Lithium battery parameter and state estimation method based on adaptive inversion sliding mode observation

Abstract

The application relates to the technical field of lithium battery state estimation, and discloses a lithium battery parameter and state estimation method based on adaptive backstepping sliding mode observation, which comprises the following steps: a lithium battery electric-thermal bidirectional coupling model is constructed, a bidirectional closed-loop coupling link is built, and standardized electric state equations and thermal state equations are derived; an observation error closed loop is constructed by taking a measured terminal voltage and a surface temperature of the battery as feedbacks; adaptive laws of corresponding parameters are derived for the electric state equations and the thermal state equations; electric parameters output by an electric observer are transmitted to a thermal model, and temperature parameters output by a thermal observer are used to correct an electric model; and based on the real-time updated electric-thermal bidirectional coupling model, online adaptive identification of electric parameters and thermal parameters of the lithium battery and joint estimation of the state of charge and the core temperature of the battery are simultaneously realized. The application solves the technical problems of low electric-thermal state estimation precision and high estimation algorithm complexity in the prior art.

Description

Technical Field

[0001] This application relates to the field of lithium battery state estimation technology, specifically a method for estimating lithium battery parameters and state based on adaptive inversion sliding mode observation. Background Technology

[0002] With the rapid development of new energy technologies, lithium batteries, as core energy storage components, are widely used in electric vehicles, smart grids, and portable electronic devices. The performance of the battery management system directly affects the safety, reliability, and lifespan of the entire energy storage system, and accurate estimation of the battery state is one of the key technologies for achieving efficient battery management. During actual battery operation, there is a strong coupling relationship between the internal electrochemical reactions and thermal effects. Temperature changes affect the battery's internal resistance, capacity, and other electrochemical characteristics, while changes in current generate Joule heat, leading to an increase in battery temperature. This electrothermal coupling characteristic makes the battery state highly nonlinear and time-varying, posing a significant challenge to state estimation. Summary of the Invention

[0003] The purpose of this application is to provide a method for estimating the parameters and state of lithium batteries based on adaptive inversion sliding mode observation, so as to solve the technical problems of low accuracy and high complexity of the estimation algorithm in the prior art.

[0004] To achieve the above objectives, this application provides a method for estimating lithium battery parameters and state based on adaptive inversion sliding mode observation, including: A bidirectional electrothermal coupling model for lithium batteries was constructed. The first-order RC Thevenin model was used to characterize the battery's electrical dynamics as the electrical model, and the second-order Cauer thermal model was used to characterize the battery's thermal dynamics as the thermal model. A bidirectional closed-loop coupling link was established, in which electrical parameters are input to the thermal model in the positive direction and thermal temperature parameters are used to correct the electrical model in the negative direction. The standardized electrical and thermal equations of state were derived. A closed loop of observation error is constructed using the measured terminal voltage and surface temperature of the battery as feedback quantities. An adaptive inversion sliding mode observer with a unified architecture is designed for the electrical state equation and the thermal state equation respectively. The inversion double-loop error control and sliding mode disturbance rejection control are integrated, and the corresponding parameter adaptive law is derived based on the Lyapunov stability criterion. Two adaptive inversion sliding mode observers interact with each other in real time. The electrical parameters output by the electrical observer are fed into the thermal model, and the temperature parameters output by the thermal observer are used to correct the electrical model. Based on the real-time updated electrothermal bidirectional coupling model, the online adaptive identification of the electrical and thermal parameters of the lithium battery and the joint estimation of the battery state of charge and core temperature are realized simultaneously.

