A lithium battery SOC estimation method based on a minimalist electrochemical model at all temperatures

Abstract

The present invention relates to a lithium battery SOC estimation method based on a minimalist electrochemical model at all temperatures, comprising the following steps: Step 1: Reconstructing a minimalist electrochemical model; Step 2: Using a genetic algorithm to perform parameter identification on the minimalist electrochemical model established in step 1; Step 3: Constructing a minimalist electrochemical model at all temperatures. Step 4: Discretizing the solid phase diffusion equation to obtain a system state space equation and a measurement equation; Step 5: Based on the system state space equation and the measurement equation in step 4, the square root volumetric Kalman filter algorithm is integrated into the lithium ion battery SOC estimation to obtain an accurate SOC value after the state is updated. Simplified processing is performed on the average electrode model, and the influence of ambient temperature factors is considered to construct a minimalist electrochemical model at all temperatures, and the square root volumetric Kalman filter algorithm is integrated into the SOC estimation to eliminate the influence of process noise, improve the operation efficiency, and solve the problem of low SOC estimation accuracy caused by ambient temperature interference.

Description

Technical Field

[0001] The present invention belongs to the technical field of lithium-ion batteries, and in particular relates to a lithium battery SOC estimation method based on a minimalist electrochemical model at all temperatures. Background Art

[0002] As a key component of energy storage and electric vehicle power source, the battery's performance and cycle life have attracted much attention. As a direct representation of the remaining battery power, the state of charge (SOC) can enable the BMS to more accurately determine the instantaneous peak power and health status, so as to ensure that the battery operates within a safe range in a timely manner.

[0003] The parameters of the equivalent circuit model are easy to identify, but they cannot fully describe the characteristics of the power battery. At the same time, they are easily disturbed by environmental factors, resulting in low SOC estimation accuracy. The mathematical calculation process of the traditional electrochemical model is relatively complex, but it can fully reflect the strong coupling relationship between its internal parameters and battery characteristics, has high accuracy, and is also a hot topic in the current battery model research. Ambient temperature is an important factor affecting the accuracy of battery SOC estimation. Most scholars study battery thermal characteristics based on accurate battery thermal models. However, the battery working process involves the mutual coupling of electrochemical fields, electric fields, and temperature fields, which increases the calculation amount of the already complex electrochemical model again. In order to better adapt to the real-time state estimation of the battery, it is necessary and meaningful to simplify and reconstruct the electrochemical model and reconstruct it into a unique attribute related to SOC estimation. The Kalman filter algorithm helps to correct the initial SOC value, solve the problem of inaccurate initial SOC value estimation and cumulative error caused by the charge accumulation method, help overcome the problem of insufficient sensor accuracy, and help eliminate the influence of electromagnetic interference. The classic Kalman filter is more suitable for linear systems, but because the battery is a highly nonlinear system, the classic Kalman filter algorithm needs to be improved. The error trade-off between the cubature Kalman filter and the unscented Kalman filter will bring about the asymmetry and non-positive definiteness of the error covariance matrix, and the extended Kalman filter has the problem of low Taylor expansion accuracy. How to use existing technology to build an efficient and accurate model, eliminate process noise in electrochemical models with strong nonlinear characteristics by introducing intelligent algorithms, and apply them to battery management systems is a key issue that needs to be solved. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a lithium battery SOC estimation method based on a minimalist electrochemical model at all temperatures to solve the problems in the prior art that the average electrode model has low calculation efficiency and cannot be efficiently applied to the battery management system, is easily disturbed by the ambient temperature, resulting in low SOC estimation accuracy, and traditional Kalman filtering cannot be applied to highly nonlinear electrochemical model systems, so as to achieve the purpose of eliminating process noise.

