SOC (State of Charge) estimation method for aqueous sodium-ion battery

Abstract

The invention discloses an SOC estimation method for a water-based sodium-ion battery. The method comprises the following steps: step 1, establishing a DP equivalent circuit model of the water-based sodium-ion battery and a voltage mathematical model of the water-based sodium-ion battery; step 2, performing Laplace transformation on the mathematical model of the terminal voltage of the aqueous sodium-ion battery obtained in the step 1, discretizing by adopting bilinear transformation, and then calculating a to-be-identified parameter by adopting an FFRLS algorithm; and step 3, estimating the SOC of the aqueous sodium ion battery based on an AUKF algorithm. According to the method, the parameter identification of the battery model is realized by adopting the least square parameter estimation method with the forgetting factor, the AUKF algorithm is improved in combination with the noise adaptive step, and the real-time SOC of the water-based sodium-ion battery energy storage system can be accurately estimated.

Description

Technical Field

[0001] The present invention relates to the field of battery SOC estimation methods, and in particular to an aqueous sodium ion battery SOC estimation method. Background Art

[0002] For aqueous sodium-ion batteries with time-varying and nonlinear characteristics, a widely studied internal SOC state estimation method is based on Kalman filter-related algorithms, including Unscented Kalman Filter (UKF) and Extended Kalman Filter (EKF), which are used to improve the accuracy of SOC estimation. The specific description is as follows:

[0003] 1. Extended Kalman filter algorithm: This algorithm has a high accuracy in estimating the real-time SOC of aqueous sodium-ion batteries. It can also improve the accuracy of SOC estimation by combining other filtering algorithms or optimizing battery models. Combining EKF with the ampere-hour integration method can effectively suppress the influence of system noise, thereby enhancing the overall accuracy of SOC estimation.

[0004] 2. Unscented Kalman Filter (AUKF): This algorithm uses unscented transformation to avoid possible errors in the linearization process. It is usually combined with a first-order RC circuit model to apply the UKF estimation algorithm to the SOC estimation of aqueous sodium-ion batteries, thereby improving the reliability and accuracy of the SOC estimation algorithm. Based on the PNGV equivalent circuit model, the SOC of SIBs is estimated using EKF and UKF, respectively. UKF is superior to EKF in terms of accuracy and stability.

[0005] However, the accuracy of the above-mentioned Kalman filter-based aqueous sodium-ion battery SOC estimation method usually depends on the accuracy of the model, and requires more computing resources due to the complexity of the algorithm. Summary of the invention

[0006] The present invention provides a method for estimating the SOC of an aqueous sodium ion battery, so as to solve the problems of high accuracy and high computing resource requirements of the prior art method for estimating the SOC of an aqueous sodium ion battery based on a Kalman filter.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is:

[0008] The method for estimating SOC of an aqueous sodium ion battery comprises the following steps:

[0009] Step 1, establish an aqueous sodium ion battery DP equivalent circuit model, and determine an aqueous sodium ion battery terminal voltage mathematical model based on the aqueous sodium ion battery DP equivalent circuit model, wherein the aqueous sodium ion battery terminal voltage mathematical model has unknown battery ohmic internal resistance, electrochemical polarization internal resistance, concentration polarization internal resistance, electrochemical polarization time constant, and concentration polarization time constant;

[0010] Step 2: Perform Laplace transformation on the terminal voltage mathematical model of the aqueous sodium ion battery obtained in step 1 and discretize it using bilinear transformation to obtain the discrete pulse transfer function G(z -1 );

[0011] Then, the FFRLS algorithm is used to calculate and identify the discrete pulse transfer function G(z -1 ), and then inversely solve the unknown battery ohmic internal resistance, electrochemical polarization internal resistance, concentration polarization internal resistance, electrochemical polarization time constant, and concentration polarization time constant in the battery terminal voltage mathematical model through the parameters to be identified, thereby obtaining the battery ohmic internal resistance, electrochemical polarization internal resistance, concentration polarization internal resistance, electrochemical polarization time constant, and concentration polarization time constant The mathematical model of the terminal voltage of the aqueous sodium ion battery is known;

[0012] Step 3: Based on the battery ohmic internal resistance, electrochemical polarization internal resistance, concentration polarization internal resistance, electrochemical polarization time constant, and concentration polarization time constant obtained in step 2, a mathematical model of the terminal voltage of the aqueous sodium ion battery is established; then, the SOC of the aqueous sodium ion battery is estimated based on the discretized state space model of the aqueous sodium ion battery using the AUKF algorithm.

