Lithium ion battery state estimation method and device, terminal equipment and storage medium

Abstract

The invention discloses a lithium ion battery state estimation method and device, terminal equipment and a storage medium. The method comprises the following steps: acquiring current, a to-be-corrected parameter value and a terminal voltage observation value of a lithium ion battery; wherein the electrochemical model is a simplified electrochemical model of a pseudo two-dimensional model; inputting the current into an electrochemical model of the lithium ion battery, and calculating to obtain a battery state data estimation value of the lithium ion battery; according to the terminal voltage observation value, the battery state data estimation value is corrected through a self-adaptive extended Kalman filtering algorithm, and a battery state data correction value of the lithium ion battery is obtained and output; according to the terminal voltage observation value, correcting the to-be-corrected parameter value through an adaptive extended Kalman filtering algorithm to obtain a parameter correction value of the lithium ion battery; and adjusting the to-be-corrected parameters of the electrochemical model according to the parameter correction value. According to the invention, data correction is carried out through observation values, and the accuracy deficiency in the prior art is made up.

Description

Technical Field

[0001] The present invention relates to the technical field of battery status detection, and in particular to a lithium-ion battery status estimation method, device, terminal equipment and storage medium. Background Art

[0002] Lithium-ion batteries are now widely used in multiple industrial fields. As battery capacity and power levels increase, the requirements for lithium-ion battery performance are also becoming increasingly higher. Lithium-ion batteries have characteristics such as coupled electrochemical behavior and nonlinear input-output relationships, making them difficult to characterize. This requires the development of effective battery models to predict battery characteristics.

[0003] In terms of battery models, in order to be able to provide rapid guidance for control, the battery model is required to have efficient computing capabilities, a high level of simulation accuracy, and universality for multiple working conditions.

[0004] Mechanistic models are complex. From a modeling perspective, they provide specific mathematical descriptions of battery particle migration and physical processes. While these models are highly applicable, they also present operational efficiency challenges. Simplifying the mechanistic model is an effective way to improve computational efficiency, but this can reduce accuracy. Summary of the Invention

[0005] The present invention provides a lithium-ion battery state estimation method, apparatus, terminal device and storage medium to solve the technical problem of reduced accuracy caused by simplifying the battery excitation model in the prior art.

[0006] In order to solve the above technical problems, an embodiment of the present invention provides a lithium-ion battery state estimation method, comprising:

[0007] During a first cycle, obtaining a current, a parameter value to be corrected, and an observed terminal voltage value of the lithium-ion battery during the first cycle; wherein the parameter value to be corrected is a numerical value of the parameter to be corrected in an electrochemical model of the lithium-ion battery; and the electrochemical model is a simplified electrochemical model of a pseudo-two-dimensional model;

[0008] Inputting the current into an electrochemical model of the lithium-ion battery to calculate an estimated value of battery state data of the lithium-ion battery;

[0009] According to the terminal voltage observation value, the battery state data estimation value is corrected by an adaptive extended Kalman filter algorithm to obtain and output a battery state data correction value of the lithium-ion battery;

[0010] According to the terminal voltage observation value, the parameter value to be corrected is corrected by an adaptive extended Kalman filter algorithm to obtain a parameter correction value of the lithium-ion battery;

[0011] In a second period, calculating a parameter correction average value based on all parameter correction values ​​corresponding to the first period in the second period; wherein the second period is composed of a plurality of consecutive first periods;

[0012] The parameters to be corrected of the electrochemical model are adjusted according to the parameter correction average value.

[0013] As a preferred solution, the process of constructing the electrochemical model includes:

[0014] Obtaining the initial lithium insertion amount of the positive electrode, the initial lithium insertion amount of the negative electrode, the positive electrode capacity, the negative electrode capacity, the total capacity, the positive electrode solid phase diffusion time constant, the negative electrode solid phase diffusion time constant, the reaction polarization coefficient, the liquid phase diffusion proportional coefficient, the liquid phase diffusion time constant, and the ohmic internal resistance of the lithium ion battery;

[0015] Constructing a solid-phase diffusion process description equation for the lithium-ion battery according to the positive electrode capacity, the negative electrode capacity, the positive electrode solid-phase diffusion time constant, and the negative electrode solid-phase diffusion time constant;

[0016] Constructing a basic working process description equation of the lithium-ion battery according to the initial lithium insertion amount of the positive electrode, the initial lithium insertion amount of the negative electrode, the positive electrode capacity, the negative electrode capacity, the total capacity, the positive electrode solid phase diffusion time constant, the negative electrode solid phase diffusion time constant and the solid phase diffusion process description equation;

[0017] Constructing a liquid phase diffusion process description equation of the lithium ion battery according to the liquid phase diffusion proportional coefficient and the liquid phase diffusion time constant;

[0018] Constructing an ohmic polarization description equation of the lithium-ion battery based on the ohmic internal resistance;

[0019] Constructing a reaction polarization description equation of the lithium-ion battery according to the positive electrode capacity, the negative electrode capacity, the reaction polarization coefficient and the basic working process description equation;

[0020] The electrochemical model is constructed based on the basic working process description equation, the solid phase diffusion process description equation, the liquid phase diffusion process description equation, the ohmic polarization description equation and the reaction polarization description equation.

[0021] As a preferred solution, the solid phase diffusion process description equation is:

[0022]

[0023] Where Δy represents the difference between the average solid-phase lithium insertion amount and the surface lithium insertion amount of the positive electrode; Δx represents the difference between the average solid-phase lithium insertion amount and the surface lithium insertion amount of the negative electrode; Δx1 and Δy1 are process state variables; Q p Indicates the positive electrode capacity; Qn represents the negative electrode capacity; τ p represents the positive electrode solid phase diffusion time constant; τ n represents the negative electrode solid phase diffusion time constant; Δt represents the simulation time difference between time t and time t+1;

[0024] The basic working process description equation is:

[0025]

[0026] x surf (t) = x avg (t)-Δx(t);

[0027]

[0028] y surf (t) = y avg (t)-Δy(t);

[0029] E ocv (t) = U p (y surf )-U n (x surf );

[0030] Where SOC represents the battery state of charge; I represents the current; t all Indicates the total simulation time from the start of the simulation to the current time t; y avg represents the average amount of lithium embedded in the positive electrode solid phase; x avg represents the average amount of lithium embedded in the negative electrode solid phase; y surf Indicates the amount of lithium embedded on the positive electrode surface; x surf represents the amount of lithium embedded in the negative electrode surface; y0 represents the initial amount of lithium embedded in the positive electrode; x0 represents the initial amount of lithium embedded in the negative electrode; Q all Indicates total capacity; E ocv Indicates open circuit voltage; U p (·) represents the positive electrode open circuit potential function; U n (·) represents the negative electrode open circuit potential function;

