Sodium-ion battery parameter identification method based on third-order equivalent model

Abstract

A sodium ion battery parameter identification method based on a third-order equivalent circuit model comprises the following steps: applying pulse current to a sodium ion battery to carry out charge and discharge test, and collecting terminal voltage, current and capacity data in discharge and shelving stages; constructing a three-order equivalent circuit model formed by connecting three groups of RC parallel modules and ohmic resistors in series, and establishing a terminal voltage dynamic equation; ohmic resistance is calculated by using voltage break variables at the beginning and the end of a discharge pulse, a polarization process is divided into a zero-state response stage and a zero-input response stage, and polarization voltage nonlinear dynamic equations of all RC modules are deduced respectively. Based on an LM algorithm, a terminal voltage nonlinear function is constructed, polarization resistance and capacitance parameters are iteratively optimized, the error sum of squares of model output and actual data is minimized, and finally high-precision parameter identification is achieved. According to the method, the performance prediction precision of the sodium battery is improved, and reliable data support is provided for related applications.

Description

Technical Field

[0001] The present invention relates to the field of sodium ion battery modeling and parameter identification, and in particular to a sodium battery parameter identification method based on a third-order equivalent model. Background Art

[0002] With the rapid popularization of electric vehicles and the continuous increase in market demand, traditional battery technology faces new challenges and opportunities. In this context, sodium batteries have gradually emerged as an important alternative for electric vehicle power systems. Compared with traditional lithium batteries, sodium-ion batteries have many advantages, including abundant resource reserves, low cost, high flexibility and superior safety. With these advantages, sodium batteries are gradually becoming an ideal choice for electric vehicle power systems.

[0003] However, to fully realize the potential of sodium batteries, it is urgent to conduct in-depth research on their internal characteristics. At present, there are relatively few studies on sodium batteries, especially in accurately describing their charge and discharge characteristics and cycle stability. The research on the identification method of model parameters is particularly important. By establishing an accurate battery model, we can better understand the behavioral characteristics of sodium batteries under different working conditions, thus laying the foundation for the development of more efficient and safe power systems for electric vehicles.

[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 parameter identification method based on a third-order equivalent model.

[0006] To achieve the above object, the present invention adopts the following technical solutions: A sodium ion battery parameter identification method based on a third-order equivalent circuit model comprises the following steps: S1. Apply pulse current to the sodium ion battery for charge and discharge test, and collect terminal voltage, current and capacity data during the discharge and standby stages; S2. constructing a third-order equivalent circuit model of a sodium-ion battery, the model consisting of three groups of RC parallel modules and an ohmic resistor in series, and establishing a terminal voltage calculation formula based on the model; S3. Calculate the ohmic resistance by the terminal voltage mutation at the beginning and end of the discharge pulse; divide the polarization process into a zero-state response stage and a zero-input response stage, and derive the polarization voltage dynamic equation of each RC module respectively; S4. Based on the polarization voltage dynamic equation, a nonlinear function of the terminal voltage is constructed, and the polarization resistance and polarization capacitance parameters are iteratively optimized using the Levenberg-Marquardt (LM) algorithm to minimize the sum of square errors between the output of the nonlinear function and the actual terminal voltage data, and finally obtain the parameter set of the third-order equivalent circuit model.

[0007] In some optional implementations, step S1 specifically includes: Apply periodic pulse current to the sodium ion battery, alternately discharge and rest operations until the battery capacity approaches the cut-off threshold, and collect terminal voltage, current and capacity data in real time during the discharge and rest stages; Based on the voltage curve under a specific state of charge, the dynamic response characteristics of voltage and current during the discharge process are extracted.

[0008] In some optional implementations, step S2 specifically includes: A third-order equivalent circuit model consisting of multiple parallel RC modules and series ohmic resistors was constructed, where each RC module was used to characterize the polarization effect at different time scales. According to the model, a dynamic correlation equation between the terminal voltage and the open circuit voltage, ohmic voltage drop and multi-order polarization voltage is established, and the open circuit voltage value is calibrated by the terminal voltage data in the long-term shelving stage.

