiment of the present application;

[0050] Figure 6c Fig. 8 shows a schematic diagram of a simulation temperature curve and a reference temperature curve of a constant current charging and discharging under 1.5C condition according to an embodiment of the present application;

[0051] Figure 6d Fig. 9 shows a schematic diagram of a simulation temperature curve and a reference temperature curve of a constant current charging and discharging under 2C condition according to an embodiment of the present application;

[0052] Figure 7a Fig. 10 shows a schematic diagram of a simulation voltage curve and a reference voltage curve of a constant current charging and discharging under 0.5C condition according to an embodiment of the present application;

[0053] Figure 7bFig. 2 shows a simulation voltage curve and a reference voltage curve of constant current charging and discharging under a 1C condition according to an embodiment of the present application;

[0054] Figure 7c Fig. 3 shows a simulation voltage curve and a reference voltage curve of constant current charging and discharging under a 1.5C condition according to an embodiment of the present application;

[0055] Figure 7d Fig. 4 shows a simulation voltage curve and a reference voltage curve of constant current charging and discharging under a 2C condition according to an embodiment of the present application;

[0056] Figure 8 Fig. 5 shows a battery simulation capacity retention rate and a reference capacity retention rate curve of constant current charging and discharging under a 2C condition according to an embodiment of the present application;

[0057] Figure 9 Fig. 6 shows a parameter identification device structure of an electrochemical-thermal-aging coupled battery model according to an embodiment of the present application. DETAILED DESCRIPTION

[0058] Exemplary embodiments of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings; however, they are not limited to the embodiments set forth herein but can be implemented in various forms. The present embodiments are provided so that this disclosure will be thorough, and will fully convey the scope of the disclosure to those skilled in the art.

[0059] The present application provides a parameter identification method of an electrochemical-thermal-aging coupled battery model, as shown in the following. Figure 1 The method comprises the following steps:

[0060] S110, constructing an electrochemical-thermal-aging coupled battery simulation model based on the actual structure of the battery.

[0061] In order to accurately describe the internal reaction process and external characteristics of the battery, the present application considers the coupling effect between the electrochemical field, the thermal field and the aging reaction, and therefore constructs an electrochemical-thermal-aging coupled battery simulation model to accurately simulate the internal electrochemical reaction, ion and electron transfer process of the battery.

[0062] In one embodiment, the electrochemical-thermal-aging coupled battery simulation model is constructed, comprising:

[0063] A three-dimensional electrochemical model of a single battery cell under a mesoscale is constructed, and a corresponding first control equation is set for the three-dimensional electrochemical model;

[0064] a solid electrolyte interphase film growth mechanism model of the battery negative electrode is constructed on the basis of the three-dimensional electrochemical model, and a corresponding second control equation is set for the solid electrolyte interphase film growth mechanism model;

[0065] a three-dimensional thermal model of the battery at a macroscopic scale is constructed, and a corresponding third control equation is set for the three-dimensional thermal model;

[0066] the electrochemical reaction heat output by the first control equation is coupled to the third control equation, the average temperature output by the third control equation is coupled to the first control equation, the local current density on the particle surface output by the first control equation is coupled to the second control equation, and the parasitic reaction current output by the second control equation is coupled to the first control equation, so that an electrochemical-thermal-aging coupled battery simulation model is obtained.

[0067] Specifically, when the battery model is constructed, the actual results of the real battery need to be constructed. The electrochemical model is a kind of three-dimensional geometry structure used for simulating the battery current collector, positive and negative active layers and the separator stack to form a battery cell; the additional dimension is the radius dimension of the electrode particles, which is used for simulating the ideal spherical electrode particles in the electrode active layer.

[0068] The thermal model mainly considers the convective heat loss on the surface of the battery, and calculates the temperature and heat source distribution of the battery.

[0069] The aging model is mainly used to characterize the aging behavior of the lithium ion battery, which is manifested as capacity attenuation, impedance increase and actual power reduction, and the aging behavior will seriously affect the service life of the battery. The main reasons for the aging of the battery are the growth of the solid electrolyte interphase (SEI) film on the negative electrode interface, the deposition of the negative electrode metal lithium and the loss of the active material, etc. Among them, the main aging side reaction is the growth of the SEI film, so the construction of the aging model only considers the influence of the SEI film growth. The aging model is established on the basis of the electrochemical model, and it is assumed that the formation of the SEI film is limited by the kinetics and diffusion process, and the aging slows down with the increase of the film thickness. In addition, the graphite electrode particles swell during insertion into the negative electrode, and the SEI film will be “broken”, which will accelerate the aging. Assuming that the SEI film formation reaction is a reduction reaction, the reaction rate is faster when the potential (i.e. the state of charge of the battery) is lower, and the SEI film growth side reaction will be coupled to the electrochemical model in the form of parasitic reaction current, and the parasitic reaction current will affect the current density in the electrochemical model.

[0070] In the present application, the electrochemical model, the thermal model and the aging model all correspond to a control equation, and the coupling between the models is completed through the control equations.

[0071] In one embodiment, the first control equations include: a lithium ion concentration equation of the solid phase electrode particle, a current density equation of the solid phase electrode particle, a current density equation of the electrolyte liquid phase, and a local current density equation of the electrode particle surface: wherein,

[0072] The lithium ion concentration equation of the solid phase electrode particle is:

[0073]

[0074] The current density equation of the solid phase electrode particle is:

[0075] i s = -σ s ×▽×φ s ; (2)

[0076] The current density equation of the electrolyte liquid phase is:

[0077]

[0078] The local current density equation of the electrode particle surface is:

[0079]

[0080] wherein,

[0081] η = φ s - φ l - U e (5)

[0082]

