Lithium battery charge state estimation method based on extended state observer
By employing a lithium battery state-of-charge estimation method based on an extended state observer, online parameter identification is performed using the Thevenin equivalent circuit model and the recursive least squares method with forgetting factors. Combined with the extended state observer for real-time estimation and dynamic compensation, the problem of non-zero mean and non-Gaussian noise disturbance in lithium battery SOC estimation is solved, and high-precision SOC estimation is achieved.
Patent Information
- Application Number
- CN202511765870.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-27
- Publication Date
- 2026-01-13
AI Technical Summary
Existing extended Kalman filtering methods cannot effectively handle non-zero mean and non-Gaussian noise disturbances in lithium battery SOC estimation, resulting in decreased estimation accuracy and poor robustness.
A lithium battery state-of-charge estimation method based on an extended state observer is adopted. By establishing a Thevenin equivalent circuit model, online parameter identification is performed using bilinear transformation and recursive least squares method with forgetting factor, and real-time estimation and dynamic compensation are performed in combination with the extended state observer.
It significantly improves the adaptability and accuracy of lithium battery SOC estimation, effectively copes with non-zero mean and non-Gaussian noise disturbances, suppresses measurement noise and battery aging effects, and maintains high-precision SOC estimation.
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Figure CN121324948A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of lithium battery technology, and more particularly to a method for estimating the state of charge of a lithium battery based on an extended state observer. Background Technology
[0002] State of Charge (SOC) is a key parameter characterizing the remaining capacity of a lithium-ion battery. With the increasing demands for global marine resource exploration and development and ecological environmental protection, the energy endurance of unmanned surface vessels (USVs), as important tools in modern marine observation systems, has become a critical factor restricting long-term operations. Pure electric USVs have become an important development direction due to their environmental advantages. However, lithium-ion battery management not only requires precise voltage input control during charging but, more importantly, the use of more accurate battery SOC estimation methods. Accurate SOC estimation not only provides key parameter support for the optimized implementation of charge and discharge control strategies but also fundamentally determines the lifespan and operational adaptability of lithium-ion batteries, representing a key breakthrough in improving the energy management level of USVs.
[0003] Currently, mainstream SOC estimation methods can be mainly divided into three categories: model-based methods, data-driven methods, and artificial intelligence-based methods. Among them, model-based methods are more widely used. Within this category, the extended Kalman filter (EPF) method has the advantages of high computational efficiency and low storage requirements. In environments with Gaussian white noise and low nonlinearity, it exhibits good stability, fast convergence characteristics, and high estimation accuracy.
[0004] However, existing extended Kalman filtering methods have significant limitations. When the system exhibits strong nonlinear characteristics, the error accumulation effect caused by linearization is aggravated, resulting in a significant decrease in estimation accuracy, and even divergence. Furthermore, traditional methods can typically only ideally handle zero-mean white noise disturbances; their estimation performance and robustness are poor for complex noise disturbances such as non-zero-mean and non-Gaussian noise present in real-world systems. Summary of the Invention
[0005] In view of this, the purpose of this invention is to propose a lithium battery state of charge estimation method based on an extended state observer, so as to solve the technical problem that the existing SOC estimation method has insufficient estimation accuracy when the system exhibits strong nonlinear characteristics.
[0006] The technical means employed in this invention are as follows: A method for estimating the state of charge of a lithium battery based on an extended state observer includes the following steps: S1. Establish the Thevenin equivalent circuit model of the lithium battery system, derive the transfer function of the Thevenin equivalent circuit model in the s domain, and use the bilinear transformation method to discretize the transfer function to obtain the discrete difference equation and terminal voltage expression of the lithium battery system containing the parameters of the model to be identified. S2. Based on the terminal voltage expression obtained in S1, define the parameter vector and data vector of the lithium battery system to be estimated, and use the recursive least squares method with a forgetting factor for online parameter identification. N The criterion function of the second observation is used to solve for the ohmic internal resistance, polarization resistance and polarization capacitance in the Thevenin equivalent circuit model; S3. Fit the mapping relationship between the state of charge (SOC) and open-circuit voltage of the lithium battery to obtain the SOC-open-circuit voltage function relationship; combine the ohmic internal resistance, polarization resistance and polarization capacitance parameters identified in S2 to construct the state space equation and output equation of the lithium battery system with SOC and polarization voltage as state variables. S4. Based on the state-space equation of the lithium battery system constructed in S3, an extended state observer is designed to observe the system state and total disturbance. The model parameters identified and updated online in S2 are input into the extended state observer in real time, and the state of charge of the lithium battery is estimated and dynamically compensated in real time through the observer.