[0031] The liquid phase diffusion process description equation is:

[0032]

[0033] Where η con represents polarization overpotential; R represents ideal gas constant; T represents battery temperature; F represents Faraday constant; t + represents the lithium ion migration number; c0 represents the initial value of the liquid phase lithium ion concentration in the positive or negative electrode direction; Δc represents the change in the liquid phase lithium ion concentration at the positive or negative electrode current collector; τ erepresents the liquid phase diffusion time constant; P con represents the liquid phase diffusion proportional coefficient;

[0034] The ohmic polarization description equation is:

[0035] η ohm =R ohm I;

[0036] Where η ohm represents the ohmic polarization overpotential; R ohm Indicates ohmic internal resistance;

[0037] The reaction polarization description equation is:

[0038]

[0039] Where η act represents the reaction polarization overpotential; P act Represents the reaction polarizability coefficient.

[0040] As a preferred solution, the battery state data estimated value includes: a battery state of charge estimated value and a terminal voltage estimated value;

[0041] Inputting the current into an electrochemical model of the lithium-ion battery to calculate an estimated value of battery state data of the lithium-ion battery includes:

[0042] Inputting the current into an electrochemical model of the lithium-ion battery to calculate an estimated value of the battery state of charge, an estimated value of the open circuit voltage, an estimated value of the polarization overpotential, an estimated value of the ohmic polarization overpotential, and an estimated value of the reaction polarization overpotential;

[0043] The terminal voltage estimate is calculated using a terminal voltage calculation formula according to the open circuit voltage estimate, the polarization overpotential estimate, the ohmic polarization overpotential estimate, and the reaction polarization overpotential estimate;

[0044] Wherein, the terminal voltage calculation formula is:

[0045] U app (t) = E ocv (t)-η con (t)-η ohm (t)-η act (t);

[0046] Where U app Indicates terminal voltage.

[0047] As a preferred solution, the screening process of the parameters to be corrected includes:

[0048] Performing parameter sensitivity analysis on each parameter of the electrochemical model and selecting several parameters that have the greatest impact on the terminal voltage as parameters to be corrected;

[0049] Among them, the parameters of the electrochemical model include: initial lithium insertion amount of the positive electrode, initial lithium insertion amount of the negative electrode, positive electrode capacity, negative electrode capacity, total capacity, positive electrode solid phase diffusion time constant, negative electrode solid phase diffusion time constant, reaction polarization coefficient, liquid phase diffusion proportional coefficient, liquid phase diffusion time constant and ohmic internal resistance.

[0050] As a preferred solution, the method of correcting the estimated value of the battery state data by an adaptive extended Kalman filter algorithm based on the terminal voltage observation value to obtain and output a corrected value of the battery state data of the lithium-ion battery includes:

[0051] Inputting the terminal voltage observation value and the battery state data estimation value into a first adaptive extended Kalman filter system, so that the first adaptive extended Kalman filter system calculates a battery state data correction value based on the terminal voltage observation value and the battery state data estimation value;

[0052] outputting the battery status data correction value;

[0053] Among them, the state transfer equation of the first adaptive extended Kalman filter system uses the battery state data estimation value as the system state transfer variable and the current as the system input; the observation equation of the first adaptive extended Kalman filter system uses the terminal voltage as the observation comparison quantity and the current as the system input.

[0054] As a preferred solution, the parameter value to be corrected is corrected by an adaptive extended Kalman filter algorithm based on the terminal voltage observation value to obtain the parameter correction value of the lithium-ion battery, including:

[0055] Inputting the terminal voltage observation value and the parameter value to be corrected into a second adaptive extended Kalman filter system, so that the second adaptive extended Kalman filter system calculates the parameter correction value according to the terminal voltage observation value and the parameter value to be corrected;

[0056] Among them, the state transfer equation of the second adaptive extended Kalman filter system uses the parameter value to be corrected as the system state transfer variable and the current as the system input; the observation equation of the second adaptive extended Kalman filter system uses the terminal voltage as the observation comparison quantity and the current as the system input.

[0057] Based on the above embodiment, another embodiment of the present invention provides a lithium-ion battery state estimation device, comprising: a data acquisition module, a battery state data correction module and an electrochemical model parameter correction module;

[0058] The data acquisition module is configured to acquire, within a first period, the current, the parameter value to be corrected, and the terminal voltage observation value of the lithium-ion battery within the first period; wherein the parameter value to be corrected is the value of the parameter to be corrected in the electrochemical model of the lithium-ion battery; and the electrochemical model is a simplified electrochemical model of a pseudo-two-dimensional model;

[0059] The battery state data correction module is configured to input the current into the electrochemical model of the lithium-ion battery in a first cycle to calculate an estimated battery state data value of the lithium-ion battery; and correct the estimated battery state data value using an adaptive extended Kalman filter algorithm based on the terminal voltage observation value to obtain and output a corrected battery state data value of the lithium-ion battery;

[0060] The electrochemical model parameter correction module is used to correct the parameter value to be corrected in the first cycle according to the terminal voltage observation value through an adaptive extended Kalman filter algorithm to obtain the parameter correction value of the lithium-ion battery; in the second cycle, calculate the parameter correction average value according to the parameter correction values ​​corresponding to all first cycles in the second cycle; wherein the second cycle is composed of several consecutive first cycles; and adjust the parameter to be corrected of the electrochemical model according to the parameter correction average value.

[0061] Based on the above embodiments, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the lithium-ion battery state estimation method described in the above embodiment of the invention is implemented.

[0062] Based on the above embodiment, another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the lithium-ion battery state estimation method described in the above embodiment of the invention.

