Power battery high-precision temperature field modeling method based on embedded optical fiber sensor

Abstract

The invention provides a power battery high-precision temperature field modeling method based on an embedded optical fiber sensor, and the method comprises the steps: building a distributed optical fiber temperature sensing system in a lossless manner, obtaining the internal and external temperature data of a battery, and only employing a small number of parameters, such as the voltage, current, temperature, etc. Establishing a battery internal temperature field prediction model based on the GRU neural network; the Bayesian optimization algorithm is introduced in the model training to perform hyper-parameter search, the training speed can be effectively improved, and the temperature field modeling precision is also ensured by taking the EKF-based battery SOC and SOH joint estimation result as the model input. The method provided by the invention can be widely applied to commercial batteries of various models, and is beneficial to early identification of various faults which may cause abnormal internal high temperature, such as negative electrode lithium precipitation, internal short circuit, overcharge and the like, so that the use safety of the battery can be improved, and the intelligent management level of the power battery is improved from the overall level.

Description

Technical Field

[0001] The present invention belongs to the technical field of battery testing and management monitoring, and in particular relates to a high-precision temperature field modeling method for a power battery based on an embedded optical fiber sensor. Background Art

[0002] Temperature has a high correlation with the working performance and cycle life of power batteries. Accurate and timely monitoring of it is helpful to diagnose and warn of battery thermal runaway failures, thereby ensuring the safety of battery use. At present, some existing technologies related to battery temperature monitoring have been expanded from the battery surface to the inside, which greatly improves the characterization effect of battery temperature compared with the early days. The internal battery temperature monitoring methods used are mainly divided into three categories: direct measurement method, model method and data-driven method. Among them, the measurement method directly measures the internal temperature through embedded sensors or indirectly measures parameters such as the electrochemical impedance of the power battery to reflect the temperature information inside the power battery. However, there is no clear conclusion on the impact of destructive sensor implantation on important performance parameters such as power battery capacity, life and safety, and the implantation process lacks clear standards. It is difficult to achieve commercial application in the short term; the measurement of electrochemical impedance parameters requires a large amount of experimental data calibration, and can only reflect the overall temperature state, which is of little help to temperature field modeling. The model method estimates the internal temperature of the battery by establishing a battery distributed parameter or lumped parameter model, but the temperature estimation result is too dependent on the accuracy of the modeling. Since most of the existing battery thermal models are simplified and approximated, it is easy to cause temperature estimation deviations and only obtain temperature estimation results at a few points. The data-driven method is based on a large amount of temperature data during the operation of the battery, and uses machine learning to directly establish a battery internal temperature prediction model, which is conducive to online application, but the quality of test data and temperature data under different experimental conditions directly affects the generalization and modeling accuracy of the model; some battery temperature modeling technologies based on artificial intelligence can improve data quality and modeling accuracy to a certain extent, but their shortcomings of complex parameter adjustment and slow learning speed are still difficult to solve. Summary of the invention

[0003] In view of this, in order to solve the technical problems existing in the art, the present invention provides a method for high-precision temperature field modeling of a power battery based on an embedded optical fiber sensor, which specifically includes the following steps:

[0004] Step 1: Place the optical fiber transmission device on the surface of the commercial power battery cell and perform non-destructive optical fiber implantation on the battery cell to form a distributed optical fiber temperature sensor system for measuring the surface and internal temperature of the power battery;

[0005] Step 2: Establish a battery bipolarization equivalent circuit model including two RC networks and a corresponding state equation for the battery cell, and discretize and linearize the state equation to obtain a form consisting of a voltage and current data matrix and a system parameter matrix;

[0006] Step 3: Use the collected battery voltage and current data to identify the system parameter matrix, and parse out the ohmic internal resistance R0 and polarization internal resistance R represented by the system parameters. D1 and R D2 And the polarization capacitance C D1 and C D2 ;

[0007] Step 4: The state variables are composed of the power battery SOC and the loop voltages of the two RC networks, and the terminal voltage is used as the observation variable. The system state equation and observation equation are established, and the SOC and SOH of the power battery are jointly estimated based on the extended Kalman filter;