[0007] Furthermore, S1 specifically includes the following steps: An equivalent circuit model of Thevenin is established, and the Thevenin equivalent circuit model is as follows:
[0008] in, It is the terminal voltage of the lithium battery; It is the open-circuit voltage of a lithium battery; It is the polarization voltage of the lithium battery. This is the operating current of the lithium battery; It is the ohmic internal resistance of a lithium battery; It is the time-varying polarization internal resistance of the lithium battery; It is the time-varying polarization capacitor of a lithium battery; The state-of-charge model of a lithium battery is established, and the expression is as follows:
[0009] in, This is the initial time. The current moment; For Coulomb efficiency; This refers to the rated capacity of the lithium battery. The difference between the terminal voltage and the open-circuit voltage of the lithium battery Using the lithium battery current as the input excitation and the system output variable as the input variable, the transfer function of the lithium battery system is established:
[0010] The transfer function is discretized using the bilinear transform method:
[0011] in:
[0012] in, , and These are the relevant parameters for calculating the Thevnin model parameters, where T is the sampling time; The difference equations for the discretization of the lithium battery system are as follows:
[0013] The expression for the lithium battery terminal voltage is as follows: .
[0014] Furthermore, S2 specifically includes the following steps: Define the parameter vector and data vector of the lithium battery system to be estimated as follows:
[0015] in ,but , , , ; The terminal voltage expressed using the recursive least squares method in Thevenin's equivalent circuit model is:
[0016] in, It is the output variable of the lithium battery system, that is , These are data variables of the lithium battery system. These are the identification parameters to be estimated for the lithium battery system. It is zero-mean white noise; For the recursive least squares method, the lithium battery system model produces a deviation between the predicted output and the measured data at each sampling time k, resulting in a criterion function for N observations:
[0017] in, It is a forgetting factor; Ohmic resistance Polarization resistance With polarization capacitor The calculation formula is as follows: .
[0018] Furthermore, the specific steps of S3 are as follows: Lithium battery terminal voltage for:
[0019] in, It is the open-circuit voltage of the lithium battery and The function; Modify the initial lithium battery settings The values were then measured and their corresponding open-circuit voltages were measured respectively. The results of the multi-order fitting relationship between the two were obtained. Based on the fitting of multi-order polynomials, the following was obtained. and The mathematical relationship is as follows:
[0020] The equation for the rate of change of charge state is as follows:
[0021] in, Uncertainties caused by errors in actual battery capacity, etc. The state equation is:
[0022] in, , It is the uncertainty of the terminal voltage; The state equation of a lithium battery is:
[0023] The output equation of the lithium battery system is:
[0024] The parameter expression for the lithium battery system is:
[0025] in, , and The parameters were obtained through online parameter identification using the recursive least squares method based on the forgetting factor.
[0026] Furthermore, S4 specifically includes the following steps: set up State variables of lithium battery system The estimation, defining the observation error :
[0027] set up yes Based on the estimation, the extended state observer for observing the battery's state of charge is:
[0028] in, , It is a positive real constant. It is a positive real constant. and It is the extended state observer gain; Based on the need for lithium battery state of charge estimation, a recursive least squares method with a forgetting factor is used to achieve online parameter identification. The battery system model and the extended state observer are cascaded and integrated to estimate the battery state of charge.
[0029] The present invention also provides a storage medium comprising a stored program, wherein, when the program is executed, any of the above-described lithium battery state-of-charge estimation methods based on an extended state observer are performed.
[0030] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes any of the above-described lithium battery state-of-charge estimation methods based on an extended state observer through the computer program.
[0031] Compared with the prior art, the present invention has the following advantages: Traditional extended Kalman filtering methods can only handle zero-mean white noise disturbances and are less effective for other types of disturbances. The method proposed in this invention breaks through this limitation and can effectively deal with non-zero-mean and non-Gaussian noise, significantly improving the adaptability of SOC estimation.
