Battery SOC (State of Charge) estimation method and device based on Kalman filtering and electronic equipment

Abstract

The invention discloses a battery SOC estimation method and device based on Kalman filtering and electronic equipment, and relates to the technical field of battery management. The method comprises the following steps: determining a first non-plateau area, and reconstructing a first SOC-OCV curve in the first non-plateau area to obtain a second SOC-OCV curve; constructing a second-order RC equivalent circuit model according to the second SOC-OCV curve; dynamic parameters of the target battery are obtained in real time from the parameter online identification model for the second-order RC equivalent circuit model, so that target RC parameters are obtained; and introducing the target RC parameter into the Kalman filtering model so as to carry out SOC estimation on the target battery. According to the technical scheme, the precision of SOC estimation of the target battery can be improved, meanwhile, dependence on early-stage complex and time-consuming off-line experiment calibration is reduced, and the stability and applicability of SOC estimation in the whole life cycle and the dynamic working condition of the target battery are enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery management, in particular to a battery SOC estimation method and device based on Kalman filtering and an electronic device. BACKGROUND

[0002] Lithium iron phosphate power batteries are widely used in electric vehicles due to their high safety and long cycle life. However, the State of Charge-Open Circuit Voltage (SOC-OCV) curve of the lithium iron phosphate power battery has a flat platform region in the middle SOC range. In the platform region, the OCV changes very little with the SOC, which makes it difficult to accurately estimate the SOC in the middle region and thus to effectively correct it. In complex and variable working conditions, this can easily lead to a sharp drop in battery capacity and poor user experience. Therefore, there is an urgent need for a solution that can accurately estimate the battery SOC. SUMMARY

[0003] The present application provides a battery SOC estimation method and device based on Kalman filtering, an electronic device and a storage medium, aiming to improve the accuracy of battery SOC estimation.

[0004] In a first aspect, a battery SOC estimation method based on Kalman filtering is provided, comprising: determining a first non-platform region, reconstructing the first SOC-OCV curve in the first non-platform region to obtain a second SOC-OCV curve; wherein the first non-platform region is located between the first platform region and the second platform region; constructing a second-order RC equivalent circuit model according to the second SOC-OCV curve; real-time obtaining the dynamic parameters of the target battery from the parameter online identification model for the second-order RC equivalent circuit model to obtain target RC parameters; introducing the target RC parameters into the Kalman filtering model to estimate the SOC of the target battery.

[0005] In some embodiments of the present application, the parameter online identification model is constructed, comprising: constructing an initial equation of the parameter online identification model, the initial equation being: ; wherein k represents the sampling time, represents the output vector at time k, represents the regression vector at time k, represents the to-be-identified parameter vector at time k, represents the identification error at time k; calculating the genetic factor, and the genetic factor is calculated according to the following formula: ; ; ; wherein, Let k represent the measured terminal voltage of the target battery at time k. Let P represent the sensitivity factor at time k, and let P represent the preset sensitivity coefficient. Indicates the length of the sliding window. This represents the variable forgetting factor at time k. This represents the minimum value of the forgetting factor. This represents the maximum value of the forgetting factor; recursive least squares is performed according to the following formula: ; ; ;in, Represents the gain vector. This represents the parameter covariance matrix updated at time k; it represents the dynamic parameters of the target battery obtained in real time from the online parameter identification model of the second-order RC equivalent circuit model, including: in the parameter vector to be identified. After convergence, the objective parameter matrix is ​​obtained. The dynamic parameters of the target battery are calculated based on the target parameter matrix, using the following formula: ; ; ; ; ;in, This represents the ohmic internal resistance of the target battery in a second-order RC equivalent circuit. Represents the first time constant. Indicates the second time constant. Indicates the sampling period. , , , and This represents the model coefficients of the online parameter identification model. Indicates the first polarization resistor. This represents the second polarization resistor.

[0006] In some embodiments of this application, the target RC parameters include: a first RC parameter and a second RC parameter; introducing the target RC parameters into the Kalman filter model to estimate the SOC of the target battery includes: introducing the first RC parameter and the second RC parameter into a first matrix and a second matrix to estimate the SOC of the target battery; wherein, the first matrix is: ;in, Indicates the first RC parameter. This represents the second RC parameter; the second matrix is: ;in, This indicates the rated capacity of the target battery.

[0007] In some embodiments of this application, the second SOC-OCV curve needs to meet the following conditions:

[0008] in, This represents the maximum voltage value in the first non-plateau region of the second SOC-OCV curve. This represents the minimum voltage value in the first non-plateau region of the second SOC-OCV curve. This represents the maximum voltage value in the first non-plateau region of the first SOC-OCV curve. This represents the minimum voltage value in the first non-plateau region of the first SOC-OCV curve. This represents the preset slope coefficient of the second SOC-OCV curve. This indicates the SOC variation range corresponding to the first non-platform region.

[0009] In some embodiments of this application, the second SOC-OCV curve of the target battery includes: a first non-plateau region, a second non-plateau region, a first plateau region, and a second plateau region; wherein, the first non-plateau region is the discharge range of 55%~60% SOC in the second SOC-OCV curve, the second non-plateau region is the discharge range of 0%~30% SOC in the second SOC-OCV curve, the first plateau region is the discharge range of 61%~95% SOC in the second SOC-OCV curve, and the second plateau region is the discharge range of 30%~54% SOC in the second SOC-OCV curve.

[0010] In some embodiments of this application, SOC estimation of the target battery includes: when the region to be estimated is a first plateau region and a second plateau region, SOC estimation of the first plateau region and the second plateau region is performed using a Kalman filter model, and the SOC estimation values ​​of the first plateau region and the second plateau region are corrected for the first time; wherein, the method further includes: obtaining the target polarization voltage, which is obtained according to the following formula: ; ; ;in, Indicates the first polarization voltage. Indicates the second polarization voltage. Indicates the target polarization voltage. This represents the real-time terminal voltage measurement value of the target battery. Indicates the first polarization capacitor. This represents the second polarization capacitor. The value represents the internal resistance in ohms; the SOC estimates for the first and second plateau regions are corrected based on the target polarization voltage; wherein, the second correction of the SOC estimates for the first and second plateau regions based on the target polarization voltage includes: when the target polarization voltage is in the first plateau region, correcting the SOC estimate for the first plateau region to the SOC estimate for the second plateau region; when the target polarization voltage is in the second plateau region, correcting the SOC estimate for the second plateau region to the SOC estimate for the first plateau region.