[0005] In order to solve the above technical problems, the technical solution of the present invention is to provide a lithium battery SOC estimation method based on a minimalist electrochemical model at all temperatures, and its innovation lies in that it includes the following steps:

[0006] Step 1: Establishment of a minimalist electrochemical model: Based on the average electrode model, only the open circuit voltage and estimated SOC are retained, and the complex equations of positive and negative electrode overpotential and liquid phase potential are simplified into a polynomial equation to reconstruct a minimalist electrochemical model;

[0007] Step 2: Parameter identification using genetic algorithm: Under full temperature conditions, based on battery charge and discharge data, use genetic algorithm to perform parameter identification on the minimalist electrochemical model established in step 1;

[0008] Step 3: Construct a minimalist electrochemical model at all temperatures: Assume C s_max represents the maximum lithium ion concentration, θ0 and θ 100 They represent the electrode utilization when SOC is 0 and 1 respectively. The parameter C related to SOC calculation in the minimalist electrochemical model obtained in step 1 is s_max , θ0 and θ 100 Fitting to a functional relationship related to ambient temperature, a minimalist electrochemical model at all temperatures was constructed;

[0009] Step 4: Discretization of the minimalist electrochemical model at all temperatures: Based on the minimalist electrochemical model at all temperatures obtained in step 3, the solid phase diffusion equation is discretized to obtain the system state space equation and measurement equation;

[0010] Step 5: Incorporate the square root volumetric Kalman filter algorithm to estimate the SOC: Based on the system state space equation and measurement equation in step 4, the square root volumetric Kalman filter algorithm is integrated into the SOC estimation of the lithium-ion battery. The terminal voltage of the minimalist electrochemical model at full temperature obtained in step 3 is used as the system output variable, and the current is used as the system input variable to obtain the precise SOC value after the state is updated.

[0011] Furthermore, the specific method for reconstructing the minimalist electrochemical model in step 1 is as follows:

[0012] The positive and negative electrode overpotentials and liquid phase potential of the average electrode model are simplified into seventh-order polynomials, and the polynomial coefficients are obtained through parameter identification methods, that is, a minimalist electrochemical model is obtained:

[0013] y=k1*T (7) +k2*T (6) +k3*T (5) +k4*T (4) +k5*T (3) +k6*T (2) +k7*T+k8

[0014] Where ki is the polynomial coefficient, i = 1, 2.....8, and y is the sum of the positive and negative electrode overpotentials and the liquid phase potential.

[0015] Furthermore, the specific process of using the genetic algorithm to perform parameter identification on the minimalist electrochemical model established in step 1 in step 2 is as follows:

[0016] Based on the battery charge and discharge data, set SOC sim and SOC exp They represent the SOC simulation value and SOC experimental value of the minimalist electrochemical model respectively. The evaluation index of the genetic algorithm individual is the SOC error function error(i), which is expressed as:

[0017] errror(i)=abs(SOC sim -SOC exp ).

[0018] Furthermore, the parameter C related to SOC calculation in the minimalist electrochemical model in step 3 is s_max , θ0 and θ 100 The functional relationships fitted to the ambient temperature are:

[0019] θ0=-2.184e-11*T (7) +3.992e-09*T (6) -2.677e-07*T (5) +7.69e-06*T (4) -6.917e-05*T (3) -0.0005659*T (2) +0.004516*T+0.467

[0020] θ 100 =-4.47e-11*T (7) +8.111e-09*T (6) -5.378e-07*T (5) +1.507e-05*T (4) -0.0001203*T(3) -0.001538*T (2) +0.01862*T+0.9576

[0021] C s_max =7.827e-07*T (7) -0.000142*7 (6) +0.00941*T (5) -0.2636*T (4) +2.106*T (3) +26.77*T (2) -324.4*T+1.591e+04

[0022] T is the ambient temperature, where the SOC calculation expression is:

[0023]

[0024] Among them, electrode utilization θ = C s / C s_max , that is, the actual average total concentration of lithium ions in the electrode solid particles C S The ratio between its maximum possible concentration, θ0 and θ 100 They represent the electrode utilization when SOC is 0 and 1 respectively, and the positive and negative electrode open circuit voltage expressions are:

[0025] U p =2.585*θ p (6) -11.5*θ p (5) +15.1*θ p (4) -7.926*θ p (3) +2.486*θ p (2) -1.683*θ p +4.399