[0083] c s is the lithium ion concentration in the solid phase electrode particle, r is the radius of the solid phase electrode particle, D s is the solid phase diffusion coefficient, i s is the current density of the solid phase electrode particle, σ s is the conductivity of the solid phase electrode particle,▽ is the gradient operator, φ s is the solid phase potential, i l is the current density of the electrolyte liquid phase, σ1 is the liquid phase conductivity, φ l is the liquid phase potential, R is the ideal gas constant, T is the temperature of the electrochemical-thermal-aging coupled battery model, F is the Faraday constant, t0 is the lithium ion transference number, f ± is the average molar activity coefficient of the electrolyte, j n is the local current density of the solid phase electrode particle surface, j0 is the reference exchange current density, a a is the transfer coefficient of the negative electrode (cathode), a c is the transfer coefficient of the positive electrode (anode), η is the interface overpotential generated by the electrochemical reaction.e k is the equilibrium potential of the electrode, i k is the reaction rate constant of the positive or negative electrode, l c is the concentration of lithium ions in the liquid phase, l,ref c is the concentration of reference lithium ions in the liquid phase, s,ref c is the concentration of lithium ions on the surface of the solid phase, s,max is the maximum concentration of lithium ions in the solid phase.

[0084] wherein the current density of the solid phase electrode particles and the current density of the electrolyte liquid phase satisfy the charge conservation formula:

[0085] ▽×i l +▽×i s = 0 (7)

[0086] The current density of the solid phase electrode particles and the local current density on the surface of the electrode particles (also referred to as the solid-liquid phase interface reaction current density) satisfy the following relationship:

[0087] ▽×i s = -a p ×j n (8)

[0088] In an embodiment, the second control equation includes: a parasitic reaction kinetics reaction equation, an overpotential equation, and a film resistance equation of a solid electrolyte interface film; wherein,

[0089] The parasitic reaction kinetics equation is:

[0090]

[0091] The overpotential equation is:

[0092]

[0093] The film resistance equation of the solid electrolyte interface film is:

[0094]

[0095] The growth rate equation of the solid electrolyte interface film is:

[0096]

[0097] i loc,SEI is the local current density on the surface of the battery model negative electrode due to the parasitic reaction kinetics reaction, HK is a dimensionless graphite expansion factor function, J is a dimensionless exchange current density of the parasitic reaction, a is a transfer coefficient of the electrochemical reduction reaction, i loc,1,ref is the local current density at 1C rate, η SEI is the overpotential generated by the parasitic reaction kinetics reaction, qSEI To represent the localized charge accumulation caused by the formation of a solid electrolyte interfacial film, R is the ideal gas constant, T is the temperature of the electrochemical-thermal-aging coupled battery model, F is the Faraday constant, f is the lumped dimensionless parameter based on the properties of the solid electrolyte interfacial film, and φ s φ1 is the solid phase potential, j is the liquid phase potential, j ... n Let a be the local current density on the surface of the solid electrode particles. p R is the specific surface area of ​​the solid electrode particles. film U is the membrane resistance of the solid electrolyte interface membrane. SEI,eq ω represents the equilibrium potential for the solid electrolyte interfacial film growth reaction. film δ represents the volume fraction of the solid electrolyte interfacial film. film Let be the thickness of the solid electrolyte interface film, and K be the conductivity of the solid electrolyte interface film.

[0098] In one embodiment, the third governing equations are: the overall heat production equation, the Ohmic heat equation, the polarization heat equation, the electrochemical reaction heat equation, the Joule heat equation, and the temperature determination equation; wherein...

[0099] The overall heat production equation is:

[0100] Q h =q ohm +q act +q rev +q tab (13)

[0101] The Ohm's heat equation is:

[0102] q ohm =i s ×▽φ s +i l ×φ1 (14)

[0103] The polarization heat equation is:

[0104] q act =a p ×j n ×η (15)

[0105] The electrochemical reaction heat equation is:

[0106]

[0107] The Joule equation is:

[0108]

[0109] The equation for determining temperature is:

[0110]

[0111] Q h q is the total heat production of the electro-thermal-aging coupled battery model ohm q is the ohmic heat act q is the polarization heat rev q is the electrochemical reaction heat tab i is the Joule heat s i is the current density of the solid phase electrode particles l is the current density of the electrolyte liquid phase s is the solid phase potential, is the liquid phase potential, is the gradient operator, a p is the specific surface area of the solid phase electrode particles n is the local current density on the surface of the solid phase electrode particles, T is the temperature of the electro-thermal-aging coupled battery model e is the electrode equilibrium potential, I is the total current passing through the tab of the electro-thermal-aging coupled battery model, R tab is the resistance of the tab tab is the volume of the tab, p is the density of the electro-thermal-aging coupled battery model p is the constant pressure heat capacity of the electro-thermal-aging coupled battery model, m is the thermal conductivity total is the convective heat transfer coefficient on the surface of the electro-thermal-aging coupled battery model, A is the surface area of the electro-thermal-aging coupled battery model surface participating in heat exchange, T f is the reference temperature.

[0112] S111, obtaining experimental data of the battery at different rates; the experimental data includes charge and discharge voltage data, temperature data and aging data.

[0113] After the above electro-thermal-aging coupled battery model is constructed, experimental data of the battery at different rates need to be obtained, and the experimental data includes charge and discharge voltage data, temperature data and aging data, so as to provide a data basis for the target function required by the subsequent genetic algorithm.

[0114] Among them, the battery rate refers to the current value required by the battery to release its rated capacity within a specified time, which is usually represented by the letter C. 1C means that the battery is charged and discharged with a current of the rated capacity value. For example, if the capacity of the battery is 2 Ah (ampere hour), discharging with a current of 2 A (ampere), the discharge rate is 1C; if discharging with a current of 4 A, the discharge rate is 2C.

[0115] S112, identifying each target parameter of the electro-thermal-aging coupled battery model according to the experimental data and the genetic algorithm, to obtain the optimal value of each target parameter.