[0008] Step 5: Establish a prediction model for the internal temperature field of the power battery based on the neural network of the gated recurrent unit GRU, and perform a hyperparameter search for the GRU neural network based on the Bayesian optimization algorithm; the prediction model for the internal temperature field of the power battery takes the joint estimation results of the battery voltage, current, SOC and SOH and the external temperature of the battery as input, and the temperature field distribution inside the battery as output;

[0009] Step 6: Use the battery surface temperature data collected by the distributed optical fiber temperature sensor system, the power battery voltage, current data, and the joint estimation results of SOC and SOH to train the established power battery internal temperature field prediction model, and use the battery internal temperature collected by the distributed optical fiber temperature sensor to verify the training accuracy of the model;

[0010] Step seven, apply the trained power battery internal temperature field prediction model online, so that it can directly output the internal temperature field distribution results according to the power battery voltage, current, SOC and SOH and external temperature data.

[0011] Furthermore, the process of non-destructive optical fiber implantation of the battery cell in step 1 includes:

[0012] (1) Negative electrode drilling: Use a drill bit with a diameter of 0.9 mm to drill a hole in the center of the negative electrode of the cylindrical power battery cell. The drill bit should drill into the depth of 0.8 to 1 mm to ensure that the negative electrode shell and the electrode are drilled open. After the negative electrode is drilled, it should be sealed with Kapton glue immediately to prevent electrolyte leakage;

[0013] (2) Positive electrode cutting: Use pipe cutting pliers to cut open the positive terminal cover of the battery cell. After cutting, check the integrity of the positive electrode sheet and use Kapton tape to insulate the positive electrode sheet from the shell.

[0014] (3) Distributed optical fiber implantation: The distributed optical fiber sensor is implanted from the negative electrode hole, and the optical fiber is led out from the positive electrode and respectively attached to the shells on both sides of the battery cell and fixed with Kapton glue, and then the positive and negative electrodes of the battery cell are re-sealed with epoxy resin glue;

[0015] (4) Battery cell performance test and temperature perception performance test: After the epoxy resin glue solidifies, measure the open circuit voltage of the battery cell. After confirmation, place the battery cell on the fixture and carry out charge and discharge tests and internal and external temperature perception performance tests of the battery cell in the incubator;

[0016] The above steps (1) to (3) are all carried out under an inert gas protection environment.

[0017] Furthermore, in step 2, the following state equation is established for the cylindrical power battery cell:

[0018] U t (t) = U OC (t)-i L (t)R0(t)-U D1 (t)-U D2 (t)

[0019]

[0020]

[0021] Where U OC is the open circuit voltage, U t is the terminal voltage, U D1 and U D2 are the loop voltages of the two RC networks, i L is the load current, R0 is the battery ohmic internal resistance, R D1 and R D2 is the polarization internal resistance of the two RC networks, C D1 and C D2 are the polarized capacitances of the two RC networks, t represents time, and the superscript · represents the derivative of the corresponding parameter;

[0022] After discretization and linearization, we get:

[0023] U t,k =Φ 2,k θ 2,k

[0024] Φ 2,k =[1,U t,k-1 ,U t,k-2 ,i L,k ,i L,k-1 ,i L,k-2 ]

[0025] θ 2,k =[(1-θ1-θ2)U OC,k ,θ1,θ2,θ3,θ4,θ5] T

[0026] Among them, Φ 2,k is the system voltage and current data matrix, θ 2,k is the system parameter matrix, and k represents the sampling time.