[0026] U n =0.7202+0.1386*θ n +0.028*θ n (0.5)-(0.0171 / u) +0.0019*θ n (-1.5) +0.2816*exp(0.9-15*θ n )-0.7896*exp(0.4598*θ n -0.4098)

[0027] Where θ = C s / C s_max ,θp and θ n are the positive and negative electrode utilization rates, U p and U n They represent the positive electrode open circuit voltage and the negative electrode open circuit voltage respectively, and θ0 and θ 100 , C s_max All are fitted into functional expressions related to the ambient temperature, thus obtaining a minimalist electrochemical model at all temperatures.

[0028] Furthermore, in step 4, the minimalist electrochemical model at all temperatures is a nonlinear system, and its discretized state equation and measurement equation are expressed as:

[0029]

[0030] where Y k is the output, u k is the input, X k represents the state vector at the kth step, ω k and v k They represent system noise and measurement noise respectively, and their covariances are Q k and R k , A k , B k , C k and D k They represent the transfer matrix, control matrix, observation matrix and feedforward matrix of the system respectively;

[0031] The terminal voltage equation of the extremely simple electrochemical model at all temperatures is:

[0032] V(t)=U p -U n +y

[0033] V(t) represents the terminal voltage. In the terminal voltage equation, the overpotential, liquid phase potential difference and ohmic voltage are all independent of the lithium ion concentration on the solid surface and are functions of the battery operating current. Therefore, they can be described by f(I). Based on the terminal voltage equation, the measurement equation is expressed as:

[0034] V(t)=U p -U n +f(I)

[0035] The solid phase diffusion equation in the minimalist electrochemical model at all temperatures obtained in step 3 is:

[0036]

[0037] After discretizing the solid phase diffusion equation, the state variable x in the system is obtained k And the coefficient matrix Ak :

[0038] x k =[SOC y 1，p y 2，p ] T

[0039]

[0040]

[0041]

[0042] C S is the solid phase lithium ion concentration, D s，p is the cathode solid phase diffusion coefficient, R p is the radius of the positive electrode solid phase lithium ion particle.

[0043] Furthermore, the specific steps of integrating the square root volumetric Kalman filter algorithm into the SOC estimation of the lithium-ion battery in step 5 are as follows:

[0044] S1: Initialization:

[0045]

[0046] Where E(·) represents the expectation, P0 represents the error covariance matrix;

[0047] S2: Calculate basic volume point:

[0048]

[0049] Among them, [1] represents a completely symmetric point set, which is generated by arranging the elements of the n-dimensional unit column vector and changing the signs of the elements;

[0050] S3: Time update:

[0051] Calculate the sampling points:

[0052]

[0053] in, and S k-1|k-1 They represent the square root form of the state error covariance and the predicted state at the k-1 step respectively;

[0054] Compute the square root of the state estimation error covariance matrix:

[0055]

[0056]

[0057] Among them, tria represents QR decomposition, S Q，|k-1 is the process noise Q k-1 The square root form of

[0058] S4: Measurement update:

[0059] Calculate the input sampling points of the measurement equation:

[0060]

[0061] Estimate the measured output at each sampling point:

[0062] Z i，k|k-1 =h(X i，k|k-1 ,u k )

[0063] Define h as the function dependent variable, X i，k|k-1 and u k is the dependent variable;

[0064] Compute the predicted value of the observed output:

[0065]

[0066] Calculate the square root of the observation error covariance S zz，k|k-1 :

[0067]

[0068] Where S R，k is the measurement noise R k The square root of

[0069] Calculate the covariance P of the state and observation output errors xz，k|k-1 :

[0070]

[0071] Calculate the filter gain:

[0072]

[0073] Status Update:

[0074]

[0075] Update the square root form of the state error covariance:

[0076] S k|k =tria([χ k|k-1 -W k ξ k|k-1 , W k S R，k ])

[0077] Based on the system state space equation and measurement equation in step 4, the terminal voltage of the minimalist electrochemical model at all temperatures obtained in step 3 is used as the system output variable, and the current is used as the system input variable. They are input into the square root volume Kalman filter algorithm to obtain the precise SOC after state update.