[0013] In the further step 1, the terminal voltage mathematical model of the aqueous sodium ion battery is shown in the following formula:

[0014]

[0015] Where: Δt is the sampling time interval; U t Indicates the battery terminal voltage; U OCV Represents the open circuit voltage; I represents the battery terminal current; R0 represents the battery ohmic internal resistance; R1 represents the electrochemical polarization internal resistance; R2 represents the concentration polarization internal resistance; τ1 represents the electrochemical polarization time constant; τ2 represents the concentration polarization time constant; R0, R1, R2, τ1, τ2 are all unknown quantities to be solved.

[0016] In a further step 2, the discrete pulse transfer function G(z -1 ) is shown in the following formula:

[0017]

[0018] Where: Z is the discretization operator; θ = [b1, b2, b3, b4, b5] is the parameter to be identified; G(z -1 ) represents the discrete pulse transfer function; b1, b2, b3, b4, b5 are the coefficients of the complex variable z; U OCV (z -1 ) represents the discrete pulse transfer function of the open circuit voltage; U t (z -1 ) represents the discrete pulse transfer function of the terminal voltage; I(z -1 ) represents the discrete pulse transfer function of the terminal current.

[0019] In the further step 2, the FFRLS algorithm is used to calculate and identify the discrete pulse transfer function G(z -1 ) is as follows:

[0020] First, initialize the covariance matrix, the parameters to be identified, the state of charge), and the open circuit voltage to obtain the covariance matrix P at the initial time, i.e., time 0 FFRLS (0), parameter to be identified θ(0), state of charge SOC(0), open circuit voltage U ocv (0);

[0021] Then, multiple rounds of recursive iterations of the FFRLS algorithm are performed, in which the forgetting factor φ is introduced into the recursive process during the k-th moment recursion. Based on the covariance matrix and the parameters to be identified at the k-th moment, the initial data of the covariance matrix, the parameters to be identified, and the gain matrix at the k+1th moment are obtained. When the estimated error of the accuracy of the parameters to be identified is less than the set value through multiple rounds of iterations, the loop is stopped to complete the identification of the parameters to be identified.

[0022] In the further step 2, the unknown battery ohmic internal resistance, electrochemical polarization internal resistance, concentration polarization internal resistance, electrochemical polarization time constant, and concentration polarization time constant in the battery terminal voltage mathematical model are inversely solved by the parameters to be identified. The calculation formulas are as follows:

[0023]

[0024] Among them: the parameters to be identified θ = [b1, b2, b3, b4, b5], where b1, b2, b3, b4, b5 are identified by the FFRLS algorithm; T is the sampling time constant; R0 is the battery ohmic internal resistance, R1 is the electrochemical polarization internal resistance, R2 is the concentration polarization internal resistance, τ1 is the electrochemical polarization time constant, and τ2 is the concentration polarization time constant.

[0025] The present invention adopts the FFRLS identification algorithm (i.e., the least squares parameter estimation method) to improve the model accuracy and reduce the computing resource requirements, and then adopts the AUKF algorithm (i.e., the unscented Kalman filter algorithm) to estimate the SOC. The unscented transformation avoids errors in the linearization process, and also improves the accuracy and real-time performance of the calculation, which can meet the requirements of online detection.