[0005] Beneficial effects: The lithium battery parameter and state estimation method based on adaptive inversion sliding mode observation proposed in this application achieves joint estimation of state of charge and core temperature while ensuring high accuracy of identification of the electrothermal equivalent parameters of lithium batteries. This reduces the cost of state estimation and improves the complexity of joint estimation of battery state and parameters in industrial applications. Attached Figure Description

[0006] To more clearly illustrate the technical solutions in the embodiments of this application 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 only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0007] Figure 1 A flowchart illustrating the lithium battery parameter and state estimation method based on adaptive inversion sliding mode observation provided in this application embodiment; Figure 2 A flowchart illustrating the construction of a bidirectional closed-loop coupling link provided in an embodiment of this application; Figure 3 A battery model framework in which the electrical and thermal models are coupled together, as provided in the embodiments of this application; Figure 4 A flowchart illustrating the derivation of the standardized electrical and thermal equations of state for embodiments of this application; Figure 5 A flowchart illustrating the design of an adaptive inversion sliding mode observer for electrical state equations provided in this application embodiment; Figure 6 A flowchart illustrating the design of an adaptive inversion sliding mode observer for the thermal equation of state, provided in an embodiment of this application; Figure 7 This application provides a parameter estimation curve for the battery electrical model in an embodiment of the present application. Figure 8 This is a battery open-circuit voltage estimation curve provided in an embodiment of this application; Figure 9 This application provides a parameter estimation curve for the battery thermal model in an embodiment of the present application. Figure 10 A battery core temperature estimation curve provided for an embodiment of this application.

[0008] The implementation, functional features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0009] The technical solutions in the embodiments of this application will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0010] In this document, the term "comprising" is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0011] In actual battery operation, there is a strong coupling relationship between the internal electrochemical reactions and thermal effects. Temperature changes affect the battery's internal resistance, capacity, and other electrochemical characteristics, while current changes generate Joule heating, leading to an increase in battery temperature. This electrothermal coupling characteristic makes the battery state highly nonlinear and time-varying, posing a significant challenge to state estimation. In response to this challenge, this embodiment discloses a lithium battery parameter and state estimation method based on adaptive inversion sliding mode observation. In summary, this embodiment combines an adaptive sliding mode observer with the electrothermal characteristics of the lithium battery to identify its electrothermal equivalent parameters and achieve joint estimation of the state of charge and core temperature.

[0012] The method for estimating lithium battery parameters and state based on adaptive inversion sliding mode observation disclosed in this embodiment will now be described in detail.

[0013] Reference Figure 1 , Figure 1 A flowchart illustrating the lithium battery parameter and state estimation method based on adaptive inversion sliding mode observation provided in this application embodiment.

[0014] like Figure 1 As shown in the figure, this embodiment discloses a method for estimating lithium battery parameters and state based on adaptive inversion sliding mode observation, including: Traditional battery state estimation methods primarily focus on the single indicator of state of charge, using equivalent circuit models or electrochemical models combined with algorithms such as Kalman filtering to predict the state. However, these methods often neglect the battery's thermal characteristics, making it difficult to accurately reflect the battery's true operating state. Especially under high-rate charge and discharge conditions, the internal temperature distribution of the battery is uneven, with a significant difference between the core temperature and the surface temperature. Relying solely on surface temperature measurements cannot accurately characterize the battery's thermal state. Therefore, this embodiment designs S10 to construct a bidirectional electrothermal coupling model and a standardized state equation, addressing the problems of disconnected electrothermal characteristics, inconsistent models, and difficulty in adapting to observers.

[0015] S10: Construct a bidirectional electrothermal coupling model for lithium batteries. The first-order RC Thevenin model is used to characterize the battery's electrical dynamics as the electrical model, and the second-order Cauer thermal model is used to characterize the battery's thermal dynamics as the thermal model. A bidirectional closed-loop coupling link is established, in which electrical parameters are input to the thermal model in the positive direction and thermal temperature parameters are used to correct the electrical model in the negative direction. Standardized electrical and thermal equations of state are derived.

[0016] Reference Figure 2 , Figure 2 A flowchart illustrating the construction of a bidirectional closed-loop coupled link provided in an embodiment of this application.

[0017] like Figure 2 As shown, specifically, the construction of a bidirectional closed-loop coupling link where electrical parameters are input to the thermal model in the forward direction and thermal temperature parameters are used to correct the electrical model in the reverse direction is as follows: S1011: The working current and electrical parameters output in real time by the electrical model are used as the calculation input for the heat generation rate of the thermal model, wherein the heat generation rate is calculated with irreversible Joule heating as the core calculation item.