[0011] In some embodiments of this application, estimating the target battery includes: estimating the SOC of the target battery, including: when the region to be estimated is a first non-platform region and a second non-platform region, estimating the first non-platform region and the second non-platform region using a Kalman filter model to obtain an estimated SOC value; and correcting the estimated SOC values ​​of the first non-platform region and the second non-platform region based on the estimated SOC value.

[0012] Secondly, a battery SOC estimation device based on Kalman filtering is also provided, comprising: The reconstruction module is used to determine the first non-platform region, and reconstruct the first SOC-OCV curve in the first non-platform region to obtain the second SOC-OCV curve; wherein, the first non-platform region is located between the first platform region and the second platform region; The building module is used to construct a second-order RC equivalent circuit model based on the second SOC-OCV curve; The acquisition module is used to obtain the dynamic parameters of the target battery in real time from the parameter online identification model for the second-order RC equivalent circuit model, so as to obtain the target RC parameters; The estimation module is used to introduce target RC parameters into the Kalman filter model to estimate the SOC of the target battery.

[0013] Thirdly, an electronic device is also provided, the electronic device including a processor and a memory, the memory storing a computer program, wherein when the processor calls the computer program in the memory, it executes the battery SOC estimation method based on Kalman filtering as provided in the first aspect.

[0014] Fourthly, a computer-readable storage medium is also provided, on which a computer program is stored, said computer program being loaded by a processor to execute the battery SOC estimation method based on Kalman filtering as provided in the first aspect.

[0015] According to the technical solution of this disclosure, by determining a first non-platform region, the first SOC-OCV curve is reconstructed in the first non-platform region to obtain a second SOC-OCV curve; a second-order RC equivalent circuit model is constructed based on the second SOC-OCV curve; the dynamic parameters of the target battery are obtained in real time from the parameter online identification model for the second-order RC equivalent circuit model to obtain the target RC parameters; the target RC parameters are introduced into the Kalman filter model to estimate the SOC of the target battery, which can improve the accuracy of SOC estimation of the target battery, while reducing the dependence on the complex and time-consuming offline experimental calibration in the early stage, and enhancing the stability and applicability of SOC estimation under the entire life cycle and dynamic operating conditions of the target battery. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the battery SOC estimation method based on Kalman filtering provided in an exemplary embodiment of this disclosure. Figure 2 This is another flowchart illustrating the battery SOC estimation method based on Kalman filtering provided in an exemplary embodiment of this disclosure; Figure 3 This is a schematic flowchart illustrating the modification of the first platform area and the second platform area provided by an exemplary embodiment of this disclosure; Figure 4 This is a schematic diagram of one embodiment of the battery SOC estimation device based on Kalman filtering provided in some embodiments of this application; Figure 5 This is a schematic diagram of an embodiment of the electronic device provided in this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] In the description of the embodiments of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0020] The use of "applies to" or "configured to" in this application implies open and inclusive language, which does not exclude the applicability to or configuration to devices performing additional tasks or steps. Additionally, the use of "based on" implies openness and inclusivity, because processes, steps, calculations, or other actions "based on" one or more of the stated conditions or values ​​may in practice be based on additional conditions or values ​​beyond those stated.

[0021] In this application, the term "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described as "exemplary" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of this application with unnecessary detail. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0022] Among related technologies, lithium iron phosphate (LFP) batteries are widely used in electric vehicles due to their high safety and long cycle life. However, the state-of-charge (SOC)-open-circuit voltage (OCV) curve of LFP batteries exhibits a flat plateau region within the middle SOC range. Within this plateau region, the rate of change of OCV with SOC is extremely small, making it impossible to accurately estimate SOC in the middle range and thus hindering effective correction. This can easily lead to a significant drop in battery capacity under complex and variable operating conditions, resulting in a poor user experience.

[0023] In existing technologies, traditional filtering algorithms are commonly used, but these algorithms heavily rely on accurate battery models. First, constructing an accurate battery model requires obtaining precise battery model parameters, followed by complex and time-consuming offline experiments for calibration. However, as the battery ages, these parameters change, leading to lower accuracy in later battery degradation calculations. Furthermore, for lithium iron phosphate batteries, the SOC value is typically corrected when the battery is fully charged or discharged to 0-30% SOC. If the battery is used in a flat, intermediate range for an extended period, this correction strategy may not be triggered, resulting in lower accuracy in battery capacity estimation.

[0024] To at least partially address one or more of the aforementioned problems and other potential issues, this disclosure proposes a battery SOC estimation scheme based on Kalman filtering. The scheme includes: determining a first non-plateau region; reconstructing a first SOC-OCV curve within the first non-plateau region to obtain a second SOC-OCV curve; wherein the first non-plateau region is located between the first and second plateau regions; constructing a second-order resistor-capacitor (RC) equivalent circuit model based on the second SOC-OCV curve; obtaining target RC parameters by using the target battery dynamic parameters obtained in real-time from the online parameter identification model based on the second-order RC equivalent circuit model; and constructing a Kalman filter model, incorporating the target RC parameters to estimate the target battery. This improves the accuracy of SOC estimation for the target battery while reducing reliance on complex and time-consuming offline experimental calibration, enhancing the stability and applicability of SOC estimation throughout the target battery's entire lifespan and under dynamic operating conditions.

