Voltage lag simulation method and device, battery simulation method and device and storage medium

Abstract

The invention provides a voltage lag simulation method and device, a battery simulation method and device and a storage medium, and the voltage lag simulation method comprises the steps: S31, obtaining the actual measurement data of a lithium removal voltage Eex and a lithiation voltage Ein; s32, setting a correction voltage E, an average voltage Eavg, an offset voltage Eoffset and a correction coefficient sigma, and establishing a correction model E (sigma); s33, introducing a lithiation state x and time t, setting identification parameters including a lag boundary alpha, a lag constant K and a critical coefficient epsilon, and establishing a dynamic equation sigma (x, t) about a correction coefficient sigma; and S34, simultaneously establishing a function E (sigma) and a function sigma (x, t), setting an initial value for the identification parameter, solving simulation discrete data (Esim, x, t) by comparing the actually measured voltage discrete data (Etest, x, t), defining an error index d (Esim, Etest), fitting the identification parameter by using an optimization algorithm, and solving the identification parameter. And a dynamic correction coefficient sigma is introduced to establish a voltage lag accurate correction model, so that high-precision mathematical description of the unbalanced behavior of the battery is realized, and the accuracy of a simulation result is improved.

Description

Technical Field

[0001] This application relates to the field of battery simulation technology, and in particular to a voltage hysteresis simulation method, battery simulation method, device and storage medium. Background Technology

[0002] Lithium-ion batteries, as important energy storage devices, are widely used in energy storage power stations, electric vehicles, portable electronic devices, and other fields. In battery simulation technology, accurately describing their electrochemical behavior is crucial for battery state estimation, lifetime prediction, and safety management.

[0003] Currently, electrochemical simulation primarily relies on pseudo-two-dimensional (P2D) models, with model equations refined or simplified to meet specific needs. While P2D models can characterize some fundamental electrochemical behaviors of batteries based on equilibrium assumptions, they deviate from equilibrium during charging and discharging when using specific cathode and anode materials, such as silicon-based anodes and lithium iron phosphate cathodes. This manifests as significant voltage hysteresis. This phenomenon stems from mechanisms such as phase transition lag, interfacial reaction impedance differences, and asymmetric ion diffusion paths during lithiation / delithiation, leading to a clear separation between charging and discharging voltage curves at the same state of charge (SOC). Traditional P2D models fail to consider this path-dependent voltage behavior, thus hindering accurate voltage hysteresis description and limiting their simulation accuracy in relevant material systems.

[0004] Therefore, developing an electrochemical simulation model that can accurately describe the voltage hysteresis phenomenon caused by the battery material system, simulate the real operating environment of the battery, and improve the accuracy and reliability of the simulation results has become an urgent need in this field. Summary of the Invention

[0005] To address the aforementioned problems and fill the gap in existing simulation techniques for characterizing voltage hysteresis, the first aspect of this application provides a voltage hysteresis simulation method, comprising the following steps: S31: acquiring basic data and measured data packages of the target battery, and filtering out the delithiation voltage E of the target electrode. ex and lithiation voltage E in S32: Set the correction voltage to E and the average voltage to E. avg The offset voltage is E offset Let the correction coefficient be σ. Solve equations I through III simultaneously to establish the correction model E(σ): (I), (II) (III); where σ is used to control the conversion of the correction voltage between the lithiation voltage and the delithiation voltage, and its value range is: -1≤σ≤1; S33: Introduce temporal parameters: lithiation state x and time t, establish identification parameters: hysteresis boundary α, hysteresis constant K and critical coefficient ε, and combine formulas IV-V to establish the dynamic equation σ(x,t) about the correction coefficient σ: (Ⅳ), (V); The lithiation state x is the ratio of the current lithium-ion concentration of the electrode to the maximum lithium-ion concentration of the electrode in the instantaneous state; S34: Obtain the measured discrete voltage data (E) of the target battery. test Using the parameters (x,t) as the optimization objective, the functions E(σ) and σ(x,t) are combined to set initial values ​​for the identification parameters. The discrete voltage data are then substituted one by one into the functions σ(x,t) and E(σ) to solve for the corresponding simulated discrete data (E). sim (x,t), define the error index d(E) sim E test Establish the objective function Obj= The identification parameters are fitted using an optimization algorithm to solve for the identification parameters.

[0006] In some optional implementations, the objective function includes one or more error indices. When multiple error indices exist, each error indice is assigned a corresponding weight i, and the objective function is a weighted sum of the error indices: Obj = .

[0007] In some optional implementations, the error index is calculated by at least one of mean square error, root mean square error, and mean absolute error, and the optimization algorithm is calculated by at least one of least squares algorithm, genetic algorithm, particle swarm optimization algorithm, and Bayesian algorithm.

[0008] In some optional implementations, the basic data includes battery capacity and "time-temperature" data, and the measured data package includes "time-current-voltage" data and "time-current-negative parameter potential" data of the target battery during charging and discharging at different rates.