[0027] Furthermore, in step 3, the system parameter matrix θ is identified based on the recursive least squares method with forgetting factor. 2,k :

[0028]

[0029] In the formula, K k is the gain matrix, P k is the covariance error matrix, I is the identity matrix, and μ is the forgetting factor;

[0030] In the identification, the total voltage and total current in the power battery test data are input in matrix format; after identification, the ohmic internal resistance R0 and polarization internal resistance R D1 and R D2 And the polarization capacitance C D1 and C D2 They are:

[0031]

[0032] Furthermore, the system state equation x established in step 4 k and the observation equation U t,k The specific form is as follows:

[0033] x k =A k x k-1 +B k I k +ω k

[0034] U t,k =C k x k -I k R0+D k-1 +v k

[0035]

[0036] The process of performing joint estimation of the SOC and SOH of the power battery based on the extended Kalman filter includes:

[0037] (1) Initialize the system state: Set the initial state, the state estimation error covariance matrix to P0, the system noise covariance matrix to Q, the measurement noise covariance matrix to R, and Q and R are constants;

[0038] (2) System state prediction: predict the system state x at the next moment k And update the error covariance matrix P k :

[0039]

[0040] (3) State update: Use the measurement data to correct the state estimate and covariance estimate, and calculate the Kalman gain matrix K in sequence k , correct the system state x k And update the covariance matrix P k :

[0041]

[0042] (4) System state recursion: Let And start the next iteration;

[0043] Use EKF to quickly recurse and get the SOC result in the battery cycle; for adjacent sampling points, count the changes in battery capacity and SOC to get the actual maximum available capacity of the battery and estimate the battery SOH:

[0044]

[0045] After a charge and discharge cycle, the average value of the maximum available capacity of the battery in the entire cycle is calculated, and the capacity parameters of the system state equation in the EKF method are updated in real time.

[0046] Furthermore, the process of searching for hyperparameters of the GRU neural network through Bayesian optimization in step 5 includes:

[0047] (1) Initialization exploration: Randomly sample the initial observation points in the set hyperparameter space to obtain the observation points x1,…,x t , and then obtain the GRU model hyperparameter objective function observations F(x1),…,F(x t );

[0048] (2) Surrogate function fitting: Based on the existing observation points and observation values, solve the distribution parameter Θ of the surrogate function and calculate the 95% confidence interval of all unexplored spaces;

[0049] (3) Exploration point selection: The exploration utility function of the hyperparameter objective function is obtained based on the upper bound of the confidence interval of the objective function. Then, the maximum value of the exploration utility function is calculated, which is the next exploration point x.* ;

[0050] (4) Iterative exploration: Obtain the hyperparameter objective function observation value F(x) of the new exploration point * ), repeat the above steps (2) and (3) until the target accuracy requirement or the search number limit is reached, and finally output the optimal hyperparameter combination obtained by the search.

[0051] The high-precision temperature field modeling method of the power battery based on the embedded optical fiber sensor provided by the present invention above acquires the temperature data inside and outside the battery by building a distributed optical fiber temperature sensing system in a non-destructive way, and only uses a small number of parameters such as battery voltage, current, and temperature to establish a battery internal temperature field prediction model based on the GRU neural network; the Bayesian optimization algorithm is introduced in the training of the model for hyperparameter search, which can effectively improve the training speed, and the joint estimation results of the battery SOC and SOH based on the EKF are used as the model input, so that the accuracy of temperature field modeling is also guaranteed. The method of the present invention can be widely applied to commercial batteries of various models, which is helpful to early identify various faults such as negative electrode lithium deposition, internal short circuit, overcharging, etc. that may cause abnormal internal high temperature, thereby improving the safety of battery use and improving the level of intelligent management of power batteries from an overall level. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A flowchart of the method provided by the present invention;

[0053] Figure 2 A flow chart for non-destructive distributed optical fiber implantation into battery cells;

[0054] Figure 3 Flowchart for modeling the GRU neural network based on the Bayesian optimization algorithm. DETAILED DESCRIPTION

[0055] The technical solution of the present invention will be described clearly and completely below 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] The invention provides a high-precision temperature field modeling method for a power battery based on an embedded optical fiber sensor. Figure 1 As shown, the specific steps include:

[0057] Step 1: Place the optical fiber transmission device on the surface of the commercial power battery cell and perform non-destructive optical fiber implantation on the battery cell to form a distributed optical fiber temperature sensor system for measuring the surface and internal temperature of the power battery;