[0025] Figure 1 This is a flowchart illustrating the battery SOC estimation method based on Kalman filtering provided in an exemplary embodiment of this disclosure, as shown below. Figure 1 As shown, the method includes at least the following steps: S101: Determine the first non-platform region, reconstruct the first SOC-OCV curve in the first non-platform region to obtain the second SOC-OCV curve; wherein, the first non-platform region is located between the first platform region and the second platform region; S102: Construct a second-order RC equivalent circuit model based on the second SOC-OCV curve; S103: The dynamic parameters of the target battery are obtained in real time from the online parameter identification model for the second-order RC equivalent circuit model in order to obtain the target RC parameters; S104: Introduce the target RC parameter into the Kalman filter model to estimate the SOC of the target battery.

[0026] S101: Determine the first non-platform region, and reconstruct the first SOC-OCV curve in the first non-platform region to obtain the second SOC-OCV curve; wherein, the first non-platform region is located between the first platform region and the second platform region.

[0027] In some embodiments, the first SOC-OCV curve is an experimentally obtained SOC-OCV curve. This first SOC-OCV curve can be plotted by fully charging the target battery under standard test conditions such as specific temperature and rate, followed by constant current discharge, and continuously collecting OCV data at different SOC states. For example, at an ambient temperature of 25°C, the fully charged target battery can be discharged to the cutoff voltage at a rate of 0.5, and the OCV value can be recorded every 5% SOC to obtain the first SOC-OCV curve.

[0028] In some embodiments, the second SOC-OCV curve is obtained by reconstructing the first SOC-OCV curve in the first non-plateau region. The reconstruction of the first SOC-OCV curve can employ interpolation, curve fitting, or a correction model based on electrochemical mechanisms to finely adjust and optimize the SOC-OCV data points in the first non-plateau region, thereby enhancing the sensitivity of OCV to SOC changes within the first non-plateau region. For example, by increasing the sampling point density in the first non-plateau region and using the least squares method to perform high-order polynomial fitting on the collected discrete data, the second SOC-OCV curve can more accurately reflect the nonlinear relationship between SOC and OCV in the first non-plateau region, providing a more reliable curve basis for subsequent SOC estimation and correction based on the second SOC-OCV curve.

[0029] In some embodiments, the second SOC-OCV curve of the target battery includes: a first non-plateau region, a second non-plateau region, a first plateau region, and a second plateau region. The first non-plateau region is the discharge range of 55% to 60% SOC in the second SOC-OCV curve, the second non-plateau region is the discharge range of 0% to 30% SOC in the second SOC-OCV curve, the first plateau region is the discharge range of 61% to 95% SOC in the second SOC-OCV curve, and the second plateau region is the discharge range of 30% to 54% SOC in the second SOC-OCV curve.

[0030] It should be noted that the non-platform region is the steep section in the second SOC-OCV curve where the OCV changes significantly with SOC; the plateau region is the gentle section where the OCV changes very little with SOC. The above division of the SOC ranges for the first non-platform region, the second non-platform region, the first plateau region, and the second plateau region is only an illustrative example, and can be adapted according to the type, characteristics, and actual application scenarios of the target battery.

[0031] In some embodiments, the first non-platform area is located between the first platform area and the second platform area.

[0032] S102: Construct a second-order RC equivalent circuit model based on the second SOC-OCV curve.

[0033] In some embodiments, the second-order RC equivalent circuit model is a circuit model composed of an ohmic resistor, two parallel RC networks, and an ideal voltage source connected in series. The voltage value of the ideal voltage source is determined by a second SOC-OCV curve; that is, the corresponding OCV value is retrieved from the second SOC-OCV curve based on a preset SOC value as the output of the ideal voltage source. The ohmic resistor is used to simulate the ohmic internal resistance of the target battery, reflecting the sum of the resistance of the electrolyte, electrode materials, and current collector within the target battery. The two parallel RC networks are used to simulate the electrochemical polarization process and concentration polarization process of the target battery, respectively. Each RC network consists of a polarization resistor and a polarization capacitor connected in parallel. The polarization resistor characterizes the resistance during the polarization process, and the polarization capacitor characterizes the storage capacity of polarization charge. By connecting the above components according to a specific topology, the second-order RC equivalent circuit model can simulate the dynamic voltage response characteristics of the target battery during charging and discharging. This provides an accurate model basis for subsequent SOC estimation of the target battery.

[0034] S103: The dynamic parameters of the target battery are obtained in real time from the online parameter identification model for the second-order RC equivalent circuit model to obtain the target RC parameters.

[0035] In some embodiments, the online parameter identification model can be constructed using the Recursive Least Squares (RLS) algorithm. Specifically, by real-time acquisition of the target battery's charging and discharging current and terminal voltage data, and based on the discretized state equations and output equations of the second-order RC equivalent circuit model, the estimated values ​​of the model parameters are continuously updated iteratively to achieve online tracking of dynamic parameters. The online parameter identification model acquires the real-time current and measured terminal voltage of the target battery once according to a preset sampling period (e.g., every 100 milliseconds), and combines the parameter estimates and covariance matrix from the previous moment, calculates the dynamic parameters of the target battery, such as the ohmic internal resistance R0, the first polarization resistance R1, the first polarization capacitance C1, the first polarization resistance R2, and the second polarization capacitance C2, using the recursive formula of the RLS algorithm. The aforementioned dynamic parameters are affected by various factors such as the temperature, aging degree, and charge / discharge rate of the target battery. The parameter online identification model can capture the parameter changes caused by these factors in real time, thereby obtaining the target RC parameters (i.e., R1, C1, R2, C2) and ohmic resistance R0 that can accurately reflect the state of the target battery.

[0036] In some embodiments, the target battery is a lithium iron phosphate battery. Due to the material characteristics of lithium iron phosphate batteries, the SOC-OCV curve of lithium iron phosphate batteries exhibits a steep slope region, i.e., the first non-plateau region, in the discharge range of 55% to 60% SOC.