[0063] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0064] The present invention obtains, during a first cycle, the current, the parameter value to be corrected, and the terminal voltage observation value of the lithium-ion battery during the first cycle; wherein, the parameter value to be corrected is the numerical value of the parameter to be corrected in the electrochemical model of the lithium-ion battery; the electrochemical model is a simplified electrochemical model of a pseudo-two-dimensional model; the current is input into the electrochemical model of the lithium-ion battery to calculate the estimated value of the battery state data of the lithium-ion battery; based on the terminal voltage observation value, the estimated value of the battery state data is corrected by an adaptive extended Kalman filter algorithm to obtain and output the corrected value of the battery state data of the lithium-ion battery; based on the terminal voltage observation value, the parameter value to be corrected is corrected by an adaptive extended Kalman filter algorithm to obtain the corrected value of the parameter of the lithium-ion battery; during a second cycle, a parameter correction average value is calculated based on the parameter correction values ​​corresponding to all first cycles within the second cycle; wherein, the second cycle is composed of several consecutive first cycles; and based on the parameter correction average value, the parameter to be corrected of the electrochemical model is adjusted. Existing technologies improve computational efficiency by simplifying mechanism models, but this simplification reduces simulation accuracy. The present invention, compared to existing technologies, ensures a certain level of accuracy. The present invention uses an adaptive extended Kalman filter algorithm to correct battery state data and electrochemical model parameters based on terminal voltage observations. This creates a closed-loop structure that integrates observations and a simplified electrochemical model. Compared to open-loop battery state estimation methods that rely solely on simplified models, this closed-loop approach compensates for the accuracy loss caused by model simplification. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a flow chart of a lithium-ion battery state estimation method provided by one embodiment of the present invention;

[0066] Figure 2 This is a schematic diagram of the lithium-ion battery structure;

[0067] Figure 3 is the current excitation diagram of the parameter identification working condition;

[0068] Figure 4 It is the voltage curve diagram of parameter identification working condition;

[0069] Figure 5 This is the current excitation diagram for DST condition;

[0070] Figure 6 This is the DST working condition simulation effect diagram;

[0071] Figure 7 The figure is a schematic structural diagram of a lithium-ion battery state estimation device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0072] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0073] Example 1

[0074] Please refer to Figure 1 , is a flow chart of a lithium-ion battery state estimation method provided by one embodiment of the present invention, comprising:

[0075] S1. In a first cycle, obtain the current, the parameter value to be corrected, and the terminal voltage observation value of the lithium-ion battery in the first cycle; wherein the parameter value to be corrected is the numerical value of the parameter to be corrected in the electrochemical model of the lithium-ion battery; and the electrochemical model is a simplified electrochemical model of a pseudo-two-dimensional model.

[0076] In a preferred embodiment, the process of constructing the electrochemical model includes:

[0077] Obtaining the initial lithium insertion amount of the positive electrode, the initial lithium insertion amount of the negative electrode, the positive electrode capacity, the negative electrode capacity, the total capacity, the positive electrode solid phase diffusion time constant, the negative electrode solid phase diffusion time constant, the reaction polarization coefficient, the liquid phase diffusion proportional coefficient, the liquid phase diffusion time constant, and the ohmic internal resistance of the lithium ion battery;

[0078] Constructing a solid-phase diffusion process description equation for the lithium-ion battery according to the positive electrode capacity, the negative electrode capacity, the positive electrode solid-phase diffusion time constant, and the negative electrode solid-phase diffusion time constant;

[0079] Constructing a basic working process description equation of the lithium-ion battery according to the initial lithium insertion amount of the positive electrode, the initial lithium insertion amount of the negative electrode, the positive electrode capacity, the negative electrode capacity, the total capacity, the positive electrode solid phase diffusion time constant, the negative electrode solid phase diffusion time constant and the solid phase diffusion process description equation;

[0080] Constructing a liquid phase diffusion process description equation of the lithium ion battery according to the liquid phase diffusion proportional coefficient and the liquid phase diffusion time constant;

[0081] Constructing an ohmic polarization description equation of the lithium-ion battery based on the ohmic internal resistance;

[0082] Constructing a reaction polarization description equation of the lithium-ion battery according to the positive electrode capacity, the negative electrode capacity, the reaction polarization coefficient and the basic working process description equation;

[0083] The electrochemical model is constructed based on the basic working process description equation, the solid phase diffusion process description equation, the liquid phase diffusion process description equation, the ohmic polarization description equation and the reaction polarization description equation.

[0084] In a preferred embodiment, the solid-phase diffusion process description equation is:

[0085]

[0086]

[0087] Where Δy represents the difference between the average solid-phase lithium insertion amount and the surface lithium insertion amount of the positive electrode; Δx represents the difference between the average solid-phase lithium insertion amount and the surface lithium insertion amount of the negative electrode; Δx1 and Δy1 are process state variables; Q p Indicates the positive electrode capacity; Q n represents the negative electrode capacity; τ p represents the positive electrode solid phase diffusion time constant; τ n represents the negative electrode solid phase diffusion time constant; Δt represents the simulation time difference between time t and time t+1;

[0088] The basic working process description equation is:

[0089]

[0090] x surf (t) = x avg (t)-Δx(t);

[0091]

[0092] y surf (t) = y avg (t)-Δy(t);

[0093] E ocv (t) = U p (y surf )-U n (x surf );

[0094] Where SOC represents the battery state of charge; I represents the current; t all Indicates the total simulation time from the start of the simulation to the current time t; y avg represents the average amount of lithium embedded in the positive electrode solid phase; x avg represents the average amount of lithium embedded in the negative electrode solid phase; y surf Indicates the amount of lithium embedded on the positive electrode surface; x surf represents the amount of lithium embedded in the negative electrode surface; y0 represents the initial amount of lithium embedded in the positive electrode; x0 represents the initial amount of lithium embedded in the negative electrode; Q all Indicates total capacity; Eocv Indicates open circuit voltage; U p (·) represents the positive electrode open circuit potential function; U n (·) represents the negative electrode open circuit potential function;

[0095] The liquid phase diffusion process description equation is:

[0096]

[0097] Where η con represents polarization overpotential; R represents ideal gas constant; T represents battery temperature; F represents Faraday constant; t + represents the lithium ion migration number; c0 represents the initial value of the liquid phase lithium ion concentration in the positive or negative electrode direction; Δc represents the change in the liquid phase lithium ion concentration at the positive or negative electrode current collector; τ e represents the liquid phase diffusion time constant; P con represents the liquid phase diffusion proportional coefficient;

[0098] The ohmic polarization description equation is:

[0099] η ohm =R ohm I;

[0100] Where η ohm represents the ohmic polarization overpotential; R ohm Indicates ohmic internal resistance;

[0101] The reaction polarization description equation is:

[0102]

[0103] Where η act represents the reaction polarization overpotential; P act Represents the reaction polarizability coefficient.

[0104] It should be noted that the basic structure of lithium-ion batteries is as follows: Figure 2 As shown in Figure 1, the positive and negative active materials can generally be considered to be composed of several spherical active particles. During the battery charging and discharging process, there are physical and chemical processes such as solid phase diffusion, electrochemical reactions on the particle surface, and liquid phase diffusion. Figure 2 The discharge process of a lithium-ion battery is shown.

