Model parameter and SOE online joint estimation method and device and electronic equipment

Abstract

The invention provides a model parameter and SOE online joint estimation method and device and electronic equipment, and relates to the technical field of battery management. The method comprises the following steps: establishing an equivalent circuit model of a target battery; carrying out charging and discharging test on the target battery to obtain a fitting curve of the SOE (State of Energy) and the OCV (Open Circuit Voltage) of the target battery; according to the fitted curve, performing parameter identification on a voltage equation of the equivalent circuit model by adopting a particle swarm algorithm to obtain an initial parameter of the equivalent circuit model; according to the initial parameters, establishing an SOE estimation state equation and an SOE estimation observation equation of the target battery; and performing SOE online estimation on the target battery according to the SOE estimation state equation and the SOE estimation observation equation to obtain a current SOE estimation value of the target battery. Therefore, by establishing the equivalent circuit model, the solid-phase diffusion and liquid-phase diffusion effects in the battery are fully considered, and the estimation precision and accuracy of the SOE are improved.

Description

Technical Field

[0001] The present application relates to the field of battery management technology, and in particular to a method, device and electronic device for online joint estimation of model parameters and SOE. Background Art

[0002] Battery State of Energy (SOE) is a key parameter for measuring a battery's remaining energy. Its estimation accuracy directly impacts the development of battery energy management strategies, lifespan optimization, and system safety. In applications such as electric vehicles and energy storage systems, accurate battery SOE estimation is a key prerequisite for optimal battery scheduling and preventing overcharging and over-discharging.

[0003] Existing SOE estimation methods mainly include power integration, open-circuit voltage, data-driven methods, and filter-based model methods. The filter-based model method is widely used because it balances accuracy and real-time performance. This method relies on a battery equivalent circuit model to construct a state-space equation.

[0004] However, the traditional battery equivalent circuit model does not take into account the solid-phase diffusion and liquid-phase diffusion effects inside the battery, and only describes the polarization characteristics through a simple RC network, resulting in significant deviations in the terminal voltage simulation under dynamic conditions (such as rapid acceleration and frequent starts and stops), which in turn affects the estimation accuracy of SOE. Summary of the Invention

[0005] The purpose of the embodiments of the present application is to provide an online joint estimation method, device and electronic device for model parameters and SOE. By establishing an equivalent circuit model, the solid-phase diffusion and liquid-phase diffusion effects inside the battery are fully considered, thereby improving the estimation precision and accuracy of SOE.

[0006] In order to achieve the above objectives, the technical solutions adopted in the embodiments of the present application are as follows: In a first aspect, an embodiment of the present application provides an online joint estimation method of model parameters and SOE, comprising: Establish an equivalent circuit model of the target battery; Performing a charge and discharge test on the target battery to obtain a fitting curve of the state of energy (SOE) and the open circuit voltage (OCV) of the target battery; According to the fitting curve, a particle swarm algorithm is used to perform parameter identification on the voltage equation of the equivalent circuit model to obtain initial parameters of the equivalent circuit model; According to the initial parameters, establishing the SOE estimation state equation and SOE estimation observation equation of the target battery; According to the SOE estimation state equation and the SOE estimation observation equation, the SOE of the target battery is estimated online to obtain a current SOE estimation value of the target battery.

[0007] Optionally, establishing an equivalent circuit model of the target battery includes: The equivalent circuit model is established based on the open circuit voltage, battery internal resistance, first electrochemical polarization potential and second electrochemical polarization potential of the target battery, wherein the first electrochemical polarization potential is the electrochemical reaction polarization potential caused by the polarization capacitance effect, and the second electrochemical polarization potential is the electrochemical reaction polarization potential caused by the solid-liquid diffusion of the lithium ions.

[0008] Optionally, the voltage equation of the equivalent circuit model is: , in, is the battery terminal voltage; is the open circuit voltage of the target battery; is the current at time t; is the internal resistance of the battery; is the first electrochemical polarization potential; is the second electrochemical polarization potential.

[0009] Optionally, after establishing the equivalent circuit model of the target battery, the method further includes: Discretizing the voltage equation of the equivalent circuit model to obtain a voltage discrete equation of the equivalent circuit model; The method of performing parameter identification on the equivalent circuit model using a particle swarm algorithm according to the fitting curve to obtain initial parameters of the equivalent circuit model includes: According to the fitting curve, the particle swarm algorithm is used to perform parameter identification on the voltage discrete equation to obtain the initial parameters.

[0010] Optionally, performing a charge and discharge test on the target battery to obtain a fitting curve of the battery state of energy (SOE) and open circuit voltage (OCV) of the target battery includes: After the target battery is fully charged, a discharge test is performed on the target battery using a preset low current to obtain the state of charge (SOC) value of the target battery at multiple OCV values; Calculating SOE values ​​at the multiple OCV values ​​respectively according to the SOC values ​​at the multiple OCV values; According to the SOE values ​​under the multiple OCV values, a least squares method is used to perform curve fitting to obtain the fitting curve.

[0011] Optionally, the performing parameter identification on the voltage equation of the equivalent circuit model using a particle swarm algorithm according to the fitting curve to obtain initial parameters of the equivalent circuit model includes: constructing a fitness function based on a root mean square error between an estimated voltage and a measured voltage of the equivalent circuit model; According to the fitting curve and the fitness function, a preset particle swarm algorithm is used to perform parameter identification on the voltage equation of the equivalent circuit model to obtain initial parameters of the equivalent circuit model.

[0012] Optionally, establishing the SOE estimation state equation and SOE estimation observation equation of the target battery according to the initial parameters includes: The power integration method is used to establish the SOE calculation equation of the target battery; Discretizing the SOE calculation equation to obtain a SOE discrete equation; Establishing the SOE estimation state equation according to the SOE discrete equation and the initial parameters; The SOE estimation observation equation is established according to the SOE estimation state equation.

[0013] Optionally, performing online SOE estimation on the target battery according to the SOE estimation state equation and the SOE estimation observation equation to obtain a current SOE estimation value of the target battery includes: According to the SOE estimated value of the target battery at time k, the SOE estimated state equation is used to perform an online SOE estimation on the target battery, and the SOE estimated value of the target battery at time k+1 is obtained as the state variable at time k+1; According to the state variable at the k+1 moment, the SOE estimation observation equation is used to make a prediction to obtain the observation variable at the k+1 moment; The state change at time k+1 is corrected according to the observed variables at time k+1, and an optimal state estimate at time k+1 is obtained as the optimal SOE estimate of the target battery at time k+1.