[0037] In some embodiments, the dynamic parameters of the target battery may include: the ohmic internal resistance R0, the first polarization resistance R1, the first polarization capacitance C1, the second polarization resistance R2, and the second polarization capacitance C2. The ohmic internal resistance R0 is the sum of all purely resistive components within the target battery, directly affecting the voltage drop during high-current charging and discharging. The first polarization resistance R1 and the first polarization capacitance C1 simulate the electrochemical polarization effect caused by the limitation of the electrochemical reaction rate during charging and discharging; their values ​​are related to factors such as the activity of the electrode material and the ion migration rate of the electrolyte. The second polarization resistance R2 and the second polarization capacitance C2 mainly correspond to the concentration polarization process, i.e., the polarization phenomenon caused by the difference in ion concentration between the electrode surface and the electrolyte interior. These parameters change significantly with changes in charge / discharge rate and the increasing aging of the battery. The online parameter identification model continuously collects and dynamically adjusts the real-time operating data of the target battery to ensure that these parameters accurately reflect the true state of the target battery under the current operating conditions, providing reliable parameter input for the subsequent SOC estimation of the Kalman filter model.

[0038] In some embodiments, the target RC parameters refer to key parameters acquired in real time through an online parameter identification model, used to characterize the dynamic characteristics of the two RC networks in the second-order RC equivalent circuit model. These target RC parameters include: a first RC parameter and a second RC parameter; the first RC parameter includes: a first polarization resistor R1 and a first polarization capacitor C1, used to simulate the electrochemical polarization process of the target battery; wherein R1 reflects the resistance during the electrochemical polarization process, and C1 reflects the storage capacity of polarization charge during this process. The second RC parameter includes: a second polarization resistor R2 and a second polarization capacitor C2, used to simulate the concentration polarization process of the target battery; wherein R2 characterizes the resistance to concentration polarization, and C2 characterizes the storage capacity of concentration polarization charge. By acquiring and updating these two sets of target RC parameters in real time, the second-order RC equivalent circuit model can accurately capture the polarization dynamic response of the target battery at different charging and discharging stages, different aging degrees, and different ambient temperatures, thereby providing more realistic model support for the subsequent SOC estimation of the Kalman filter model, effectively avoiding the problem of decreased estimation accuracy caused by parameter mismatch when the battery state changes in traditional offline parameter calibration methods.

[0039] S104: Introduce the target RC parameter into the Kalman filter model to estimate the SOC of the target battery.

[0040] In some embodiments, the Kalman filter model can employ the Extended Kalman Filter (EKF) algorithm, suitable for nonlinear systems. Specifically, the State of Charge (SOC) of the target battery is used as the system state variable, and the state-space model of the EKF is constructed by combining the state equations and output equations of the second-order RC equivalent circuit model. The state equations describe the dynamic changes of the SOC and polarization voltage in the second-order RC equivalent circuit model. For example, the SOC update can be based on the ampere-hour integral method, considering factors such as coulomb efficiency to correct the charging and discharging currents before accumulation; the polarization voltage update can be calculated based on the target RC parameters (R1, C1, R2, C2) and the real-time current. The output equations describe the relationship between the battery terminal voltage calculated based on the state variables and model parameters and the measured terminal voltage. In the prediction step of the EKF (Earning Kalman Filter) model, the previous SOC estimate and covariance matrix are used in conjunction with the state equation to predict the prior SOC estimate and prior covariance matrix for the current time step. In the update step, the terminal voltage estimate calculated by the output equation after introducing the target RC parameters is compared with the measured terminal voltage to obtain the residual. Then, the Kalman gain is used to correct the prior estimate to obtain the posterior estimate of the SOC for the current time step, and the covariance matrix is ​​updated. By continuously iterating through the prediction and update steps, the Kalman filter model can integrate the observed information such as battery current and voltage with the dynamic characteristics of the model in real time, thereby achieving high-precision estimation of the target battery SOC and effectively suppressing the impact of measurement noise and model uncertainty on the estimation results.

[0041] In some embodiments, the second-order RC equivalent circuit model is used to simulate the dynamic characteristics of the target battery. This model accurately characterizes the electrochemical polarization and concentration polarization processes of the target battery. According to Kirchhoff's laws, the target battery terminal voltage Ut is: ; Where I represents the operating current of the target battery. This represents the terminal voltage of the first RC circuit. This represents the terminal voltage of the second RC circuit.

[0042] In some embodiments, constructing a Kalman filter model includes: constructing the state equation: ; in, This represents the state vector at time k. This represents the state vector at time k-1. This represents the input variable at time k. Indicates the noise at time k; Construct the observation equation: ; in, Denotes the observation vector at time k. This represents the open-circuit voltage function at time k. This represents the operating current at time k. This represents the first polarization voltage at time k. This represents the second polarization voltage at time k. This represents the observation noise at time k; A Kalman filter model is constructed based on the state equation and the observation equation.

[0043] In some embodiments, the target RC parameters include: a first RC parameter and a second RC parameter; introducing the target RC parameters into the Kalman filter model to estimate the SOC of the target battery includes: introducing the first RC parameter and the second RC parameter into a first matrix and a second matrix to estimate the SOC of the target battery; wherein, the first matrix is: ; in, Indicates the first RC parameter. Indicates the second RC parameter; The second matrix is: ; in, This indicates the rated capacity of the target battery.

[0044] In some embodiments, discretization is required to implement discrete-time recursive computation on a microprocessor. The model's state vector x, input u, and observation vector z are defined as follows: ; Where k represents the sampling time, This represents the state of charge at time k. This represents the polarization voltage of the first-order RC network at time k. This represents the polarization voltage of the second-order RC network at time k.

[0045] In some embodiments, the derivation formula of the Kalman filter model is as follows: ； ； ； ； ; in, This represents the state vector at time k. This represents the input variable at time k. This represents the process noise at time k. express The state covariance matrix at time t, This represents the first matrix (i.e., the state transition matrix). This represents the transpose of the state transition matrix. The covariance matrix representing the process noise. Indicates the Kalman gain at time k. Represents the observation matrix. This represents the transpose of the observation matrix. This represents the observation noise covariance at time k. This represents the state vector after correction at time k. Represents the observation value at time k. This represents the open-circuit voltage at time k. Represents the identity matrix.

