Sodium-ion battery SOC estimation method based on extended Kalman filtering

Abstract

The invention discloses a sodium ion battery SOC estimation method based on extended Kalman filtering. The method comprises the following steps: acquiring charging and discharging voltage, current and open-circuit voltage OCV data of a sodium battery through an HPPC test and a capacity test; and fitting a function relationship between the OCV and the SOC based on the standing balanced voltage data. And constructing a three-order RC equivalent circuit model, and identifying ohmic resistance, polarization resistance and polarization capacitance parameters in the model through curve fitting. Based on the model, a state vector containing SOC and polarization voltage is defined, and a nonlinear state equation and a terminal voltage observation equation are established. And predicting the state vector and the covariance matrix at the current moment according to the state vector and the covariance matrix at the previous moment by using an extended Kalman filtering algorithm, calculating Kalman gain by combining the actually measured terminal voltage, correcting the prediction result, and updating the SOC estimation value. And through loop iteration, real-time high-precision dynamic estimation of the SOC of the sodium-ion battery is realized. The method provides an efficient and accurate SOC estimation solution for a sodium ion battery management system.

Description

Technical Field

[0001] The present invention relates to the technical field of sodium ion battery management systems, and in particular to a sodium ion battery SOC estimation method based on extended Kalman filtering. Background Art

[0002] With the growth of global energy demand and the advancement of sustainable development, the research on new energy battery technology has become a hot topic. In recent years, sodium-ion batteries have gradually become an important alternative to lithium-ion batteries due to their advantages such as abundant resources, low cost and high safety, showing broad application prospects in energy storage power stations, electric transportation and smart grids. However, compared with lithium-ion batteries, sodium-ion batteries still have certain challenges in cycle life, rate performance and stability, especially the accurate estimation of battery state of charge (SOC), which is directly related to the safety and service life of the battery, and therefore has become one of the key issues in the research of battery management systems.

[0003] SOC represents the remaining available power of the battery and is an important parameter for measuring the operating status of the battery. Accurate SOC estimation can not only prevent the battery from overcharging and over-discharging, reduce safety risks, and increase the service life of the battery, but also optimize energy management and improve system operating efficiency. However, due to the complex electrochemical characteristics of sodium-ion batteries, the relationship between SOC and factors such as voltage, current, and temperature is nonlinear and hysteretic, making it difficult for traditional SOC estimation algorithms based on coulomb counting or open circuit voltage methods to meet high-precision requirements. Therefore, how to develop a high-precision and highly robust SOC estimation method has become an important challenge for sodium-ion battery management systems.

[0004] It should be noted that the information disclosed in the above background technology section is only used for understanding the background of the present application, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the invention

[0005] The main purpose of the present invention is to overcome the defects existing in the above-mentioned background technology and provide a sodium ion battery SOC estimation method based on extended Kalman filtering.

[0006] To achieve the above object, the present invention adopts the following technical solutions: A sodium ion battery SOC estimation method based on extended Kalman filtering comprises the following steps: S1. Obtain sodium battery HPPC test and capacity test data; S2. Based on the static equilibrium voltage data of step S1, fitting the function expression of OCV on SOC; S3. Establish a third-order RC equivalent circuit model and identify the values ​​of ohmic resistance, polarization resistance and polarization capacitance by curve fitting; S4. Based on the equivalent model of step S3, define a state vector including SOC and polarization voltage, and establish a nonlinear state equation and terminal voltage observation equation; S5. Using the extended Kalman filter algorithm, the state vector and covariance matrix of the previous moment are predicted based on the state vector and covariance matrix of the current moment; S6. Based on the predicted covariance matrix and the terminal voltage observation equation of step S5, the Kalman gain is calculated in combination with the measured terminal voltage; S7. The predicted state vector and covariance matrix are corrected by the Kalman gain of step S6 to update the SOC estimate; S8. Execute steps S5 to S7 in a loop to achieve real-time and high-precision dynamic estimation of the SOC of the sodium-ion battery.