[0078] Compared with the prior art, the present invention has the following beneficial effects:

[0079] (1) Simplify the average electrode model to obtain a minimalist electrochemical model, which greatly reduces the model complexity and improves the calculation efficiency;

[0080] (2) Considering the influence of ambient temperature factors, a minimalist electrochemical model under all temperatures is constructed to solve the problem of low SOC estimation accuracy caused by ambient temperature interference;

[0081] (3) The square root volumetric Kalman filter is innovatively introduced into the SOC calculation of the electrochemical model, which can not only solve the asymmetry and non-positive definiteness problems of the error covariance matrix caused by the error trade-off between the volumetric Kalman filter and the unscented Kalman filter;

[0082] (4) The innovative introduction of the square root volumetric Kalman filter into the SOC calculation of the electrochemical model can overcome the problem of low Taylor expansion accuracy of the extended Kalman filter and obtain a more accurate battery SOC. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 The present invention is a flowchart of the specific steps of a lithium battery SOC estimation method based on a minimalist electrochemical model at all temperatures.

[0084] Figure 2 This is a diagram of the terminal voltage result and error result of the minimalist electrochemical model of the present invention under an ambient temperature of 25°C.

[0085] Figure 3 This is a SOC estimation result diagram and an error result diagram of the present invention under an ambient temperature of 25°C. DETAILED DESCRIPTION

[0086] In order to enable those skilled in the art to better understand the scheme of the present invention, the present invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0087] In the present invention, a lithium battery SOC estimation method based on a minimalist electrochemical model at all temperatures is provided. The method can be applied to terminal devices such as electric vehicle battery management systems. The lithium ion battery SOC estimation method provided by the present invention is as follows: Figure 1 As shown, the following steps are included:

[0088] Step 1: Establishment of a minimalist electrochemical model:

[0089] On the basis of the average electrode model, only the open circuit voltage and the estimated SOC are retained, and the complex equations of the positive and negative electrode overpotentials and the liquid phase potential are simplified into a polynomial equation to reconstruct the minimalist electrochemical model; wherein, the specific method of reconstructing the minimalist electrochemical model is as follows: the positive and negative electrode overpotentials and the liquid phase potential of the average electrode model are simplified into seventh-order polynomials, and the polynomial coefficients are obtained by the parameter identification method:

[0090] y=k1*T (7) +k2*T (6) +k3*T (5) +k4*T (4) +k5*T (3) +k6*T (2) +k7*T+k8

[0091] Where ki (i = 1, 2.....8) is the polynomial coefficient, y is the sum of the positive and negative electrode overpotentials and the liquid phase potential;

[0092] The relatively complex overpotential equation and liquid potential equation of the average electrode model are simplified into simple polynomial equations, which greatly reduces the amount of calculation of the average electrode model and improves the calculation speed. Therefore, the terminal voltage expression of the minimalist electrochemical model can be abbreviated as:

[0093] V(t)=U p -U n +y

[0094] U p and U n Represent the positive open circuit voltage and the negative open circuit voltage respectively.

[0095] Step 2: Parameter identification using genetic algorithm:

[0096] Under all temperature conditions, based on the battery charge and discharge data, a genetic algorithm is used to identify the parameters of the minimalist electrochemical model established in step 1. The specific process is as follows: In this embodiment, the number of individuals in the genetic algorithm is set to 50, the genetic generation is set to 20, the variable dimension is set to 3, the crossover rate and the mutation rate are set to 0.7 and 0.008 respectively, and the parameter identification is preferably performed under the conditions of ambient temperatures of -20℃, 0℃, 20℃, 40℃ and 60℃. Based on the battery charge and discharge data, SOC sim and SOC exp They represent the SOC simulation value and SOC experimental value of the minimalist electrochemical model respectively. The evaluation index of the genetic algorithm individual is the SOC error function error(i), which is expressed as:

[0097] errror(i)=abs(SOC sim-SOC exp ).