[0046] Figure 2 This is another flowchart illustrating the battery SOC estimation method based on Kalman filtering provided in this exemplary embodiment of the present disclosure, as shown below. Figure 2 As shown, the process may include: S201: Beginning; S202: Reconstruct the first SOC-OCV curve to obtain the second SOC-OCV curve; S203: Construct a second-order RC equivalent circuit model and import the second SOC-OCV curve into the second-order RC equivalent circuit model; S204: The dynamic parameters of the target battery are obtained in real time from the online parameter identification model for the second-order RC equivalent circuit model in order to obtain the target RC parameters; S205: Construct a Kalman filter model; S206: Is this a platform area? If yes, proceed to S207; otherwise, proceed to S208. S207: Determine whether the region to be estimated is the first plateau region or the second plateau region using the Kalman filter model; S208: The first and second non-plateau regions are estimated using a Kalman filter model to obtain the SOC estimate. S209: Correct the SOC estimates for the first and second non-platform regions based on the SOC estimates.

[0047] The solution of this embodiment determines a first non-platform region, reconstructs the first SOC-OCV curve in the first non-platform region to obtain a second SOC-OCV curve, constructs a second-order RC equivalent circuit model based on the second SOC-OCV curve, obtains the dynamic parameters of the target battery in real time from the parameter online identification model for the second-order RC equivalent circuit model to obtain the target RC parameters, and introduces the target RC parameters into the Kalman filter model to estimate the SOC of the target battery. This can improve the accuracy of SOC estimation of the target battery, reduce the dependence on the complex and time-consuming offline experimental calibration in the early stage, and enhance the stability and applicability of SOC estimation under the entire life cycle and dynamic operating conditions of the target battery.

[0048] In this embodiment of the disclosure, constructing an online parameter identification model includes: constructing an initial equation for the online parameter identification model, wherein the initial equation is: ; Where k represents the sampling time, This represents the output vector at time k. This represents the regression vector at time k. Let k represent the vector of parameters to be identified at time k. This represents the identification error at time k; Calculate the genetic factors using the following formula: ; ; ; in, Let k represent the measured terminal voltage of the target battery at time k. Let P represent the sensitivity factor at time k, and let P represent the preset sensitivity coefficient. Indicates the length of the sliding window. This represents the variable forgetting factor at time k. This represents the minimum value of the forgetting factor. This represents the maximum value of the forgetting factor; Recursive least squares is performed according to the following formula: ; ; ; in, Represents the gain vector. This represents the parameter covariance matrix updated at time k; The dynamic parameters of the target battery are obtained in real time from the online parameter identification model for the second-order RC equivalent circuit model, including: In the parameter vector to be identified After convergence, the objective parameter matrix is ​​obtained. ; The dynamic parameters of the target battery are calculated based on the target parameter matrix, using the following formula: ; ; ; ; ; in, This represents the ohmic internal resistance of the target battery in a second-order RC equivalent circuit. Represents the first time constant. Indicates the second time constant. Indicates the sampling period. , , , and This represents the model coefficients of the online parameter identification model. Indicates the first polarization resistor. This represents the second polarization resistor.

[0049] Thus, in the online parameter identification model, a variable forgetting factor is introduced. It can dynamically adjust the weight of historical data in parameter estimation, when the target battery is under dynamic operating conditions or the parameters change significantly. It will approach To increase the weight of new data and accelerate parameter convergence to adapt to changes; while when the battery state is relatively stable, near This approach fully utilizes historical data to improve the smoothness and accuracy of parameter estimation, effectively balancing the conflict between identification speed and estimation accuracy. Furthermore, by setting a sliding window length M, it ensures that parameter identification is based solely on recent, valid battery operating data, avoiding interference from outdated data on current parameter estimation. This further enhances the real-time performance and reliability of online identification of target RC parameters, providing continuously accurate dynamic parameter input for the Kalman filter model.

[0050] In this embodiment of the disclosure, the target RC parameter includes: a first RC parameter and a second RC parameter; the target RC parameter is introduced into the Kalman filter model to estimate the SOC of the target battery, including: The first RC parameter and the second RC parameter are incorporated into the first matrix and the second matrix to estimate the SOC of the target battery; wherein, the first matrix is: ; in, Indicates the first RC parameter. Indicates the second RC parameter; The second matrix is: ; in, This indicates the rated capacity of the target battery.

[0051] In some embodiments, to improve the accuracy of the Kalman filter model in SOC estimation, the initial SOC value of the target battery is optimized. Specifically, this includes: based on the target battery's factory parameters and historical charge / discharge data, using an ampere-hour integration method combined with open-circuit voltage calibration to initially determine the range of initial SOC values; then, by introducing a particle swarm optimization algorithm, iteratively optimizing candidate values ​​within this initial range to minimize the mean square error between the initial SOC value output by the model and the actual battery state, thereby obtaining a more accurate initial SOC estimate, which serves as the starting point for the iterative calculation of the Kalman filter model.

[0052] In some embodiments, to further enhance the robustness of the Kalman filter model under complex operating conditions, the process noise covariance matrix Q and the observation noise covariance matrix R can be adaptively adjusted. For example, when the target battery is detected to be under dynamic conditions such as high-current charging and discharging, drastic temperature changes, or the SOC value being in the transition phase between the plateau and non-plateau regions, the weights of the elements related to the SOC state in the Q matrix can be increased to enhance the model's ability to track state changes. Simultaneously, the R matrix is ​​adjusted in real time according to the fluctuation of the observed voltage. If the observed voltage fluctuates significantly due to disturbances, the value of R is increased to reduce the impact of the observed value on state correction; conversely, the value of R is decreased to increase the weight of the observed value. Specifically, the values ​​of Q and R can be dynamically updated by calculating the variance of the observed residuals over the most recent N sampling periods or by using a fuzzy logic control strategy, thereby enabling the Kalman filter model to adapt to different battery operating environments and further reduce estimation errors.

[0053] Thus, by introducing the first RC parameter and the second RC parameter into the first matrix and the second matrix to estimate the SOC of the target battery, the prediction and correction process of SOC can be continuously optimized through real-time updated target RC parameters, thereby effectively improving the overall accuracy and stability of SOC estimation and providing reliable state-of-charge data support for the efficient and safe operation of the battery management system.