[0007] In some optional embodiments, in step S1, the charge and discharge voltage, current and open circuit voltage OCV data of the sodium battery are obtained through HPPC testing and capacity testing; the HPPC test includes the following steps: charging the sodium battery to a fully charged state and then allowing it to stand until thermal equilibrium, then performing a cycle of constant current discharge, standing, constant current charging and standing at a preset SOC interval until a target SOC value is reached, and recording the voltage, current and open circuit voltage data after standing equilibrium during the charge and discharge process.

[0008] In some optional embodiments, in step S2, the OCV-SOC function expression is established in the following manner: selecting the static equilibrium voltage before the charge and discharge pulse as the OCV value, collecting data corresponding to different SOC points, and using a polynomial fitting algorithm to generate a continuous and differentiable OCV-SOC relationship curve.

[0009] In some optional embodiments, in step S3, the parameter identification of the equivalent circuit model is specifically as follows: based on the dynamic response data of charge and discharge, the ohmic resistance and the polarization resistance and polarization capacitance of each RC link are solved by a nonlinear curve fitting algorithm, wherein the polarization parameters are obtained by fitting the transient response characteristics of the battery terminal voltage by a multi-time constant exponential function.

[0010] In some optional embodiments, in step S4, the state vector in the state space equation includes SOC and multiple polarization voltage components, and the nonlinear state equation is obtained by discretizing the continuous time equation, and a process noise covariance matrix is ​​introduced during the discretization process to characterize the model uncertainty.

[0011] In some optional embodiments, in step S5, the state transfer matrix is ​​generated by performing Jacobian matrix linearization processing on the nonlinear state equation, and the process noise covariance matrix is ​​dynamically adjusted according to the current measurement error and the polarization voltage estimation uncertainty.

[0012] In some optional embodiments, in step S6, the measurement Jacobian matrix includes the derivative of the OCV-SOC function and the linear contribution of the polarization voltage to the terminal voltage, and gain optimization is achieved by fusing the prediction covariance matrix with the measurement noise covariance.

[0013] In some optional implementations, in step S8, the actual SOC reference value is calculated by the ampere-hour integration method, and dynamic error correction is performed with the extended Kalman filter estimation result to verify the real-time estimation accuracy.

[0014] A computer-readable storage medium stores a computer program, which implements the method when executed by a processor.

[0015] A computer program product comprises a computer program, which implements the method when executed by a processor.

[0016] The present invention has the following beneficial effects: The present invention proposes a sodium ion battery SOC estimation method based on extended Kalman filtering. By constructing a third-order RC equivalent circuit model and combining the extended Kalman filtering algorithm, the nonlinear and hysteresis problems caused by the complex electrochemical characteristics in the state of charge (SOC) estimation of sodium ion batteries are effectively solved. Compared with the traditional method, the method of the present invention can accurately describe the strong polarization characteristics of sodium ion batteries. By dynamically integrating the equivalent circuit model with real-time observation data, the state space equation and the terminal voltage observation equation are used to iteratively predict and correct the SOC and polarization voltage, which significantly improves the estimation accuracy and robustness. At the same time, the method introduces the process noise covariance matrix and the measurement noise optimization mechanism, which effectively suppresses the influence of current measurement error and environmental interference on the estimation results, and realizes the real-time high-precision dynamic tracking of SOC, which can not only prevent the battery from overcharging and over-discharging and extend the service life, but also provide reliable battery management system (BMS) technical support for the application of sodium ion batteries in energy storage power stations, electric transportation and other fields, and further promote the practical development of sodium ion battery technology.

[0017] Other beneficial effects of the embodiments of the present invention will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flow chart of an embodiment of the present invention.

[0019] Figure 2 4 is an equivalent circuit diagram of a three-layer RC network according to an embodiment of the present invention.

[0020] Figure 3 This is a discharge voltage curve of a 200Ah sodium battery according to an embodiment of the present invention.

[0021] Figure 4This is a discharge current curve of a 200Ah sodium battery according to an embodiment of the present invention.