[0116] After the experimental data is obtained, each target parameter of the electrochemical-thermal-aging coupling battery model is identified according to the experimental data and the genetic algorithm, and optimal values of the target parameters are obtained, including:

[0117] The target parameters of the electrochemical model are encoded by using a preset encoding mode to generate a first initial population, the first initial population containing a plurality of first individuals, each first individual representing a set of solutions of the target parameters of the electrochemical model; in each iteration process, a first objective function is constructed according to the experimental data of the electrochemical model and the simulation data of the electrochemical model; the fitness value of each first individual is calculated by using the first objective function; the parent generation is selected based on the fitness value of each first individual, and the parent generation is subjected to a cross operation to generate new offspring, thereby obtaining a new population; the above iteration process is repeated until the iteration condition is met, and the optimal first individual is output, the optimal first individual being the optimal solution of the target parameters of the electrochemical model;

[0118] The optimal solution of the target parameters of the electrochemical model is substituted into the electrochemical model, the target parameters of the thermal model are encoded by using a preset encoding mode to generate a second initial population, the second initial population containing a plurality of second individuals, each second individual representing a set of solutions of the target parameters of the thermal model; in each iteration process, a second objective function is constructed according to the experimental data of the thermal model and the simulation data of the thermal model; the fitness value of each second individual is calculated by using the second objective function; the parent generation is selected based on the fitness value of each second individual, and the parent generation is subjected to a cross operation to generate new offspring, thereby obtaining a new population; the iteration process is repeated until the iteration condition is met, and the optimal second individual is output, the optimal second individual being the optimal solution of the target parameters of the thermal model;

[0119] The optimal solution of the target parameters of the thermal model is substituted into the thermal model, the target parameters of the aging model are encoded by using a preset encoding mode to generate a third initial population, the third initial population containing a plurality of third individuals, each third individual representing a set of solutions of the target parameters of the aging model; in each iteration process, a third objective function is constructed according to the experimental data of the aging model and the simulation data of the aging model; the fitness value of each third individual is calculated by using the third objective function; the parent generation is selected based on the fitness value of each third individual, and the parent generation is subjected to a cross operation to generate new offspring, thereby obtaining a new population; the above iteration process is repeated until the iteration condition is met, and the optimal third individual is output, the optimal third individual being the optimal solution of the target parameters of the aging model.

[0120] Specifically, since the to-be-identified parameters of the electrochemical-thermal-aging coupled battery model include many, the application is to identify the target parameters of the electrochemical model first, then substitute the corresponding parameter values identified into the electrochemical model, identify the target parameters of the thermal model, substitute the corresponding parameters identified into the thermal model, and finally identify the target parameters of the aging model.

[0121] When the target parameters of each model are identified by using the genetic algorithm, the steps of identification are the same except that the experimental data and the objective functions needed are different.

[0122] Taking the electrochemical model as an example, the target parameters of the electrochemical model also include many, in order to ensure the identification accuracy and improve the identification efficiency, the application divides the target parameters of the electrochemical model into three categories according to the sensitivity of the target parameters of the electrochemical model under different working conditions, which are the first type of parameters, the second type of parameters and the third type of parameters. The first type of parameters is the lithium intercalation parameter of the motor, including: the maximum lithium intercalation of the positive electrode of the battery, the maximum lithium intercalation of the negative electrode of the battery, the minimum lithium intercalation of the positive electrode of the battery and the minimum lithium intercalation of the negative electrode of the battery. The second type of parameter is the electrode capacity parameter, including: the volume fraction of the positive electrode of the battery, the maximum lithium ion concentration of the positive electrode of the battery, the volume fraction of the negative electrode of the battery and the maximum lithium ion concentration of the negative electrode of the battery. The third type of parameter is the battery impedance influence parameter, including: the electrolyte conductivity, the diffusion coefficient and the electrode exchange current density.

[0123] Then in one embodiment, a first objective function is constructed according to the experimental data of the electrochemical model and the simulation data of the electrochemical model, including:

[0124] According to formula (19), the first objective function corresponding to the first type of parameters is constructed:

[0125]

[0126] According to formula (20), the first objective function corresponding to the second type of parameters is constructed:

[0127]

[0128] According to formula (21), the first objective function corresponding to the third type of parameters is constructed:

[0129]

[0130] Wherein, the χ1 is the first type of parameter, the θ max,p is the maximum lithium intercalation of the positive electrode of the battery, the θ max,n is the maximum lithium intercalation of the positive electrode of the battery, the θ min,p is the minimum lithium intercalation of the positive electrode of the battery, and θ min,nminimum lithium intercalation for the battery anode, N is the number of data points, s is the data point number, U ch,full,ref (x full ) is the corresponding reference voltage under 0.01 rate charging condition, U ch,sim,ref (x full ) is the corresponding simulated voltage under 0.01 rate charging condition, U dis,full,ref (x full ) is the corresponding reference voltage under 0.01 rate discharging condition, U dis,sim,ref (x full ) is the corresponding simulated voltage under 0.01 rate discharging condition, χ2 is the second type parameter, ε p is the volume fraction of the battery cathode, ε n is the volume fraction of the battery cathode, cs max_p is the maximum lithium ion concentration of the battery cathode, cs max_n is the maximum lithium ion concentration of the battery anode, U 0.05C,ch,ref (t) is the corresponding reference charging voltage under 0.05 rate condition, U 0.05C,ch,sim (t) is the corresponding simulated voltage under 0.05 rate charging condition, U 0.05C,diss,ref (t) is the corresponding reference voltage under 0.05 rate discharging condition, U 0.05C,diss,sim (t) is the corresponding simulated voltage under 0.05 rate discharging condition, χ3 is the third type parameter, σ e is the electrolyte conductivity, D e is the diffusion coefficient, i ref,p is the exchange current density of the battery cathode, i ref,n is the exchange current density of the battery anode, U 0.5C,ch,ref (x full ) is the corresponding reference voltage under 0.5 rate charging condition, U 0.5C,ch,sim (x full ) is the corresponding simulated voltage under 0.5 rate charging condition, is the corresponding reference voltage under 0.5 rate discharging condition, is the corresponding simulated voltage under 0.5 rate discharging condition, U 2C,ch,ref (x full ) is the corresponding reference voltage under 2 rate charging condition, U 2C,diss,ref (x full ) is the corresponding reference voltage under 2 rate discharging condition, U 2C,ch,sim (x full ) is the corresponding simulated voltage under 2 rate charging condition, U 2C,diss,sim (x full ) is the corresponding simulated voltage under 2 rate discharging condition, full is full battery mode, xfull Any one coordinate point in the battery model for full battery mode.