[0098] The genetic algorithm was used to perform parameter identification at ambient temperatures of -20°C, 0°C, 20°C, 40°C and 60°C to obtain parameters related to SOC calculation. The identified result parameters are shown in Table 1:

[0099] Table 1

[0100]

[0101] Step 3: Construct a minimalist electrochemical model at all temperatures:

[0102] Let C s_max represents the maximum lithium ion concentration, θ0 and θ 100 They represent the electrode utilization when SOC is 0 and 1 respectively. The parameter C related to SOC calculation in the minimalist electrochemical model obtained in step 1 is s_max , θ0 and θ 100 The function relationship related to the ambient temperature is fitted to construct a minimalist electrochemical model under all temperatures; the parameter C related to SOC calculation in the minimalist electrochemical model s_max , θ0 and θ 100 The functional relationships fitted to the ambient temperature are:

[0103] θ0=-2.184e-11*T (7) +3.992e-09*T (6) -2.677e-07*T (5) +7.69e-06*T (4) -6.917e-05*T (3) -0.0005659*T (2) +0.004516*T+0.467

[0104] θ 100 =-4.47e-11*T (7) +8.111e-09*T (6) -5.378e-07*T (5) +1.507e-05*T (4) -0.0001203*T (3) -0.001538*T (2) +0.01862*T+0.9576

[0105] C s_max =7.827e-07*T (7) -0.000142*7 (6) +0.00941*T (5) -0.2636*T(4) +2.106*T (3) +26.77*T (2) -324.4*T+1.591e+04

[0106] T is the ambient temperature, where the SOC calculation expression is:

[0107]

[0108] Among them, electrode utilization θ = C s / C s_max , that is, the actual average total concentration of lithium ions in the electrode solid particles C S The ratio between its maximum possible concentration, θ0 and θ 100 They represent the electrode utilization when SOC is 0 and 1 respectively, and the positive and negative electrode open circuit voltage expressions are:

[0109] U p =2.585*θ p (6) -11.5*θ p (5) +15.1*θ p (4) -7.926*θ p (3) +2.486*θ p (2) -1.683*θ p +4.399U n =0.7202+0.1386*θ n +0.028*θ n (0.5)-(0.0171 / u) +0.0019*θ n (-1.5) +0.2816*exp(0.9-15*θ n )-0.7896*exp(0.4598*θ n -0.4098)

[0110] Where θ = C s / C s_max ,θ p and θ n are the positive and negative electrode utilization rates, U p and U n They represent the positive electrode open circuit voltage and the negative electrode open circuit voltage respectively, and θ0 and θ 100 , C s_max All are fitted into functional expressions related to the ambient temperature, thus obtaining a minimalist electrochemical model at all temperatures.

[0111] By reconstructing the minimalist electrochemical model under full temperature, the influence of ambient temperature interference on the model accuracy can be eliminated, so that the model can adapt to the full working temperature. Figure 2 This is a terminal voltage result diagram and an error result diagram of the minimalist electrochemical model of the present invention under an ambient temperature of 25°C. The average error is below 0.02V, the maximum error is 0.07V, and the model accuracy meets the requirements.

[0112] Step 4: Discretization of the minimalist electrochemical model at all temperatures:

[0113] Based on the minimalist electrochemical model at all temperatures obtained in step 3, the solid phase diffusion equation is discretized to obtain the system state space equation and measurement equation. Specifically, the minimalist electrochemical model at all temperatures is a nonlinear system, and its discretized state equation and measurement equation are expressed as:

[0114]

[0115] where Y k is the output, u k is the input, X k represents the state vector at the kth step, ω k and v k They represent system noise and measurement noise respectively, and their covariances are Q k and R k , A k , B k , C k and D k They represent the transfer matrix, control matrix, observation matrix and feedforward matrix of the system respectively;

[0116] The terminal voltage equation of the extremely simple electrochemical model at all temperatures is:

[0117] V(t)=U p -U n +y

[0118] V(t) represents the terminal voltage. In the terminal voltage equation, the overpotential, liquid phase potential difference and ohmic voltage are independent of the lithium ion concentration on the solid surface and are functions of the battery operating current, so they can be described by f(I). The measurement equation is an equation that describes the algebraic relationship between the output and state of the system according to the observable voltage. Therefore, according to the terminal voltage equation, the measurement equation is expressed as:

[0119] V(t)=U p -U n +f(I)

[0120] The solid phase diffusion equation in the minimalist electrochemical model at all temperatures obtained in step 3 is:

[0121]

[0122] After discretizing the solid phase diffusion equation, the state variable x in the system is obtained k And the coefficient matrix A k :

[0123] x k =[SOC y 1，p y 2，p ] T

[0124]

[0125]

[0126]

[0127] C S is the solid phase lithium ion concentration, D s，p is the cathode solid phase diffusion coefficient, R p is the radius of the positive electrode solid phase lithium ion particle.

[0128] Step 5: Incorporate the square root volumetric Kalman filter algorithm to estimate the SOC: Based on the system state space equation and measurement equation in step 4, the square root volumetric Kalman filter algorithm is integrated into the SOC estimation of the lithium-ion battery. The terminal voltage of the minimalist electrochemical model at full temperature obtained in step 3 is used as the system output variable, and the current is used as the system input variable to obtain the precise SOC value after the state is updated.

[0129] Among them, the system state variable is x k =[y 1，p y 2，p SOC] T , the input variable is u k =I k , the output variable is Y k =y t，p The specific steps of integrating the square root volumetric Kalman filter algorithm into the SOC estimation of lithium-ion batteries are as follows:

[0130] S1: Initialization:

[0131]

[0132] Where E(·) represents the expectation, P0 represents the error covariance matrix;

[0133] S2: Calculate basic volume point:

[0134]

[0135] Among them, [1] represents a completely symmetric point set, which is generated by arranging the elements of the n-dimensional unit column vector and changing the signs of the elements;

[0136] S3: Time update:

[0137] Calculate the sampling points:

[0138]

[0139] in, and S k-1|k-1 They represent the square root form of the state error covariance and the predicted state at the k-1 step respectively;

[0140] Estimate the state at each sampling point:

[0141]

[0142] Calculate the next forecast value:

[0143]

[0144] Compute the square root of the state estimation error covariance matrix:

[0145]

[0146]

[0147] Among them, tria represents QR decomposition, S Q，|k-1 is the process noise Q k-1 The square root form of .

[0148] S4: Measurement update:

[0149] Calculate the input sampling points of the measurement equation:

[0150]

[0151] Estimate the measured output at each sampling point:

[0152] Z i，k|k-1 =h(X i，k|k-1 ,u k )

[0153] Define h as the function dependent variable, X i，k|k-1 and u k is the dependent variable;

[0154] Compute the predicted value of the observed output:

[0155]

[0156] Calculate the square root of the observation error covariance Szz，k|k-1 :

[0157]

[0158] Where S R，k is the measurement noise R k The square root of

[0159] Calculate the covariance P of the state and observation output errors xz，k|k-1 :

[0160]

[0161] Calculate the filter gain:

[0162]

[0163] Status Update:

[0164]

[0165] Update the square root form of the state error covariance:

[0166] S k|k =tria([χ k|k-1 -W k ξ k|k-1 , W k S R，k ])

[0167] Based on the system state space equation and measurement equation in step 4, the terminal voltage of the minimalist electrochemical model at all temperatures obtained in step 3 is used as the system output variable, and the current is used as the system input variable, which are input into the square root volumetric Kalman filter algorithm in step 5 for state update. While solving the asymmetry and non-positive definiteness problems of the error covariance matrix caused by the error trade-off of the volumetric Kalman filter and the unscented Kalman filter, it can also overcome the low accuracy of the Taylor expansion of the extended Kalman filter and obtain a more accurate battery SOC. Figure 3 This is a SOC estimation result diagram and an error result diagram of the present invention under the condition of an ambient temperature of 25°C. The SOC estimation error is within 2%, and the accuracy meets the requirements.