[0032] To address the issue of parameter drift in lithium batteries during use, this invention utilizes the forgetting factor recursive least squares method to achieve online identification of model parameters. Combined with the perturbation observation capability of the extended state observer, it effectively compensates for the estimation error caused by parameter drift.
[0033] This invention uses an extended state observer to observe and compensate for internal and external disturbances of the system as new states. This not only suppresses external disturbances of measurement noise, but also overcomes the influence of internal disturbances caused by battery aging, enabling SOC estimation to maintain high accuracy under various complex operating conditions. Attached Figure Description
[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0035] Figure 1 This is a flowchart of the method of the present invention.
[0036] Figure 2 This is a circuit diagram of the Thevnin lithium battery model in this invention.
[0037] Figure 3 This is a diagram showing the identification results of the ohmic internal resistance of the lithium battery in this invention.
[0038] Figure 4 This is a diagram showing the identification results of the polarization resistance of the lithium battery in this invention.
[0039] Figure 5 This is a diagram showing the identification results of the polarized capacitor of the lithium battery in this invention.
[0040] Figure 6 This is a graph showing the multi-stage fitting results of the SOC and open-circuit voltage of the lithium battery in this invention.
[0041] Figure 7 The graph shows the linear fitting results of the SOC and open-circuit voltage of the lithium battery in this invention.
[0042] Figure 8 This is a comparison diagram of the actual terminal voltage of the lithium battery and the identified calculated terminal voltage in this invention.
[0043] Figure 9 This is a graph showing the SOC estimation results of the lithium battery during discharge in this invention.
[0044] Figure 10 The graph shows the SOC estimation results of the lithium battery during charging in this invention. Detailed Implementation
[0045] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.
[0046] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0047] like Figure 1 As shown, this invention provides a method for estimating the state of charge of a lithium battery based on an extended state observer, comprising the following steps: S1. Establish the Thevenin equivalent circuit model of the lithium battery system, derive the transfer function of the Thevenin equivalent circuit model in the s-domain, and discretize the transfer function using the bilinear transformation method to obtain the discrete difference equations and terminal voltage expressions of the lithium battery system containing the parameters of the model to be identified; specifically: Establish the Thevnin equivalent circuit model, such as Figure 2 As shown. Based on Kirchhoff's laws, the mathematical equations for the Thevnin model can be established as follows:
[0048] in, It is the terminal voltage of the lithium battery; It is the open-circuit voltage of a lithium battery; It is the polarization voltage of the lithium battery. This is the operating current of the lithium battery; It is the ohmic internal resistance of a lithium battery; It is the time-varying polarization internal resistance of the lithium battery; It is a time-varying polarized capacitor in a lithium battery.
[0049] The state-of-charge (SOC) model of a lithium battery is established, i.e., the SOC expression for a lithium battery is:
[0050] in, This is the initial time. The current moment; For Coulomb efficiency; This refers to the rated capacity of the lithium battery.
[0051] If we consider the difference between the lithium battery terminal voltage and the open-circuit voltage... If the lithium battery current is considered as the system output variable and the lithium battery current is considered as the input excitation, then the transfer function model of the lithium battery system can be established:
[0052] Discretize using the bilinear transform method:
[0053] in:
[0054] in, , and These are the parameters related to the calculation of Thevnin model parameters. T Given the sampling time, the discretized difference equations for the lithium battery system can be derived as follows:
[0055] Considering the sampling period is sufficiently small, the fluctuation of the open-circuit voltage of the lithium battery within a single sampling interval is negligible and can be approximated as a constant value. Therefore, the terminal voltage of a lithium battery can be written as follows:
[0056] S2. Based on the terminal voltage expression obtained in S1, define the parameter vector and data vector to be estimated for the lithium battery system. Use the recursive least squares method with a forgetting factor to perform online parameter identification, obtaining the criterion function for N observations. Solve for the ohmic internal resistance, polarization resistance, and polarization capacitance in the Thevenin equivalent circuit model. Specifically: Define the parameter vector and data vector of the lithium battery system to be estimated as follows:
[0057] In formula (8) ,but , , , .
[0058] Therefore, the terminal voltage expressed by the recursive least squares method used in the Thevenin model is:
[0059] in, It is the output variable of the lithium battery system, that is , These are data variables of the lithium battery system. These are the identification parameters to be estimated for the lithium battery system. It is white noise with zero mean.