[0131] Wherein, f(x1) includes f1(x1)~f2(x1), f(x2) includes f1(x2)~f2(x2), f(x3) includes f1(x3)~f4(x3).

[0132] Specifically, for lithium-ion batteries, the open-circuit voltage OCV of the battery reaches the lower cut-off voltage when SOC=0, and reaches the upper cut-off voltage when SOC=1. The OCV curve in the electrochemical model is usually expressed as a function of the state-of-lithiation (SOL) of the electrode. It is worth noting that after the electrode material is made into a finished battery, only part of its theoretical capacity is utilized. To prevent lithium precipitation, the negative electrode graphite is usually not completely filled; due to the formation of SEI film, the positive electrode material is also not completely filled. In addition, the electrode material is not completely de-embedded at a low lithiation state due to its unstable thermodynamic properties. Therefore, the actual OCV range of the electrode is not the theoretical range obtained by half-cell measurement, and the electrode OCV working range needs to be re-identified when simulating the full battery, and the first type of parameters is identified using a multi-objective genetic algorithm.

[0133] The steps of identifying the first type of parameters using a multi-objective genetic algorithm are as follows:

[0134] (1) Perform half-cell experiments to obtain the open-circuit voltage OCV curves of the positive and negative electrodes. The battery negative electrode used in the experiment is a silicon-carbon composite electrode, and the presence of silicon causes voltage hysteresis in the composite electrode during charging and discharging. The positive electrode OCV curve is as shown in Figure 2 , and the negative electrode OCV curve is as shown in Figure 3 .

[0135] In Figure 2 , neg_charge_OCV is the OCV curve of the negative electrode under charging conditions, and neg_discharge_OCV is the OCV curve of the negative electrode under discharging conditions. In Figure 3 , pos_OCV is the OCV curve of the positive electrode under discharging conditions. Figure 2 and Figure 3 , the abscissa is the state of charge SOC, and the ordinate is the battery voltage, Figure 2 and Figure 3 , θ0 is the first data point, and θ 100 is the 101st data point.

[0136] (2) Perform 0.01C constant current charging and discharging experiments on the full battery, and the obtained OCV curve is as shown in Figure 4 . At this time, the charging and discharging data includes: the reference charging voltage U ch,full,ref(x full ) and the discharge voltage U dis,full,ref (x full ) under the 0.01C discharge condition.

[0137] In the formula (21), fullcell_charge_OCV is the OCV curve under the full cell charging condition, and fullcell_discharge_OCV is the OCV curve under the full cell discharging condition. Figure 4

[0138] The open circuit voltage OCV curves of the positive and negative electrodes determined in the step (1) are used to calculate U ch,full,sim (x full ) and U dis,full,sim (x full ):

[0139] U ch,full,sim (x full ) = U pos,sim (x pos ) - U ch,neg,sim (x neg ) (22)

[0140] U diss,full,sim (x full ) = U pos,sim (x pos ) - U dis,neg,sim (x neg ) (23)

[0141] wherein U pos,sim (x pos ) is the simulation voltage of the positive electrode of the battery, U ch,neg,sim (x neg ) is the charging simulation voltage of the negative electrode of the battery, and U dis,neg,sim (x neg ) is the discharging simulation voltage of the negative electrode of the battery.

[0142] (3) defining χ1 as the to-be-identified parameter, U ch,full,ref (x full ) and U dis,full,ref (x full ) as the reference charging and discharging voltages, U ch,sim,ref (x full ) and U dis,sim,ref (x full ) as the simulation charging and discharging voltages, constructing the first objective function described in the formula (19), taking the error between the reference charging and discharging voltage data and the simulation charging and discharging voltage data as the objective function, taking the first type of parameters as the variables, and using the genetic algorithm to iteratively calculate to obtain the specific value of the first parameter corresponding to the minimum value of the first objective function, thereby completing the identification of the first type of parameters. ​

[0143] When the first type of parameter identification is completed, the specific value of χ1 identified is taken as a known value, equivalent to a known electrode working interval, and the second type of parameter is identified again under low rate (0.5C) conditions by using a single-target genetic algorithm. The steps are as follows:

[0144] (1) Perform full battery 0.05C constant current charge and discharge experiments, and take the charge and discharge voltage data under 0.05C working conditions as reference charge and discharge voltages. The reference voltage curve in the process of the charge and discharge experiment and the simulation voltage curve in the process of the charge and discharge simulation are as shown in Figure 5 . Figure 5 The blue curve in represents the charge simulation voltage curve, and the red curve represents the reference voltage curve. Figure 5 0.05C_exp in means the reference voltage curve corresponding to the battery charge and discharge experiment under 0.05C working conditions, and 0.05C_sim means the simulation voltage curve corresponding to the battery charge and discharge experiment under 0.05C working conditions. Figure 5 The horizontal axis in represents time, and the vertical axis represents voltage. Figure 5 The upward trend curve in is the voltage curve under the charge working condition, and the downward trend curve is the voltage curve under the discharge working condition.

[0145] (2) Take χ2 as the parameter to be identified, U 0.05C,ch,ref (x 0.05C,diss,ref ) and U 0.05C,ch,sim (x 0.05C,diss,sim ) as the reference charge and discharge voltages, and U full (x 2C,ch,ref ) and U full (x 0.5C,diss,ref ) as the simulation charge and discharge voltages.