[0026] The present invention uses the least squares parameter estimation method with forgetting factor to realize the parameter identification of the battery model, and improves the AUKF algorithm in combination with the noise adaptive step, which can accurately estimate the real-time SOC of the aqueous sodium ion battery energy storage system, so that each energy storage module can be in the best working state, realize the dynamic distribution of power, extend the service life of the parallel energy storage system, and ensure the long-term stable operation of the energy storage system. A simulation model is established to analyze the divided working modes and verify the effectiveness of the proposed strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a diagram of the DP equivalent circuit model of an aqueous sodium ion battery established in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0029] This embodiment discloses a method for estimating the SOC of an aqueous sodium ion battery, comprising the following steps:

[0030] Step 1: Establish the DP equivalent circuit model of aqueous sodium ion battery Figure 1 shown. Figure 1 Middle U OCV Indicates the open circuit voltage of aqueous sodium ion battery; U t represents the terminal voltage of the aqueous sodium ion battery; I represents the terminal current of the battery; R0 represents the ohmic internal resistance of the battery; R1 represents the electrochemical polarization internal resistance, C1 represents the electrochemical polarization capacitor; R2 represents the concentration polarization internal resistance; C2 represents the concentration polarization capacitor; U1 represents the voltage across R1 C1; U2 represents the voltage across R2C2. Figure 1 In the DP equivalent circuit model of the aqueous sodium-ion battery shown, two RC networks composed of resistors R1, C1, R2, and C2 are used to describe the resistance generated by the ohmic effect and polarization effect inside the aqueous sodium-ion battery.

[0031] Based on Figure 1 The DP equivalent circuit model of the aqueous sodium-ion battery shown in FIG. 1 can provide an initial estimate of the SOC state of the aqueous sodium-ion battery as shown in formula (1.1):

[0032]

[0033] In formula (1.1), SOC0 represents the initial value of the state of charge, C N represents the rated capacity, η represents the coulombic efficiency (η≈1); SOc represents the current state of charge of the battery; I d represents the battery terminal current; t represents the charge and discharge time.

[0034] And based on Figure 1 The DP equivalent circuit model of the aqueous sodium ion battery shown in the figure can obtain the mathematical model of the terminal voltage of the aqueous sodium ion battery as shown in formula (1.2):

[0035]

[0036] In formula (1.2): Δt is the sampling time interval; U t Indicates the battery terminal voltage; U OCV represents the open circuit voltage; I represents the battery terminal current; R0 represents the battery ohmic internal resistance; R1 represents the electrochemical polarization internal resistance; R2 represents the concentration polarization internal resistance; τ1 represents the electrochemical polarization time constant; τ2 represents the concentration polarization time constant.

[0037] In the above mathematical model of the terminal voltage of the aqueous sodium-ion battery, the battery ohmic internal resistance R0, electrochemical polarization internal resistance R1, concentration polarization internal resistance R2, electrochemical polarization time constant τ1, and concentration polarization time constant τ2 are all unknown quantities.

[0038] Step 2, solve the unknown battery ohmic internal resistance, electrochemical polarization internal resistance, concentration polarization internal resistance, electrochemical polarization time constant, and concentration polarization time constant in the battery terminal voltage mathematical model, thereby obtaining the battery ohmic internal resistance, electrochemical polarization internal resistance, concentration polarization internal resistance, electrochemical polarization time constant, and concentration polarization time constant The mathematical model of the terminal voltage of the aqueous sodium ion battery is known, and the process is as follows:

[0039] (2.1) The terminal voltage mathematical model of the aqueous sodium ion battery obtained in step 1 as shown in formula (1.2) is Laplace transformed and discretized using bilinear transformation to obtain the discrete pulse transfer function G(z -1 ) as shown in formula (2.1):

[0040]

[0041] In formula (2.1), Z is the discretization operator; θ = [b1, b2, b3, b4, b5] is the parameter to be identified; G(z -1 ) represents the discrete pulse transfer function; b1, b2, b3, b4, b5 are the coefficients of the complex variable z; U OCV (z -1 ) represents the discrete pulse transfer function of the open circuit voltage; U t (z -1) represents the discrete pulse transfer function of the terminal voltage; I(z -1 ) represents the discrete pulse transfer function of the terminal current.