[0009] In some optional implementations, step S3 specifically includes: Based on the terminal voltage mutation at the beginning and end of the discharge pulse, the ohmic resistance is calculated by averaging the voltage differences of multiple segments; The battery polarization process is divided into a zero-state response stage and a zero-input response stage, and dynamic decay models of polarization voltage are established respectively. In the zero-state response stage, according to the initial polarization effect under the action of current pulse, the exponential dynamic rise equation of polarization voltage is derived; In the zero-input response stage, based on the final value of the polarization voltage in the previous stage as the initial condition, the exponential dynamic decay equation of the polarization voltage is derived to characterize the dynamic decay process of the polarization effect.

[0010] In some optional implementations, step S4 specifically includes: The dynamic equations of terminal voltage and polarization voltage are combined to construct a nonlinear terminal voltage function containing multi-order polarization attenuation terms. Based on the objective of minimizing the residual sum of squares between the nonlinear terminal voltage function and the actual terminal voltage data, a Levenberg-Marquardt algorithm is used to perform iterative parameter optimization; In each iteration, the partial derivatives of the residual vector with respect to the parameters are approximately solved by numerical differentiation methods, the Jacobian matrix is ​​constructed, and the damping factor is dynamically adjusted to balance the convergence speed and the global search capability. According to the comprehensive effect of the residual vector, Jacobian matrix and damping factor, the polarization resistance and polarization capacitance parameters are updated until the objective function converges to the preset accuracy and the optimal parameter set is output.

[0011] In some optional implementations, in step S4, the parameter iteration optimization process specifically includes: (1) Calculate the residual vector between the nonlinear terminal voltage function output and the actual observed data under the current parameters to evaluate the model fitting effect; (2) Approximately solve the partial derivatives of the residual vector with respect to the parameters through numerical differentiation methods and construct the Jacobian matrix that describes the sensitivity of the parameters; (3) According to the Levenberg-Marquardt update formula, the incremental correction of the parameter vector is calculated by combining the Jacobian matrix, damping factor and identity matrix; (4) Dynamically adjust the damping factor based on the change direction of the objective function: if the residual sum of squares decreases, the damping factor is reduced to accelerate local convergence; if the residual sum of squares increases, the damping factor is increased to enhance global stability.

[0012] A computer-readable storage medium stores a computer program, wherein the computer program implements the sodium-ion battery parameter identification method when executed by a processor.

[0013] A computer program product comprises a computer program, wherein when the computer program is executed by a processor, the sodium ion battery parameter identification method is implemented.

[0014] The present invention has the following beneficial effects: Aiming at the research gap of sodium ion battery model and parameter identification method, the present invention proposes an innovative method based on the third-order equivalent circuit model. By constructing a refined circuit model including three groups of RC parallel modules and ohmic resistors, combined with the dynamic response division of the polarization process (zero state response and zero input response) and nonlinear function construction, the model's ability to characterize the internal characteristics of the battery is significantly improved. The Levenberg-Marquardt algorithm is used for parameter iterative optimization, and the damping factor is dynamically adjusted to balance the convergence speed and global stability, so as to achieve high-precision identification of polarization resistance and capacitance, and control the terminal voltage estimation error within 20mV. Compared with traditional first-order and second-order models, the present invention has significant advantages in adaptability, prediction accuracy and robustness, and can keenly capture subtle characteristic changes during battery charging and discharging, providing reliable theoretical support and data basis for health status assessment, aging performance optimization and practical applications (such as electric vehicle power systems and energy storage systems) of sodium batteries, helping to break through the technical bottleneck of sodium batteries and promote their large-scale application.

[0015] Other beneficial effects of the embodiments of the present invention will be further described below. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of a method according to an embodiment of the present invention; Figure 2 The voltage curve of the sodium battery when SOC=60% is discharged; Figure 3 This is a third-order RC equivalent circuit diagram of an embodiment of the present invention; Figure 4 It is a flowchart of the LM algorithm iteration according to an embodiment of the present invention; Figure 5 1 is a comparison diagram between the estimated value and the actual value of the terminal voltage according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] 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.