Claims

1. A lithium battery SOC estimation method based on a minimalist electrochemical model at all temperatures, characterized in that: The following steps are involved: Step 1: Establishment of a minimalist electrochemical model: Based on the average electrode model, only the open circuit voltage and estimated SOC are retained, and the complex equations of positive and negative electrode overpotential and liquid phase potential are simplified into a polynomial equation to reconstruct a minimalist electrochemical model; Step 2: Parameter identification using genetic algorithm: Under full temperature conditions, based on battery charge and discharge data, use genetic algorithm to perform parameter identification on the minimalist electrochemical model established in step 1; Step 3: Construct a minimalist electrochemical model at all temperatures: Assume C s_max represents the maximum lithium ion concentration, θ0 and θ 100 They represent the electrode utilization when SOC is 0 and 1 respectively. The parameter C related to SOC calculation in the minimalist electrochemical model obtained in step 1 is s_max , θ0 and θ 100 Fitting to a functional relationship related to ambient temperature, a minimalist electrochemical model at all temperatures was constructed; Parameter C related to SOC calculation in the minimalist electrochemical model s_max , θ0 and θ 100 The functional relationships fitted to the ambient temperature are: θ0=-2.184e-11*T (7) +3.992e-09*T (6) -2.677e-07*T (5) +7.69e -06*T (4) -6.917e-05*T (3) -0.0005659*T (2) +0.004516*T+0.467 θ 100 =-4.47e-11*T (7) +8.111e-09*T (6) -5.378e-07*T (5) +1.507e -05*T (4) -0.0001203*T (3) -0.001538*T (2) +0.01862*T+0.9576 C s_max =7.827e-07*T (7) -0.000142*7 (6) +0.00941*T (5) -0.2636*T (4) +2.106*T (3) +26.77*T (2) -324.4*T+1.591e+04 T is the ambient temperature, where the SOC calculation expression is: Among them, electrode utilization θ = C s / C s_max , that is, the actual average total concentration of lithium ions in the electrode solid particles C S The ratio between its maximum possible concentration, θ0 and θ 100 They represent the electrode utilization when SOC is 0 and 1 respectively, and the positive and negative electrode open circuit voltage expressions are: U p =2.585*θ p (6) -11.5*θ p (5) +15.1*θ p (4) -7.926*θ p (3) +2.486*θ p (2) -1.683*θ p +4,399 U n =0.7202+0.1386*θ n +0.028*θ n (0.5)-(0.0171 / u) +0.0019*θ n (-1.5) + 0.2816*exp(0.9-15*θ n )-0.7896*exp(0.4598*θ n -0.4098) Where θ = C s / C s_max ,θ p and θ n are the positive and negative electrode utilization rates, U p and U n They represent the positive electrode open circuit voltage and the negative electrode open circuit voltage respectively, and θ0 and θ 100 , C s_max All of them are fitted into functional expressions related to the ambient temperature, thus obtaining a minimalist electrochemical model at all temperatures; Step 4: Discretization of the minimalist electrochemical model at all temperatures: Based on the minimalist electrochemical model at all temperatures obtained in step 3, the solid phase diffusion equation is discretized to obtain the system state space equation and measurement equation; Step 5: Incorporate the square root volumetric Kalman filter algorithm to estimate the SOC: Based on the system state space equation and measurement equation in step 4, the square root volumetric Kalman filter algorithm is integrated into the SOC estimation of the lithium-ion battery. The terminal voltage of the minimalist electrochemical model at full temperature obtained in step 3 is used as the system output variable, and the current is used as the system input variable to obtain the precise SOC value after the state is updated.