[0058] Step 2: Establish a battery bipolarization equivalent circuit model including two RC networks and a corresponding state equation for the battery cell, and discretize and linearize the state equation to obtain a form consisting of a voltage and current data matrix and a system parameter matrix;

[0059] Step 3: Use the collected battery voltage and current data to identify the system parameter matrix, and parse out the ohmic internal resistance R0 and polarization internal resistance R represented by the system parameters. D1 and R D2 And the polarization capacitance C D1 and C D2 ;

[0060] Step 4: The state variables are composed of the power battery SOC and the loop voltages of the two RC networks, and the terminal voltage is used as the observation variable. The system state equation and observation equation are established, and the SOC and SOH of the power battery are jointly estimated based on the extended Kalman filter;

[0061] Step 5: Establish a prediction model for the internal temperature field of the power battery based on the neural network of the gated recurrent unit GRU, and perform a hyperparameter search for the GRU neural network based on the Bayesian optimization algorithm; the prediction model for the internal temperature field of the power battery takes the estimated results of the battery voltage, current, SOC and SOH and the external temperature as input, and the temperature field distribution inside the battery as output;

[0062] Step 6: Use the battery surface temperature data collected by the distributed optical fiber temperature sensor system, the power battery voltage, current data, and the SOC, SOH estimation results to train the established power battery internal temperature field prediction model, and use the battery internal temperature collected by the distributed optical fiber temperature sensor to verify the training accuracy of the model;

[0063] Step seven, apply the trained power battery internal temperature field prediction model online, so that it can directly output the internal temperature field distribution results according to the power battery voltage, current, SOC and SOH and external temperature data.

[0064] In a preferred embodiment of the present invention, the process of non-destructive optical fiber implantation of the battery monomer in step 1 is as follows: Figure 2 As shown, including:

[0065] (1) Negative electrode drilling: Use a drill bit with a diameter of 0.9 mm to drill a hole in the center of the negative electrode of the cylindrical power battery cell. The drill bit should drill into the depth of 0.8 to 1 mm to ensure that the negative electrode shell and the electrode are drilled open. After the negative electrode is drilled, it should be sealed with Kapton glue immediately to prevent electrolyte leakage;

[0066] (2) Positive electrode cutting: Use pipe cutting pliers to cut open the positive terminal cover of the battery cell. After cutting, check the integrity of the positive electrode sheet and use Kapton tape to insulate the positive electrode sheet from the shell.

[0067] (3) Distributed optical fiber implantation: The distributed optical fiber sensor is implanted from the negative electrode hole, and the optical fiber is led out from the positive electrode and respectively attached to the shells on both sides of the battery cell and fixed with Kapton glue, and then the positive and negative electrodes of the battery cell are re-sealed with epoxy resin glue;

[0068] (4) Battery cell performance test and temperature perception performance test: After the epoxy resin glue solidifies, measure the open circuit voltage of the battery cell. After confirmation, place the battery cell on the fixture and carry out charge and discharge tests and internal and external temperature perception performance tests of the battery cell in the incubator;

[0069] The above steps (1) to (3) are all carried out under an inert gas protection environment.

[0070] The above process does not require disassembly of the battery cells, so it will not affect the normal performance and safety of the battery.

[0071] In a preferred embodiment of the present invention, in step 2, the following state equation is established for the cylindrical power battery cell:

[0072] U t (t) = U OC (t)-i L (t)R0(t)-U D1 (t)-U D2 (t)

[0073]

[0074] Where U OC is the open circuit voltage, U t is the terminal voltage, U D1 and U D2 are the loop voltages of the two RC networks, i L is the load current, R0 is the battery ohmic internal resistance, R D1 and R D2 is the polarization internal resistance of the two RC networks, C D1 and C D2 are the polarized capacitances of the two RC networks, t represents time, and the superscript · represents the derivative of the corresponding parameter;

[0075] After discretization and linearization, we get:

[0076] U t,k =Φ 2,k θ 2,k

[0077] Φ 2,k =[1,U t,k-1 ,U t,k-2 ,i L,k ,i L,k-1 ,i L,k-2 ]

[0078] θ 2,k =[(1-θ1-θ2)U OC,k ,θ1,θ2,θ3,θ4,θ5] T

[0079] Among them, Φ 2,k is the system voltage and current data matrix, θ 2,k is the system parameter matrix, and k represents the sampling time.