[0009] The second aspect of this application provides a battery simulation method, comprising the following steps: S1: acquiring the electrochemical and physical parameters of the target battery to construct an electrochemical simulation model; S2: inputting simulation conditions and calculation requirements into the electrochemical simulation model to generate simulation calculation mathematical equations, and screening out voltage terms with voltage hysteresis errors in the simulation calculation mathematical equations to be corrected; S3: establishing the correction model using the voltage hysteresis simulation method described in any of the preceding steps; S4: inputting the voltage terms to be corrected into the correction model, and the correction model correcting the voltage terms to be corrected into correction model terms according to the voltage hysteresis simulation method; S5: substituting the correction model terms back into the simulation calculation mathematical equations to perform simulation calculations, synchronously correcting the related terms of the voltage terms to be corrected, and outputting the final simulation results.

[0010] In some optional embodiments, the electrochemical parameters include: battery capacity, composition of the positive and negative electrode active material layers, maximum lithium concentration, electrolyte composition, diffusion coefficient, conductivity, lithium-ion transference number, solid-phase diffusion coefficient of the positive and negative electrodes, reaction rate constants of the positive and negative electrodes, transfer coefficient of electrode reactions, initial state of charge, positive electrode open-circuit voltage, and open-circuit voltage of negative electrode lithiation and delithiation; the physical parameters include particle size of the positive and negative electrode particles, thickness and porosity of the positive and negative electrode active material layers, electrode area, and membrane thickness and porosity; the electrochemical simulation model is any one of the following: P2D model, single-particle model, P3D model, P4D model, and multiphysics coupling model.

[0011] In some optional embodiments, the voltage term to be corrected includes the negative open-circuit voltage and the positive open-circuit voltage. If the electrode is a composite electrode composed of two or more materials, the voltage term to be corrected also includes the open-circuit voltage of one or more of the materials.

[0012] A third aspect of this application provides a battery simulation apparatus for correcting voltage hysteresis, used to execute the battery simulation method as described in any of the preceding claims, comprising: a parameter module for acquiring electrochemical and physical parameters of a target battery; a simulation module comprising a modeling unit and a calculation unit, wherein the modeling unit is data-connected to the parameter module, calls the parameters in the parameter module to construct an electrochemical simulation model of the target battery, the calculation unit is data-connected to the modeling unit, generates a simulation calculation mathematical equation according to simulation conditions and calculation requirements, and outputs simulation results after correcting the voltage term with voltage hysteresis error in the simulation calculation mathematical equation; and a voltage correction module, data-connected to the calculation unit, acquiring the voltage term with voltage hysteresis error in the simulation calculation mathematical equation, executing the voltage hysteresis simulation method as described in any of claims 1-4, correcting the voltage term to be corrected into a corrected model term and feeding it back to the simulation calculation mathematical equation of the calculation unit.

[0013] In some optional implementations, the voltage correction module further includes: a correction model unit for building the correction model and receiving the voltage term to be corrected; and a parameter identification unit connected to the correction model unit for obtaining the measured discrete voltage data of the target battery as the optimization target and solving for the values ​​of the identified parameters in the correction model.

[0014] A fourth aspect of this application provides a storage medium including a stored computer program, wherein, when the computer program is executed, it controls a device in which the storage medium is located to perform a battery simulation method for correcting voltage hysteresis according to any one of the preceding claims, the battery simulation method employing a voltage hysteresis simulation method as described in any one of the preceding claims to correct voltage hysteresis errors.

[0015] This application has at least the following technical effects:

[0016] 1) The first aspect of this application provides a voltage hysteresis simulation method. By constructing a voltage hysteresis accurate correction model containing a dynamic correction coefficient σ, the macroscopic voltage hysteresis phenomenon is introduced into the microscopic electrode dynamics process, realizing a high-precision mathematical description of the non-equilibrium behavior of the battery. This effectively solves the common problem that traditional battery models cannot simulate voltage hysteresis loops, enabling the simulation results to reproduce the real differences in charging and discharging voltage paths. This provides a highly reliable simulation platform for accurate battery state estimation, lifetime prediction, and the development of advanced battery management algorithms, and contributes to the improvement of battery design level and application safety.

[0017] 2) The second aspect of this application also provides a battery simulation method for correcting voltage hysteresis. First, a traditional basic electrochemical simulation model is created. This model generates mathematical equations for simulation calculations based on the simulation conditions and computational requirements. Then, voltage terms with voltage hysteresis errors in these mathematical equations are selected and replaced with correction model terms. Correlated terms are corrected simultaneously, resulting in a more accurate simulation value reflecting the voltage hysteresis phenomenon. This achieves high-precision simulation of the physical phenomenon of battery voltage hysteresis. It eliminates the need for complex deconstruction of the basic electrochemical model and coupling with the correction model, preserving the computational efficiency of the original electrochemical simulation model. It only refines specific voltage terms in the mathematical equations used for simulation calculations, avoiding the problem of drastically increased computational complexity caused by directly embedding complex hysteresis dynamics into the basic electrochemical model, thus saving computational resources.