[0014] In a second aspect, an embodiment of the present application provides an online joint estimation device for model parameters and SOE, comprising: A first building module is used to build an equivalent circuit model of a target battery; A test module, configured to perform a charge and discharge test on the target battery to obtain a fitting curve of the state of energy (SOE) and the open circuit voltage (OCV) of the target battery; an identification module, configured to perform parameter identification on the voltage equation of the equivalent circuit model using a particle swarm algorithm according to the fitting curve to obtain initial parameters of the equivalent circuit model; A second establishing module is used to establish the SOE estimation state equation and SOE estimation observation equation of the target battery according to the initial parameters; An estimation module is used to perform online SOE estimation on the target battery according to the SOE estimation state equation and the SOE estimation observation equation to obtain a current SOE estimation value of the target battery.

[0015] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor and a memory, wherein the memory stores machine-executable instructions that can be executed by the processor, and the processor can execute the machine-executable instructions to implement any of the methods described in the first aspect.

[0016] The present application provides an online joint estimation method, device and electronic device for model parameters and SOE, wherein the method accurately captures the dynamic behavior of the battery by establishing an equivalent circuit model of the target battery, and the equivalent circuit model of the present application takes into account the reaction mechanism inside the battery; the target battery is subjected to charge and discharge tests to obtain a fitting curve of the energy state SOE and the open circuit voltage OCV of the target battery, and a data-driven battery characteristic characterization is established, providing a high-precision open circuit voltage benchmark for the equivalent circuit model; based on the fitting curve, a particle swarm algorithm is used to perform parameter identification on the voltage equation of the equivalent circuit model to obtain the initial parameters of the equivalent circuit model, overcoming the problem that traditional gradient algorithms are prone to falling into local optimality. Problem, make the equivalent circuit model parameters closer to the actual battery dynamic characteristics, so as to provide a high-precision basis for obtaining more accurate results when estimating SOE online; according to the initial parameters, establish the SOE estimation state equation and SOE estimation observation equation of the target battery to effectively describe the nonlinear dynamic behavior of the battery, especially suitable for complex working conditions such as variable current and variable temperature, to achieve the essential upgrade of the battery from power to energy, and then realize the dynamic tracking of SOE; according to the SOE estimation state equation and SOE estimation observation equation, perform online estimation of SOE on the target battery to obtain the current SOE estimation value of the target battery, so as to output a high-precision SOE estimation value in real time, reduce the estimation error, and support the optimization control of the battery management system. Therefore, the online joint estimation method of model parameters and SOE provided in this application can improve the accuracy and robustness of model parameter and SOE state estimation in a noisy environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 Schematic diagram of the process of online joint estimation method of model parameters and SOE provided in the embodiment of the present application Figure 1 ; Figure 2 A schematic structural diagram of an improved equivalent circuit model provided in an embodiment of the present application; Figure 3 Schematic diagram of the process of online joint estimation method of model parameters and SOE provided in the embodiment of the present application Figure 2 ; Figure 4 A schematic diagram of a fitting curve provided in an embodiment of the present application; Figure 5 Schematic diagram of the process of online joint estimation method of model parameters and SOE provided in the embodiment of the present application Figure 3 ; Figure 6 Schematic diagram of the process of online joint estimation method of model parameters and SOE provided in the embodiment of the present application Figure 4 ; Figure 7 Schematic diagram of the process of online joint estimation method of model parameters and SOE provided in the embodiment of the present application Figure 5 ; Figure 8 Schematic diagram of the estimation result verification process of the online joint estimation method of model parameters and SOE provided in an embodiment of the present application; Figure 9 A schematic diagram of the structure of an online joint estimation device for model parameters and SOE provided in an embodiment of the present application; Figure 10 A schematic diagram of the electronic device structure provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0020] In order to better understand the various solutions provided in the embodiments of the present application, the technologies involved in the following embodiments are first explained.

[0021] SOE (State of Energy): It reflects the ratio of the battery's current releasable energy to the battery's maximum available energy. It is an important indicator for measuring battery energy usage and can reflect the battery's remaining energy.

[0022] The following describes in detail the online joint estimation method, device and electronic device of model parameters and SOE according to the embodiments of the present application through multiple embodiments in conjunction with the accompanying drawings. Figure 1 Schematic diagram of the process of online joint estimation method of model parameters and SOE provided in the embodiment of the present application Figure 1 The execution subject of this method may be a processor in an electronic device. Figure 1As shown, the method may include: S110. Establish an equivalent circuit model of the target battery.

[0023] In one possible implementation, by selecting a suitable equivalent circuit model, the dynamic characteristics of the battery are simulated through circuit elements (such as resistors, capacitors, etc.) to simplify the complex electrochemical process into circuit equations, which facilitates mathematical modeling and parameter identification.

[0024] S120 , performing charge and discharge tests on the target battery to obtain a fitting curve of the state of energy (SOE) and the open circuit voltage (OCV) of the target battery.

[0025] In one possible implementation method, a charge and discharge cycle test is performed on the target battery in a constant temperature environment, and the open circuit voltage (OCV) at different energy states (SOE) during the charge and discharge test is recorded. Then, based on multiple open circuit voltages (OCV), a fitting curve of the energy state (SOE) and the open circuit voltage (OCV) of the target battery is obtained by fitting, so as to establish a deterministic relationship between the energy state (SOE) and the open circuit voltage (OCV), thereby providing a basis for subsequent parameter identification and state estimation.

[0026] S130 . Based on the fitting curve, a particle swarm algorithm is used to perform parameter identification on the voltage equation of the equivalent circuit model to obtain initial parameters of the equivalent circuit model.

[0027] In one possible implementation method, based on the fitting curve, the particle swarm algorithm is used to perform parameter identification on the voltage equation of the equivalent circuit model to obtain the initial parameters of the equivalent circuit model. Then, based on the initial parameters of the equivalent circuit model, the parameters of the equivalent circuit model are optimized to make it more consistent with the actual battery characteristics, avoid local optimality, and improve the accuracy of the equivalent circuit model.