[0105] Currently, the most accurate electrochemical model is the pseudo-two-dimensional model. While highly accurate, the P2D model suffers from low computational efficiency. This is because the partial differential equations describing the liquid-phase and solid-phase diffusion processes require gridding in two spatial dimensions: electrode thickness l and particle radius r. Algebraic equations are then established at the grid nodes for numerical iterative calculations. Assuming 50 grids in the r dimension within the active particle, and 80, 40, and 80 grids in the l dimension for the negative electrode, separator, and positive electrode regions, respectively, the P2D model requires iterative calculations of over 10,000 algebraic equations for a single time step. Therefore, finding approximate analytical solutions to the partial differential equations for liquid-phase and solid-phase diffusion processes in spatial dimensions is an effective means of reducing the computational complexity of electrochemical models. Therefore, this paper develops a simplified electrochemical model based on simplified computational methods for liquid-phase and solid-phase diffusion.

[0106] In order to construct the electrochemical model, a certain amount of preliminary offline identification work is required to obtain the model parameter set. In addition to the battery's inherent characteristic parameters, the electrochemical model parameters and their physical meanings that need to be obtained through identification methods are shown in the following table:

[0107]

[0108]

[0109] Under the same current excitation, the response time of each battery reaction process is different, which is why the design is based on the parameter identification method. From the mathematical description of each process, the reaction polarization and ohmic polarization are stimulated by current, which means that polarization overpotential is generated, while solid-phase diffusion is a change of state, and the establishment of its state variables takes time, which has a significant time difference with polarization. At the same time, when faced with the same magnitude of current excitation, the intensity of the response of different processes is also different. Based on this difference, the various parts of the terminal voltage calculation can be separated and extracted, and then the parameters can be gradually acquired through the relationship between the open-circuit voltage, the overpotential of each part, and the corresponding parameters.

[0110] In order to identify the electrochemical parameters y0, x0, Q p , Q n , Q all , τ p , τ n 、P act 、P con and τ e According to the basic principle of excitation response analysis and the requirement of parameter decoupling identification, a set of parameter identification conditions are designed with reference to the establishment time of the steady-state diffusion process of the P2D model. The current excitation form is as follows: Figure 3As shown in the figure, the constant current discharge time of each section is set to 20 minutes, the constant current charging time is set to 10 minutes, the shelf time is set to 15 minutes, and the charge and discharge current rate range is 0.16~0.5C to reduce the impact of the internal temperature change of the battery on the electrochemical parameter identification results. Figure 4 This is the voltage curve obtained for the corresponding identification condition.

[0111] The specific identification order of electrochemical parameters is as follows:

[0112] (1) Use an ohm internal resistance meter to measure the battery ohm internal resistance R at the reference temperature ohm ;

[0113] (2) After applying the excitation condition, the open circuit voltage curve is formed by extracting the voltage data points at the end of each static period, and the least squares fitting method is used to obtain the initial lithium insertion amount y0, x0 of the positive and negative electrodes and the positive and negative electrodes and battery capacity Q p , Q n , Q all ;

[0114] (3) By extracting the sudden change ΔU of the battery terminal voltage each time a current excitation is suddenly applied, this part of the instantaneous voltage change is determined by the ohmic polarization overpotential and the reaction polarization overpotential. The ohmic internal resistance of the battery has been tested in advance, so P can be identified by this. act ;

[0115] (4) At the end of short-time constant current charge and discharge, the solid-liquid phase diffusion process has entered a stable stage, and these data points can be extracted to identify τ p , τ n 、P con ;

[0116] (5) Finally, the concentration polarization overpotential at different times is calculated by transient data in the working condition, and the other parameters obtained are used to reversely solve τ e .

[0117] At this point, the parameter set of the required electrochemical model can be obtained.

[0118] In the process of constructing the electrochemical model, descriptive equations were established for the basic working process of lithium-ion batteries, solid-phase diffusion process, liquid-phase diffusion process, ohmic polarization, and reaction polarization. The specific construction process of the electrochemical model is as follows:

[0119] (1) Description of basic working process:

[0120] Assuming uniform reaction distribution across the battery, isotropic plates, and the same lithium ion concentration at all locations along the radius r, all active particles in the positive and negative electrodes are characterized by single particles. The basic operating process of a battery is characterized by the average lithium insertion amount at the positive electrode and the average lithium insertion amount at the negative electrode, which are related to the initial lithium insertion amount at the positive electrode, the initial lithium insertion amount at the negative electrode, the positive electrode capacity, the negative electrode capacity, the battery capacity, and the battery state of charge.

[0121] (2) Description of solid phase diffusion process:

[0122] Considering the influence of solid-phase diffusion under the basic operating process, Fick's second law of diffusion is used to establish the variation of solid-phase lithium ion concentration in the radius r. The parameters describing the solid-phase diffusion process are the positive electrode solid-phase diffusion time constant and the negative electrode solid-phase diffusion time constant. These parameters determine the duration of the battery's transition to steady-state, the difference between the average solid-phase lithium insertion amount and the surface lithium insertion amount of the positive electrode, and the average solid-phase lithium insertion amount and the surface lithium insertion amount of the negative electrode.

[0123] (3) Description of liquid phase diffusion process:

[0124] Assuming the change in the liquid-phase lithium-ion concentration at the positive and negative current collectors is the same, a gradient exists in the liquid-phase lithium-ion concentration distribution in the direction of the positive and negative plates, leading to the generation of polarization overpotential. Concentration polarization is described by the liquid-phase diffusion proportional coefficient and the liquid-phase diffusion time constant, which reflect the degree of polarization and the time it takes to establish a concentration gradient.

[0125] (4) Description of ohmic polarization effect: The calculation of ohmic polarization overpotential follows Ohm's law.

[0126] (5) Description of reaction polarization: Assuming that the reaction rate and the radius of the active particle are the same, the description parameter is the reaction polarization coefficient Pact, which reflects the difficulty of the electrochemical reaction inside the battery.

[0127] It should also be noted that the initial values ​​of the process state variables Δx1 and Δy1 are 0. ohm The ohmic internal resistance is expressed in lumped parameter form. The input of the electrochemical model is current, and the output is estimated battery state data, including terminal voltage and battery state of charge.