[0018] 3) The third aspect of this application provides a battery simulation device for correcting voltage hysteresis. By modularizing functions such as parameter management, basic simulation and voltage hysteresis correction, a highly integrated and automated high-precision battery simulation process is realized. The modular division of labor and data flow closed-loop feedback mechanism significantly improves the efficiency, accuracy and practicality of the simulation project.

[0019] 4) A fourth aspect of this application provides a storage medium including a stored computer program, wherein, when the computer program is running, it controls the device where the storage medium is located to execute the battery simulation method according to any of the preceding claims, wherein the battery simulation method employs the voltage hysteresis simulation method as described in any of the preceding claims to simulate the voltage hysteresis phenomenon in actual battery operation, thereby improving the accuracy and reliability of the simulation results. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of an optional embodiment of a battery simulation method proposed in this application;

[0022] Figure 2 This is a structural principle block diagram of an optional embodiment of a battery simulation device proposed in this application;

[0023] Figure 3 This is a comparison and verification diagram of simulation results and experimental results for embodiments of this application. Detailed Implementation

[0024] The embodiments of this implementation are described in detail below. In the description of this implementation, "several" means one or more, "multiple" means two or more, "greater than," "less than," and "exceeding" are understood to exclude the stated number, while "above," "below," and "within" are understood to include the stated number. If "first" and "second" are mentioned, they are only used to distinguish technical features and should not be construed as indicating or implying relative importance or implicitly indicating the number of indicated technical features or the order of the indicated technical features.

[0025] In the description of this embodiment, unless otherwise explicitly limited, terms such as setting, installing, and connecting should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this embodiment in conjunction with the specific content of the technical solution.

[0026] Battery simulation technology is evolving towards digital, high-fidelity models that deeply integrate electrochemical mechanisms. The core objective is to build a "digital twin" of a physical battery, that is, to create a digital copy in virtual space that closely matches the physical battery, thereby enabling non-destructive detection of battery status, accurate prediction of performance, and early assessment of lifespan.

[0027] It is generally accepted that constructing a high-fidelity battery simulation model relies on a comprehensive parameter system. These parameters span multiple physical scales, including geometric, electrochemical, and thermodynamic parameters. Geometric parameters such as electrode thickness, porosity, and active material particle radius define the battery's physical structure; intrinsic electrochemical parameters such as the equilibrium potential curves of the positive and negative electrode materials, lithium-ion solid-phase diffusion coefficient, and electrochemical reaction rate constant describe the material's inherent properties; and transport parameters such as electrolyte conductivity and lithium-ion transference number characterize the movement of ions within the battery. These parameters can be obtained through direct physical measurement, electrochemical testing of semi-finished or finished batteries (such as GITT and EIS), or by directly adopting empirical and reference values ​​from existing technical manuals. After establishing the parameter system, a set of mathematical models that can describe the internal physicochemical processes is established and solved; this is the battery simulation model. The most classic example is the pseudo-two-dimensional model (P2D model), which achieves a precise characterization of the battery's internal state by coupling the solid-phase equation describing lithium-ion diffusion within electrode particles with the liquid-phase equation describing ion migration in the electrolyte.

[0028] However, the pseudo-two-dimensional model is essentially based on the equilibrium assumption and mainly relies on the state of charge (SOC) for voltage estimation. It does not fully consider the voltage hysteresis effect brought about by special materials, and therefore it is difficult to accurately describe the voltage hysteresis, which limits its simulation accuracy in relevant material systems.

[0029] Based on this, this application provides a voltage hysteresis simulation method, including the following steps: S31: acquiring basic data and measured data packages containing the target battery, and filtering out the delithiation voltage E of the target electrode. ex and lithiation voltage E in S32: Set the correction voltage to E and the average voltage to E. avg The offset voltage is E offset Let the correction coefficient be σ. Solve equations I through III simultaneously to establish the correction model E(σ): (I), (II) (III); where σ is used to correct the voltage transition between lithiation voltage and delithiation voltage, and its value range is: -1≤σ≤1; S33: Introduce temporal parameters: lithiation state x and time t, establish identification parameters: hysteresis boundary α, hysteresis constant K and critical coefficient ε, and simultaneously solve formulas IV-V to establish the dynamic equation σ(x,t) about the correction coefficient σ: (Ⅳ), (V); The lithiation state x is the ratio of the current lithium-ion concentration of the electrode to the maximum lithium-ion concentration of the electrode in the instantaneous state; S34: Obtain the measured discrete voltage data (E) of the target battery. testUsing the parameters (x,t) as the optimization objective, the functions E(σ) and σ(x,t) are combined to set initial values ​​for the identification parameters. The discrete voltage data are then substituted one by one into the functions σ(x,t) and E(σ) to solve for the corresponding simulated discrete data (E). sim (x,t), define the error index d(E) sim E test Establish the objective function Obj= The identification parameters are fitted using an optimization algorithm to solve for the identification parameters.