[0018] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0019] The present invention aims to solve the current lack of sodium ion battery models and parameter identification methods, establish an equivalent model that can reflect the charging and discharging characteristics and internal performance of sodium ion batteries, and select and optimize appropriate methods to accurately identify the internal parameters of the battery. Ensure that the established model and parameter identification method have good adaptability and can maintain accuracy under different charging and discharging conditions, thereby providing a reliable theoretical basis for the health status estimation and aging performance optimization of sodium ion batteries. The method of the present invention mainly includes the following processes: establishing a third-order equivalent circuit model of a sodium ion battery; identifying the battery ohmic resistance based on the voltage curve; deriving the sodium ion battery polarization voltage calculation formula in sections; and fitting the polarization resistance and capacitance values ​​through the LM algorithm.

[0020] See also Figure 1 The embodiment of the present invention provides a sodium ion battery parameter identification method based on a third-order equivalent circuit model, comprising the following steps: S1. Apply pulse current to the sodium ion battery for charge and discharge test, and collect terminal voltage, current and capacity data during the discharge and standby stages; S2. constructing a third-order equivalent circuit model of a sodium-ion battery, the model consisting of three groups of RC parallel modules and an ohmic resistor in series, and establishing a terminal voltage calculation formula based on the model; S3. Calculate the ohmic resistance by the terminal voltage mutation at the beginning and end of the discharge pulse; divide the polarization process into a zero-state response stage and a zero-input response stage, and derive the polarization voltage dynamic equation of each RC module respectively; S4. Based on the polarization voltage dynamic equation, a nonlinear function of the terminal voltage is constructed, and the polarization resistance and polarization capacitance parameters are iteratively optimized using the Levenberg-Marquardt algorithm to minimize the sum of squares of errors between the output of the nonlinear function and the actual terminal voltage data, and finally obtain a parameter set of the third-order equivalent circuit model.

[0021] The specific embodiments of the present invention and algorithm examples thereof are further described below.

[0022] The present invention proposes a sodium ion battery parameter identification method based on a third-order equivalent circuit model, comprising the following steps: S1. Carry out charge and discharge experiments on sodium ion batteries and collect voltage and current data; S2. Establish a third-order equivalent circuit model of a sodium-ion battery, which consists of three groups of RC parallel modules and one ohmic resistor in series, and obtain a terminal voltage calculation formula; S3. Identify the internal parameters of the battery. Calculate the ohmic resistance by the voltage difference between the start and end of discharge; analyze the internal polarization effect of the battery, divide the battery polarization process into two stages and obtain the calculation formula of the polarization voltage; S4. Use the LM algorithm to fit the parameter values. Combined with the polarization voltage formula, the calculation method of the CE phase terminal voltage is obtained and used as a custom nonlinear function; after setting the initial value, iteratively search for the optimal parameters, minimize the sum of square errors between the output of the custom function and the actual voltage data, and use the lsqcurvefit function to fit the optimal parameter set.

[0023] For the parameter identification of sodium-ion batteries, the innovative method based on the third-order equivalent model proposed in this invention can keenly capture subtle changes in the internal characteristics of the battery, and its adaptability far exceeds that of traditional models. With the help of the LM algorithm, iterative fitting is performed to obtain accurate internal battery parameters by continuously adjusting the parameters. Ultimately, the terminal voltage estimation error is successfully controlled within 20mv, significantly improving the accuracy of sodium-ion battery performance prediction. This lays a solid foundation for the practical application of sodium batteries in many fields such as energy storage systems and electric vehicles.