[0028] Among them, the particle swarm algorithm can accelerate the initial parameter identification process.

[0029] S140 , establishing the SOE estimation state equation and SOE estimation observation equation of the target battery according to the initial parameters.

[0030] In one possible implementation, based on the initial parameters , establish the SOE estimation state equation and SOE estimation observation equation of the target battery. Since the construction of the SOE estimation state equation and SOE estimation observation equation of this application both provide a standard input format with the adaptive extended Kalman filter (AEKF) algorithm, that is, the SOE is estimated using AEKF. For example, for a nonlinear discrete system, the state equation of the AEKF algorithm can be expressed by the following formula (1); the observation equation 13 formula (1): Formula (1) Where x is the state vector; u is the system input vector; w is white noise with zero mean and covariance Q.

[0031] Formula (2) Where y is the system output vector, v is white noise with zero mean and covariance R. w and v are independent of each other.

[0032] Expand the state equation of formula (1) and the observation equation of formula (2) using the first-order Taylor expansion, and use the following formulas (3) and (4) to obtain the linearized state equation and observation equation: Formula (3) Formula (4) Among them: A, B, C, and D are the coefficient matrices after linearization.

[0033] From the above formulas (1) to (4), we can see that according to the initial parameters , the SOE estimation state equation and SOE estimation observation equation of the target battery are established, and the SOE estimation state equation and SOE estimation observation equation need to be discretized to realize real-time calculation of the target battery.

[0034] S150 , performing online SOE estimation on the target battery according to the SOE estimation state equation and the SOE estimation observation equation to obtain a current SOE estimation value of the target battery.

[0035] In one possible implementation method, the SOE at the next moment is predicted based on the SOE estimation state equation, and then the predicted value is corrected using the voltage observation value of the SOE estimation observation equation, and then the SOE estimation is iteratively updated to achieve online SOE estimation of the target battery. By updating the initial parameters or state variables of the equivalent circuit model in real time, the current SOE estimation value of the target battery is obtained. The current SOE estimation value of the target battery can adapt to the characteristic drift caused by battery aging, temperature change, etc.

[0036] The online joint estimation method of model parameters and SOE provided in the present application accurately captures the dynamic behavior of the battery by establishing an equivalent circuit model of the target battery, and the equivalent circuit model of the present application takes into account the reaction mechanism inside the battery; charge and discharge tests are performed on the target battery to obtain a fitting curve of the energy state SOE and open circuit voltage OCV of the target battery, establish a data-driven battery characteristic characterization, and provide a high-precision open circuit voltage benchmark for the equivalent circuit model; according to the fitting curve, the particle swarm algorithm is used to perform parameter identification on the voltage equation of the equivalent circuit model to obtain the initial parameters of the equivalent circuit model, which overcomes the problem that traditional gradient algorithms are prone to falling into local optimality and makes the equivalent circuit model more precise. The model parameters are closer to the actual battery dynamic characteristics, providing a high-precision basis for obtaining more accurate results when estimating SOE online; based on the initial parameters, the SOE estimation state equation and SOE estimation observation equation of the target battery are established to effectively describe the nonlinear dynamic behavior of the battery, which is especially suitable for complex working conditions such as variable current and variable temperature, realizing the essential upgrade of the battery from power to energy, and then realizing the dynamic tracking of SOE; based on the SOE estimation state equation and SOE estimation observation equation, the SOE of the target battery is estimated online to obtain the current SOE estimation value of the target battery, so as to output a high-precision SOE estimation value in real time, reduce the estimation error, and support the optimization control of the battery management system. Therefore, the online joint estimation method of model parameters and SOE provided in this application can improve the accuracy and robustness of the estimation of model parameters and SOE state in a noisy environment.

[0037] Optionally, establishing the equivalent circuit model of the target battery may include: According to the open circuit voltage of the target battery , battery internal resistance , first electrochemical polarization potential and the second electrochemical polarization potential , establish an equivalent circuit model.

[0038] Among them, the first electrochemical polarization potential is the electrochemical reaction polarization potential caused by the polarization capacitance effect, the second electrochemical polarization potential It is the polarization potential of the electrochemical reaction caused by the solid-liquid diffusion of lithium ions.

[0039] For example, Figure 2 This is a schematic diagram of the structure of the improved equivalent circuit model provided in the embodiment of the present application. Figure 2 As shown in the figure, the discharge current of the improved equivalent circuit model is specified to be positive and the charging current is specified to be negative. Then, based on Kirchhoff's voltage law, the terminal voltage equation of the improved equivalent circuit model is obtained. The voltage equation of the equivalent circuit model can be expressed by the following formula (5): Formula (5) in, is the battery terminal voltage; is the open circuit voltage of the target battery; is the current at time t; is the internal resistance of the battery; is the first electrochemical polarization potential; is the second electrochemical polarization potential. It is used to characterize the thermodynamic equilibrium potential of the battery, that is, the open circuit voltage of the target battery, which has a nonlinear relationship with SOE.

[0040] in, It can represent the polarization potential of the electrochemical reaction caused by the double-layer capacitance effect of the target battery and can be expressed by the following formula (6): = Formula (6) Among them, in formula (2) is the polarization resistance, and accordingly, is a polarized capacitor. The first electrochemical polarization potential The corresponding polarization current.

[0041] It can represent the polarization potential of the electrochemical reaction caused by the solid-liquid diffusion of lithium ions and can be expressed by the following formula (7): = Formula (7) in, The second electrochemical polarization potential The coefficient of variation with temperature.

[0042] From formula (3), we can see that the second electrochemical polarization potential With the first electrochemical polarization potential Exponential change of the first electrochemical polarization potential The smaller the second electrochemical polarization potential A faster response means a faster polarization of the double-layer capacitance of the target battery.

[0043] The online joint estimation method of model parameters and SOE provided in this application establishes an equivalent circuit model based on the open circuit voltage, battery internal resistance, first electrochemical polarization potential and second electrochemical polarization potential of the target battery. As a result, the equivalent circuit model of this application can not only simulate the rapid polarization of the double layer effect, but also simulate the slow polarization of the diffusion effect. At the same time, the introduction of the coefficient of the second electrochemical polarization potential that varies with temperature can be used to reflect the relationship between the polarization potential in electrochemistry and the potential caused by the solid-liquid diffusion of lithium ions, so that the equivalent circuit model of this application effectively improves the accuracy of the target battery model without increasing the complexity of the model.