[0022] Figure 5 The SOC estimation curve and the SOC actual value curve of the 200Ah sodium battery in the embodiment of the present invention are shown.

[0023] Figure 6 This is a 200Ah sodium battery SOC estimation error curve according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following is a detailed description of the embodiments of the present invention. It should be emphasized that the following description is only exemplary and is not intended to limit the scope and application of the present invention.

[0025] The extended Kalman filter (EKF) is an algorithm for predicting SOC based on the model method. It has significant advantages over the traditional Kalman filter (KF) in SOC estimation. The traditional Kalman filter is only applicable to linear systems, while the change process of SOC usually exhibits nonlinear characteristics. Especially in sodium-ion batteries, due to the influence of factors such as electrode material properties, electrolyte transport mechanism, and temperature effects, the relationship between SOC and parameters such as battery voltage and internal resistance is highly nonlinear. EKF performs Taylor series expansion and linearization on the nonlinear system, so that the filter can adapt to the nonlinear characteristics of SOC estimation to a certain extent and improve the estimation accuracy. In addition, EKF can effectively integrate battery models and measurement data, dynamically correct errors, and improve robustness to noise, thereby achieving more stable and accurate SOC estimation under complex working conditions.

[0026] A method for estimating the SOC of a sodium-ion battery based on an extended Kalman filter (EKF), the process is as follows Figure 1 As shown, the following steps are included: Step S1. Performing capacity test and HPPC test on the sodium ion battery to obtain experimental data; Step S2. Fitting the open circuit voltage OCV-SOC curve to obtain a function expression of OCV with respect to SOC; Step S3. Establish a third-order RC equivalent circuit model, and identify parameters such as the ohmic resistance R0, polarization resistances R1, R2, R3, and polarization capacitances C1, C2, C3 included in the circuit model; Step S4. Based on the equivalent circuit model established in step S2, construct a state space equation, including a state equation and an observation equation; Step S5. predicting the state vector (SOC and each polarization voltage) value and covariance matrix; Step S6. Observe the terminal voltage value and calculate the Kalman gain; Step S7. Update the state variable values ​​and covariance matrix values; Step S8: loop iteration to obtain an accurate SOC estimation value.

[0027] The specific embodiments of the present invention and algorithm examples thereof are further described below.

[0028] A sodium ion battery SOC estimation method based on extended Kalman filter (EKF) mainly includes the following steps: Step S1. Obtaining sodium battery HPPC test and capacity test data; Step S2. Obtain a functional expression of OCV with respect to SOC; Step S3. construct a third-order RC equivalent circuit model and implement parameter identification; Step S4. constructing state space equations; Step S5. predicting the state vector and covariance matrix; Step S6. Calculate the Kalman gain; Step S7. Correcting the state vector and the covariance matrix; Step S8: loop iteration to accurately estimate SOC.

[0029] The details are as follows: Furthermore, in step S1, the sodium battery capacity test is mainly used to measure the actual capacity (Ah), energy density, cycle life and other performance of the sodium battery. The HPPC test is carried out under UDDS conditions. The battery is first charged to SOC=100% and then left to stand for 2 hours to reach thermal equilibrium. The SOC is set to decrease at a rate of 5%, and each SOC point performs the following cycle steps: constant current (1C) discharge for 6 minutes - stand for 30 minutes - constant current (1C) charge for 6 minutes - stand for 30 minutes until the target SOC is reached. In the HPPC test, the voltage and current of the battery charge and discharge, as well as the open circuit voltage OCV data can be obtained.

[0030] Furthermore, in step S2, the open circuit voltage OCV is a function of SOC. In the acquired voltage data, the voltage value when the system reaches equilibrium at rest is taken as OCV. Generally, the stable voltage value before the charge / discharge pulse arrives is taken to obtain the OCV value at different SOC points, and then the OCV-SOC curve is obtained by polynomial fitting.