[0060] For the recursive least squares method, the lithium battery system model at each sampling time k Both produce deviations between the predicted output and the measured data, making it easy to obtain the criterion function for N observations:
[0061] in, It is the forgetting factor, which is generally taken as 0.95~1.0.
[0062] The traditional least squares method suffers from covariance matrix degradation during the recursive process, which manifests as follows: As the number of iterations increases, the value gradually approaches zero, leading to data saturation and reducing the algorithm's convergence speed. A forgetting factor is introduced. It can better adapt to changes in lithium battery systems. When the criterion function is as shown in equation (10), it can be derived that... The basic formula for recursive least squares with forgetting factor at each time step:
[0063] in, yes The estimated identification parameter values at time, yes The estimated identification parameter values at time. It is the gain matrix. yes The covariance matrix at time t. yes The covariance matrix at time t, It is an identity matrix.
[0064] When validating the recursive least squares method with a forgetting factor, the following parameters need to be initialized:
[0065] in, It is a sufficiently large positive real number. It is a relatively small real number. It is an identity matrix.
[0066] Equation (11) can be solved using the recursive least squares method with a forgetting factor to obtain the parameter vector matrix. .
[0067] From equation (4) z plane to s The inverse solution of the plane is given by the following transformation formula:
[0068] Substituting equation (13) into equation (4), we get:
[0069] By comparing the coefficients of equation (14) with those of equation (3), we can obtain:
[0070] Finally, all the parameters required in the lithium battery system model are obtained by combining the parameter vector to be identified in equation (8) and equation (15):
[0071] To verify the effectiveness of the least squares method with a forgetting factor, an online parameter identification simulation was performed. Figure 3 The results of the lithium battery ohmic internal resistance identification show that the lithium battery ohmic internal resistance... Stable and perfectly matching the preset model parameters; by Figure 4 The results of lithium battery polarization resistance identification show that the polarization resistance The change with discharge time shows a dynamic trend of first rising and then slowly decreasing; from Figure 5 The identification results of the polarized capacitor in the lithium battery show that the polarized capacitor... The value increases slowly as the discharge time changes. This result in the simulation is related to the sampling accuracy, the initial covariance matrix of parameter identification, and the selection of estimated identification parameter values.
[0072] S3. Fit the mapping relationship between the state of charge (SOC) and open-circuit voltage of the lithium battery to obtain the SOC-open-circuit voltage function relationship; combine the ohmic internal resistance, polarization resistance and polarization capacitance parameters identified in S2 to construct the state space equation and output equation of the lithium battery system with SOC and polarization voltage as state variables. The charging and discharging current direction of a lithium battery is defined as follows: the current is positive during charging and negative during discharging. Therefore, the lithium battery terminal voltage... for:
[0073] in, It is the open-circuit voltage of the lithium battery and The function.
[0074] Modify the initial lithium battery settings The values were then measured and their corresponding open-circuit voltages were measured respectively. The results of the multi-order fitting relationship between the two are as follows: Figure 6 As shown. Based on the fitting of a multi-order polynomial, the following is derived: and The mathematical relationship is as follows: (18) To reduce the complexity of solving high-order systems, the state equations are derived, assuming... It has a linear relationship with the open-circuit voltage, that is... , and The coefficients represent the linear relationship, according to... Figure 7 The discrete data points in the SOC vs. open-circuit voltage characteristic curve of the lithium battery are fitted to obtain... and The first-order linear relationship yields the following results: Figure 7 As shown.
[0075] Equation for the rate of change of charge state:
[0076] in, This refers to the uncertainty caused by errors in the actual battery capacity, etc.
[0077] Substituting equation (17) into equation (19), we get:
[0078] in, .
[0079] The polarization voltage in the RC circuit is expressed as:
[0080] in, , , It is an uncertain quantity of polarization voltage.
[0081] In the derivation of equation (17), considering the dynamic characteristics of the RC circuit, when the sampling time interval is sufficiently small, the rate of change of the terminal voltage relative to the rate of change of the current can be ignored, i.e. Therefore, the second term in the derivative of equation (17) can be ignored. Combining the derivations of equations (19), (20), and (21), we finally obtain... The state equation is:
[0082] in, , It is the uncertainty of the terminal voltage.