2. According to claim 1, a lithium battery SOC estimation method based on a minimalist electrochemical model at all temperatures is characterized in that: The specific method for reconstructing the minimalist electrochemical model in step 1 is as follows: The positive and negative electrode overpotentials and liquid phase potential of the average electrode model are simplified into seventh-order polynomials, and the polynomial coefficients are obtained through parameter identification methods, that is, a minimalist electrochemical model is obtained: y=k1*T (7) +k2*T (6) +k3*T (5) +k4*T (4) +k5*T (3) +k6*T (2) +k7*T+k8 Where ki is the polynomial coefficient, i=1,2...8, and y is the sum of the positive and negative electrode overpotentials and the liquid phase potential.

3. The method for estimating SOC of a lithium battery based on a minimalist electrochemical model at all temperatures according to claim 1, characterized in that: The specific process of using genetic algorithm to perform parameter identification on the minimalist electrochemical model established in step 1 in step 2 is as follows: Based on the battery charge and discharge data, set SOC sim and SOC exp They represent the SOC simulation value and SOC experimental value of the minimalist electrochemical model respectively. The evaluation index of the genetic algorithm individual is the SOC error function error(i), which is expressed as: errror(i)=abs(SOC sim -SOC exp )。 4. The method for estimating SOC of a lithium battery based on a minimalist electrochemical model at all temperatures according to claim 1, characterized in that: In step 4, the minimalist electrochemical model at all temperatures is a nonlinear system, and its discretized state equation and measurement equation are expressed as: where Y k is the output, u k is the input, X k represents the state vector at the kth step, ω k and v k They represent system noise and measurement noise respectively, and their covariances are Q k and R k , A k , B k , C k and D k They represent the transfer matrix, control matrix, observation matrix and feedforward matrix of the system respectively; The terminal voltage equation of the extremely simple electrochemical model at all temperatures is: V(t)=U p -U n +y V(t) represents the terminal voltage. In the terminal voltage equation, the overpotential, liquid phase potential difference and ohmic voltage are all independent of the lithium ion concentration on the solid surface and are functions of the battery operating current. Therefore, they can be described by f(I). Based on the terminal voltage equation, the measurement equation is expressed as: V(t)=U p -U n +f(I) The solid phase diffusion equation in the minimalist electrochemical model at all temperatures obtained in step 3 is: After discretizing the solid phase diffusion equation, the state variable x in the system is obtained k And the coefficient matrix A k : x k =[SOC y 1,p y 2,p ] T C S is the solid phase lithium ion concentration, D s,p is the cathode solid phase diffusion coefficient, R p is the radius of the positive electrode solid phase lithium ion particle.

5. The method for estimating SOC of a lithium battery based on a minimalist electrochemical model at all temperatures according to claim 1, characterized in that: The specific steps of integrating the square root volumetric Kalman filter algorithm into the SOC estimation of the lithium-ion battery in step 5 are as follows: S1: Initialization: Where E(·) represents the expectation, P0 represents the error covariance matrix; S2: Calculate basic volume point: Among them, [1] represents a completely symmetric point set, which is generated by arranging the elements of the n-dimensional unit column vector and changing the signs of the elements; S3: Time update: Calculate the sampling points: in, and S k-1k-1 They represent the square root form of the state error covariance and the predicted state at the k-1 step respectively; Compute the square root of the state estimation error covariance matrix: Among them, tria represents QR decomposition, S Q,k-1 is the process noise Q k-1 The square root form of S4: Measurement update: Calculate the input sampling points of the measurement equation: Estimate the measured output at each sampling point: Z i,kk-1 =h(X i,kk-1 ,u k ) Define h as the function dependent variable, X i,kk-1 and u k is the dependent variable; Compute the predicted value of the observed output: Calculate the square root of the observation error covariance S zz,kk-1 : Where S R,k is the measurement noise R k The square root of Calculate the covariance P of the state and observation output errors xz,kk-1 : Calculate the filter gain: Status Update: Update the square root form of the state error covariance: S kk =tria([x kk-1 -W k x kk-1 ,W k S R,k ]) Based on the system state space equation and measurement equation in step 4, the terminal voltage of the minimalist electrochemical model at all temperatures obtained in step 3 is used as the system output variable, and the current is used as the system input variable. They are input into the square root volume Kalman filter algorithm to obtain the precise SOC after state update.

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