[0042] (2.2) The FFRLS algorithm is used to calculate and identify the discrete pulse transfer function G(z -1 ) in the parameter to be identified θ, and then the unknown battery ohmic internal resistance R0, electrochemical polarization internal resistance R1, concentration polarization internal resistance R2, electrochemical polarization time constant τ1, and concentration polarization time constant τ2 in the battery terminal voltage mathematical model are inversely solved through the parameter to be identified, thereby obtaining the battery ohmic internal resistance R0, electrochemical polarization internal resistance R1, concentration polarization internal resistance R2, electrochemical polarization time constant τ1, and concentration polarization time constant τ2. The mathematical model of the terminal voltage of the aqueous sodium ion battery with known battery ohmic internal resistance R0, electrochemical polarization internal resistance R1, concentration polarization internal resistance R2, electrochemical polarization time constant τ1, and concentration polarization time constant τ2 is obtained. The FFRLS algorithm identification process is as follows:

[0043] 2.2a) Initialize the covariance matrix, parameters to be identified, state of charge), open circuit voltage, and obtain the covariance matrix P at the initial time, i.e. time 0 FFRLS (0), parameter to be identified θ(0), state of charge SOC(0), open circuit voltage U ocv (0).

[0044] And, let P FFRLS (k),θ(k),SOC(k),U ocv (k) respectively represent the covariance matrix, the parameter to be identified, the state of charge, and the open circuit voltage at time k, P FFRLS (k+1), θ(k+1), SOC(k+1), U ocv (k+1) respectively represent the covariance matrix, the parameter to be identified, the state of charge, and the open circuit voltage at the k+1 moment.

[0045] 2.2b) Then, multiple rounds of FFRLS algorithm are repeated, and the forgetting factor φ is introduced into the recursive process during the k-th moment recursion. Based on the covariance matrix and the parameters to be identified at the k-th moment, the initial data of the covariance matrix, the parameters to be identified, and the gain matrix at the k+1th moment are obtained. The calculation process is shown in formula (2.2):

[0046]

[0047] In formula (2.2): Φ(k+1) represents the system observation data at time k+1, which is a column vector composed of the difference between the open circuit voltage and the terminal voltage at different times; K(k+1) is the gain matrix at time k+1, which is an intermediate parameter in the algorithm calculation process; The meaning is consistent with θ(k) and θ(k+1), which represent the parameters to be identified at the kth moment and the k+1th moment respectively; φ is the forgetting factor, which is generally taken as 0.95~0.99.

[0048] Will get K(k+1),P FFRLS (k+1) is used as the initial data for the recursion at the k+1th moment to continue the recursion, thereby realizing multiple rounds of cyclic recursion until the estimated error of the accuracy of the parameter to be identified is less than the set value ε, then the cycle is stopped and the identification of the parameter to be identified is completed.

[0049] 2.2c) The unknown battery ohmic internal resistance R0, electrochemical polarization internal resistance R1, concentration polarization internal resistance R2, electrochemical polarization time constant τ1, and concentration polarization time constant τ2 in the battery terminal voltage mathematical model are inversely solved by the identified parameters to be identified. The calculation formula is as follows:

[0050]

[0051] Among them: the parameters to be identified θ = [b1, b2, b3, b4, b5], where b1, b2, b3, b4, b5 are identified by the FFRLS algorithm; T is the sampling time constant; R0 is the battery ohmic internal resistance, R1 is the electrochemical polarization internal resistance, R2 is the concentration polarization internal resistance, τ1 is the electrochemical polarization time constant, and τ2 is the concentration polarization time constant.