[0083] Based on the above derivations, the state equation for a lithium battery can be established as follows:
[0084] It can be represented in the following matrix form:
[0085] in, , , , For system uncertainties, , .
[0086] The output equation of a lithium battery system can be expressed as:
[0087] The parameter expression for a lithium battery system can be represented as:
[0088] in, , and Online parameter identification was performed using the recursive least squares method with forgetting factor, and the parameter estimates obtained at each sampling time showed dynamic differences.
[0089] Figure 8 The comparison chart of the actual terminal voltage and the calculated terminal voltage of the lithium battery shows that the actual terminal voltage of the lithium battery... Terminal voltage calculated from curve and model identification results The curves largely overlap, verifying the effectiveness of the parameter representation method.
[0090] S4. Based on the state-space equation of the lithium battery system constructed in S3, an extended state observer is designed to observe the system state and total disturbance. The model parameters identified and updated online in S2 are input into the extended state observer in real time, and the state of charge of the lithium battery is estimated and dynamically compensated in real time through the observer.
[0091] set up State variables of lithium battery system The estimation, defining the observation error :
[0092] set up yes Based on the estimation, the extended state observer for observing the battery's state of charge is designed as follows:
[0093] in, , It is a positive real constant. is a positive real constant, and is the gain of the designed extended state observer.
[0094] Based on the requirement of lithium battery state of charge estimation, the recursive least squares method with forgetting factor is used to realize online parameter identification. The battery system model represented by Equation (24) and the extended state observer designed by Equation (28) are cascaded and integrated to estimate the battery state of charge.
[0095] To verify the effectiveness of the proposed method, SOC estimation simulations were performed using the proposed method combined with the traditional least squares method and the extended Kalman filter method. A signal generator was added as a disturbance when measuring the lithium battery current, and the noise immunity of the two SOC estimation methods was analyzed. The SOC estimation results under discharge conditions are shown below. Figure 9 As shown, by adjusting the SOC parameters, the SOC estimation results under charging conditions are obtained as follows: Figure 10 As shown, the proposed method exhibits significant advantages in the main operating range. Simulations of SOC estimation during lithium battery charging and discharging verify the effectiveness of the proposed method in estimating SOC under disturbances, demonstrating its ability to provide accurate SOC values for unmanned surface vessel energy management.
[0096] This invention proposes a lithium battery state of charge (SOC) estimation method based on an extended state observer. By constructing an online parameter identification model using the forgetting factor recursive least squares method and combining it with an extended state observer, it effectively solves the problem of decreased estimation accuracy caused by complex disturbances and noise such as non-zero mean and non-Gaussian noise in lithium battery SOC estimation. Compared with the traditional forgetting factor recursive least squares method combined with extended Kalman filtering, the method of this invention can more accurately track the true SOC value. Through online parameter identification and disturbance observation compensation, it can perform real-time estimation and dynamic correction of SOC and system disturbances, significantly improving estimation accuracy and anti-interference capability. At the same time, this method overcomes the limitation of traditional extended Kalman filtering on zero-mean white noise and still has strong robustness to complex disturbances such as non-zero mean and non-Gaussian noise. The effectiveness of its SOC estimation under complex disturbance environments is verified by lithium battery charge and discharge simulation experiments, which can provide reliable SOC estimation support for unmanned surface vessel energy management systems and further promote the application of lithium batteries in complex operating conditions.
[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method for estimating the state of charge of a lithium battery based on an extended state observer, characterized in that, Includes the following steps: S1. Establish the Thevenin equivalent circuit model of the lithium battery system, derive the transfer function of the Thevenin equivalent circuit model in the s domain, and use the bilinear transformation method to discretize the transfer function to obtain the discrete difference equation and terminal voltage expression of the lithium battery system containing the parameters of the model to be identified. S2. Based on the terminal voltage expression obtained in S1, define the parameter vector and data vector of the lithium battery system to be estimated, and use the recursive least squares method with a forgetting factor for online parameter identification. N The criterion function of the second observation is used to solve for the ohmic internal resistance, polarization resistance and polarization capacitance in the Thevenin equivalent circuit model; S3. Fit the mapping relationship between the state of charge (SOC) and open-circuit voltage of the lithium battery to obtain the SOC-open-circuit voltage function relationship; combine the ohmic internal resistance, polarization resistance and polarization capacitance parameters identified in S2 to construct the state space equation and output equation of the lithium battery system with SOC and polarization voltage as state variables. S4. Based on the state-space equation of the lithium battery system constructed in S3, an extended state observer is designed to observe the system state and total disturbance. The model parameters identified and updated online in S2 are input into the extended state observer in real time, and the state of charge of the lithium battery is estimated and dynamically compensated in real time through the observer.