[0054] In this embodiment of the disclosure, the second SOC-OCV curve needs to meet the following conditions:

[0055] in, This represents the maximum voltage value in the first non-plateau region of the second SOC-OCV curve. This represents the minimum voltage value in the first non-plateau region of the second SOC-OCV curve. This represents the maximum voltage value in the first non-plateau region of the first SOC-OCV curve. This represents the minimum voltage value in the first non-plateau region of the first SOC-OCV curve. This represents the preset slope coefficient of the second SOC-OCV curve. This indicates the SOC variation range corresponding to the first non-platform region. This represents the maximum threshold value for the preset error between the first SOC-OCV curve and the second SOC-OCV curve.

[0056] In some embodiments, a brief non-plateau region exists at 55%~60% SOC of the target battery. To improve the accuracy of identifying the first and second plateau regions, the first SOC-OCV curve is reconstructed in the first non-plateau region (55%~60% SOC) to obtain the second SOC-OCV curve. This increases the slope of the voltage curve in the first non-plateau region without causing significant errors to the first SOC-OCV curve.

[0057] In some embodiments, the voltage curve of the second SOC-OCV curve needs to satisfy a value greater than 1. Without causing significant errors to the first SOC-OCV curve, the slope of the voltage curve in the first non-plateau region is increased, for example, by presetting a slope coefficient. By increasing the voltage change rate in this region to 1.2-1.5 times the original slope, small changes in SOC in the first non-plateau region can elicit a more significant OCV response. This enhances the sensitivity of the observed values ​​to the SOC state in the Kalman filter model, helping to reduce the uncertainty in SOC estimation in this non-plateau region. Simultaneously, the root mean square error (RMSE) of the curves before and after reconstruction is constrained to not exceed a preset threshold. (For example, 0.5mV) ensures the consistency of the second SOC-OCV curve with the original first SOC-OCV curve in the overall trend, avoids the introduction of additional systematic errors due to local slope adjustment, and provides a more accurate and robust mapping relationship for subsequent SOC estimation based on OCV.

[0058] In this embodiment of the disclosure, the second SOC-OCV curve of the target battery includes: a first non-plateau region, a second non-plateau region, a first plateau region, and a second plateau region; wherein, the first non-plateau region is the discharge range of 55%~60% SOC in the second SOC-OCV curve, the second non-plateau region is the discharge range of 0%~30% SOC in the second SOC-OCV curve, the first plateau region is the discharge range of 61%~95% SOC in the second SOC-OCV curve, and the second plateau region is the discharge range of 30%~54% SOC in the second SOC-OCV curve.

[0059] In some embodiments, the observation equations of the Kalman filter model can be optimized by partitioning them according to the characteristics of different plateau and non-plateau regions. For example, in the first plateau region (61%~95% SOC) and the second plateau region (30%~54% SOC), since the OCV changes relatively smoothly with the SOC, the observed voltage is less sensitive to the SOC. In this case, the weights related to the SOC state in the process noise covariance matrix Q can be appropriately increased. At the same time, combined with the stability of the open-circuit voltage method in the plateau region, the SOC estimate can be calibrated periodically (e.g., every N sampling cycles or when the battery resting time reaches a preset threshold). In the first non-plateau region (55%~60% SOC) and the second non-plateau region (0%~30% SOC), given the high sensitivity of OCV to SOC changes, the state correction can be mainly based on the real-time observed voltage. By reducing the value of the observation noise covariance matrix R, the contribution of the observed information to the SOC update can be increased, thereby making full use of the significant change characteristics of OCV in the non-plateau region to improve the estimation accuracy. In practice, the boundary SOC thresholds for each interval can be preset, and the interval in which the current SOC is located can be determined in real time during the Kalman filter iteration process. The corresponding Q and R matrix adjustment strategies can be automatically switched to achieve adaptive optimization of the model at different SOC stages.

[0060] Thus, by defining the second SOC-OCV curve in partitions and utilizing the differences in the sensitivity of OCV to SOC changes in different regions, a comprehensive improvement in estimation accuracy is achieved across the entire SOC variation range of the target battery, significantly enhancing the accuracy and robustness of SOC estimation.

[0061] In this embodiment of the disclosure, the SOC estimation of the target battery includes: when the region to be estimated is a first plateau region and a second plateau region, the SOC of the first plateau region and the second plateau region is estimated by using a Kalman filter model, and the SOC estimation values ​​of the first plateau region and the second plateau region are corrected for the first plateau region and the second plateau region. The battery SOC estimation method based on Kalman filtering further includes: obtaining the target polarization voltage, which is obtained according to the following formula: ; ; ; in, Indicates the first polarization voltage. Indicates the second polarization voltage. Indicates the target polarization voltage. This represents the real-time terminal voltage measurement value of the target battery. Indicates the first polarization capacitor. This represents the second polarization capacitor. Indicates the internal resistance of the ohm; The SOC estimates for the first and second plateau regions are corrected based on the target polarization voltage. The second correction is made to the SOC estimates of the first and second plateau regions based on the target polarization voltage, including: When the target polarization voltage is in the first plateau region, the SOC estimate of the first plateau region is corrected to the SOC estimate of the second plateau region. When the target polarization voltage is in the second plateau region, the SOC estimate of the second plateau region is corrected to the SOC estimate of the first plateau region.

[0062] Figure 3 This is a schematic flowchart illustrating the modification of the first platform area and the second platform area provided by an exemplary embodiment of this disclosure, such as... Figure 3 As shown, the process may include: S301: Start; S302: Estimate the SOC of the first and second plateau regions using a Kalman filter model, and make a first correction to the estimated SOC values ​​of the first and second plateau regions; S303: Determine whether the region to be estimated is the first plateau region or the second plateau region using the Kalman filter model; if it is the first plateau region, proceed to S304; if it is the second plateau region, proceed to S305. S304: Correct the SOC estimate of the first platform area to the SOC estimate of the second platform area; S305: Correct the SOC estimate of the second platform area to the SOC estimate of the first platform area.