[0052] Step 3: Estimate the SOC of the aqueous sodium-ion battery based on the AUKF algorithm. The specific process is as follows:

[0053] (3.1) Based on the battery ohmic internal resistance R0, electrochemical polarization internal resistance R1, concentration polarization internal resistance R2, electrochemical polarization time constant τ1, and concentration polarization time constant τ2 obtained in step 2, the mathematical model of the terminal voltage of the aqueous sodium ion battery is known, and the discrete state space model of the aqueous sodium ion battery is established as shown in formulas (3.1) and (3.2):

[0054]

[0055] U t (k+1)=U OCV -R0I(k)-U1(k)-U2(k)(3.2)

[0056] Where: k is a discrete time index, k = 0, 1, 2…n; SOC(k) is the state of charge at time k; U1(k) is the voltage across R1C1 at time k; U2(k) is the voltage across R2C2 at time k; I(k) is the terminal current at time k; U t (k+1) is the terminal voltage at time k.

[0057] (3.2) The discretized state space model of the aqueous sodium ion battery is described in the form shown in formula (3.3):

[0058]

[0059] In formula (3.3): w k is state noise, with a mean of 0 and a covariance of Q k ;v k is the observation noise, with a mean of 0 and a covariance of R k ;x k Represents the state quantity, y k Represents the observed value U t (k);u k represents the input quantity I(k); SOC(k) is the state of charge at time k; U1(k) is the voltage across R1C1 at time k; U2(k) is the voltage across R2C2 at time k; I(k) is the terminal current at time k; U t (k+1) is the terminal voltage at time k. f() represents the equation of state quantity; h() represents the equation of observation quantity.

[0060] (3.3) Then, the AUKF algorithm is used to calculate the state quantity x in the discretized state space model of the aqueous sodium ion battery. k The SOC of the aqueous sodium-ion battery is estimated. The process is as follows:

[0061] 3.3a) For the state quantity x at time k, T Perform an untraceable transformation to obtain the sigma point set and corresponding weight of the state quantity x, as shown in formulas (3.4) and (3.5):

[0062]

[0063] In formulas (3.4) and (3.5): x is a 2D signal, then n is 2, and the superscript i is 0 to 4; and P represent the mean and variance of the state quantity x, respectively. (i) (k|k) is the mean value of the state quantity x at time k, and P(k|k) represents the variance of the state quantity x at time k. m is the weight of the state prediction mean; ω c is the covariance weight; the two values ​​here are related to x (i) (k|k) has a one-to-one correspondence, such as That is, (0) The weight corresponding to (k|k).

[0064] Parameter λ = α 2(n+κ)-n, the influence of high-order terms on the results can be adjusted by controlling the value of α, which is a very small positive constant. The selection of κ needs to ensure that the matrix (n+λ)P is semi-positive definite; the parameter β is a non-negative weight coefficient. In this paper, 0≤α≤1, β=2, κ=3-n are set.

[0065] 3.3b) Substitute the obtained 2n+1 sigma points into formula (3.1) to obtain the further predicted value of sigma as shown in formula (3.6):

[0066] x (i) (k+1|k)=f[k,x (i) (k|k)](3.6)

[0067] In formula (3.6): x (i) (k+1|k) represents the predicted value of the sigma point at time k+1, f[] is the prediction function, and i ranges from 0 to 4.

[0068] 3.3c) For further prediction of value x (i) (k+1|k) is weighted summed and the weight ω is obtained by formula (3.5) c (i) , we can get the predicted mean of the state quantity and its covariance matrix, the result is shown in formula (3.7):

[0069]

[0070] In formula (3.7): x (i) (k+1|k) represents the predicted value of the sigma point at time k+1. Indicates the corresponding x (i) (k+1|k), Q is the covariance of the state noise, and n is 2.

[0071] 3.3d) The predicted mean value obtained from equation (3.7) Perform an unscented transformation, and the updated sigma point set is shown in formula (3.8):

[0072]

[0073] In formula (3.8): Represents the updated sigma point mean, n takes 2 parameters, λ=α 2 (n+κ)-n, the influence of high-order terms on the results can be adjusted by controlling the value of α, which is a very small positive constant. The selection of κ needs to ensure that the matrix (n+λ)P is semi-positive definite; the parameter β is a non-negative weight coefficient. In this paper, 0≤α≤1, β=2, κ=3-n are set.