[0146] Take the error between the reference voltage data and the simulation voltage data under the full battery mode 0.05C constant current charge and discharge working condition as the objective function, take the second type of parameter as the variable, and use the genetic algorithm to continuously iterate and calculate to obtain the specific value of the second type of parameter corresponding to the minimum value of the first objective function, thereby completing the identification of the second type of parameter.

[0147] When the second type of parameter identification is completed, the specific value of χ2 identified is taken as a known value, and the third type of parameter is continuously identified, and the steps are as follows:

[0148] (1) Perform full battery 0.5C and 2C constant current charge and discharge experiments, and take the charge and discharge voltage data under 0.5C working conditions and 2C working conditions as reference charge and discharge voltages.

[0149] (2) Take χ3 as the parameter to be identified, U 0.5C,ch,ref (x full ), U 2C,ch,ref (x full ) as the reference charge voltage, and U 0.5C,diss,ref (x full ) and U 2C,diss,ref(x full ) as the reference discharge voltage, U 0.5C,ch,sim (x full ) and U 2C,ch,sim (x full ) as the simulation charging voltage, U 0.5C,diss,sim (x full ) and U 2C,diss,sim (x full ) as the simulation discharge voltage.

[0150] The error between the reference voltage data and the simulation voltage data under the full battery mode 0.5C and 2C constant current charging and discharging working conditions is taken as the objective function, the third type of parameters is taken as the variable, and the genetic algorithm is used for continuous iteration calculation, so as to obtain the specific value of the third type of parameters corresponding to the minimum value of the first objective function, and the identification of the third type of parameters is completed.

[0151] After the identification of the to-be-identified parameters of the electrochemical model is completed, the target parameters of the thermal model are further identified based on the above. In an embodiment, the target parameters of the thermal model include the overall entropy heat coefficient and the convective heat transfer coefficient; a second objective function is constructed according to the experimental data of the thermal model and the simulation data of the thermal model, including:

[0152] The second objective function is constructed according to formula (24):

[0153]

[0154] χ4 is the target parameter of the thermal model, is the overall entropy heat coefficient, h total is the convective heat transfer coefficient, N is the number of data points, s is the data point number, T 0.5C,ch,ref (x full ) is the reference temperature corresponding to the 0.5C charging working condition, T 0.5C,ch,sim (x full ) is the simulation temperature corresponding to the 0.5C charging working condition, T 2C,ch,ref (x full ) is the reference temperature corresponding to the 2C charging working condition, T 2C,ch,sim (x full ) is the reference temperature corresponding to the 2C charging working condition, T 0.5C,diss,ref (x full ) is the reference temperature corresponding to the 0.5C discharging working condition, T 0.5C,diss,sim (x full ) is the simulation temperature corresponding to the 0.5C discharging working condition, T 2C,diss,ref (x full ) is the reference temperature corresponding to the 2C discharging working condition, T 2C,diss,sim (x full) is the corresponding simulation temperature under 2C discharge condition. Wherein, f(x4) includes f1(x4)~f4(x4).

[0155] Then, according to the same method, the target parameters of the thermal model are identified by using the multi-objective genetic algorithm, the error between the reference temperature data and the simulation voltage temperature under the 0.5C and 2C constant current charge and discharge conditions of the full battery mode is taken as the objective function, and the target parameters of the thermal model are taken as the variables. The multi-objective genetic algorithm is used for continuous iterative calculation, and the specific value of the target parameters corresponding to the minimum value of the second objective function is obtained, so as to complete the identification of the target parameters of the thermal model.

[0156] After the identification of the target parameters of the thermal model is completed, the aging type target parameters are continuously identified on this basis. In one embodiment, the target parameters of the aging model include the overall entropy thermal coefficient and the convective heat transfer coefficient; a third objective function is constructed according to the experimental data of the aging model and the simulation data of the aging model, including:

[0157] The third objective function is constructed according to formula (25):

[0158]

[0159] x5 is the target parameter of the aging model, δ film is the thickness of the solid electrolyte interface film, j n is the local current density on the surface of the solid phase electrode particles, N is the number of data points, s is the data point number, R 0.5C,ch,ref,film (x full ) is the corresponding reference solid electrolyte interface film resistance under 0.5C charge condition, R 0.5C,ch,sim,film (x full ) is the corresponding simulation solid electrolyte interface film resistance under 0.5C charge condition, R 0.5C,diss,ref,film (x full ) is the corresponding reference solid electrolyte interface film resistance under 0.5C discharge condition, R 0.5C,diss,sim,film (x full ) is the corresponding simulation solid electrolyte interface film resistance under 0.5C discharge condition, R 2C,ch,ref,film (x full ) is the corresponding reference solid electrolyte interface film resistance under 2C charge condition, R 2C,ch,sim,film (x full ) is the corresponding simulation solid electrolyte interface film resistance under 2C charge condition, R 2C,diss,ref,film (x full ) is the corresponding reference solid electrolyte interface film resistance under 2C discharge condition, R 2C,diss,sim,film (x full) is 2 times the corresponding simulated solid electrolyte interface film resistance under the discharge condition. Wherein, f(x5) includes f1(x5)~f4(x5).

[0160] Then, according to the same method, the target parameters of the thermal model are identified by using the multi-objective genetic algorithm, the error between the reference solid electrolyte interface film resistance and the simulated solid electrolyte interface film resistance under the 0.5C and 2C constant current charge and discharge conditions of the full battery mode is taken as the objective function, the target parameters of the aging model are taken as variables, the multi-objective genetic algorithm is used for continuous iteration calculation, the specific value of the target parameter corresponding to the minimum value of the third objective function is obtained, and the identification of the target parameters of the aging model is completed.

[0161] In order to verify the accuracy of the electrochemical-thermal-aging coupled battery model after parameter identification, the identified electrochemical-thermal-aging coupled battery model is also verified under various rates.