[0128] In a preferred embodiment, the process of screening the parameters to be modified includes:

[0129] Performing parameter sensitivity analysis on each parameter of the electrochemical model and selecting several parameters that have the greatest impact on the terminal voltage as parameters to be corrected;

[0130] Among them, the parameters of the electrochemical model include: initial lithium insertion amount of the positive electrode, initial lithium insertion amount of the negative electrode, positive electrode capacity, negative electrode capacity, total capacity, positive electrode solid phase diffusion time constant, negative electrode solid phase diffusion time constant, reaction polarization coefficient, liquid phase diffusion proportional coefficient, liquid phase diffusion time constant and ohmic internal resistance.

[0131] It should be noted that the parameters that have a greater impact on the model are determined as the update objects of the subsequent electrochemical model parameter correction link, and the number of correction parameters is reduced to improve the operation efficiency of the model algorithm. Therefore, it is necessary to adjust the basic model parameters y0, x0, Q p , Q n , Q all , τ p , τ n 、P act 、P con , τ e and R ohm Work on parameter sensitivity analysis is performed to determine their correlation with the external characteristics of the battery.

[0132] For each parameter, the 10 values ​​that change on average in the range of 10% to 90% as the initial parameter increases are taken as the initial value of the sensitivity analysis. The parameter value obtained in the early stage through the stimulus response analysis method is used as the initial value of the sensitivity analysis. The average absolute difference between the voltage at the rear end of the parameter change and the original curve is used as the basis for quantitative comparison of the sensitivity, that is, the sensitive value. The larger the sensitive value, the greater its impact on the terminal voltage. Selecting the parameter with a larger sensitive value as the parameter to be corrected can achieve accurate simulation of the battery state while ensuring that the number of parameters to be corrected is as small as possible.

[0133] S2. Inputting the current into an electrochemical model of the lithium-ion battery to calculate and obtain an estimated value of battery state data of the lithium-ion battery.

[0134] In a preferred embodiment, the battery state data estimated value includes: a battery state of charge estimated value and a terminal voltage estimated value;

[0135] Inputting the current into an electrochemical model of the lithium-ion battery to calculate an estimated value of battery state data of the lithium-ion battery includes:

[0136] Inputting the current into an electrochemical model of the lithium-ion battery to calculate an estimated value of the battery state of charge, an estimated value of the open circuit voltage, an estimated value of the polarization overpotential, an estimated value of the ohmic polarization overpotential, and an estimated value of the reaction polarization overpotential;

[0137] The terminal voltage estimate is calculated using a terminal voltage calculation formula according to the open circuit voltage estimate, the polarization overpotential estimate, the ohmic polarization overpotential estimate, and the reaction polarization overpotential estimate;

[0138] Wherein, the terminal voltage calculation formula is:

[0139] U app (t) = E ocv (t)-η con (t)-η ohm (t)-η act (t);

[0140] Where U app Indicates terminal voltage.

[0141] It should be noted that without considering the influence of temperature, the battery terminal voltage Uapp can be calculated from the open circuit voltage and the three-part polarization overpotential.

[0142] S3. According to the terminal voltage observation value, the battery status data estimation value is corrected by an adaptive extended Kalman filter algorithm to obtain and output a battery status data correction value of the lithium-ion battery.

[0143] It should be noted that in order to solve the problem that the initial noise value is fixed but it is difficult to converge in the face of dynamic and changeable working conditions, the adaptive extended Kalman filter algorithm (AKEF) adds a noise covariance matching algorithm on the basis of the extended Kalman filter algorithm (EKF), and adaptively adjusts the noise according to the working conditions. It has better convergence and estimation accuracy in nonlinear systems with complex and changeable working conditions.

[0144] The present invention constructs an interaction mode between a simplified electrochemical model and an actual battery system based on an adaptive extended Kalman filter algorithm. By feedback of observation data of the actual modeling object (terminal voltage), the model simulation accuracy is adaptively improved, forming a dual adaptive extended Kalman filter structure, which respectively realizes battery state estimation and online correction of electrochemical model parameters. In subsequent strategy development work, input control of the actual battery is realized based on the fusion method, forming data exchange in both directions of the model and the physical system.

[0145] After completing the initial value setting of the electrochemical model, the state optimal estimation iterative process of the nonlinear discrete system in the adaptive extended Kalman filter algorithm is entered. The specific iterative optimization process is as follows:

[0146] (1) Predict the system state and obtain the prior estimate of the system state:

[0147] Using t k-1 State variables at time and input variable u k-1 , calculate t by the state equation f(·) k A priori estimate of the system state at time like:

[0148]

[0149] (2) Update the error covariance matrix estimate:

[0150] P k - =A k-1 P k-1 + A k-1 T +Q k-1 ;

[0151] Where, t k A priori estimate of the error covariance matrix at time t; t k-1 The process noise covariance matrix at time ; A k-1 t k-1 The state matrix at the moment;

[0152] (3) Calculate the Kalman gain:

[0153] K k =P k - C k T (C k P k - C k T +R k ) -1 ;

[0154] Where K k t k Kalman gain matrix at time t; R k t k The observation noise covariance matrix at the moment; C k t k The observation matrix at time t;

[0155] (4) Conduct observation error assessment:

[0156] The system state prior estimate obtained in step (1) is used to calculate the value of t k Input variables at time t and calculate t through observation equation g(·) k The theoretical output at the moment and the observed value y k Compare and get the observation error e k , as shown in the formula:

[0157]

[0158] (5) Correct the system state and obtain the posterior estimate:

[0159] The prior estimate of the system state obtained in step (1) The updated posterior estimate of the system state is As shown in the formula:

[0160]

[0161] (6) Correct the error covariance matrix:

[0162] P k + =(IK k C k )P k - ;

[0163] Where I represents the identity matrix; t k The posterior estimate of the error covariance matrix at time t;

[0164] (7) Perform adaptive matching calculation of noise covariance matrix:

[0165]

[0166]

[0167] Where H k t k The moment noise covariance update matrix, M is the window size.

[0168] In a preferred embodiment, the method of correcting the estimated battery state data value by an adaptive extended Kalman filter algorithm based on the terminal voltage observation value to obtain and output a corrected battery state data value of the lithium-ion battery includes:

[0169] Inputting the terminal voltage observation value and the battery state data estimation value into a first adaptive extended Kalman filter system, so that the first adaptive extended Kalman filter system calculates a battery state data correction value based on the terminal voltage observation value and the battery state data estimation value;

[0170] outputting the battery status data correction value;

[0171] Among them, the state transfer equation of the first adaptive extended Kalman filter system uses the battery state data estimation value as the system state transfer variable and the current as the system input; the observation equation of the first adaptive extended Kalman filter system uses the terminal voltage as the observation comparison quantity and the current as the system input.