[0031] Further, in step S3, a third-order RC equivalent circuit model is constructed for the sodium ion battery. Compared with lithium ion batteries, the polarization characteristics of sodium ion batteries are relatively significant, and the present invention constructs a third-order RC equivalent circuit model for this. Figure 2 As shown, the third-order RC equivalent circuit model mainly includes an open circuit voltage source OCV, an ohmic resistor R0 and three RC links. In the present invention, curve fitting is used to identify the model parameters to obtain the ohmic resistor R0, the polarization resistor , , and polarization charge , , data.

[0032] The fitting function is as follows:

[0033] in,

[0034] Function fitting can be performed using methods such as exponential fitting and nonlinear least squares fitting.

[0035] It can be obtained from Ohm's law based on the rebound curve at the moment when the discharge voltage ends. The formula is as follows:

[0036] Further, in step S4, the space equations are constructed including the state equation and the measurement equation.

[0037] Equation of state: Define the state variables of the system as:

[0038] in, refer to k The SOC value at the moment, 、 and They are the voltages of the three polarization links respectively.

[0039] The continuous time equation is as follows:

[0040] Where Cap is the nominal capacity of the sodium battery.

[0041] The discretization is as follows (the sampling time is ):

[0042] Right now,

[0043] in, is the process noise and Q is the covariance matrix.

[0044] Measurement equation: The observation equation is to observe the terminal voltage value. The specific equation is as follows:

[0045] Right now,

[0046] in, is the measurement noise, and the covariance matrix is , depends on the error of the voltage sensor.

[0047] Further, in step S5, the state variables and covariance are predicted. Before the prediction begins, the initial values ​​of the state vector and covariance matrix are set. , It is a diagonal matrix.

[0048] According to the state equation Predict the SOC value at time k+1.

[0049] The covariance prediction equation is as follows:

[0050] Among them, the state transfer matrix By the equation of state Performing Jacobian linearization yields:

[0051] Process noise Adjust in the experiment:

[0052] in, represents the uncertainty in SOC estimation, which can be adjusted for current measurement error. represents the uncertainty in the polarization voltage estimate.

[0053] Further, in step S6, the Kalman gain Calculations are performed to correct the predicted values ​​of the state quantities and covariance matrices.

[0054] Kalman Gain

[0055] in, is the measurement Jacobian matrix. is the measurement prediction covariance.

[0056]

[0057] Further, in step S7, based on the Kalman gain calculated in step S6 Update the state vector and covariance matrix.

[0058] State vector update:

[0059] Covariance matrix update:

[0060] Furthermore, the accurate SOC real-time estimation is achieved by continuous loop iteration and compared with the actual SOC. The actual SOC is generally calculated by interpolation using ampere-hour integration. The formula is as follows:

[0061] In summary, the sodium ion battery SOC estimation method based on extended Kalman filtering of the present invention effectively solves the nonlinear and hysteresis problems caused by the complex electrochemical characteristics in the state of charge (SOC) estimation of sodium ion batteries by constructing a third-order RC equivalent circuit model and combining the extended Kalman filtering algorithm. By real-time observation of the terminal voltage, the state quantity (SOC and polarization voltage) and the covariance matrix are cyclically predicted and dynamically corrected. This method can accurately describe the strong polarization characteristics of sodium ion batteries and significantly improve the accuracy and real-time performance of SOC estimation. Compared with traditional technologies, its iterative optimization mechanism that integrates equivalent models and observation data enhances the robustness of the battery management system, effectively prevents overcharging and over-discharging, and prolongs battery life. At the same time, the invention provides high-reliability technical support for the application of sodium ion batteries in energy storage power stations, electric transportation and other fields, promotes the practical development of sodium ion battery modeling and management systems, and further promotes the large-scale promotion of sodium ion battery technology.

[0062] An embodiment of the present invention further provides a storage medium for storing a computer program, which at least performs the above method when executed.

[0063] An embodiment of the present invention further provides a control device, comprising a processor and a storage medium for storing a computer program; wherein the processor is configured to execute at least the method described above when executing the computer program.

[0064] An embodiment of the present invention further provides a processor, wherein the processor executes a computer program and at least executes the method described above.