[0162] The verification curve of the electrochemical model is as shown in Figure 6a to Figure 6d The red curve in Figure 6a to Figure 6d represents the simulated temperature curve, the black curve represents the reference temperature curve, the horizontal axis represents time, and the vertical axis represents temperature, in units of K. Figure 6a In Figure 6b , 0.5C_exp represents the reference temperature under the 0.5C charge and discharge condition, and 0.5C_sim represents the simulated temperature under the 0.5C charge and discharge condition; in Figure 6c , 1C_exp represents the reference temperature under the 1C charge and discharge condition, and 1C_sim represents the simulated temperature under the 1C charge and discharge condition; in Figure 6d , 1.5C_exp represents the reference temperature under the 1.5C charge and discharge condition, and 1.5C_sim represents the simulated temperature under the 1.5C charge and discharge condition; in , 2C_exp represents the reference temperature under the 2C charge and discharge condition, and 2C_sim represents the simulated temperature under the 2C charge and discharge condition.

[0163] Figure 6a to Figure 6d In , from left to right, the first curve with an upward trend is the temperature curve under the charge condition, the second curve with a downward trend is the temperature curve under the static condition, and the third curve with an upward trend is the temperature curve under the discharge condition.

[0164] Under various rates, the curve fitting is good, and the maximum error occurs in the initial stage of charging and discharging, which is due to the fact that the experimental negative electrode material is silicon-carbon material, and the OCV curve has voltage hysteresis. The root mean square (RSME, Root Mean Square Error) error of the voltage under various rates (the error between the reference voltage and the simulated voltage) is shown in Table 1.

[0165] Table 1

[0166] Magnification RSME (mV) 0.5C 34.76 1C 28.18 1.5C 40.63 2C 50.17

[0167] In Table 1, the root mean square error of the voltage at each multiplier meets the error requirements.

[0168] The validation curves for the thermal model are as follows: Figure 7a to Figure 7d As shown, the temperature curves fluctuate significantly at low rates (0.5C, 1C), while the curves fit better at high rates. The maximum error occurs at the end of charging or discharging. The RSME error at each rate is shown in Table 2. The maximum RSME error occurs at the 2C rate, at 1.52℃, which also meets the error requirements.

[0169] exist Figure 7a In this context, 0.5C_exp represents the reference voltage under 0.5C charge / discharge conditions, and 0.5C_sim represents the simulated voltage under 0.5C charge / discharge conditions; Figure 7b In this context, 1C_exp represents the reference voltage under 1C charge / discharge conditions, and 1C_sim represents the simulated voltage under 1C charge / discharge conditions; Figure 7c In this context, 1.5C_exp represents the reference voltage under 1.5C charge / discharge conditions, and 1.5C_sim represents the simulated voltage under 1.5C charge / discharge conditions; Figure 7d In this context, 2C_exp represents the reference voltage under 2C charge / discharge conditions, and 2C_sim represents the simulated voltage under 2C charge / discharge conditions.

[0170] exist Figure 7a to Figure 7d In the diagram, viewed from left to right, the first segment, showing an upward trend, represents the voltage curve under charging conditions; the second segment, showing a flat trend, represents the voltage curve under resting conditions; and the third segment, showing a downward trend, represents the voltage curve under discharging conditions.

[0171] Table 2

[0172] Magnification RSME (°C) 0.5C 0.90 1C 0.91 1.5C 1.14 2C 1.52

[0173] The validation curves for the aging model are as follows: Figure 8 As shown in Table 3, the error of the SEI film resistance at 1x is as follows: the maximum root mean square error is 0.005, which meets the error requirements.

[0174] exist Figure 8 In the diagram, the horizontal axis represents the number of cycles, the vertical axis represents the time capacity retention rate, 1C_exp represents the reference capacity retention rate under 1C discharge conditions, and 1C_sim represents the simulated capacity retention rate under 1C discharge conditions.

[0175] Table 3

[0176] Magnification Number of cycles RSME 1C 1020 0.005

[0177] In summary, the target parameters identified by the present application can meet the accuracy requirement.

[0178] Based on the same inventive concept as in the foregoing embodiments, the present embodiment also provides a parameter identification device for an electrochemical-thermal-aging coupled battery model, which comprises: Figure 9 As shown in the figure, the device comprises:

[0179] A construction unit 91, configured to construct an electrochemical-thermal-aging coupled battery simulation model based on the actual structure of the battery;

[0180] An acquisition unit 92, configured to acquire experimental data of the battery under different rates; the experimental data comprises charge-discharge voltage data, temperature data and aging data;

[0181] An identification unit 93, configured to identify each target parameter of the electrochemical-thermal-aging coupled battery model according to the experimental data and a genetic algorithm, to obtain the optimal value of each target parameter.

[0182] Since the device introduced in the present embodiment is the device used for the parameter identification method of the electrochemical-thermal-aging coupled battery model of the present embodiment, the specific structure and deformation of the device can be understood by those skilled in the art based on the method introduced in the present embodiment, and thus will not be described here. Any device used by the method of the present embodiment belongs to the scope of the present application.

[0183] Based on the same inventive concept, the present embodiment provides a computer device, which comprises a memory, a processor and a computer program stored on the memory and executable on the processor, and the processor implements any step of the method described above when executing the computer program.

[0184] Based on the same inventive concept, the present embodiment provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of any method described above.

[0185] By one or more embodiments of the present application, the present application has the following beneficial effects or advantages: the present application provides a parameter identification method, device, medium and equipment of an electrochemical-thermal-aging coupled battery model, the method comprising: constructing an electrochemical-thermal-aging coupled battery simulation model based on the actual structure of the battery; obtaining experimental data of the battery under different rates; the experimental data includes charge-discharge voltage data, temperature data and aging data; identifying each target parameter of the electrochemical-thermal-aging coupled battery model according to the experimental data and a genetic algorithm, to obtain the optimal value of each target parameter; in this way, the present application considers the coupling effect between the electrochemical field, the thermal field and the aging reaction, so the electrochemical reaction, the ion and electron transfer process in the battery can be accurately simulated, and the performance state change of the battery can be accurately reflected; when the genetic algorithm is used to identify the parameters of the coupled battery model, the global optimization ability of the genetic algorithm is strong, so the accuracy of the optimal solution can be ensured, and the identification accuracy of the battery model parameters can be ensured, and the battery quality can be ensured.