[0080] In a preferred embodiment of the present invention, in step 3, the system parameter matrix θ is specifically identified based on the recursive least squares method with a forgetting factor. 2,k :

[0081]

[0082] In the formula, K k is the gain matrix, P k is the covariance error matrix, I is the identity matrix, and μ is the forgetting factor;

[0083] In the identification, the total voltage and total current in the power battery test data are input in matrix format; after identification, the ohmic internal resistance R0 and polarization internal resistance R D1 and R D2 And the polarization capacitance C D1 and C D2 They are:

[0084]

[0085] In a preferred embodiment of the present invention, the system state equation x established in step 4 is k and the observation equation U t,k The specific form is as follows:

[0086] x k =A k x k-1 +B k I k +ω k

[0087] U t,k =C k x k -I k R0+D k-1 +v k

[0088]

[0089] The process of performing joint estimation of the SOC and SOH of the power battery based on the extended Kalman filter includes:

[0090] (1) Initialize the system state: Set the initial state, the state estimation error covariance matrix to P0, the system noise covariance matrix to Q, the measurement noise covariance matrix to R, and Q and R are constants;

[0091] (2) System state prediction: predict the system state x at the next moment k And update the error covariance matrix P k :

[0092]

[0093] (3) State update: Use the measurement data to correct the state estimate and covariance estimate, and calculate the Kalman gain matrix K in sequence k , correct the system state x k And update the covariance matrix P k :

[0094]

[0095] (4) System state recursion: Let And start the next iteration;

[0096] Use EKF to quickly recurse and get the SOC result in the battery cycle; for adjacent sampling points, count the changes in battery capacity and SOC to get the actual maximum available capacity of the battery and estimate the battery SOH:

[0097]

[0098] After a charge and discharge cycle, the average value of the maximum available capacity of the battery in the entire cycle is calculated, and the capacity parameters of the system state equation in the EKF method are updated in real time.

[0099] Since the hyperparameters of GRU, such as the number of network layers and the number of neurons in each layer, will greatly affect the model training speed and modeling results, the present invention adopts a hyperparameter search based on Bayesian optimization to conduct a rapid search of high-dimensional and complex hyperparameter spaces to optimize the modeling results. Bayesian optimization mainly includes the fitting of surrogate functions and the calculation of exploration utility functions. The present invention uses a multivariate normal distribution surrogate function to approximate the objective function. For the already explored sampling points x1,…,x t (abbreviated as x 1:t ) and the observed values ​​of the objective function F(x1),…,F(x t)(abbreviated as F(x 1:t ), the multivariate normal distribution it obeys is:

[0100]

[0101] Among them, σ t 2 is the noise covariance matrix, μ t is the mean of the multivariate normal distribution, K t is the covariance of the distribution, and the probability density function that obeys the above multivariate normal distribution law is as follows:

[0102]

[0103] Among them, Θ is the parameter set of the multivariate normal distribution. Since the observation points and observation values ​​are known, the probability density function only takes the parameter set as a variable. Through maximum likelihood estimation, the parameter value that maximizes the probability density can be selected. Convert the probability density function to the log-likelihood function:

[0104]

[0105] This function is a function whose gradient can be calculated by a known formula. The optimal value of the parameter set of the multivariate normal distribution can be solved by the marginal likelihood partial derivative and the gradient descent method, and the joint Gaussian prior distribution of the target function can be fitted according to the existing observations. * ,y * ), the posterior distribution condition it satisfies is as follows:

[0106]

[0107] Among them, μ(y * ) is the mean based on the observed values, σ 2 (y * ) is the variance of the observed point data. For all undetected points, the 95% confidence interval of the observed value can be calculated as follows:

[0108] 95% CI C =μ(y * )±1.96×σ 2 (y * )