[0044] Optionally, after establishing the equivalent circuit model of the target battery, the method further includes: The voltage equation of the equivalent circuit model is discretized to obtain the voltage discrete equation of the equivalent circuit model.

[0045] In one possible implementation, the voltage equation of the equivalent circuit model of the above formula (5) is discretized, and the voltage discrete equation of the equivalent circuit model is obtained according to the following formula (8).

[0046] Formula (8) Among them, the first electrochemical polarization potential It can be expressed by the following formula (9): = Formula (9) Among them, the polarization current in the above formula (5) is It can be expressed by the following formula (10): = +(1- ) Formula (10) Among them, the second electrochemical polarization potential It can be expressed by the following formula (11): = Formula (11) It should be noted that the voltage discrete equation of the above equivalent circuit model can also be called the circuit terminal voltage equation.

[0047] Optionally, the method of performing parameter identification on the equivalent circuit model using a particle swarm algorithm based on the fitting curve to obtain initial parameters of the equivalent circuit model may include: According to the fitting curve, the particle swarm algorithm is used to identify the parameters of the voltage discrete equation and obtain the initial parameters.

[0048] The particle swarm optimization (PSO) algorithm is a metaheuristic algorithm used to solve optimization problems. It simulates the behavior of a flock of birds to perform an optimization search, using a fitness function as an evaluation criterion to optimize parameters and obtain the parameters to be identified. The PSO algorithm has the advantages of simplicity, fast convergence, strong optimization capabilities, and adaptability to nonlinear systems. It also has high accuracy in identifying the initial model parameters and is easy to implement in engineering projects.

[0049] In one possible implementation method, according to the fitting curve, the particle swarm algorithm is used to identify the parameters of the voltage discrete equation and obtain the initial parameters. , using the initial parameters obtained by the particle swarm algorithm, the prediction error fluctuation range of the equivalent circuit model under different working conditions is reduced, and the adaptability of the equivalent circuit model to complex driving scenarios is enhanced.

[0050] This application provides an online joint estimation method for model parameters and SOE. This method discretizes the voltage equation of a continuous equivalent circuit model to obtain a discrete voltage equation for the equivalent circuit model. This discretized voltage equation depends only on the previous state and current measurements (e.g., current and voltage). This eliminates the need to store large amounts of historical data, reduces memory usage, and enables stable operation of the equivalent circuit model on resource-constrained embedded devices. Based on the fitted curve, a particle swarm algorithm is used to identify the parameters of the discrete voltage equation to obtain initial parameters. These initial parameters, obtained using the particle swarm algorithm, reduce the fluctuation range of the equivalent circuit model's prediction error under different operating conditions, enhance the model's adaptability to complex driving scenarios, and lay a reliable foundation for subsequent accurate estimation of battery SOE. Thus, by combining discretization with particle swarm parameter identification, this application implements a complete optimization chain from digitization of the equivalent circuit model to parameter precision, improving the terminal voltage prediction accuracy of the equivalent circuit model under dynamic operating conditions and reducing the estimation error of the battery SOE.

[0051] Figure 3 Schematic diagram of the process of online joint estimation method of model parameters and SOE provided in the embodiment of the present application Figure 2 .like Figure 3 As shown, the above charge and discharge test is performed on the target battery to obtain a fitting curve of the battery state of energy SOE and open circuit voltage OCV of the target battery, including: S210 , after the target battery is fully charged, a preset low current is used to perform a discharge test on the target battery to obtain SOC (State of Charge) values ​​of the target battery at multiple OCV values.

[0052] S220 , calculating SOE values ​​at multiple OCV values ​​respectively according to the SOC values ​​at multiple OCV values.

[0053] S230 , performing curve fitting using a least squares method according to the SOE values ​​at multiple OCV values ​​to obtain a fitting curve.

[0054] In one possible implementation, the battery polarization effect can be ignored due to the continuous preset small current discharge, and the battery terminal voltage at this time can be approximated as the open circuit voltage. , design a small current charge and discharge static test, collect target battery data, and then obtain the open circuit voltage of the target battery —SOE relationship. The specific test steps are as follows: 1) After fully charging the target battery, let it rest for a preset time (e.g. 3 hours) to eliminate the polarization effect of the target battery after charging and obtain a stable reference open circuit voltage. .

[0055] 2) Discharge the target battery at a preset constant current rate (e.g., 1 / 3 constant current rate) until the SOC of the target battery drops by 5%, then leave it for a preset first time (e.g., 3 hours), and record the open circuit voltage of the battery at this time. and the battery's SOC.

[0056] 3) Repeat step 2) until the battery SOC drops to 0%.

[0057] The SOE corresponding to the battery SOC is calculated using the following formula (12).

[0058] Formula (12) Among them, SOE (SOC) is used to indicate the SOE value corresponding to SOC at rest; is the rated energy of the battery; is the energy charge and discharge efficiency, usually 1; I( ) and V( ) are the current and voltage corresponding to the SOC at time t.

[0059] Based on the above formula (12), the preset least square method is used to calculate the collected open circuit voltage through the following formula (13): The calculated SOE of the battery is fitted to obtain the OCV-SOE fitting curve.

[0060] Formula (13) Among them, y represents , x represents SOE.

[0061] For example, Figure 4 Schematic diagram of the fitting curve provided in the embodiment of this application. Figure 4 As shown in the figure, the dots are the collected OCV-SOE points, and the line segments are the fitting curves of OCV-SOE.

[0062] The present application provides an online joint estimation method of model parameters and SOE. After the target battery is fully charged, a preset small current is used in combination with a long period of rest to perform a discharge test on the target battery. This can effectively suppress ohmic polarization, double-layer polarization and diffusion polarization, and obtain the state of charge (SOC) value of the target battery at multiple OCV values. According to the SOC values ​​at multiple OCV values, the SOE values ​​at multiple OCV values ​​are calculated respectively, which can solve the defect of traditional SOC that ignores voltage fluctuations. According to the SOE values ​​at multiple OCV values, the least squares method is used for curve fitting to obtain a fitting curve, which provides a benchmark for subsequent calculation accuracy and provides a reliable basis for battery overcharge and over-discharge protection and energy balancing strategy.