[0030] Specifically, the lithium delithiation voltage E is introduced. ex and lithiation voltage E in The average voltage and offset voltage are defined, and a key variable, the correction coefficient σ, is introduced. By establishing a dynamic differential equation for the correction coefficient σ with respect to the lithiation state x and time t, and introducing identification parameters such as the hysteresis boundary α, the hysteresis constant K, and the critical coefficient ε, the model can accurately describe the dynamics of the smooth switching of the voltage state between different paths: under strong charge and discharge currents, σ ​​will rapidly tend to -1 or +1, making the voltage close to E. ex or E in Under static or low-current conditions, σ spontaneously tends towards 0, simulating the voltage relaxation recovery phenomenon, which greatly improves the response fidelity of the simulation model under dynamic conditions. Furthermore, for the negative electrode, σ tends towards 1 during the delithiation process after a period of static storage, and towards -1 during the lithium insertion process. When the current is zero or very small, σ tends towards 0. Finally, by optimizing the fitting of key identification parameters α, K, and ε based on measured voltage data, the mathematical accuracy and physical realism of the modified model are balanced.

[0031] It should be noted that the criterion for determining the optimal solution for obtaining the identification parameters is: the objective function Obj = Within 10 mV, or the optimization algorithm used has converged. Convergence is defined as follows: the error index calculated using the algorithm in 10 to 50 consecutive iterations is below a given minimum change. Specifically, the minimum change can be: 1e -6 -1e -3 between.

[0032] By constructing a voltage hysteresis accurate correction model that includes a dynamic correction coefficient σ, the macroscopic voltage hysteresis phenomenon is introduced into the microscopic electrode dynamics process, realizing a high-precision mathematical description of the non-equilibrium thermodynamic behavior of the battery. This effectively solves the common problem that traditional battery models cannot simulate voltage hysteresis loops, enabling simulation results to reproduce the real differences in charge and discharge voltage paths. It provides a highly reliable simulation platform for accurate battery state estimation, lifetime prediction, and the development of advanced battery management algorithms, thus contributing to the improvement of battery design level and application safety.

[0033] In some optional implementations, the objective function includes one or more error indices. When multiple error indices exist, each error indice is assigned a corresponding weight i, and the objective function is a weighted sum of the error indices: Obj = By coupling multiple error indices through weighted assignment, the hysteresis effect affecting the voltage term can be described more comprehensively from multiple dimensions, further improving simulation accuracy. Specifically, the weights i of each error index can be normalized to more intuitively reflect the correlation between the error index terms.

[0034] In some optional implementations, the error index is calculated by at least one of mean square error, root mean square error, and mean absolute error, and the optimization algorithm is calculated by at least one of least squares algorithm, genetic algorithm, particle swarm optimization algorithm, and Bayesian algorithm.

[0035] In some alternative implementations, the basic data includes battery capacity and "time-temperature" data, and the measured data package includes "time-current-voltage" data and "time-current-negative parameter potential" data of the target battery during charging and discharging at different rates.

[0036] The second aspect of this application provides a battery simulation method, see [link to relevant documentation]. Figure 1 The method includes the following steps: S1: Obtain the electrochemical and physical parameters of the target battery to construct an electrochemical simulation model; S2: Input simulation conditions and calculation requirements into the electrochemical simulation model to generate simulation calculation mathematical equations, and screen out voltage terms with voltage hysteresis errors in the simulation calculation mathematical equations to be corrected; S3: Establish the correction model using the voltage hysteresis simulation method described in any of the preceding steps; S4: Input the voltage terms to be corrected into the correction model, and the correction model corrects the voltage terms to be corrected into correction model terms according to the voltage hysteresis simulation method; S5: Substitute the correction model terms back into the simulation calculation mathematical equations, perform simulation calculations, synchronously correct the related terms of the voltage terms to be corrected, and output the final simulation results.

[0037] First, a traditional basic electrochemical simulation model is created. This model generates mathematical equations for simulation calculations based on the simulation conditions and computational requirements. Then, voltage terms with voltage hysteresis errors in these equations are selected and replaced with correction model terms. Correlated terms are simultaneously corrected, resulting in more accurate simulation values ​​reflecting voltage hysteresis. This achieves high-precision simulation of the physical phenomenon of battery voltage hysteresis. This approach eliminates the need for complex deconstruction of the basic electrochemical model and coupling with the correction model, preserving the computational efficiency of the original electrochemical simulation model. It refines only the specific voltage terms in the mathematical equations used for simulation calculations, avoiding the dramatic increase in computational complexity caused by directly embedding complex hysteresis dynamics into the basic electrochemical model, thus saving computational resources.