[0109] Specifically in a preferred embodiment of the present invention, the process of searching for hyperparameters by Bayesian optimization for the GRU neural network includes:

[0110] (1) Initialization exploration: Randomly sample the initial observation points in the set hyperparameter space to obtain the observation points x1,…,x t, and then obtain the GRU model hyperparameter objective function observations F(x1),…,F(x t );

[0111] (2) Surrogate function fitting: Based on the existing observation points and observation values, solve the distribution parameter Θ of the surrogate function and calculate the 95% confidence interval of all unexplored spaces;

[0112] (3) Exploration point selection: The exploration utility function of the hyperparameter objective function is obtained based on the upper bound of the confidence interval of the objective function. Then, the maximum value of the exploration utility function is calculated, which is the next exploration point x. * ;

[0113] (4) Iterative exploration: Obtain the hyperparameter objective function observation value F(x) of the new exploration point * ), repeat the above steps (2) and (3) until the target accuracy requirement or the search number limit is reached, and finally output the optimal hyperparameter combination obtained by the search.

[0114] It should be understood that the size of the serial numbers of the steps in the embodiment of the present invention does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0115] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A high-precision temperature field modeling method for a power battery based on an embedded optical fiber sensor, characterized in that: The specific steps include: Step 1: Place the optical fiber transmission device on the surface of the commercial power battery cell and perform non-destructive optical fiber implantation on the battery cell to form a distributed optical fiber temperature sensor system for measuring the surface and internal temperature of the power battery; Step 2: Establish a battery bipolarization equivalent circuit model including two RC networks and a corresponding state equation for the battery cell, and discretize and linearize the state equation to obtain a form consisting of a voltage and current data matrix and a system parameter matrix; Step 3: Use the collected battery voltage and current data to identify the system parameter matrix, and parse out the ohmic internal resistance R0 and polarization internal resistance R represented by the system parameters. D1 and R D2 And the polarization capacitance C D1 and C D2 ; Step 4: The state variables are composed of the power battery SOC and the loop voltages of the two RC networks, and the terminal voltage is used as the observation variable. The system state equation and observation equation are established, and the SOC and SOH of the power battery are jointly estimated based on the extended Kalman filter; Step 5: Establish a prediction model for the internal temperature field of the power battery based on the gated recurrent unit GRU neural network, and perform a hyperparameter search for the GRU neural network based on the Bayesian optimization algorithm; the prediction model for the internal temperature field of the power battery takes the joint estimation results of the battery voltage, current, SOC and SOH and the external temperature of the battery as input, and the temperature field distribution inside the battery as output; Step 6: Use the battery surface temperature data collected by the distributed optical fiber temperature sensor system, the power battery voltage, current data, and the joint estimation results of SOC and SOH to train the established power battery internal temperature field prediction model, and use the battery internal temperature collected by the distributed optical fiber temperature sensor to verify the training accuracy of the model; Step seven, apply the trained power battery internal temperature field prediction model online, so that it can directly output the internal temperature field distribution results according to the power battery voltage, current, SOC and SOH and external temperature data.

2. The method according to claim 1, characterized in that: The process of non-destructive optical fiber implantation of the battery cell in step 1 includes: (1) Negative electrode drilling: Use a drill bit with a diameter of 0.9 mm to drill a hole in the center of the negative electrode of the cylindrical power battery cell. The drill bit should drill into the depth of 0.8 to 1 mm to ensure that the negative electrode shell and the electrode are drilled open. After the negative electrode is drilled, it should be sealed with Kapton glue immediately to prevent electrolyte leakage; (2) Positive electrode cutting: Use pipe cutting pliers to cut open the positive terminal cover of the battery cell. After cutting, check the integrity of the positive electrode sheet and use Kapton tape to insulate the positive electrode sheet from the shell. (3) Distributed optical fiber implantation: The distributed optical fiber sensor is implanted from the negative electrode hole, and the optical fiber is led out from the positive electrode and respectively attached to the shells on both sides of the battery cell and fixed with Kapton glue, and then the positive and negative electrodes of the battery cell are re-sealed with epoxy resin glue; (4) Battery cell performance test and temperature perception performance test: After the epoxy resin glue solidifies, measure the open circuit voltage of the battery cell. After confirmation, place the battery cell on the fixture and carry out charge and discharge tests and internal and external temperature perception performance tests of the battery cell in the incubator; The above steps (1) to (3) are all carried out under an inert gas protection environment.