[0018] S1012: The core temperature and surface temperature output in real time from the thermal model are used as temperature correction inputs for the electrical parameters of the electrical model, forming a bidirectional closed-loop coupling between electrothermal and thermal systems.

[0019] Reference Figure 3 , Figure 3 The battery model framework provided in this application embodiment is a battery model in which the electrical model and the thermal model are coupled together.

[0020] In the specific application of this embodiment, such as Figure 3 As shown, this embodiment combines the Thevenin model and the Cauer model to simulate the electrothermal dynamics of lithium-ion batteries by balancing the accuracy, flexibility and computational workload of different equivalent models.

[0021] For the electrical model that describes the electrical dynamics of a battery using a first-order RC Thevenin model, it is based on an ideal voltage source. It consists of equivalent electrical parameters, including ohmic resistance. Polarization resistance and polarization capacitor The specific formulas for the electric model are as follows: in: Polarization voltage The first derivative with respect to time; It is a time constant and ; This refers to the operating current of the lithium battery. The battery's terminal voltage; the battery's open-circuit voltage. By being in a charged state For the input nonlinear function Obtain.

[0022] A thermal model for characterizing the thermal dynamics of a battery using a second-order Cauer thermal model, including the core heat capacity. Surface heat capacity Thermal conductivity impedance and thermal convection resistance The specific formula for the thermal model is as follows: in: Indicates the surface temperature of a lithium battery The first derivative with respect to time; Indicates the ambient temperature of the lithium battery; Indicates the core temperature of the lithium battery The first derivative with respect to time; The heat generation rate, or thermal power, consists of irreversible and reversible heat. However, the entropy heat of the battery is negligible because, at standard current rates, its magnitude is much smaller than that of the resistive heat source. Dissipative heating leads to irreversible heat generation. Therefore, the heat generation rate is determined... The calculation method is as follows: Reference Figure 4 , Figure 4 A flowchart illustrating the derivation of the standardized electrical and thermal equations of state provided for embodiments of this application.

[0023] like Figure 4 As shown, specifically, the derivation yields standardized electrical and thermal equations of state, which are used to achieve decoupled standardized expressions, specifically as follows: S1021: Based on the electrical model, extract the dynamic change law corresponding to the battery polarization voltage and terminal voltage, and construct the electrical state equation; perform matrix processing to clarify the electrical regression matrix and electrical parameter vector of the electrical model.

[0024] In a specific application of this embodiment, the matrix transformation of the electrical state equations is as follows: Based on the relevant equations of the aforementioned lithium battery electrical model, the polarization voltage of the battery is extracted respectively. First derivative with respect to time With the battery terminal voltage First derivative with respect to time The electrical state equation is obtained as follows: but in, This is the electrical regression matrix of the electrical model. This is the electrical parameter vector of the electrical model.

[0025] S1022: Based on the thermal model, extract the dynamic change law corresponding to the core temperature and surface temperature of the battery, and construct the thermal equation of state; perform matrix processing to clarify the thermal regression matrix and thermal parameter vector of the thermal model.

[0026] In the specific application of this embodiment, the matrix transformation of the thermal equation of state is as follows: Based on the relevant equations of the aforementioned thermal model of lithium batteries, the core temperature of the battery is extracted respectively. First derivative with respect to time and the surface temperature of the battery First derivative with respect to time The thermodynamic equation of state is obtained as follows: but in, The thermal regression matrix represents the thermal model. This represents the vector of thermal parameters of the thermal model. Furthermore, Defined as the first regression vector of the thermal model. Defined as the second regression vector of the thermal model, i.e., the thermal regression matrix. Including the first regression vector Second regression vector .

[0027] Based on this, this embodiment builds a bidirectional electrothermal coupling model and a closed-loop link to achieve dynamic electrothermal linkage characterization, derives standardized state equations, solves the problems of disconnection between electrothermal characteristics and difficulty in balancing model accuracy and complexity, and provides a unified basis for subsequent observer design.