[0038] Furthermore, step S2 filters out the "voltage terms to be corrected," thus concentrating computational resources on the aspects most in need of correction. Specifically, the voltage terms to be corrected may be process values ​​or result values ​​in the simulation calculation. Using this method, the calculation process of the electrochemical simulation model before referencing the voltage terms to be corrected will not be interfered with, ensuring the original computational efficiency. Subsequently, in step S5, the corrected model terms are substituted back and their related terms are corrected simultaneously. This ensures the consistency of the internal states of the model (such as lithiation concentration, overpotential, etc.), avoids theoretical distortion caused by local corrections, and makes the final simulation results have higher physical consistency and reliability overall.

[0039] In practical implementation, this battery simulation method effectively corrects simulation errors caused by voltage hysteresis. The final simulation results more realistically reflect the battery's performance under actual dynamic operating conditions, such as more accurately predicting the battery's available energy, power limits, and thermal behavior. This provides a far more reliable data foundation than traditional simulation methods for battery management system (BMS) algorithm verification, battery pack thermal management design, and vehicle energy management strategy optimization, thereby significantly reducing R&D testing costs and accelerating product development cycles.

[0040] In some optional embodiments, the electrochemical parameters include: battery capacity, composition of the positive and negative electrode active material layers, maximum lithium concentration, electrolyte composition, diffusion coefficient, conductivity, lithium-ion transference number, solid-phase diffusion coefficient of the positive and negative electrodes, reaction rate constants of the positive and negative electrodes, transfer coefficient of electrode reactions, initial state of charge, positive electrode open-circuit voltage, and open-circuit voltage of negative electrode lithiation and delithiation; the physical parameters include particle size of the positive and negative electrode particles, thickness and porosity of the positive and negative electrode active material layers, electrode area, and membrane thickness and porosity; the electrochemical simulation model is any one of the following: P2D model, single-particle model, P3D model, P4D model, and multiphysics coupling model.

[0041] Understandably, the correction model for the same target battery will differ depending on the level of use, and the corresponding identification parameters will need to be resolved. Therefore, a series of correction models can be established according to the level of use, making them plug-in, matching existing electrochemical models, and performing full-cycle simulation calculations of the battery to provide a data foundation for battery life management.

[0042] In some alternative embodiments, the voltage term to be corrected includes the negative open-circuit voltage and the positive open-circuit voltage. If the electrode is a composite electrode composed of two or more materials, the voltage term to be corrected also includes the open-circuit voltage of one or more of the materials.

[0043] A third aspect of this application provides a battery simulation device, see [link to relevant documentation]. Figure 2 The method is used to execute the battery simulation method described in any of the preceding claims, comprising: a parameter module for acquiring electrochemical and physical parameters of the target battery; a simulation module including a modeling unit and a calculation unit, wherein the modeling unit is data-connected to the parameter module, calls the parameters in the parameter module to construct an electrochemical simulation model of the target battery, the calculation unit is data-connected to the modeling unit, generates simulation calculation mathematical equations according to simulation conditions and calculation requirements, and outputs simulation results after correcting voltage terms with voltage hysteresis errors in the simulation calculation mathematical equations; and a voltage correction module, data-connected to the calculation unit, acquiring voltage terms with voltage hysteresis errors in the simulation calculation mathematical equations, executing the voltage hysteresis simulation method described in any of the preceding claims, correcting the voltage terms to be corrected into corrected model terms and feeding them back to the simulation calculation mathematical equations of the calculation unit.

[0044] Specifically, the electrochemical and physical parameters can be input by the user or retrieved from a relevant database. It is understood that different battery models may require different parameters, and the relevant parameters will be selected based on specific needs during implementation.

[0045] By modularizing functions such as parameter management, basic simulation, and voltage hysteresis correction, a highly integrated and automated high-precision battery simulation process has been achieved. In particular, the modular division of labor and the closed-loop feedback mechanism of data flow significantly improve the efficiency, accuracy, and practicality of the simulation project.

[0046] Specifically, this device uses a dedicated voltage correction module as an independent and flexibly accessible core processor, working in conjunction with the basic simulation module. This ensures the computational efficiency of the basic electrochemical model while refining critical hysteresis errors through the dedicated module, achieving optimal allocation of computational resources. Users no longer need to deconstruct complex basic electrochemical simulation models and then couple the correction model to the corresponding module to build entirely new full-cell models. They can easily obtain advanced simulation capabilities to correct voltage hysteresis effects simply by calling the voltage correction module through the data interface, greatly reducing the technical threshold and complexity of high-precision simulation.

[0047] Furthermore, an efficient closed-loop optimization process is formed within the device. The simulation module is responsible for the initial calculation and diagnosis of the "voltage term to be corrected". The voltage correction module then replaces the original voltage term to be corrected with a correction model. The simulation calculation mathematical equations are synchronized and iterated to avoid the errors and inefficiencies introduced by traditional manual intervention, and ensure the overall physical consistency and numerical accuracy of the final simulation results.