[0063] Figure 5 Schematic diagram of the process of online joint estimation method of model parameters and SOE provided in the embodiment of the present application Figure 3 .like Figure 5 As shown above, based on the fitting curve, the particle swarm algorithm is used to perform parameter identification on the voltage equation of the equivalent circuit model to obtain the initial parameters of the equivalent circuit model, which may include: S310 , constructing a fitness function according to a root mean square error between the estimated voltage of the equivalent circuit model and the measured voltage.

[0064] S320 , according to the fitting curve and the fitness function, a preset particle swarm algorithm is used to perform parameter identification on the voltage equation of the equivalent circuit model to obtain initial parameters of the equivalent circuit model.

[0065] In one possible implementation method, the particle swarm algorithm (PSO) is used to identify the parameters of the voltage discrete equation for the fitting curve. Since the particle swarm algorithm (PSO) is based on the fitness function as the evaluation standard for parameter optimization, the root mean square error between the terminal voltage and the measured voltage in the voltage discrete equation is selected as the fitness function according to the principle of minimum estimation error. Then, the initial parameters are obtained based on the fitness function. Among them, the fitness function can be expressed by the following formula (14): Formula (14) Among them, the above formula (14) is the terminal voltage estimated by the improved model at the i-th moment, U(i) is the measured voltage at the i-th moment, and N is the number of sampling points.

[0066] According to the above formula (14), the initial parameters are obtained , the initial parameters It can be expressed by the following formula (15): Formula (15) It should be noted that the initial parameters obtained by the above formula (11) are , are the parameters of the voltage discrete equation of the equivalent circuit model that change with temperature. Among them, due to the effect of temperature on the polarization capacitance The influence of can be ignored, and the polarized capacitance can be identified offline. , the polarization capacitance is a constant value during the actual charging and discharging process. Therefore, in the subsequent model building process, only the battery internal resistance is considered. , polarization resistance and polarized capacitance .

[0067] The present application provides an online joint estimation method for model parameters and SOE. According to the root mean square error between the estimated voltage and the measured voltage of the equivalent circuit model, a fitness function is constructed to amplify the weight of the larger error by squaring the error between the estimated voltage and the measured voltage, so that the optimization process pays more attention to the performance of the model under conditions with large errors, effectively avoiding large deviations of the model in some scenarios. According to the fitting curve and the fitness function, a preset particle swarm algorithm is used to perform parameter identification on the voltage equation of the equivalent circuit model to obtain the initial parameters of the equivalent circuit model. Therefore, the present application combines the fitting curve with the fitness function, and the preset particle swarm algorithm comprehensively considers the characteristics of the battery under different states of charge during the parameter identification process. By traversing the voltage errors under various working conditions, the optimized equivalent circuit model can maintain a high prediction accuracy in different standard test conditions and actual complex driving conditions, narrow the fluctuation range of the prediction error, and effectively improve the adaptability of the equivalent circuit model to different application scenarios.

[0068] Figure 6 Schematic diagram of the process of online joint estimation method of model parameters and SOE provided in the embodiment of the present application Figure 4 .like Figure 6 As shown, the SOE estimation state equation and SOE estimation observation equation of the target battery are established based on the initial parameters, which may include: S410: Use the power integration method to establish a SOE calculation equation for the target battery.

[0069] In one possible implementation, the power integration method is used to establish the SOE calculation equation of the target battery using the following formula (16).

[0070] Formula (16) in, and Respectively SOE value at any time and at any moment; is the rated energy of the battery; is the energy charge and discharge efficiency, usually 1; I(t) is the current at time t, where charging current is defined as negative and discharging current is defined as positive; V(t) is the voltage at time t.

[0071] S420 , discretizing the SOE calculation equation to obtain a SOE discrete equation.

[0072] In one possible implementation, the above formula (16) is discretized and the SOE discrete equation is obtained by the following formula (17).

[0073]

[0074] Formula (17) S430: Establishing a SOE estimation state equation based on the SOE discrete equation and initial parameters.

[0075] In one possible implementation, the SOE discretization equation according to the above formula (14) and the initial parameters , let the system state variable be X= , the load current is taken as the system input, and the battery terminal voltage is taken as the system output. The SOE estimation state equation of the combination of SOE and initial parameters can be established by the following formula (18).

[0076] Formula (18) Where X(k) is the quantity to be estimated; I(k) is the current value; W(k) is the process noise; A and B are coefficient matrices.

[0077] The above formula (18) is discretized and the discrete state equation is obtained through the following formula (19).

[0078] Formula (19) Among them, the coefficient matrix ; Coefficient matrix .

[0079] S440: Establish an SOE estimation observation equation based on the SOE estimation state equation.

[0080] In one possible implementation, based on the above formula (18), the SOE estimation observation equation can be established by the following formula (20).

[0081] CX(k)+D(k) formula (20) Where C is the observation matrix; X(k) is the state equation; Z(k) is the observation data obtained by sensor measurement; D(k) is the measurement noise, which represents the measurement error of the sensor on the parameter, and its covariance matrix is ​​R.

[0082] Among them, the measurement matrix C in the above formula (20) is obtained according to the preset Jacobian matrix, and the measurement matrix at time k is as follows (21): Formula (21) in, It can be expressed by the following formula (22): Formula (22) in, It can be expressed by the following formula (23): Formula (23) in, It can be expressed by the following formula (24): Formula (24) in, It can be expressed by the following formula (25): Formula (25) It should be noted that since the SOE estimation observation equation in the above formula (20) is a nonlinear equation, it is necessary to perform a first-order Taylor expansion on it to linearize the nonlinear system.

[0083] The present application provides an online joint estimation method for model parameters and SOE, which adopts the power integration method to establish the SOE calculation equation of the target battery to capture the energy changes of the battery in the charging and discharging process in real time. Whether it is steady-state small current charging and discharging or transient large current impact, the SOE can be dynamically updated through power integration, providing a reliable basis for energy scheduling of the energy storage system; the SOE calculation equation is discretized to obtain the SOE discrete equation to reduce memory usage and can run stably on resource-constrained embedded devices; based on the SOE discrete equation and initial parameters, the SOE estimated state equation is established; based on the SOE estimated state equation, the SOE estimated observation equation is established to provide a basis for the accuracy of subsequent SOE estimation based on the SOE estimated state equation and the SOE estimated observation equation.