[0024] In some embodiments, the circuit model consists of three sets of RC parallel modules and one ohmic resistor Series, RC parallel modules include their own polarization resistors , , And polarized capacitor , , ; The calculation formula of terminal voltage is:

[0025] In some embodiments, the voltage drop at the beginning of the current pulse and the voltage increase at the end of the pulse are both caused by the ohmic resistance. The voltage at the moment before and after the discharge of the segment starts and ends, and the average value of the difference is calculated to obtain the formula for calculating the ohmic resistance:

[0026] In some embodiments, when a current pulse is applied, concentration polarization and electrochemical polarization occur inside the battery, generating a gradually decaying polarization current, resulting in a polarization voltage Nonlinear change, the formula is as follows:

[0027] In the BC stage, since the battery has been stored for a long time, the polarization effect is almost zero, which can be regarded as a zero-state response. The initial value of the polarization voltage is is 0, and the polarization voltage formula is:

[0028] For smaller The polarization voltage at point C can be approximately regarded as ; In the CE stage, the pulse current drops to 0 instantly, which is regarded as a zero input response. The initial value is the voltage value at point C , the polarization voltage formula at this time is:

[0029] In some embodiments, the voltage at the middle terminal of S3 and the polarization voltage calculation formula are combined to set a custom nonlinear function:

[0030] The initial parameter guess vector is set, and the goal is to minimize the sum of squared errors between the output of the custom function and the actual voltage data.

[0031] The LM algorithm calculates the residual vector from the initial point and the Jacobian matrix , according to the update formula of LM algorithm, calculate , and obtain the new parameter vector , the minimum residual sum of squares Adjust the damping factor according to the dynamics .

[0032] After multiple iterations, the lsqcurvefit function will converge to an optimal set of parameters. By matching these parameters with the battery terminal voltage calculation formula, we can get three sets of polarization resistance and capacitance identification results.

[0033] A sodium battery parameter identification method based on a third-order equivalent model specifically comprises the following steps: S1. Carry out charge and discharge experiments on sodium ion batteries; S11. Apply a 0.5C pulse current to discharge the sodium ion battery for 10 seconds and leave it for 30 minutes after discharge, and repeat this operation until the capacity is approximately 0, during which the terminal voltage, current, capacity and other data of the battery are collected; S12. Draw the voltage and current data graph during the discharge process, select =60% voltage curve analysis; S2. Establish a third-order equivalent circuit model of sodium-ion battery; S21. The circuit model consists of three sets of RC parallel modules and one ohm resistor Series, RC parallel modules include their own polarization resistors , , And polarized capacitor , , ; The structure of the third-order equivalent circuit model is as follows Figure 3 As shown; S22. In the third-order equivalent circuit model, the parameters involved include terminal voltage 、Power supply electromotive force , pulse current , Ohm resistance , and three polarization voltages , , : The calculation formula of S23. terminal voltage is:

[0034] S23. Since the polarization effect of the battery almost disappears after a long period of storage and there is no current, the terminal voltage at this time can be regarded as the open circuit voltage , extract each The corresponding open circuit voltage value ; S3. Identify various internal parameters of the battery.

[0035] S31. By Figure 2 It can be seen that the sudden drop in voltage at the beginning of the current pulse and the sudden increase in voltage at the end of the pulse are both caused by the ohmic resistance; Extract each The voltage before the discharge of the segment starts , discharge instantaneous voltage , the voltage before the end of discharge , the instantaneous voltage at the end of discharge , from which we get:

[0036] Taking the average value, we can get the formula for calculating ohmic resistance:

[0037] S32. Formula for calculating polarization voltage of a single RC module; The relationship between the three groups of polarization resistors and polarization capacitors and their corresponding polarization voltages is similar, with only different time constants. , For example.

[0038] The relationship between the power supply current and the polarization resistance and polarization capacitance is:

[0039] The polarization voltage is derived by shifting the integral The formula is:

[0040] When a current pulse is applied, concentration polarization and electrochemical polarization will occur inside the battery, generating a gradually decaying polarization current, resulting in a polarization voltage Nonlinear change, the formula is as follows:

[0041] The polarization process of the battery is divided into two stages. In the BC stage, since the battery has been shelved for a long time, the polarization effect is almost zero, which can be regarded as a zero state response. The initial value of the polarization voltage is is 0, and the polarization voltage formula is:

[0042] For smaller The polarization voltage at point C can be approximately regarded as ; In the CE stage, the pulse current drops to 0 instantly, which is regarded as a zero input response. The initial value is the voltage value at point C. , the polarization voltage formula at this time is:

[0043] S4. Use LM algorithm to fit and obtain parameter values.