[0048] In practical implementation, the high-fidelity results output by the battery simulation device proposed in this application can be directly used to guide the optimization of battery state estimation, life prediction and system management strategies. It provides a digital experimental platform for battery research and development, operation and maintenance and battery management system algorithm development, and has high engineering application value and promotion prospects.

[0049] See Figure 2 In some optional embodiments, the voltage correction module further includes: a correction model unit for building the correction model and receiving the voltage term to be corrected; and a parameter identification unit connected to the correction model unit for obtaining the measured discrete voltage data of the target battery as the optimization target and solving for the values ​​of the identified parameters in the correction model.

[0050] Different simulation conditions and calculation requirements result in different voltage hysteresis situations. The parameter identification unit can match appropriate identification parameters to the correction model unit according to different simulation conditions and calculation requirements, thereby improving the overall scalability of the voltage correction module and increasing simulation accuracy.

[0051] A fourth aspect of this application also provides a storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the storage medium is located to execute the battery simulation method as described in any of the preceding claims. The battery simulation method employs the voltage hysteresis simulation method as described in any of the preceding claims to simulate the voltage hysteresis phenomenon in actual battery operation, thereby improving the accuracy and reliability of the simulation results.

[0052] The technical solution of this application is described below with reference to embodiments.

[0053] This embodiment provides a battery simulation method, including:

[0054] S1: Obtain the electrochemical and physical parameters of the target battery to construct an electrochemical simulation P2D model. See the table below for specific parameters:

[0055] Parameter name symbol numerical values unit Battery capacity <![CDATA[Q cell ]]> 4 Ah negative electrode thickness <![CDATA[L neg ]]> 42 μm Positive electrode thickness <![CDATA[L pos ]]> 40 μm Diaphragm thickness <![CDATA[L sep ]]> 10 μm Negative electrode porosity <![CDATA[ε neg ]]> 0.35 — Positive electrode porosity <![CDATA[ε pos ]]> 0.25 — Diaphragm porosity <![CDATA[ε sep ]]> 0.4 — Negative electrode solid-phase diffusion coefficient <![CDATA[D s,neg ]]> <![CDATA[6×10 -15 ]]> <![CDATA[m 2 / s]]> Positive electrode solid-phase diffusion coefficient <![CDATA[D s,pos ]]> <![CDATA[2×10 -15 ]]> <![CDATA[m 2 / s]]> negative electrode reaction rate constant <![CDATA[k neg ]]> <![CDATA[4×10 -8 ]]> m / s Positive electrode reaction rate constant <![CDATA[k pos ]]> <![CDATA[3×10 -9 ]]> m / s electrolyte concentration <![CDATA[c l ]]> 1 M Electrolyte conductivity <![CDATA[σ l ]]> 1.1 S / m Electrolyte diffusion coefficient <![CDATA[D l ]]> <![CDATA[8×10 -10 ]]> <![CDATA[m 2 / s]]> Open circuit voltage of lithium intercalation at negative electrode <![CDATA[E ocv,neg,in ]]> <![CDATA[E ocv,neg,in (x neg )]]> V Open circuit voltage of negative electrode lithium removal <![CDATA[E ocv,neg,ex ]]> <![CDATA[E ocv,neg,ex (x neg )]]> V Positive open circuit voltage <![CDATA[E ocv,pos ]]> <![CDATA[E ocv,pos (x pos )]]> V Composition of positive electrode active material layer Nickel-cobalt-manganese ternary materials - - Composition of negative electrode active material layer 95wt% graphite + 5wt% silicon oxide material - -

[0056] P2D model

[0057] S2: Input simulation conditions and calculation requirements into the electrochemical simulation model to generate simulation calculation mathematical equations, and filter out voltage terms that have voltage hysteresis errors in the simulation calculation mathematical equations to be corrected.

[0058] Taking the simulated battery polarization voltage as an example, the electrochemical model established in this embodiment satisfies equation E cell =E ocv + η, where η is the polarization voltage of the battery, and E cell Battery voltage, battery open-circuit voltage E ocv =E ocv, pos –E ocv, neg That is, the difference between the positive open-circuit voltage and the negative open-circuit voltage, and E is known. neg = E ocv, neg +η neg E neg η is the negative parameter potential of the battery's negative electrode. neg This is the negative electrode polarization voltage of the battery. Because the negative electrode material contains silicon-based materials in the form of silicon oxide, there is a very significant voltage hysteresis phenomenon. Therefore, the negative electrode open-circuit voltage E is selected as the optimal value. ocv, neg The term to be corrected is the voltage term. Additionally, there exists an open-circuit voltage E at the negative terminal. ocv, neg Related terms for the calculation basis: Battery voltage E cell With negative parameter potential E neg ;