[0084] Figure 7 Schematic diagram of the process of online joint estimation method of model parameters and SOE provided in the embodiment of the present application Figure 5 .like Figure 7 As shown, the above-mentioned SOE estimation state equation and SOE estimation observation equation are used to perform online estimation of the target battery to obtain the current SOE estimation value of the target battery, which may include: S510 , based on the SOE estimation value of the target battery at time k, the SOE estimation state equation is used to perform online SOE estimation on the target battery, and the SOE estimation value of the target battery at time k+1 is obtained as the state variable at time k+1.

[0085] S520: Based on the state variables at time k+1, the observation equation is estimated using the SOE to make predictions to obtain the observed variables at time k+1.

[0086] S530 , correcting the state change at time k+1 according to the observed variables at time k+1, and obtaining the optimal state estimate at time k+1 as the optimal SOE estimate of the target battery at time k+1.

[0087] Before explaining the optimal SOE estimation, the AEKF algorithm process steps are introduced.

[0088] 1) Initialization, set the initial value of the state observer: , , , .

[0089] 2) Prior state estimation: Estimate the k moment based on the k-1 moment state and covariance. The state estimation formula is as follows: The estimated system state is as follows: Formula (26) The estimated error covariance is as follows (27): Formula (27) in, and are the system state and covariance at time k-1, and are the system prior estimates of state and covariance, respectively.

[0090] 3) Delay estimation correction: calculating the error of the new information and the Kalman gain matrix , and update the noise covariance adaptively, and then according to the k-time observation value Corrected state estimate and covariance estimation , the formula is as follows: The updated information matrix is ​​as follows: Formula (28) The Kalman gain matrix is ​​calculated as follows (29): Formula (29) The adaptive noise covariance is updated as follows: Formula (30) in, ; .

[0091] The system status update result is as follows: Formula (31) The error covariance correction is as follows (32): Formula (32) It should be noted that the output The state and covariance matrix at time t are used for state estimation at time (k+1).

[0092] In one possible implementation, based on the SOE estimated value of the target battery at time k, the SOE estimation state equation of formula (13) is used to perform online SOE estimation on the target battery, and the SOE estimated value of the target battery at time k+1 is obtained as the state variable x(k+1) at time k+1. At the same time, the state variable x(k+1) is observed using the SOE estimation observation equation of formula (16) to obtain the observed variable Z(k+1) at time (k+1). Then, the observed variable Z(k+1) at time (k+1) is used to correct the state variable x(k+1) at time k+1, and the above formulas (23) to (25) can be used. Subsequently, the optimal SOE estimated value at time (k+1) is obtained according to the above formula (26), and it is used as the initial value of the next estimation to achieve estimation of the nonlinear system.

[0093] This application provides an online joint estimation method for model parameters and SOE. Based on the SOE estimated value of the target battery at time k, the SOE estimation state equation is used to perform online SOE estimation on the target battery, and the SOE estimated value of the target battery at time k+1 is obtained as the state variable at time k+1; based on the state variable at time k+1, the SOE estimation observation equation is used for prediction to obtain the observed variable at time k+1; based on the observed variable at time k+1, the state variable at time k+1 is corrected to obtain the optimal state estimate at time k+1 as the optimal SOE estimated value of the target battery at time k+1. Therefore, this application can improve the accuracy and reliability of SOE estimation while ensuring computational efficiency, providing core support for battery life cycle management.

[0094] To facilitate understanding of the above-mentioned online joint estimation method of model parameters and SOE, the embodiment of the present application further provides an example of a process for verifying the estimation results of the online joint estimation method of model parameters and SOE, which is further described below with reference to the accompanying drawings. Figure 8 Schematic diagram of the estimation result verification process of the online joint estimation method of model parameters and SOE provided in the embodiment of the present application. Figure 8 As shown, the schematic diagram provided in the embodiment of the present application may include: S610: Conduct a low-current charge and discharge static test using two preset working conditions.

[0095] Among them, two preset operating conditions can be selected according to actual conditions. For example, the two preset operating conditions can be selected from CLTC (China Car Driving Conditions) and NEDC (New European Driving Cycle).

[0096] S620 , applying an online joint estimation method of model parameters and SOE to perform online estimation of the SOE of the target battery to obtain a current estimated SOE value of the target battery.

[0097] In one possible implementation, low-current charge and discharge static tests are performed on CLTC (China Car Operating Conditions) and NEDC (New European Driving Cycle), respectively, to obtain fitting curves of the state of energy (SOE) and open-circuit voltage (OCV) of the target battery corresponding to the two preset operating conditions. Based on the fitting curves, a particle swarm algorithm is then used to perform parameter identification on the voltage equation of the equivalent circuit model to obtain the initial parameters of the equivalent circuit model. Based on the initial parameters, the SOE estimation state equation and SOE estimation observation equation of the target battery are established. Finally, based on the SOE estimation state equation and SOE estimation observation equation, the SOE of the target battery is estimated online to obtain the current SOE estimated value of the target battery.

[0098] For example, for CLTC (China Driving Cycle) and NEDC (New European Driving Cycle), the results are compared based on the second-order model combined with the EKF algorithm, the improved model combined with the EKF algorithm, and the online joint estimation method of the model parameters and SOE of this application, that is, the improved model combined with the AEKF algorithm. The SOE obtained by energy integration is used as a reference value, and the Maximum error, RMSE (root mean square error), and MAE (mean absolute error) are calculated to measure the SOE estimation accuracy.

[0099] For example, Table 1 is a table of estimated SOE values ​​for NEDC operating conditions provided in an embodiment of the present application. Table 2 is a table of estimated SOE values ​​for CLTC operating conditions provided in an embodiment of the present application.