[0172] It should be noted that the function of the state estimation AEKF system (first adaptive extended Kalman filter system) is mainly to correct the external characteristics simulation state of the lithium-ion battery. The state output estimation values ​​of the model are SOC, terminal voltage U app , but the terminal voltage U app (t k-1 ) and U app (t k ) is indirectly connected through other process parameters, including the open circuit voltage E ocv , and the corrected SOC itself can help complete E ocv In order to make the corresponding relationship more direct and avoid repeated calculations in subsequent actual online simulations to improve calculation efficiency, the polarization voltage U of the difference between the terminal voltage and the open circuit voltage can be eta Replacement terminal voltage U app As one of the system state transfer variables of the state estimation AEKF, it is as follows:

[0173]

[0174] i k =[I] k ;

[0175] y k =[U app ] k ;

[0176] Correspondingly, the system state transfer equation and observation equation are shown as follows:

[0177]

[0178] Where w k 、v k t k The process noise and sensor observation noise at the moment are expressed as Q k 、R k Two covariance matrices are represented;

[0179] The measurement matrix Ck is approximated by the difference method, as shown below:

[0180]

[0181] Where Δx k =0.01x k , the error covariance matrix is ​​initialized to P0=0, Q0 and R0 represent the system model error and sensor observation error initialized at time t0 respectively.

[0182] S4. According to the terminal voltage observation value, the parameter value to be corrected is corrected by using an adaptive extended Kalman filter algorithm to obtain a parameter correction value of the lithium-ion battery.

[0183] In a preferred embodiment, the parameter value to be corrected is corrected by an adaptive extended Kalman filter algorithm based on the terminal voltage observation value to obtain the parameter correction value of the lithium-ion battery, including:

[0184] Inputting the terminal voltage observation value and the parameter value to be corrected into a second adaptive extended Kalman filter system, so that the second adaptive extended Kalman filter system calculates the parameter correction value according to the terminal voltage observation value and the parameter value to be corrected;

[0185] Among them, the state transfer equation of the second adaptive extended Kalman filter system uses the parameter value to be corrected as the system state transfer variable and the current as the system input; the observation equation of the second adaptive extended Kalman filter system uses the terminal voltage as the observation comparison quantity and the current as the system input.

[0186] It should be noted that the input matrix of the parameter correction AEKF system (the second adaptive extended Kalman filter system) is the current I. Assuming that the parameters to be corrected are P1, P2, and P3, the system state transition variables and observation quantities are respectively shown as follows:

[0187]

[0188] The corresponding state transfer equation, system state transfer matrix, and system observation equation are shown as follows:

[0189]

[0190] Similarly, the observation matrix is ​​obtained by differential approximation, as shown in the following formula:

[0191]

[0192] Where θ is the symbol for the parameter-corrected AEKF system, which is used to distinguish it from the state-estimation AEKF system. The error covariance matrix is ​​initialized to P0=0.

[0193] It should also be noted that the nonlinear systems in each adaptive extended Kalman filter system are unified into the simplified electrochemical model of lithium ions constructed above.

[0194] S5. In a second period, calculate a parameter correction average value according to the parameter correction values ​​corresponding to all first periods in the second period; wherein the second period is composed of several consecutive first periods.

[0195] It should be noted that the present invention adopts the design of connecting two AEKF filtering algorithms in series with the electrochemical part of the model to realize the update of the battery state estimation value and the online iterative adaptive correction of the model parameters. The design of the filtering algorithm for online parameter correction adopts the design of splitting the filtering algorithm substructure for parallel calculation based on the differences in the parameter update cycle and the corresponding observation relationship.

[0196] The state output estimate is basically in a state of continuous change, and its change period is regarded as the observation comparison value sampling period (taken as 1s), while the model parameters respond much more slowly to the stimulus of the working condition change. Combined with the experimental data, its update period is set to 500s.

[0197] The estimated electrochemical parameters of the model obtained by the parameter correction AEKF structure are first recorded. Instead of updating the parameters in each iteration cycle, when the algorithm runs to the end of the set recording cycle (i.e., 500s), the above records are averaged to complete a parameter update, as shown in the following formula:

[0198]

[0199] Where L is the number of iterations in a recording cycle:

[0200] At this point, the update cycle of the entire parameter correction AEKF is completed, and its parameter correction results will be updated to the state estimation AEKF to improve the accuracy of battery external characteristic estimation.

[0201] In summary, the main process of the embodiment of the present invention is as follows: based on the parameter sensitivity analysis work and analysis results, in order to seek a balance between computational efficiency and simulation accuracy, the electrochemical parameters with relatively high sensitivity values ​​are used as the online update objects. The electrochemical model uses the initial values ​​of the model parameters at the reference temperature to initialize the module. The electrochemical model uses the operating current as input. After entering the iterative cycle, the state simulation estimation of the battery electrochemical behavior is first performed. This step requires the electrochemical part of the simplified electrochemical model of the monomer to cooperate with the state estimation module to correct the polarization voltage U calculated by the model under the current time sequence. eta (t k )、SOC(t k ) is used as a priori estimate of the electrochemical behavior of the coupled model, and the state estimation AEKF system (the first adaptive extended Kalman filter system) is corrected. The parameter update step is then performed using the parameter correction AEKF system (the second adaptive extended Kalman filter system).

[0202] S6. Adjust the parameters to be corrected of the electrochemical model according to the parameter correction average value.

[0203] It should be noted that the main advantage of the present invention over the prior art is that it ensures a certain level of accuracy. Prior art can also improve computational efficiency by simplifying the mechanism model, but after the model is simplified, the simulation accuracy will decrease. Generally speaking, a balance is chosen between the two. However, the present invention selects the AEKF algorithm, whose basic principle is to correct the battery state data and electrochemical model parameters based on the terminal voltage observation value. This constitutes a fusion closed-loop structure of observation data and simplified electrochemical model. Compared with the open-loop method of battery state estimation that relies solely on the simplified model, such a closed-loop method can compensate for the accuracy loss caused by the model simplification.