[0186] Although the preferred embodiments of the present application have been described, those skilled in the art who understand the basic inventive concept can make further changes and modifications to the embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0187] The above is only a preferred embodiment of the present application, and is not intended to limit the protection scope of the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A parameter identification method of an electrochemical-thermal-aging coupled battery model, characterized in that, The method comprises: constructing an electrochemical-thermal-aging coupled battery simulation model based on the actual structure of the battery; obtaining experimental data of the battery under different rates; the experimental data comprises charge-discharge voltage data, temperature data and aging data; identifying each target parameter of the electrochemical-thermal-aging coupled battery model according to the experimental data and a genetic algorithm, to obtain optimal values of each target parameter.

2. The method of claim 1, wherein, The method of constructing the electrochemical-thermal-aging coupled battery simulation model comprises: constructing a three-dimensional electrochemical model of a single battery unit at a mesoscale, and setting a corresponding first control equation for the three-dimensional electrochemical model; constructing a solid electrolyte interface film growth mechanism model of a battery negative electrode based on the three-dimensional electrochemical model, and setting a corresponding second control equation for the solid electrolyte interface film growth mechanism model; constructing a three-dimensional thermal model of the battery at a macro scale, and setting a corresponding third control equation for the three-dimensional thermal model; coupling electrochemical reaction heat output by the first control equation to the third control equation, coupling average temperature output by the third control equation to the first control equation, coupling local current density on the surface of a particle output by the first control equation to the second control equation, and coupling parasitic reaction current output by the second control equation to the first control equation, to obtain the electrochemical-thermal-aging coupled battery simulation model.

3. The method of claim 2, wherein, The first control equation comprises a lithium ion concentration equation of a solid-phase electrode particle, a current density equation of the solid-phase electrode particle, a current density equation of an electrolyte liquid phase, and a local current density equation on the surface of an electrode particle; wherein, The equation for the lithium ion concentration of the solid phase electrode particles is: The current density equation for the solid phase electrode particles is: The current density equation of the electrolyte liquid phase is: The local current density equation for the surface of the electrode particles is: The c s Where r is the lithium-ion concentration in the solid-phase electrode particles, and D is the radius of the solid-phase electrode particles. s Let i be the solid-phase diffusion coefficient. s The current density of the solid electrode particles is σ. s The conductivity of the solid electrode particles is given by the following formula: For the gradient operator, the φ s For solid-state potential, the i l The current density of the electrolyte liquid phase, σ1 is the liquid phase conductivity, φ1 is the liquid phase potential, R is the ideal gas constant, T is the temperature of the electrochemical-thermal-aging coupled battery model, F is the Faraday constant, t0 is the lithium-ion transport number, and f is the... ± The j represents the average molar activity coefficient of the electrolyte. n The local current density on the surface of the solid electrode particles is given by j0, which is the reference exchange current density, and a is the local current density on the surface of the solid electrode particles. a The a is the cathode transfer coefficient. c η is the anode transfer coefficient, η is the interfacial overpotential generated by the electrochemical reaction, and exp is an exponential function.

4. The method of claim 1, wherein, The electrochemical-thermal-aging coupled battery model comprises an electrochemical model, a thermal model and an aging model; and the method of identifying each target parameter of the electrochemical-thermal-aging coupled battery model according to the experimental data and the genetic algorithm comprises: encoding the target parameters of the electrochemical model by using a preset encoding mode, to generate a first initial population; the first initial population comprises a plurality of first individuals, and each first individual represents a set of solutions of the target parameters of the electrochemical model; in each iteration process, a first objective function is constructed according to experimental data of the electrochemical model and simulation data of the electrochemical model; the fitness value of each first individual is calculated by using the first objective function; parents are selected based on the fitness value of each first individual, and the parents are subjected to a crossover operation to generate new offspring, to obtain a new population; the above iteration process is repeated until an iteration condition is met, and an optimal first individual is output; the optimal first individual is the optimal solution of each target parameter of the electrochemical model. The optimal solution of each target parameter of the electrochemical model is substituted into the electrochemical model, the target parameters of the thermal model are encoded by using a preset encoding mode, to generate a second initial population, the second initial population contains a plurality of second individuals, each second individual represents a set of solutions of the target parameters of the thermal model; in each iteration process, a second objective function is constructed according to the experimental data of the thermal model and the simulation data of the thermal model; the fitness value of each second individual is calculated by using the second objective function; the parent generation is selected based on the fitness value of each second individual, the parent generation is subjected to a cross operation to generate new offspring, and a new population is obtained; the above iteration process is repeated until the iteration condition is met, and an optimal second individual is output, the optimal second individual is the optimal solution of each target parameter of the thermal model; The optimal solution of each target parameter of the thermal model is substituted into the thermal model, the target parameters of the aging model are encoded by using a preset encoding mode, to generate a third initial population, the third initial population contains a plurality of third individuals, each third individual represents a set of solutions of the target parameters of the aging model; in each iteration process, a third objective function is constructed according to the experimental data of the aging model and the simulation data of the aging model; the fitness value of each third individual is calculated by using the third objective function; the parent generation is selected based on the fitness value of each third individual, the parent generation is subjected to a cross operation to generate new offspring, and a new population is obtained; the above iteration process is repeated until the iteration condition is met, and an optimal third individual is output, the optimal third individual is the optimal solution of each target parameter of the aging model.