[0028] In the application of lithium battery state estimation, existing methods have been found to be insufficient in handling model parameter uncertainties and external disturbances. During long-term use, battery parameters drift due to aging, temperature changes, and other factors, while external disturbances such as ambient temperature changes and measurement noise also affect estimation accuracy. Traditional observers often experience increased estimation errors and slower convergence speeds under these uncertainties. In recent years, although some battery state estimation methods considering electrothermal coupling have emerged, most suffer from high computational complexity and poor real-time performance, making them difficult to promote and apply in practical engineering. Therefore, this embodiment designs S20 to construct a dual-observer closed-loop and adaptive inversion sliding mode control, addressing the problems of poor estimation accuracy and lack of convergence guarantee under time-varying parameters and external disturbances.

[0029] S20: Construct an observation error closed loop using the measured terminal voltage and surface temperature of the battery as feedback quantities. Design a unified adaptive inversion sliding mode observer for both the electrical state equation and the thermal state equation. Integrate the inversion dual-loop error control and sliding mode disturbance rejection control. Derive the corresponding parameter adaptive law based on the Lyapunov stability criterion.

[0030] Reference Figure 5 , Figure 5 The flowchart for designing an adaptive inversion sliding mode observer for electrical state equations is provided in the embodiments of this application.

[0031] Specifically, such as Figure 5 As shown, the adaptive inversion sliding mode observer designed for the electrical state equation is an electrical observer. The design of this electrical observer is as follows: S2011: Construct a first-order closed loop for terminal voltage observation error using the difference between the measured terminal voltage of the lithium battery and the estimated terminal voltage output by the electrical observer.

[0032] From a technical standpoint, observing the actual battery terminal voltage... Continuous and It is differentiable of order 1, and Bounded, that is and The purpose of an adaptive inversion sliding mode observer is to design a reasonable parameter adaptive law so that the estimated battery electrothermal parameters asymptotically approach the ideal value, while ensuring that the closed-loop system state variables are bounded. The design of an adaptive inversion sliding mode observer for a thermal model follows the same principle.

[0033] First-order terminal voltage observation error in constructing the electrical model for: in: This represents the measured terminal voltage of the lithium battery. The estimated terminal voltage output of the electrical observation device is, i.e. for The estimated value.

[0034] Will Taking the derivative with respect to time, we get: in: for The estimated value; ,Right now The parameter vector to be estimated for the electric model The estimation error.

[0035] Define the Lyapunov function of the electric model : in, Let Lyapunov be the positive definite matrix set up for the electric model, used for adaptive gain. Differentiation yields: S2012: Based on the inversion design method, the second-order terminal voltage observation error is defined. A positive gain coefficient is introduced for the electrical model to dynamically control the first-order terminal voltage observation error, and an inversion dual-loop error control architecture for the electrical model is constructed.

[0036] Define the second-order terminal voltage observation error of the electrical model for: in, Represented as positive constants set in the electrical model, Taking the derivative with respect to time, we get: In this embodiment, Can be converted to: in, These represent the positive constants set for the electrical model.

[0037] S2013: Defines the sliding mode switching function of the electrical model, introduces the sliding mode control law, and suppresses the uncertainty and external disturbances caused by electrical parameter drift, measurement noise, and operating condition fluctuations.

[0038] Define the sliding mode switching function of the electric model : Then for Taking the derivative, we get: S2014: Construct a positive definite Lyapunov function for the electric model, and derive an adaptive law of electrical parameters to ensure system stability based on the Lyapunov stability criterion.

[0039] Construct a positive definite Lyapunov function for the electric model, and define the Lyapunov function. : but in: This indicates that the result is obtained by performing component indexing. The components of the first row; This indicates that the result is obtained by performing component indexing. The components in the second row; This indicates that the result is obtained by performing component indexing. The components in the third row; This indicates that the result is obtained by performing component indexing. The components of the 4th row.