3. The method according to claim 1, characterized in that: In step 2, the following state equation is established for the cylindrical power battery cell: U t (t)=U OC (t)-i L (t)R0(t)-U D1 (t)-U D2 (t) Where U OC is the open circuit voltage, U t is the terminal voltage, U D1 and U D2 are the loop voltages of the two RC networks, i L is the load current, R0 is the battery ohmic internal resistance, R D1 and R D2 is the polarization internal resistance of the two RC networks, C D1 and C D2 are the polarized capacitances of the two RC networks, t represents time, and the superscript · represents the derivative of the corresponding parameter; After discretization and linearization, we get: U t,k =Φ 2,k i 2,k Φ 2,k =[1,U t,k-1 ,IN t,k-2 ,and L,k ,and L,k-1 ,and L,k-2 ] i 2,k =[(1-θ1-θ2)U OC,k ,θ1,θ2,θ3,θ4,θ5] T Among them, Φ 2,k is the system voltage and current data matrix, θ 2,k is the system parameter matrix, and k represents the sampling time.

4. The method according to claim 3, characterized in that: In step 3, the system parameter matrix θ is identified based on the recursive least squares method with forgetting factor. 2,k : In the formula, K k is the gain matrix, P k is the covariance error matrix, I is the identity matrix, and μ is the forgetting factor; In the identification, the total voltage and total current in the power battery test data are specifically input in a matrix format; After identification, the ohmic internal resistance R0 and polarization internal resistance R D1 and R D2 And the polarization capacitance C D1 and C D2 They are:

5. The method according to claim 4, characterized in that: The system state equation x established in step 4 k and the observation equation U t,k The specific form is as follows: x k =A k x k-1 +B k I k +ω k U t,k =C k x k -I k R0+D k-1 +v k The process of performing joint estimation of the SOC and SOH of the power battery based on the extended Kalman filter includes: (1) Initialize the system state: Set the initial state, the state estimation error covariance matrix to P0, the system noise covariance matrix to Q, the measurement noise covariance matrix to R, and Q and R are constants; (2) System state prediction: predict the system state x at the next moment k And update the error covariance matrix P k : (3) State update: Use the measurement data to correct the state estimate and covariance estimate, and calculate the Kalman gain matrix K in sequence k , correct the system state x k And update the covariance matrix P k : (4) System state recursion: Let And start the next iteration; Use EKF to quickly recurse and get the SOC result in the battery cycle; for adjacent sampling points, count the changes in battery capacity and SOC to get the actual maximum available capacity of the battery and estimate the battery SOH: After a charge and discharge cycle, the average value of the maximum available capacity of the battery in the entire cycle is calculated, and the capacity parameters of the system state equation in the EKF method are updated in real time.

6. The method according to claim 1, characterized in that: The process of searching for hyperparameters through Bayesian optimization for the GRU neural network in step 5 includes: (1) Initialization exploration: Randomly sample the initial observation points in the set hyperparameter space to obtain the observation points x1,…,x t , and then obtain the GRU model hyperparameter objective function observations F(x1),…,F(x t ); (2) Surrogate function fitting: Based on the existing observation points and observation values, solve the distribution parameter Θ of the surrogate function and calculate the 95% confidence interval of all unexplored spaces; (3) Exploration point selection: The exploration utility function of the hyperparameter objective function is obtained based on the upper bound of the confidence interval of the objective function. Then, the maximum value of the exploration utility function is calculated, which is the next exploration point x. * ; (4) Iterative exploration: Obtain the hyperparameter objective function observation value F(x) of the new exploration point * ), repeat the above steps (2) and (3) until the target accuracy requirement or the search number limit is reached, and finally output the optimal hyperparameter combination obtained by the search.

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