5. The method of claim 4, wherein, The target parameters of the electrochemical model include first-type parameters, second-type parameters and third-type parameters; the first objective function is constructed according to the experimental data of the electrochemical model and the simulation data of the electrochemical model, and includes: According to the formula constructing a first objective function corresponding to the first type of parameters; According to the formula constructing a first objective function corresponding to the second type of parameters; According to the formula constructing a first objective function corresponding to the third type of parameters; wherein, χ1 is the first type of parameter, θ max,p is the maximum lithium intercalation of the battery cathode, θ max,n is the maximum lithium intercalation of the battery cathode, θ min,p is the minimum lithium intercalation of the battery cathode, θ min,n is the minimum lithium intercalation of the battery anode, N is the number of data points, s is the data point number, U ch,full,ref (x full ) is the corresponding reference voltage under 0.01 rate charging conditions, U ch,sim,ref (x full ) is the corresponding simulation voltage under 0.01 rate charging conditions, U dis,full,ref (x full ) is the corresponding reference voltage under 0.01 rate discharging conditions, U dis,sim,ref (x full ) is the corresponding simulation voltage under 0.01 rate discharging conditions, χ2 is the second type of parameter, ε p is the volume fraction of the battery cathode, ε n is the volume fraction of the battery cathode, cs max_p is the maximum lithium ion concentration of the battery cathode, cs max_n is the maximum lithium ion concentration of the battery anode, U 0.05C,ch,ref (t) is the corresponding reference charging voltage under 0.05 rate conditions, U 0.05C,ch,sim (t) is the corresponding simulation voltage under 0.05 rate charging conditions, U 0.05C,diss,ref (t) is the corresponding reference voltage under 0.05 rate discharging conditions, U 0.05C,diss,sim (t) is the corresponding simulation voltage under 0.05 rate discharging conditions, χ3 is the third type of parameter, σ e is the electrolyte conductivity, D e is the diffusion coefficient, i ref,p is the exchange current density of the battery cathode, i ref,n is the exchange current density of the battery anode, U 0.5C,ch,ref (x full ) is the corresponding reference voltage under 0.5 rate charging conditions, U 0.5C,ch,sim (x full ) is the corresponding simulation voltage under 0.5 rate charging conditions, U 0.5C,dis,sref (x ful ) l is the corresponding reference voltage under 0.5 rate discharging conditions, U 0.5C,diss,sim (x full ) is the corresponding simulation voltage under 0.5 rate discharging conditions, U 2C,ch,ref (x full ) is the reference voltage corresponding to the 2C charging condition, U 2C,diss,ref (x full ) is the reference voltage corresponding to the 2C discharging condition, U 2C,ch,sim (x full ) is the simulated voltage corresponding to the 2C charging condition, U 2C,diss,sim (x full ) is the simulated voltage corresponding to the 2C discharging condition, full is the full cell mode, x full is any coordinate point in the battery model of the full cell mode.

6. The method of claim 4, wherein, The target parameters of the thermal model include an overall entropy thermal coefficient and a convection heat transfer coefficient; the second objective function is constructed according to the experimental data of the thermal model and the simulation data of the thermal model, and includes: According to the formula constructing the second objective function; wherein, χ4 is a target parameter of the thermal model, and the is the overall entropy heat coefficient, h total is the convective heat transfer coefficient, N is the number of data points, s is the data point number, T 0.5C,ch,ref (x full ) is the reference temperature corresponding to the 0.5 rate charging condition, T 0.5C,ch,sim (x full ) is the simulation temperature corresponding to the 0.5 rate charging condition, T 2C,ch,ref (x full ) is the reference temperature corresponding to the 2 rate charging condition, T 2C,ch,sim (x full ) is the reference temperature corresponding to the 2 rate charging condition, T 0.5C,diss,ref (x full ) is the reference temperature corresponding to the 0.5 rate discharging condition, T 0.5C,diss,sim (x full ) is the simulation temperature corresponding to the 0.5 rate discharging condition, T 2C,diss,ref (x full ) is the reference temperature corresponding to the 2 rate discharging condition, T 2C,diss,sim (x full ) is the simulation temperature corresponding to the 2 rate discharging condition.

7. The method of claim 4, wherein, The target parameters of the aging model include an overall entropy thermal coefficient and a convection heat transfer coefficient; the third objective function is constructed according to the experimental data of the aging model and the simulation data of the aging model, and includes: According to the formula constructing the third objective function; wherein, χ5 is a target parameter of the aging model, δ film is a thickness of a solid electrolyte interface film, j n is a local current density on a surface of a solid-phase electrode particle, N is a number of data points, s is a data point number, R 0.5C,ch,ref,film (x full ) is a reference solid electrolyte interface film resistance corresponding to a 0.5 rate charging condition, R 0.5C,ch,sim,film (x full ) is a simulated solid electrolyte interface film resistance corresponding to a 0.5 rate charging condition, R 0.5C,diss,ref,film (x full ) is a reference solid electrolyte interface film resistance corresponding to a 0.5 rate discharging condition, R 0.5C,diss,sim,film (x full ) is a simulated solid electrolyte interface film resistance corresponding to a 0.5 rate discharging condition, R 2C,ch,ref,film (x full ) is a reference solid electrolyte interface film resistance corresponding to a 2 rate charging condition, R 2C,ch,sim,film (x full ) is a simulated solid electrolyte interface film resistance corresponding to a 2 rate charging condition, R 2C,diss,ref,film (x full ) is a reference solid electrolyte interface film resistance corresponding to a 2 rate discharging condition, R 2C,diss,sim,film (x full ) is a simulated solid electrolyte interface film resistance corresponding to a 2 rate discharging condition.

8. A device for parameter identification of an electrochemical-thermal-aging coupled battery model, characterized in that The device includes: The construction unit is configured to construct an electrochemical-thermal-aging coupled battery simulation model based on an actual structure of a battery; The acquisition unit is configured to acquire experimental data of the battery under different rates; the experimental data include charge-discharge voltage data, temperature data and aging data; The identification unit is configured to identify each target parameter of the electrochemical-thermal-aging coupled battery model according to the experimental data and a genetic algorithm, and obtain an optimal value of each target parameter.

9. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program, when executed by a processor, implements the steps of the method of any one of claims 1-7.

10. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor, when executing the program, implements the steps of the method of any one of claims 1-7.

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