[0044] The LM algorithm is used to solve nonlinear least squares problems. The goal is to minimize the residual vector The sum of squares , and its parameter update formula is:

[0045] in, It is The parameter vector for the iteration, is the residual vector About parameters The Jacobian matrix of is the damping factor, is the identity matrix.

[0046] S41. Combine the terminal voltage in S3 and the polarization voltage calculation formula to get the terminal voltage in the CE stage formula:

[0047] Add unknowns and set a custom nonlinear function:

[0048] S42. Settings is a parameter variable, is the time variable, the residual vector Represents the difference between the observed data and the output of the custom function. The goal is to minimize the sum of squared errors between the output of the custom function and the actual voltage data, that is, to find parameters that minimize the sum of squared residuals.

[0049] S43 sets the initial parameter guess vector, and the LM algorithm starts iteratively searching for the optimal parameters from this initial point, and sets the lower and upper bound vectors of the parameters to ensure that the parameter values ​​are always within a reasonable range during the iteration process.

[0050] The iterative optimization process of the LM algorithm is as follows: Figure 4 As shown, it includes residual calculation, Jacobian matrix update, parameter correction and dynamic adjustment of damping factor.

[0051] S44. Use the lsqcurvefit function to fit. In each iteration, it completes the following key steps: (1) Calculate the residual vector , that is, the difference between the observed data and the output of the custom function, to evaluate the fitting effect of the model under the current parameters; (2) Calculate the Jacobian matrix , the partial derivatives are approximately solved by numerical differentiation to describe the residual vector About parameters The rate of change of (3) According to the update formula of the LM algorithm, calculate , and add it to the current parameter vector On the other hand, we get a new parameter vector ; (4) Dynamic adjustment of damping factor If the current iteration makes the objective function Reduce, then reduce to speed up the convergence; if the objective function Increase, then increase to enhance the global search capability of the algorithm.

[0052] S45. After multiple iterations, the lsqcurvefit function converges to an optimal parameter set. By matching these parameters with the battery terminal voltage calculation formula, three sets of polarization resistance and capacitance identification results can be obtained.

[0053] The identification result obtained by fitting is brought into the terminal voltage calculation formula to obtain the terminal voltage estimation value. Therefore, the present invention builds a third-order equivalent circuit model, cooperates with the parameter fitting based on the LM algorithm, keeps the estimated error of the terminal voltage within 20mv, ensures the accuracy of the sodium ion battery parameter identification, and accurately reflects the internal characteristics of the battery.

[0054] In summary, the present invention proposes a sodium battery parameter identification method based on a third-order equivalent model. Compared with the existing first-order and second-order models, the third-order equivalent model has higher adaptability and can more accurately reflect the changes in the internal characteristics of the sodium-ion battery. Iterative fitting is performed through the LM algorithm, and the precise internal parameters of the battery are obtained by continuously adjusting the parameters. The obtained parameter values ​​control the estimated error of the terminal voltage within 20mv. This result not only significantly improves the performance prediction accuracy of the sodium battery, but also provides reliable data support for related applications. The innovative method based on the third-order equivalent model of the present invention can keenly capture subtle changes in the internal characteristics of the battery, and its adaptability far exceeds that of the traditional model, which significantly improves the accuracy of the performance prediction of the sodium-ion battery, and lays a solid foundation for the practical application of sodium batteries in energy storage systems, electric vehicles and many other fields.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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 scheme of this embodiment.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] The methods disclosed in the several method embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0065] The features disclosed in several product embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0066] 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.

[0067] 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 parameter identification method based on a third-order equivalent circuit model, characterized in that: The following steps are involved: S1. Apply pulse current to the sodium ion battery for charge and discharge test, and collect terminal voltage, current and capacity data during the discharge and standby stages; S2. construct a third-order equivalent circuit model of a sodium ion battery, the model consisting of three groups of RC parallel modules and an ohmic resistor in series, and establish a terminal voltage calculation formula based on the model; S3. Calculate the ohmic resistance by the terminal voltage mutation at the beginning and end of the discharge pulse; The polarization process is divided into a zero-state response stage and a zero-input response stage, and the polarization voltage dynamic equations of each RC module are derived respectively; S4. Based on the polarization voltage dynamic equation, a nonlinear function of the terminal voltage is constructed, and the polarization resistance and polarization capacitance parameters are iteratively optimized using the Levenberg-Marquardt algorithm to minimize the sum of squares of errors between the output of the nonlinear function and the actual terminal voltage data, and finally the parameter set of the third-order equivalent circuit model is obtained.