2. The lithium battery state-of-charge estimation method based on an extended state observer according to claim 1, characterized in that, S1 specifically includes the following steps: An equivalent circuit model of Thevenin is established, and the Thevenin equivalent circuit model is as follows: in, It is the terminal voltage of the lithium battery; It is the open-circuit voltage of a lithium battery; It is the polarization voltage of the lithium battery. This is the operating current of the lithium battery; It is the ohmic internal resistance of a lithium battery; It is the time-varying polarization internal resistance of the lithium battery; It is the time-varying polarization capacitor of a lithium battery; The state-of-charge model of a lithium battery is established, and the expression is as follows: in, This is the initial time. The current moment; For Coulomb efficiency; This refers to the rated capacity of the lithium battery. The difference between the terminal voltage and the open-circuit voltage of the lithium battery Using the lithium battery current as the input excitation and the system output variable as the input variable, the transfer function of the lithium battery system is established: The transfer function is discretized using the bilinear transform method: in: in, , and These are the relevant parameters for calculating the Thevnin model parameters, where T is the sampling time; The difference equations for the discretization of the lithium battery system are as follows: The expression for the lithium battery terminal voltage is as follows: 。 3. The lithium battery state-of-charge estimation method based on an extended state observer according to claim 1, characterized in that, S2 specifically includes the following steps: Define the parameter vector and data vector of the lithium battery system to be estimated as follows: in ,but , , , ; The terminal voltage expressed using the recursive least squares method in Thevenin's equivalent circuit model is: in, It is the output variable of the lithium battery system, that is , These are data variables of the lithium battery system. These are the identification parameters to be estimated for the lithium battery system. It is zero-mean white noise; For the recursive least squares method, the lithium battery system model produces a deviation between the predicted output and the measured data at each sampling time k, resulting in a criterion function for N observations: in, It is a forgetting factor; Ohmic resistance Polarization resistance With polarization capacitor The calculation formula is as follows: 。 4. The lithium battery state-of-charge estimation method based on an extended state observer according to claim 1, characterized in that, The specific steps for S3 are as follows: Lithium battery terminal voltage for: in, It is the open-circuit voltage of the lithium battery and The function; Modify the initial lithium battery settings The values were then measured and their corresponding open-circuit voltages were measured respectively. The results of the multi-order fitting relationship between the two were obtained. Based on the fitting of multi-order polynomials, the following was obtained. and The mathematical relationship is as follows: The equation for the rate of change of charge state is as follows: in, Uncertainties caused by errors in actual battery capacity, etc. The state equation is: in, , It is the uncertainty of the terminal voltage; The state equation of a lithium battery is: The output equation of the lithium battery system is: The parameter expression for the lithium battery system is: in, , and The parameters were obtained through online parameter identification using the recursive least squares method based on the forgetting factor.
5. The lithium battery state-of-charge estimation method based on an extended state observer according to claim 1, characterized in that, S4 specifically includes the following steps: set up State variables of lithium battery system The estimation, defining the observation error : set up yes Based on the estimation, the extended state observer for observing the battery's state of charge is: in, , It is a positive real constant. It is a positive real constant. and It is the extended state observer gain; Based on the need for lithium battery state of charge estimation, a recursive least squares method with a forgetting factor is used to achieve online parameter identification. The battery system model and the extended state observer are cascaded and integrated to estimate the battery state of charge.
6. A storage medium, characterized in that, The storage medium includes a stored program, wherein when the program is executed, it performs the lithium battery state-of-charge estimation method based on an extended state observer as described in any one of claims 1 to 5.
7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the lithium battery state-of-charge estimation method based on an extended state observer as described in any one of claims 1 to 5 through the computer program.