[0063] In some embodiments, the first correction is a preliminary correction to the SOC estimates of the first and second plateau regions. For example, the SOC values ​​of the plateau regions can be linearly compensated or proportionally corrected based on the cumulative error trend or historical estimation deviation of the ampere-hour integration method to eliminate systematic errors caused by long-term drift.

[0064] In some embodiments, the second correction is a deep correction combined with the dynamic characteristics of the target polarization voltage. Its core logic lies in using the correlation of polarization voltage in different plateau regions to achieve cross-calibration. Specifically, when the target battery is working in the first plateau region (61%~95% SOC), the target polarization voltage exhibits a similar trend of change as the typical polarization characteristics of the second plateau region (30%~54% SOC) (such as polarization voltage amplitude, decay rate, etc.). Then, the current SOC estimate of the first plateau region can be finely adjusted towards the SOC estimate of the second plateau region, and the relatively stable estimation result of the second plateau region can be used to back-calibrate the possible deviations in the first plateau region. Conversely, when the target polarization voltage is in the second plateau region, if a significant correlation is detected between its dynamic characteristics and those of the first plateau region, the SOC estimate of the second plateau region is corrected towards the estimate of the first plateau region. This bidirectional correction mechanism based on polarization voltage correlation can effectively utilize the estimation advantages of the two plateau regions, further reduce the estimation ambiguity caused by the smooth OCV characteristics of the plateau region, and enable the SOC estimation value to form a mutually supportive and dynamically balanced calibration relationship between the two plateau regions, thereby significantly improving the consistency and reliability of SOC estimation in the entire plateau region.

[0065] In this embodiment of the disclosure, the SOC estimation of the target battery includes: when the region to be estimated is a first non-platform region and a second non-platform region, the first non-platform region and the second non-platform region are estimated using a Kalman filter model to obtain an estimated SOC value; and the estimated SOC values ​​of the first non-platform region and the second non-platform region are corrected based on the estimated SOC values.

[0066] In some embodiments, in the first non-plateau region (55%~60% SOC) and the second non-plateau region (0%~30% SOC), since the OCV is highly sensitive to changes in SOC, the correction of the SOC estimate can focus more on the real-time feedback of the observed values. Specifically, based on the deviation between the SOC estimate and the theoretical OCV value obtained by reverse lookup from the second SOC-OCV curve, combined with the dynamic adjustment result of the observation noise covariance matrix R, the SOC estimate can be corrected by proportional-integral (PI) or adaptive gain correction. For example, when the deviation between the SOC estimate and the SOC value corresponding to the theoretical OCV is large, the correction gain can be increased to accelerate the convergence speed; when the deviation is small, the correction gain can be decreased to avoid oscillations in the estimate caused by over-correction. At the same time, considering the possible dynamic response delay of the battery in the non-plateau region, the estimation deviation at the previous moment can be introduced as a reference for the correction term, forming a correction mechanism with hysteresis compensation characteristics. This ensures that the SOC estimate can quickly and smoothly track the actual SOC changes, further improving the dynamic accuracy and anti-interference capability of the SOC estimate in the non-plateau region.

[0067] Figure 4 This is a schematic diagram of one embodiment of the battery SOC estimation device based on Kalman filtering provided in some embodiments of this application, such as... Figure 4 As shown, the battery SOC estimation device based on Kalman filtering includes: The reconstruction module 401 is used to determine the first non-platform region, and reconstruct the first SOC-OCV curve in the first non-platform region to obtain the second SOC-OCV curve; wherein, the first non-platform region is located between the first platform region and the second platform region; Module 402 is used to construct a second-order RC equivalent circuit model based on the second SOC-OCV curve; The acquisition module 403 is used to acquire the dynamic parameters of the target battery in real time from the parameter online identification model for the second-order RC equivalent circuit model, so as to obtain the target RC parameters; The estimation module 404 is used to introduce the target RC parameters into the Kalman filter model to estimate the SOC of the target battery.

[0068] The battery SOC estimation device based on Kalman filtering in this disclosure can improve the accuracy of SOC estimation for target batteries, while reducing the reliance on complex and time-consuming offline experimental calibration in the early stage, and enhancing the stability and applicability of SOC estimation throughout the entire life cycle and under dynamic operating conditions of the target battery.

[0069] The acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0070] Figure 5 This is a structural block diagram of a computer device according to an embodiment of the present disclosure. Figure 5 As shown, the computer device includes a memory 510 and a processor 520. The memory 510 stores a computer program that can run on the processor 520. The number of memories 510 and processors 520 can be one or more. The memory 510 can store one or more computer programs, which, when executed by the computer device, cause the computer device to perform the methods provided in the above-described method embodiments. The computer device may also include a communication interface 530 for communicating with external devices and exchanging data.

[0071] If the memory 510, processor 520, and communication interface 530 are implemented independently, they can be interconnected via a bus to communicate with each other. This bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0072] Optionally, in a specific implementation, if the memory 510, processor 520, and communication interface 530 are integrated on a single chip, then the memory 510, processor 520, and communication interface 530 can communicate with each other through an internal interface.

[0073] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. General-purpose processors can be microprocessors or any conventional processor. It is worth noting that the processor can be a processor supporting Advanced Reduced Instruction Set Machines (ARM) architecture.

[0074] Furthermore, the aforementioned memory may include read-only memory and random access memory, and may also include non-volatile random access memory. The memory may be volatile or non-volatile, or may include both. Non-volatile memory may include read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which serves as an external cache. Many forms of RAM are available by way of example, but not limitation. Examples include Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate Synchronous DRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct RAMBUS RAM (DR RAM).

[0075] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this disclosure is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line, DSL) or wireless (e.g., infrared, Bluetooth, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., Digital Versatile Discs (DVDs)), or semiconductor media (e.g., Solid State Disks (SSDs)). It is worth noting that the computer-readable storage media mentioned in this disclosure can be non-volatile storage media; in other words, they can be non-transient storage media.

[0076] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0077] In the description of the embodiments of this disclosure, the reference to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refers to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of this disclosure. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0078] The above are merely exemplary embodiments of this disclosure and are not intended to limit this disclosure. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the protection scope of this disclosure.