[0204] In order to specifically illustrate the method used in this study, a 42Ah ternary lithium square battery was used as the experimental verification object for the implementation case. The specific parameters of the battery are shown in the following table:

[0205]

[0206]

[0207] The initial identification results of the model obtained by the stimulus response analysis method are shown in the following table:

[0208]

[0209] The parameter sensitivity analysis results are shown in the following table, using the average absolute difference between the voltage at the end of parameter change and the original curve as the basis for quantitative comparison of sensitivity, that is, the sensitivity value:

[0210]

[0211] From the data in the table, we can see that when other parameters remain unchanged, the influence of each parameter on the terminal voltage is different. p , Q n , Q all and τ n It has a great influence on the terminal voltage and is a highly sensitive model parameter, so it is used as a parameter to be corrected.

[0212] In order to verify the accuracy and applicability of the model, the DST (Dynamic Stress Test, DST) dynamic working condition model verification was carried out. The current working conditions used in the dynamic verification are as follows: Figure 5 shown.

[0213] Under the DST condition, the fitting effect and fitting error output by the fusion method are as follows: Figure 6 As shown, Figure 6In the figure, (a) is the terminal voltage simulation effect diagram, (b) is the terminal voltage simulation error diagram, (c) is the SOC simulation effect diagram, (d) is the SOC simulation error diagram, (e) is the positive electrode capacity parameter change curve, (f) is the negative electrode capacity parameter change curve, and (g) is the negative electrode diffusion time constant parameter change curve. Figure 6 It can be seen that the mean absolute error of voltage is 12.3mV and the mean absolute error of SOC is 8.3%, which has high accuracy.

[0214] Example 2

[0215] Please refer to Figure 7 , is a schematic structural diagram of a lithium-ion battery state estimation device provided by one embodiment of the present invention, comprising: a data acquisition module, a battery state data correction module and an electrochemical model parameter correction module;

[0216] The data acquisition module is configured to acquire, within a first period, the current, the parameter value to be corrected, and the terminal voltage observation value of the lithium-ion battery within the first period; wherein the parameter value to be corrected is the value of the parameter to be corrected in the electrochemical model of the lithium-ion battery; and the electrochemical model is a simplified electrochemical model of a pseudo-two-dimensional model;

[0217] The battery state data correction module is configured to input the current into the electrochemical model of the lithium-ion battery in a first cycle to calculate an estimated battery state data value of the lithium-ion battery; and correct the estimated battery state data value using an adaptive extended Kalman filter algorithm based on the terminal voltage observation value to obtain and output a corrected battery state data value of the lithium-ion battery;

[0218] The electrochemical model parameter correction module is used to correct the parameter value to be corrected in the first cycle according to the terminal voltage observation value through an adaptive extended Kalman filter algorithm to obtain the parameter correction value of the lithium-ion battery; in the second cycle, calculate the parameter correction average value according to the parameter correction values ​​corresponding to all first cycles in the second cycle; wherein the second cycle is composed of several consecutive first cycles; and adjust the parameter to be corrected of the electrochemical model according to the parameter correction average value.

[0219] Example 3

[0220] Accordingly, an embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the lithium-ion battery state estimation method described in the above-mentioned embodiment of the invention.

[0221] Example 4

[0222] Accordingly, an embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the lithium-ion battery state estimation method described in the above embodiment of the invention.

[0223] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive work.

[0224] Those skilled in the art will clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0225] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0226] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the device, connecting various parts of the entire device using various interfaces and lines.

[0227] The memory can be used to store the computer program, and the processor realizes various functions of the device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0228] The storage medium is a storage medium, and the computer program is stored in the storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned method embodiments. The computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0229] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A lithium-ion battery state estimation method, characterized in that: include: During a first cycle, obtaining a current, a parameter value to be corrected, and an observed terminal voltage value of the lithium-ion battery during the first cycle; wherein the parameter value to be corrected is a numerical value of the parameter to be corrected in an electrochemical model of the lithium-ion battery; and the electrochemical model is a simplified electrochemical model of a pseudo-two-dimensional model; Inputting the current into an electrochemical model of the lithium-ion battery to calculate an estimated value of battery state data of the lithium-ion battery; According to the terminal voltage observation value, the battery state data estimation value is corrected by an adaptive extended Kalman filter algorithm to obtain and output a battery state data correction value of the lithium-ion battery; According to the terminal voltage observation value, the parameter value to be corrected is corrected by an adaptive extended Kalman filter algorithm to obtain a parameter correction value of the lithium-ion battery; In a second period, calculating a parameter correction average value based on all parameter correction values ​​corresponding to the first period in the second period; wherein the second period is composed of a plurality of consecutive first periods; The parameters to be corrected of the electrochemical model are adjusted according to the parameter correction average value.

2. The lithium-ion battery state estimation method according to claim 1, wherein: The electrochemical model construction process includes: Obtaining the initial lithium insertion amount of the positive electrode, the initial lithium insertion amount of the negative electrode, the positive electrode capacity, the negative electrode capacity, the total capacity, the positive electrode solid phase diffusion time constant, the negative electrode solid phase diffusion time constant, the reaction polarization coefficient, the liquid phase diffusion proportional coefficient, the liquid phase diffusion time constant, and the ohmic internal resistance of the lithium ion battery; Constructing a solid-phase diffusion process description equation for the lithium-ion battery according to the positive electrode capacity, the negative electrode capacity, the positive electrode solid-phase diffusion time constant, and the negative electrode solid-phase diffusion time constant; Constructing a basic working process description equation of the lithium-ion battery according to the initial lithium insertion amount of the positive electrode, the initial lithium insertion amount of the negative electrode, the positive electrode capacity, the negative electrode capacity, the total capacity, the positive electrode solid phase diffusion time constant, the negative electrode solid phase diffusion time constant and the solid phase diffusion process description equation; Constructing a liquid phase diffusion process description equation of the lithium ion battery according to the liquid phase diffusion proportional coefficient and the liquid phase diffusion time constant; Constructing an ohmic polarization description equation of the lithium-ion battery based on the ohmic internal resistance; Constructing a reaction polarization description equation of the lithium-ion battery according to the positive electrode capacity, the negative electrode capacity, the reaction polarization coefficient and the basic working process description equation; The electrochemical model is constructed based on the basic working process description equation, the solid phase diffusion process description equation, the liquid phase diffusion process description equation, the ohmic polarization description equation and the reaction polarization description equation.