[0059] S3: Establish the correction model: S31: Obtain the basic data and measured data package of the battery in the example, and select the lithium delithiation voltage E of the negative electrode to be simulated. ex and lithiation voltage E in S32: Set the correction voltage to E and the average voltage to E. avg The offset voltage is E offset Let the correction coefficient be σ. Solve equations I through III simultaneously to establish the correction model E(σ): (I), (II) (Ⅲ); where σ is used to control the conversion of the correction voltage between lithiation and delithiation voltages, and its value range is: -1≤σ≤1; S33: Introduce temporal parameters: lithiation state x and time t, establish identification parameters: hysteresis boundary α, hysteresis constant K and critical coefficient ε, and simultaneously solve formulas IV-V to establish the dynamic equation σ(x,t) about the correction coefficient σ: (Ⅳ), (V); The lithiation state x is the ratio of the current lithium-ion concentration of the negative electrode to the maximum lithium-ion concentration of the negative electrode in the instantaneous state; S34: Obtain the measured discrete voltage data (E) of the target battery. test Using the parameters (x,t) as the optimization objective, the functions E(σ) and σ(x,t) are combined to set initial values ​​for the identification parameters. The discrete voltage data are then substituted one by one into the functions σ(x,t) and E(σ) to solve for the corresponding simulated discrete data (E). sim (x,t), define the error index d(E) sim E test Establish the objective function Obj= The identification parameters are fitted using an optimization algorithm to solve for the identification parameters;

[0060] S4: Input the voltage term to be corrected into the correction model, and the correction model corrects the voltage term to be corrected into a correction model term according to the voltage hysteresis simulation method.

[0061] Specifically, the voltage term to be corrected, i.e., the negative open-circuit voltage E, is input into the correction model. ocv, neg Replace with the corrected voltage correction value, i.e.: , , The correction coefficient σ is obtained from the dynamic equations of formulas IV-V: , ,

[0062] This embodiment uses the measured charge and discharge data of the battery voltage (E). cell,test ) and negative parameter potential (E) neg,test ) and simulation results (E) cell,sim E neg,sim The weighted sum of the root mean square error (RMSE) of E is used as the objective function (Obj), i.e.: Obj = i·RMSE(E cell,test E cell,sim ) + (1-i)·RMSE(E neg,test E neg,sim ), where i is the battery voltage E cell The weights range from 0 to 1. Finally, a genetic algorithm is used as the optimization algorithm to identify the parameters, and the values ​​of the identified parameters are shown in the table below:

[0063] Coefficient symbol numerical values unit 15 h 100 — 2.5 —

[0064] After solving for the identification parameters, the dynamic equation σ(x,t) of the correction coefficient σ is combined with the correction model E(σ). The simulation conditions are set, such as operating conditions, initial state, external environment, etc. The simulation model is then run to obtain information such as voltage response and current density distribution.

[0065] S5: Substitute the corrected model terms back into the simulation calculation mathematical equations, perform simulation calculations, simultaneously correct the related terms of the voltage term to be corrected, and output the final simulation results (see attached figure). Figure 3 -b).

[0066] Appendix Figure 3 -a shows the battery voltage E generated by the battery simulation model under different charge-discharge cycles when the voltage hysteresis simulation method is not used. cell and negative parameter potential E neg The simulation curve (solid line) and the battery voltage E of the battery in the example under the same cycle conditions. cell and negative parameter potential E neg Measured values ​​(circled). (Attached) Figure 3 -b shows the battery voltage E generated by the battery simulation model under different charge-discharge cycles after using the voltage hysteresis simulation method. cell and negative parameter potential E neg The simulation curve (solid line) and the battery voltage E of the battery in the example under the same cycle conditions. cell and negative parameter potential E neg The measured value (circle).

[0067] By comparison Figure 3 -a and Figure 3 -b indicates that the battery simulation model equipped with the correction model not only has high overall accuracy, but also can simulate the real electrochemical operating state of a physical battery with high accuracy in the region where the current direction changes, i.e., where there is originally a significant voltage lag. This provides a more accurate data basis for subsequent simulation calculations of the electrochemical state of the battery in other dimensions, improves the reliability of simulation results, and guides the direction of battery improvement.

[0068] Although embodiments of this implementation have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of this implementation, the scope of which is defined by the claims and their equivalents.

Claims

1. A voltage hysteresis simulation method, characterized in that, The steps include: S31: Obtaining basic data and measured data packages of the target battery, and selecting the delithiation voltage E of the target electrode. ex and lithiation voltage E in S32: Set the correction voltage to E and the average voltage to E. avg The offset voltage is E offset Let the correction coefficient be σ. Solve equations I through III simultaneously to establish the correction model E(σ): (I), (II) (III); where σ is used to control the conversion of the correction voltage between the lithiation voltage and the delithiation voltage, and its value range is: -1≤σ≤1; S33: Introduce temporal parameters: lithiation state x and time t, establish identification parameters: hysteresis boundary α, hysteresis constant K and critical coefficient ε, and combine formulas IV-V to establish the dynamic equation σ(x,t) about the correction coefficient σ: (Ⅳ), (V); The lithiation state x is the ratio of the current lithium-ion concentration of the electrode to the maximum lithium-ion concentration of the electrode in the instantaneous state; S34: Obtain the measured discrete voltage data (E) of the target battery. test Using the parameters (x,t) as the optimization objective, the functions E(σ) and σ(x,t) are combined to set initial values ​​for the identification parameters. The discrete voltage data are then substituted one by one into the functions σ(x,t) and E(σ) to solve for the corresponding simulated discrete data (E). sim (x,t), define the error index d(E) sim E test Establish the objective function Obj= The identification parameters are fitted using an optimization algorithm to solve for the identification parameters.