Claims

1. A battery SOC estimation method based on Kalman filtering, characterized in that, The method includes: A first non-platform region is determined, and the first SOC-OCV curve is reconstructed in the first non-platform region to obtain a second SOC-OCV curve; wherein, the first non-platform region is located between the first platform region and the second platform region; Construct a second-order RC equivalent circuit model based on the second SOC-OCV curve; The dynamic parameters of the target battery are obtained in real time from the online parameter identification model for the second-order RC equivalent circuit model to obtain the target RC parameters; A target RC parameter is introduced into the Kalman filter model to estimate the SOC of the target battery.

2. The method according to claim 1, characterized in that, Constructing the online parameter identification model includes: The initial equations for constructing the online parameter identification model are as follows: ； Where k represents the sampling time, This represents the output vector at time k. This represents the regression vector at time k. Let k represent the vector of parameters to be identified at time k. This represents the identification error at time k; Calculate the genetic factors using the following formula: ； ； ； in, Let k represent the measured terminal voltage of the target battery at time k. Let P represent the sensitivity factor at time k, and let P represent the preset sensitivity coefficient. Indicates the length of the sliding window. This represents the variable forgetting factor at time k. This represents the minimum value of the forgetting factor. This represents the maximum value of the forgetting factor; Recursive least squares is performed according to the following formula: ； ； ； in, Represents the gain vector. This represents the parameter covariance matrix updated at time k; The process involves obtaining the dynamic parameters of the target battery in real time from the online parameter identification model using the second-order RC equivalent circuit model, including: In the parameter vector to be identified After convergence, the objective parameter matrix is ​​obtained. ; The dynamic parameters of the target battery are calculated based on the target parameter matrix, and the dynamic parameters of the target battery are calculated according to the following formula: ； ； ； ； ； = * ； = * ； in, This represents the ohmic internal resistance of the target battery in the second-order RC equivalent circuit. Represents the first time constant. Indicates the second time constant. Indicates the sampling period. , , , and This represents the model coefficients of the online identification model for the parameters. Indicates the first polarization resistor. This represents the second polarization resistor.

3. The method according to claim 2, characterized in that, The target RC parameters include: a first RC parameter and a second RC parameter; the step of introducing the target RC parameters into the Kalman filter model to estimate the SOC of the target battery includes: The first RC parameter and the second RC parameter are incorporated into a first matrix and a second matrix to estimate the SOC of the target battery; wherein, the first matrix is: ； in, This represents the first RC parameter. This represents the second RC parameter; The second matrix is: ； in, This indicates the rated capacity of the target battery.

4. The method according to claim 3, characterized in that, The second SOC-OCV curve needs to meet the following conditions: in, This represents the maximum voltage value in the first non-plateau region of the second SOC-OCV curve. This represents the minimum voltage value in the first non-plateau region of the second SOC-OCV curve. This represents the maximum voltage value in the first non-plateau region of the first SOC-OCV curve. This represents the minimum voltage value in the first non-plateau region of the first SOC-OCV curve. This represents the preset slope coefficient of the second SOC-OCV curve. This represents the SOC variation range corresponding to the first non-platform region.

5. The method according to claim 4, characterized in that, The second SOC-OCV curve of the target battery includes: the first non-platform region, the second non-platform region, the first platform region, and the second platform region; Wherein, the first non-plateau region is the discharge range of 55%~60% SOC in the second SOC-OCV curve, the second non-plateau region is the discharge range of 0%~30% SOC in the second SOC-OCV curve, the first plateau region is the discharge range of 61%~95% SOC in the second SOC-OCV curve, and the second plateau region is the discharge range of 30%~54% SOC in the second SOC-OCV curve.

6. The method according to claim 5, characterized in that, The SOC estimation of the target battery includes: When the regions to be estimated are the first platform region and the second platform region, the SOC of the first platform region and the second platform region is estimated by the Kalman filter model, and the SOC estimated values ​​of the first platform region and the second platform region are corrected for the first time. The method further includes: Obtain the target polarization voltage according to the following formula: ； ； ； in, Indicates the first polarization voltage. Indicates the second polarization voltage. This represents the target polarization voltage. This represents the real-time terminal voltage measurement value of the target battery. Indicates the first polarization capacitor. This represents the second polarization capacitor. Indicates the internal resistance of the ohm; The SOC estimates for the first and second plateau regions are corrected based on the target polarization voltage. The step of performing a second correction on the SOC estimates of the first and second plateau regions based on the target polarization voltage includes: When the target polarization voltage is in the first plateau region, the SOC estimate in the first plateau region is corrected to the SOC estimate in the second plateau region; When the target polarization voltage is in the second plateau region, the SOC estimate in the second plateau region is corrected to the SOC estimate in the first plateau region.

7. The method according to claim 5, characterized in that, The SOC estimation of the target battery includes: When the regions to be estimated are the first non-platform region and the second non-platform region, the first non-platform region and the second non-platform region are estimated using the Kalman filter model to obtain the SOC estimate value; The SOC estimates for the first non-platform region and the second non-platform region are corrected based on the SOC estimates.

8. A battery SOC estimation device based on Kalman filtering, characterized in that, The device includes: The reconstruction module is used to determine a first non-platform region, and reconstruct the first SOC-OCV curve in the first non-platform region to obtain a second SOC-OCV curve; wherein, the first non-platform region is located between the first platform region and the second platform region; The construction module is used to construct a second-order RC equivalent circuit model based on the second SOC-OCV curve; The acquisition module is used to acquire the dynamic parameters of the target battery in real time from the parameter online identification model for the second-order RC equivalent circuit model, so as to obtain the target RC parameters; The estimation module is used to introduce target RC parameters into the Kalman filter model to estimate the SOC of the target battery.

9. An electronic device comprising a processor and a memory, wherein the memory stores a computer program, and the processor, when invoking the computer program in the memory, executes the battery SOC estimation method based on Kalman filtering as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which is loaded by a processor to execute the battery SOC estimation method based on Kalman filtering as described in any one of claims 1 to 7.

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