3. The lithium-ion battery state estimation method according to claim 2, wherein: The solid phase diffusion process description equation is: Where Δy represents the difference between the average amount of lithium embedded in the solid phase of the positive electrode and the amount of lithium embedded on the surface; Δx represents the difference between the average amount of lithium embedded in the solid phase of the negative electrode and the amount of lithium embedded on the surface; Δx1 and Δy1 are process state variables; Q p Indicates the positive electrode capacity; Q n represents the negative electrode capacity; τ p represents the positive electrode solid phase diffusion time constant; τ n represents the negative electrode solid phase diffusion time constant; Δt represents the simulation time difference between time t and time t+1; The basic working process description equation is: x surf (t)=x avg (t)-Δx(t); y surf (t)=y avg (t)-Δy(t); E ocv (t)=U p (y surf )-U n (x surf ); Where SOC represents the battery state of charge; I represents the current; t all Indicates the total simulation time from the start of the simulation to the current time t; y avg represents the average amount of lithium embedded in the positive electrode solid phase; x avg represents the average amount of lithium embedded in the negative electrode solid phase; y surf Indicates the amount of lithium embedded on the positive electrode surface; x surf represents the amount of lithium embedded in the negative electrode surface; y0 represents the initial amount of lithium embedded in the positive electrode; x0 represents the initial amount of lithium embedded in the negative electrode; Q all Indicates total capacity; E ocv Indicates open circuit voltage; U p (·) represents the positive electrode open circuit potential function; U n (·) represents the negative electrode open circuit potential function; The liquid phase diffusion process description equation is: Where η con represents polarization overpotential; R represents ideal gas constant; T represents battery temperature; F represents Faraday constant; t + represents the lithium ion migration number; c0 represents the initial value of the liquid phase lithium ion concentration in the positive or negative electrode direction; Δc represents the change in the liquid phase lithium ion concentration at the positive or negative electrode current collector; τ e represents the liquid phase diffusion time constant; P con represents the liquid phase diffusion proportional coefficient; The ohmic polarization description equation is: or ohm =R ohm I; Where η ohm represents the ohmic polarization overpotential; R ohm Indicates ohmic internal resistance; The reaction polarization description equation is: Where η act represents the reaction polarization overpotential; P act Represents the reaction polarizability coefficient.

4. The lithium-ion battery state estimation method according to claim 2, wherein: The battery state data estimated value includes: a battery state of charge estimated value and a terminal voltage estimated value; Inputting the current into an electrochemical model of the lithium-ion battery to calculate an estimated value of battery state data of the lithium-ion battery includes: Inputting the current into an electrochemical model of the lithium-ion battery to calculate an estimated value of the battery state of charge, an estimated value of the open circuit voltage, an estimated value of the polarization overpotential, an estimated value of the ohmic polarization overpotential, and an estimated value of the reaction polarization overpotential; The terminal voltage estimate is calculated using a terminal voltage calculation formula according to the open circuit voltage estimate, the polarization overpotential estimate, the ohmic polarization overpotential estimate, and the reaction polarization overpotential estimate; Wherein, the terminal voltage calculation formula is: U app (t)=E ocv (t)-η con (t)-η ohm (t)-η act (t); Where U app Indicates terminal voltage.

5. The lithium-ion battery state estimation method according to claim 2, wherein: The screening process of the parameters to be corrected includes: Performing parameter sensitivity analysis on each parameter of the electrochemical model and selecting several parameters that have the greatest impact on the terminal voltage as parameters to be corrected; Among them, the parameters of the electrochemical model include: initial lithium insertion amount of the positive electrode, initial lithium insertion amount of the negative electrode, positive electrode capacity, negative electrode capacity, total capacity, positive electrode solid phase diffusion time constant, negative electrode solid phase diffusion time constant, reaction polarization coefficient, liquid phase diffusion proportional coefficient, liquid phase diffusion time constant and ohmic internal resistance.

6. The lithium-ion battery state estimation method according to claim 1, wherein: The method of correcting the battery status data estimation value by using an adaptive extended Kalman filter algorithm based on the terminal voltage observation value to obtain and output a battery status data correction value of the lithium-ion battery includes: Inputting the terminal voltage observation value and the battery state data estimation value into a first adaptive extended Kalman filter system, so that the first adaptive extended Kalman filter system calculates a battery state data correction value based on the terminal voltage observation value and the battery state data estimation value; outputting the battery status data correction value; Among them, the state transfer equation of the first adaptive extended Kalman filter system uses the battery state data estimation value as the system state transfer variable and the current as the system input; the observation equation of the first adaptive extended Kalman filter system uses the terminal voltage as the observation comparison quantity and the current as the system input.

7. The lithium-ion battery state estimation method according to claim 1, wherein: The method of correcting the parameter value to be corrected by using an adaptive extended Kalman filter algorithm based on the terminal voltage observation value to obtain a parameter correction value of the lithium-ion battery includes: Inputting the terminal voltage observation value and the parameter value to be corrected into a second adaptive extended Kalman filter system, so that the second adaptive extended Kalman filter system calculates the parameter correction value according to the terminal voltage observation value and the parameter value to be corrected; Among them, the state transfer equation of the second adaptive extended Kalman filter system uses the parameter value to be corrected as the system state transfer variable and the current as the system input; the observation equation of the second adaptive extended Kalman filter system uses the terminal voltage as the observation comparison quantity and the current as the system input.

8. A lithium-ion battery state estimation device, characterized in that: include: Data acquisition module, battery status data correction module and electrochemical model parameter correction module; The data acquisition module is configured to acquire, within a first period, the current, the parameter value to be corrected, and the terminal voltage observation value of the lithium-ion battery within the first period; wherein the parameter value to be corrected is the value of the parameter to be corrected in the electrochemical model of the lithium-ion battery; and the electrochemical model is a simplified electrochemical model of a pseudo-two-dimensional model; The battery state data correction module is configured to input the current into the electrochemical model of the lithium-ion battery in a first cycle to calculate an estimated battery state data value of the lithium-ion battery; and correct the estimated battery state data value using an adaptive extended Kalman filter algorithm based on the terminal voltage observation value to obtain and output a corrected battery state data value of the lithium-ion battery; The electrochemical model parameter correction module is used to correct the parameter value to be corrected in the first cycle according to the terminal voltage observation value through an adaptive extended Kalman filter algorithm to obtain the parameter correction value of the lithium-ion battery; in the second cycle, calculate the parameter correction average value according to the parameter correction values ​​corresponding to all first cycles in the second cycle; wherein the second cycle is composed of several consecutive first cycles; and adjust the parameter to be corrected of the electrochemical model according to the parameter correction average value.

9. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for estimating the state of a lithium-ion battery according to any one of claims 1 to 7 is implemented.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the lithium-ion battery state estimation method according to any one of claims 1 to 7.

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