2. The voltage hysteresis simulation method according to claim 1, characterized in that, The objective function includes one or more error indicators. When there are multiple error indicators, each error indicator is assigned a corresponding weight i. The objective function is a weighted sum of the error indicators: Obj = .

3. The voltage hysteresis simulation method according to claim 1, characterized in that, The error index calculation method includes at least one of mean square error, root mean square error, and mean absolute error, and the optimization algorithm includes at least one of least squares method, genetic algorithm, particle swarm optimization algorithm, and Bayesian algorithm.

4. The voltage hysteresis simulation method according to claim 1, characterized in that, The basic data includes battery capacity and "time-temperature" data, while the measured data package includes "time-current-voltage" data and "time-current-negative parameter potential" data of the target battery during charging and discharging at different rates.

5. A battery simulation method, characterized in that, The method includes the following steps: S1: Obtaining the electrochemical and physical parameters of the target battery to construct an electrochemical simulation model; S2: Inputting simulation conditions and calculation requirements into the electrochemical simulation model to generate simulation calculation mathematical equations, and screening out voltage terms with voltage hysteresis errors in the simulation calculation mathematical equations to be corrected; S3: Establishing the correction model using the voltage hysteresis simulation method as described in any one of claims 1-4; S4: Inputting the voltage terms to be corrected into the correction model, and the correction model corrects the voltage terms to be corrected into correction model terms according to the voltage hysteresis simulation method; S5: Substituting the correction model terms back into the simulation calculation mathematical equations, performing simulation calculations, synchronously correcting the related terms of the voltage terms to be corrected, and outputting the final simulation results.

6. The battery simulation method according to claim 5, characterized in that, The electrochemical parameters include: battery capacity, composition of the positive and negative electrode active material layers, maximum lithium concentration, electrolyte composition, diffusion coefficient, conductivity, lithium-ion transference number, solid-phase diffusion coefficient of the positive and negative electrodes, reaction rate constants of the positive and negative electrodes, transfer coefficient of electrode reactions, initial state of charge, positive electrode open-circuit voltage, and open-circuit voltage of negative electrode lithiation and delithiation; the physical parameters include particle size of the positive and negative electrode particles, thickness and porosity of the positive and negative electrode active material layers, electrode area, and membrane thickness and porosity; the electrochemical simulation model is any one of the following: P2D model, single-particle model, P3D model, P4D model, and multiphysics coupling model.

7. The battery simulation method according to claim 5, characterized in that, The voltage term to be corrected includes the negative open-circuit voltage and the positive open-circuit voltage. If the electrode is a composite electrode composed of two or more materials, the voltage term to be corrected also includes the open-circuit voltage of one or more of the materials.

8. A battery simulation device, characterized in that, The method for performing battery simulation as described in any one of claims 5-7 comprises: a parameter module for acquiring electrochemical and physical parameters of a target battery; a simulation module comprising a modeling unit and a calculation unit, wherein the modeling unit is data-connected to the parameter module, calls the parameters in the parameter module to construct an electrochemical simulation model of the target battery, the calculation unit is data-connected to the modeling unit, generates simulation calculation mathematical equations according to simulation conditions and calculation requirements, and outputs simulation results after correcting voltage terms with voltage hysteresis errors in the simulation calculation mathematical equations; and a voltage correction module, data-connected to the calculation unit, acquiring voltage terms with voltage hysteresis errors in the simulation calculation mathematical equations, performing the voltage hysteresis simulation method as described in any one of claims 1-4, correcting the voltage terms to be corrected into corrected model terms and feeding them back to the simulation calculation mathematical equations of the calculation unit.

9. The battery simulation device according to claim 8, characterized in that, The voltage correction module further includes: a correction model unit for building the correction model and receiving the voltage term to be corrected; and a parameter identification unit for connecting to the correction model unit for obtaining the measured discrete voltage data of the target battery as the optimization target and solving for the values ​​of the identified parameters in the correction model.

10. A storage medium, characterized in that, The storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the storage medium is located to perform the battery simulation method as described in any one of claims 5-7, wherein the battery simulation method employs the voltage hysteresis simulation method as described in any one of claims 1-4 to correct voltage hysteresis errors.

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