[0074] 3.3e) Substitute the updated sigma point set into formula (3.1) and predict the observation at time k+1, as shown in formula (3.9):

[0075] y (i) (k+1|k)=h(x i (k|k),u k ) (3.9)

[0076] In formula (3.9): y (i) (k+1|k) represents the sigma point set of the predicted value of the observation at time k+1, and h() is the prediction function.

[0077] 3.3f) Combined weight ω m , the predicted value y for the observed quantity (i) (k+1|k) is weighted and summed to obtain the predicted mean and covariance matrix of the terminal voltage prediction value as shown in formula (3.10):

[0078]

[0079] In formula (3.10): represents the mean of the predicted values ​​of the observations at time k for time k+1, P ykyk With P xkyk It represents the covariance matrix of the observation itself and the covariance matrix between the observation value and the state quantity, and R is the covariance of the observation noise.

[0080] 3.3g) Calculate the Kalman gain, the expression is shown in formula (3.11):

[0081]

[0082] In formula (3.11): P ykyk With P xkyk It represents the covariance matrix of the observation itself and the covariance matrix between the observation and the state, and K(k+1) is the Kalman gain.

[0083] 3.3h) Adaptively update the process noise Q and observation noise R in equation (3.3), and the noise adaptive equation is shown in equation (3.12):

[0084]

[0085] Formula (3.12): Q(k) is the covariance of the state noise at time k, and R(k) is the covariance of the observation noise at time k

[0086] H(k) is the approximate value of the new information covariance at time k, and its calculation formula is shown in formula (3.13):

[0087]

[0088] In formula (3.13), M is the window size, and the value of M is selected as 5; y (i) (k) = y (i) (k+1|k) represents the sigma point set of the predicted value of the observation at time k+1. It represents the mean of the predicted values ​​of the observations at time k+1, L=M.

[0089] 3.3i) Finally, update the state variables and covariance of the system as shown in formula (3.14):

[0090]

[0091] Formula (3.14): represents the predicted value of the state quantity at time k+1, and P(k+1|k+1) represents the covariance matrix of the state quantity at time k+1, which is used as the next input of the algorithm.

[0092] Repeat the above steps 3.3a)-3.3i) in sequence to complete the SOC estimation of the sodium ion battery.

[0093] The preferred embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. The embodiments described in the present invention are merely descriptions of the preferred embodiments of the present invention, and do not limit the concept and scope of the present invention. The various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction, and such combinations should also be regarded as the contents disclosed in the present disclosure as long as they do not violate the concept of the present invention. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0094] The present invention is not limited to the specific details of the above-mentioned embodiments. Within the technical concept of the present invention and without departing from the design concept of the present invention, various modifications and improvements made to the technical solution of the present invention by technical personnel in this field should fall within the protection scope of the present invention. The technical contents for which protection is sought in the present invention have been fully recorded in the claims.