2. The sodium ion battery parameter identification method based on the third-order equivalent circuit model according to claim 1, characterized in that: Step S1 specifically includes: Apply periodic pulse current to the sodium ion battery, alternately discharge and rest operations until the battery capacity approaches the cut-off threshold, and collect terminal voltage, current and capacity data in real time during the discharge and rest stages; Based on the voltage curve under a specific state of charge, the dynamic response characteristics of voltage and current during the discharge process are extracted.

3. The sodium ion battery parameter identification method based on the third-order equivalent circuit model according to claim 1, characterized in that: Step S2 specifically includes: A third-order equivalent circuit model consisting of multiple parallel RC modules and series ohmic resistors was constructed, where each RC module was used to characterize the polarization effect at different time scales. According to the model, a dynamic correlation equation between the terminal voltage and the open circuit voltage, ohmic voltage drop and multi-order polarization voltage is established, and the open circuit voltage value is calibrated by the terminal voltage data in the long-term shelving stage.

4. The sodium ion battery parameter identification method based on the third-order equivalent circuit model according to claim 1, characterized in that: Step S3 specifically includes: Based on the terminal voltage mutation at the beginning and end of the discharge pulse, the ohmic resistance is calculated by averaging the voltage differences of multiple segments; The battery polarization process is divided into a zero-state response stage and a zero-input response stage, and dynamic decay models of polarization voltage are established respectively. In the zero-state response stage, according to the initial polarization effect under the action of current pulse, the exponential dynamic rise equation of polarization voltage is derived; In the zero-input response stage, based on the final value of the polarization voltage in the previous stage as the initial condition, the exponential dynamic decay equation of the polarization voltage is derived to characterize the dynamic decay process of the polarization effect.

5. The sodium ion battery parameter identification method based on the third-order equivalent circuit model according to claim 1, characterized in that: Step S4 specifically includes: The dynamic equations of terminal voltage and polarization voltage are combined to construct a nonlinear terminal voltage function containing multi-order polarization attenuation terms. Based on the objective of minimizing the residual sum of squares between the nonlinear terminal voltage function and the actual terminal voltage data, a Levenberg-Marquardt algorithm is used to perform iterative parameter optimization; In each iteration, the partial derivatives of the residual vector with respect to the parameters are approximately solved by numerical differentiation methods, the Jacobian matrix is ​​constructed, and the damping factor is dynamically adjusted to balance the convergence speed and the global search capability. According to the comprehensive effect of the residual vector, Jacobian matrix and damping factor, the polarization resistance and polarization capacitance parameters are updated until the objective function converges to the preset accuracy and the optimal parameter set is output.

6. The sodium ion battery parameter identification method based on the third-order equivalent circuit model according to claim 5, characterized in that: In step S4, the parameter iteration optimization process specifically includes: (1) Calculate the residual vector between the nonlinear terminal voltage function output and the actual observed data under the current parameters to evaluate the model fitting effect; (2) Approximately solve the partial derivatives of the residual vector with respect to the parameters through numerical differentiation methods and construct the Jacobian matrix that describes the sensitivity of the parameters; (3) According to the Levenberg-Marquardt update formula, the incremental correction of the parameter vector is calculated by combining the Jacobian matrix, damping factor and identity matrix; (4) Dynamically adjust the damping factor based on the change direction of the objective function: if the residual sum of squares decreases, the damping factor is reduced to accelerate local convergence; if the residual sum of squares increases, the damping factor is increased to enhance global stability.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the sodium ion battery parameter identification method according to any one of claims 1 to 6 is implemented.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the sodium ion battery parameter identification method according to any one of claims 1 to 6 is implemented.

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