[0100] Table 1

[0101] As can be seen from Table 1, under NEDC operating conditions, the method of the present application has the smallest maximum error, RMSE (root mean square error), and MAE (mean absolute error) compared to the second-order model combined with the EKF algorithm and the improved model combined with the EKF algorithm. In other words, the online joint estimation method of model parameters and SOE provided by the present application performs well in SOE estimation accuracy compared to the traditional second-order model combined with the extended Kalman filter (EKF) algorithm and the improved model combined with the EKF algorithm, and can more accurately reflect the actual energy state of the battery.

[0102] Table 2

[0103] As can be seen from Table 1, under the CLTC working conditions, the method of the present application has the smallest maximum error, RMSE (root mean square error) and MAE (mean absolute error) compared with the second-order model combined with the EKF algorithm and the improved model combined with the EKF algorithm. In other words, the online joint estimation method of model parameters and SOE provided by the present application performs well in SOE estimation accuracy compared with the traditional second-order model combined with the extended Kalman filter (EKF) algorithm and the improved model combined with the EKF algorithm, and can more accurately reflect the actual energy state of the battery.

[0104] In summary, according to Tables 1 and 2 above, the online joint estimation method of model parameters and SOE provided in this application performs well in terms of SOE estimation accuracy compared to the traditional second-order model combined with the extended Kalman filter (EKF) algorithm, and the improved model combined with the EKF algorithm, and can more accurately reflect the actual energy state of the battery.

[0105] The online joint estimation method for model parameters and SOE provided in this application uses two preset operating conditions to conduct low-current charge and discharge static tests. This method then estimates the SOE of a target battery online using this method to obtain the target battery's current SOE estimate. Because the method takes into account the solid-phase diffusion and liquid-phase diffusion effects of the battery, a particle swarm algorithm is used to identify the initial model parameters. Simultaneously, an adaptive extended Kalman filter algorithm is used to achieve online joint estimation of model parameters and SOE. This online identification of model parameters can improve the model accuracy of the battery under different temperatures, operating conditions, and aging conditions, while also improving the SOE estimation accuracy under different environments and dynamic operating conditions. Therefore, the online joint estimation method for model parameters and SOE provided in this application performs superiorly in terms of SOE estimation accuracy compared to traditional second-order models combined with extended Kalman filters (EKFs) and improved models combined with EKF algorithms, and can more accurately reflect the actual energy state of the battery.

[0106] Figure 9 This is a schematic diagram of the structure of an online joint estimation device for model parameters and SOE provided in an embodiment of the present application. Figure 9 As shown, the online joint estimation device 70 of the model parameters and SOE may include: A first establishing module 71 is used to establish an equivalent circuit model of a target battery; A test module 72 is used to perform charge and discharge tests on the target battery to obtain a fitting curve of the state of energy (SOE) and open circuit voltage (OCV) of the target battery; An identification module 73 is used to perform parameter identification on the voltage equation of the equivalent circuit model based on the fitting curve using a particle swarm algorithm to obtain initial parameters of the equivalent circuit model; A second establishing module 74 is used to establish the SOE estimation state equation and SOE estimation observation equation of the target battery according to the initial parameters; The estimation module 75 is used to perform online SOE estimation on the target battery according to the SOE estimation state equation and the SOE estimation observation equation to obtain the current SOE estimation value of the target battery.

[0107] In an optional embodiment, the first establishment module 71 is specifically used to establish an equivalent circuit model based on the open circuit voltage, battery internal resistance, first electrochemical polarization potential and second electrochemical polarization potential of the target battery, wherein the first electrochemical polarization potential is the electrochemical reaction polarization potential caused by the polarization capacitance effect, and the second electrochemical polarization potential is the electrochemical reaction polarization potential caused by the solid-liquid diffusion of lithium ions.

[0108] In an optional embodiment, the voltage equation of the equivalent circuit model is: ,in, is the battery terminal voltage; is the open circuit voltage of the target battery; is the current at time t; is the internal resistance of the battery; is the first electrochemical polarization potential; is the second electrochemical polarization potential.

[0109] In an optional embodiment, the first establishment module 71 is also used to: discretize the voltage equation of the equivalent circuit model to obtain the voltage discrete equation of the equivalent circuit model; the identification module 73 is specifically used to: use the particle swarm algorithm according to the fitting curve to perform parameter identification on the voltage discrete equation to obtain initial parameters.

[0110] In an optional embodiment, the test module 72 is specifically used to: after fully charging the target battery, use a preset small current to perform a discharge test on the target battery to obtain the state of charge (SOC) value of the target battery at multiple OCV values; calculate the SOE values ​​at multiple OCV values ​​according to the SOC values ​​at the multiple OCV values; and use the least squares method to perform curve fitting according to the SOE values ​​at the multiple OCV values ​​to obtain a fitting curve.

[0111] In an optional embodiment, the identification module 73 is specifically used to: construct a fitness function based on the root mean square error between the estimated voltage and the measured voltage of the equivalent circuit model; and use a preset particle swarm algorithm to perform parameter identification on the voltage equation of the equivalent circuit model based on the fitting curve and the fitness function to obtain the initial parameters of the equivalent circuit model.

[0112] In an optional embodiment, the second establishment module 74 is specifically used to: establish the SOE calculation equation of the target battery using the power integration method; discretize the SOE calculation equation to obtain the SOE discrete equation; establish the SOE estimation state equation based on the SOE discrete equation and initial parameters; establish the SOE estimation observation equation based on the SOE estimation state equation.

[0113] In an optional embodiment, the estimation module 75 is specifically used to: perform online SOE estimation on the target battery based on the SOE estimation value of the target battery at time k, using the SOE estimation state equation, and obtain the SOE estimation value of the target battery at time k+1 as the state variable at time k+1; use the SOE estimation observation equation to make a prediction based on the state variable at time k+1, and obtain the observation variable at time k+1; correct the state variable at time k+1 based on the observation variable at time k+1, and obtain the optimal state estimate at time k+1 as the optimal SOE estimate value of the target battery at time k+1.

[0114] It should be noted that for details not disclosed in the online joint estimation device for model parameters and SOE in the embodiment of the present application, please refer to the details disclosed in the online joint estimation method for model parameters and SOE in the embodiment of the present application, and the details will not be repeated here.