Claims

1. A method for estimating SOC of an aqueous sodium-ion battery, characterized in that: The following steps are involved: Step 1, establish an aqueous sodium ion battery DP equivalent circuit model, and determine an aqueous sodium ion battery terminal voltage mathematical model based on the aqueous sodium ion battery DP equivalent circuit model, wherein the aqueous sodium ion battery terminal voltage mathematical model has unknown battery ohmic internal resistance, electrochemical polarization internal resistance, concentration polarization internal resistance, electrochemical polarization time constant, and concentration polarization time constant; Step 2: Perform Laplace transformation on the terminal voltage mathematical model of the aqueous sodium ion battery obtained in step 1 and discretize it using bilinear transformation to obtain the discrete pulse transfer function G(z -1 ); Then, the FFRLS algorithm is used to calculate and identify the discrete pulse transfer function G(z -1 ), and then inversely solve the unknown battery ohmic internal resistance, electrochemical polarization internal resistance, concentration polarization internal resistance, electrochemical polarization time constant, and concentration polarization time constant in the battery terminal voltage mathematical model through the parameters to be identified, thereby obtaining the battery ohmic internal resistance, electrochemical polarization internal resistance, concentration polarization internal resistance, electrochemical polarization time constant, and concentration polarization time constant The mathematical model of the terminal voltage of the aqueous sodium ion battery is known; Step 3: Based on the battery ohmic internal resistance, electrochemical polarization internal resistance, concentration polarization internal resistance, electrochemical polarization time constant, and concentration polarization time constant obtained in step 2, a mathematical model of the terminal voltage of the aqueous sodium ion battery is established; then, the SOC of the aqueous sodium ion battery is estimated based on the discretized state space model of the aqueous sodium ion battery using the AUKF algorithm.

2. The aqueous sodium ion battery SOC estimation method according to claim 1, characterized in that: In step 1, the mathematical model of the terminal voltage of the aqueous sodium ion battery is shown in the following formula: Where: Δt is the sampling time interval; U t Indicates the battery terminal voltage; U OCV Represents the open circuit voltage; I represents the battery terminal current; R0 represents the battery ohmic internal resistance; R1 represents the electrochemical polarization internal resistance; R2 represents the concentration polarization internal resistance; τ1 represents the electrochemical polarization time constant; τ2 represents the concentration polarization time constant; R0, R1, R2, τ1, τ2 are all unknown quantities to be solved.

3. The method for estimating SOC of an aqueous sodium ion battery according to claim 1, characterized in that: In step 2, the discrete pulse transfer function G(z -1 ) is shown in the following formula: Where: Z is the discretization operator; θ = [b1, b2, b3, b4, b5] is the parameter to be identified; G(z -1 ) represents the discrete pulse transfer function; b1, b2, b3, b4, b5 are the coefficients of the complex variable z; U OCV (z -1 ) represents the discrete pulse transfer function of the open circuit voltage; U t (z -1 ) represents the discrete pulse transfer function of the terminal voltage; I(z -1 ) represents the discrete pulse transfer function of the terminal current.

4. The aqueous sodium ion battery SOC estimation method according to claim 1, characterized in that: In step 2, the FFRLS algorithm is used to calculate and identify the discrete pulse transfer function G(z -1 ) is as follows: First, initialize the covariance matrix, the parameters to be identified, the state of charge), and the open circuit voltage to obtain the covariance matrix P at the initial time, i.e., time 0 FFRLS (0), parameter to be identified θ(0), state of charge SOC(0), open circuit voltage U ocv (0); Then, multiple rounds of FFRLS algorithm are repeated, wherein the forgetting factor φ is introduced into the recursive process during the k-th moment recursion, thereby obtaining the initial data of the covariance matrix, the parameters to be identified, and the gain matrix at the k+1th moment based on the covariance matrix and the parameters to be identified at the k-th moment; The process is repeated for multiple rounds until the estimated error of the precision of the parameter to be identified is less than the set value, then the cycle is stopped and the identification of the parameter to be identified is completed.

5. The aqueous sodium ion battery SOC estimation method according to claim 1, characterized in that: In step 2, the calculation formulas for the unknown battery ohmic internal resistance, electrochemical polarization internal resistance, concentration polarization internal resistance, electrochemical polarization time constant, and concentration polarization time constant in the battery terminal voltage mathematical model are inversely solved by the parameters to be identified as follows: Among them: the parameters to be identified θ = [b1, b2, b3, b4, b5], where b1, b2, b3, b4, b5 are identified by the FFRLS algorithm; T is the sampling time constant; R0 is the battery ohmic internal resistance, R1 is the electrochemical polarization internal resistance, R2 is the concentration polarization internal resistance, τ1 is the electrochemical polarization time constant, and τ2 is the concentration polarization time constant.

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