[0065] The storage medium can be implemented by any type of non-volatile storage device, or a combination thereof. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a ferromagnetic random access memory (Flash Memory), a magnetic surface memory, an optical disc, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The storage medium described in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0066] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0067] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0068] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0069] A person skilled in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, etc. Various media that can store program codes.

[0070] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0071] The methods disclosed in the several method embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0072] The features disclosed in several product embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0073] The features disclosed in several method or device embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0074] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art of the present invention, several equivalent substitutions or obvious variations can be made without departing from the concept of the present invention, and the performance or use is the same, which should be regarded as belonging to the protection scope of the present invention.

Claims

1. A sodium ion battery SOC estimation method based on extended Kalman filtering, characterized in that: The following steps are involved: S1. Obtain sodium battery HPPC test and capacity test data; S2. Based on the static equilibrium voltage data of step S1, fitting the function expression of OCV on SOC; S3. Establish a third-order RC equivalent circuit model and identify the values ​​of ohmic resistance, polarization resistance and polarization capacitance by curve fitting; S4. Based on the equivalent circuit model of step S3, define a state vector including SOC and polarization voltage, and establish a nonlinear state equation and terminal voltage observation equation; S5. Using the extended Kalman filter algorithm, the state vector and covariance matrix of the previous moment are predicted based on the state vector and covariance matrix of the current moment; S6. Based on the predicted covariance matrix and the terminal voltage observation equation of step S5, the Kalman gain is calculated in combination with the measured terminal voltage; S7. The predicted state vector and covariance matrix are corrected by the Kalman gain of step S6 to update the SOC estimate; S8. Execute steps S5 to S7 in a loop to achieve real-time and high-precision dynamic estimation of the SOC of the sodium-ion battery.

2. The method according to claim 1, characterized in that In step S1, the charge and discharge voltage, current and open circuit voltage OCV data of the sodium battery are obtained through HPPC test and capacity test; the HPPC test includes the following steps: charging the sodium battery to a fully charged state and then letting it stand to thermal equilibrium, then performing a cycle of constant current discharge, standing, constant current charging and standing at a preset SOC interval until the target SOC value is reached, and recording the voltage, current and open circuit voltage data after standing equilibrium during the charge and discharge process.

3. The method according to claim 1, characterized in that In step S2, the OCV-SOC function expression is established in the following manner: the static equilibrium voltage before the charge and discharge pulse is selected as the OCV value, data is collected corresponding to different SOC points, and a polynomial fitting algorithm is used to generate a continuous and differentiable OCV-SOC relationship curve.

4. The method according to claim 1, characterized in that: In step S3, the parameter identification of the equivalent circuit model is specifically as follows: based on the dynamic response data of charge and discharge, the ohmic resistance and the polarization resistance and polarization capacitance of each RC link are solved by a nonlinear curve fitting algorithm, wherein the polarization parameters are obtained by fitting the transient response characteristics of the battery terminal voltage through a multi-time constant exponential function.

5. The method according to claim 1, characterized in that In step S4, the state vector in the state space equation includes SOC and multiple polarization voltage components. The nonlinear state equation is obtained by discretizing the continuous time equation. During the discretization process, a process noise covariance matrix is ​​introduced to characterize the model uncertainty.

6. The method according to claim 1, characterized in that In step S5, the state transfer matrix is ​​generated by performing Jacobian matrix linearization processing on the nonlinear state equation, and the process noise covariance matrix is ​​dynamically adjusted according to the current measurement error and the polarization voltage estimation uncertainty.

7. The method according to claim 1, characterized in that In step S6, the measurement Jacobian matrix includes the derivative of the OCV-SOC function and the linear contribution of the polarization voltage to the terminal voltage, and gain optimization is achieved by fusing the prediction covariance matrix with the measurement noise covariance.

8. The method according to claim 1, characterized in that In step S8, the actual SOC reference value is calculated by the ampere-hour integration method, and dynamic error correction is performed with the extended Kalman filter estimation result to verify the real-time estimation accuracy.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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