[0115] The above modules can be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), one or more microprocessors, or one or more field programmable gate arrays (FPGAs). For example, when a module is implemented by scheduling program code through a processing element, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0116] The embodiment of the present application further provides an electronic device 80, such as Figure 10 As shown, Figure 10 The electronic device structure diagram provided for the embodiment of the present application includes: a processor 81, a memory 82, and optionally, a bus.

[0117] The memory 82 stores machine-readable instructions executable by the processor 81. When the electronic device 80 is running, the processor 81 communicates with the memory 82 via a bus. When the machine-readable instructions are executed by the processor 81, the method steps described in the aforementioned method embodiment are performed.

[0118] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the above-mentioned method for online joint estimation of model parameters and SOE are executed.

[0119] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and device described above can refer to the corresponding process in the method embodiment, and will not be repeated in this application. In the several embodiments provided in this application, it should be understood that the disclosed system, device and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0120] In addition, the functional units in the various embodiments of the present application can be integrated into a single processing unit, each unit can exist physically separately, or two or more units can be integrated into a single unit. If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0121] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. An online joint estimation method of model parameters and SOE, characterized in that: include: Establish an equivalent circuit model of the target battery; Performing a charge and discharge test on the target battery to obtain a fitting curve of the state of energy (SOE) and the open circuit voltage (OCV) of the target battery; According to the fitting curve, a particle swarm algorithm is used to perform parameter identification on the voltage equation of the equivalent circuit model to obtain initial parameters of the equivalent circuit model; According to the initial parameters, establishing the SOE estimation state equation and SOE estimation observation equation of the target battery; According to the SOE estimation state equation and the SOE estimation observation equation, the SOE of the target battery is estimated online to obtain a current SOE estimation value of the target battery.

2. The online joint estimation method of model parameters and SOE according to claim 1, characterized in that: The establishing of the equivalent circuit model of the target battery includes: The equivalent circuit model is established based on the open circuit voltage, battery internal resistance, first electrochemical polarization potential and second electrochemical polarization potential of the target battery, wherein the first electrochemical polarization potential is the electrochemical reaction polarization potential caused by the electrochemical capacitance effect, and the second electrochemical polarization potential is the electrochemical reaction polarization potential caused by the solid-liquid diffusion of lithium ions.

3. The online joint estimation method of model parameters and SOE according to claim 2, characterized in that: The voltage equation of the equivalent circuit model is: ， in, is the battery terminal voltage; is the open circuit voltage of the target battery; is the current at time t; is the internal resistance of the battery; is the first electrochemical polarization potential; is the second electrochemical polarization potential.

4. The online joint estimation method of model parameters and SOE according to claim 1, characterized in that: After establishing the equivalent circuit model of the target battery, the method further includes: Discretizing the voltage equation of the equivalent circuit model to obtain a voltage discrete equation of the equivalent circuit model; The method of performing parameter identification on the equivalent circuit model using a particle swarm algorithm according to the fitting curve to obtain initial parameters of the equivalent circuit model includes: According to the fitting curve, the particle swarm algorithm is used to perform parameter identification on the voltage discrete equation to obtain the initial parameters.

5. The online joint estimation method of model parameters and SOE according to claim 1, characterized in that: The charging and discharging test is performed on the target battery to obtain a fitting curve of the battery state of energy (SOE) and the open circuit voltage (OCV) of the target battery, including: After the target battery is fully charged, a discharge test is performed on the target battery using a preset low current to obtain the state of charge (SOC) value of the target battery at multiple OCV values; Calculating SOE values ​​at the multiple OCV values ​​respectively according to the SOC values ​​at the multiple OCV values; According to the SOE values ​​under the multiple OCV values, a least squares method is used to perform curve fitting to obtain the fitting curve.

6. The online joint estimation method of model parameters and SOE according to claim 1, characterized in that: The method of performing parameter identification on the voltage equation of the equivalent circuit model using a particle swarm algorithm according to the fitting curve to obtain initial parameters of the equivalent circuit model includes: constructing a fitness function based on a root mean square error between an estimated voltage and a measured voltage of the equivalent circuit model; According to the fitting curve and the fitness function, a preset particle swarm algorithm is used to perform parameter identification on the voltage equation of the equivalent circuit model to obtain initial parameters of the equivalent circuit model.

7. The online joint estimation method of model parameters and SOE according to claim 1 is characterized in that: The step of establishing the SOE estimation state equation and the SOE estimation observation equation of the target battery according to the initial parameters includes: The power integration method is used to establish the SOE calculation equation of the target battery; Discretizing the SOE calculation equation to obtain a SOE discrete equation; Establishing the SOE estimation state equation according to the SOE discrete equation and the initial parameters; The SOE estimation observation equation is established according to the SOE estimation state equation.

8. The online joint estimation method of model parameters and SOE according to claim 1, characterized in that: The step of performing online SOE estimation on the target battery according to the SOE estimation state equation and the SOE estimation observation equation to obtain a current SOE estimation value of the target battery includes: According to the SOE estimated value of the target battery at time k, the SOE estimated state equation is used to perform an online SOE estimation on the target battery, and the SOE estimated value of the target battery at time k+1 is obtained as the state variable at time k+1; According to the state variable at the k+1 moment, the SOE estimation observation equation is used to make a prediction to obtain the observation variable at the k+1 moment; The state change at time k+1 is corrected according to the observed variables at time k+1, and an optimal state estimate at time k+1 is obtained as the optimal SOE estimate of the target battery at time k+1.

9. An online joint estimation device for model parameters and SOE, characterized in that: include: A first building module is used to build an equivalent circuit model of a target battery; A test module, configured to perform a charge and discharge test on the target battery to obtain a fitting curve of the state of energy (SOE) and the open circuit voltage (OCV) of the target battery; an identification module, configured to perform parameter identification on the voltage equation of the equivalent circuit model using a particle swarm algorithm according to the fitting curve to obtain initial parameters of the equivalent circuit model; A second establishing module is used to establish the SOE estimation state equation and SOE estimation observation equation of the target battery according to the initial parameters; An estimation module is used to perform online SOE estimation on the target battery according to the SOE estimation state equation and the SOE estimation observation equation to obtain a current SOE estimation value of the target battery.

10. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores machine-executable instructions that can be executed by the processor, and the processor can execute the machine-executable instructions to implement the method according to any one of claims 1 to 8.

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