[0040] Based on the Lyapunov stability criterion, the adaptive law of electrical parameters to ensure system stability is derived, that is, the state estimate and parameter adaptive law of the electrical system are obtained. Specifically, the mathematical expression of the adaptive law of electrical parameters is as follows: in: , For the ohmic resistance of the electrical model, For the polarization resistance of the electrical model, The polarization capacitance is used in the electrical model. , The operating current of the electrical model, for The first derivative with respect to time, The open-circuit voltage of the electrical model is... The terminal voltage of the electrical model. for The corresponding estimated value output by the electrical model, , for The corresponding estimation error, for The first derivative with respect to time; for The second derivative with respect to time; Represents the component index, specifically as The components in the 4th row; and Positive constants are preset for the electric model. For the output of the electric model The first derivative of the estimate with respect to time; for The second derivative with respect to time; Represents the component index, specifically as The components of the first row; Represents the component index, specifically as The components in the second row; for The first derivative with respect to time; Represents the component index, specifically as The components in the third row; for The first derivative with respect to time; For the output of the electric model The second derivative of the estimate with respect to time; and The positive constants preset for the electrical model; This is a preset sliding mode switching function for the electric model. Indicates according to Defined symbolic functions; for The first derivative with respect to time; Positive definite functions are preset for the electric model; The first-order terminal voltage observation error is defined for the electrical model.

[0041] Reference Figure 6 , Figure 6 The flowchart for designing an adaptive inversion sliding mode observer for the thermal equation of state is provided in the embodiments of this application.

[0042] Specifically, such as Figure 6 As shown, an adaptive inversion sliding mode observer is designed for the thermal equation of state, adopting an architecture completely unified with the adaptive inversion sliding mode observer for the electrical model, specifically: S2021: Construct a first-order surface temperature observation error closed loop using the difference between the measured surface temperature of the lithium battery and the estimated surface temperature output by the thermal observer.

[0043] Constructing the first-order surface temperature observation error for: in, This represents the measured surface temperature of the lithium battery. The surface temperature estimate output by the thermal observer, i.e. for The estimated value; Will Taking the derivative with respect to time, we get: in: for The estimated value; ,Right now for The estimation error.

[0044] Lyapunov defines the thermal model : in, The positive definite matrix set for the thermal model, for Differentiation yields: S2022: Based on the inversion design method, the second-order surface temperature observation error is defined. A positive gain coefficient is introduced for the thermal model to dynamically control the first-order surface temperature observation error, and an inversion dual-loop error control architecture for the thermal model is constructed.

[0045] Define second-order surface temperature observation error for: in, This represents a positive constant set for the thermal model. Taking the derivative with respect to time, we get: Can Convert to: S2023: Define the sliding mode switching function of the thermal model, introduce the sliding mode control law, and suppress the uncertainty and external disturbances caused by ambient temperature fluctuations, thermal parameter aging drift.

[0046] Define the sliding mode switching function for the thermal model. for: in, The positive constants set for the thermal model.

[0047] right Taking the derivative, we get: S2024: Construct a positive definite Lyapunov function for the thermal model, and derive an adaptive law of thermal parameters to ensure system stability based on the Lyapunov stability criterion.

[0048] Construct a positive definite Lyapunov function for the thermal model, and define the Lyapunov function. : Lyapunov Taking the derivative, we get: Based on the Lyapunov stability criterion, the adaptive law of thermal parameters to ensure system stability is derived, that is, the thermal state estimate and parameter adaptive law are obtained. Specifically, the mathematical expression of the adaptive law of thermal parameters is: in: , , This represents the core temperature of the lithium battery in the thermal model. This represents the surface temperature of the lithium battery in the thermal model. The ambient temperature of the lithium battery in the thermal model. The heat generation rate of the lithium battery in the thermal model is given by [the relevant parameter]. , The operating current of the electrical model, For the ohmic resistance of the electrical model, The polarization voltage of the electrical model; , The thermal conductivity impedance of the thermal model. For the surface hot melting of the thermal model, The thermal convection impedance of the thermal model. The core of the thermal model is thermal melting; for The estimated value; Represents the component index, specifically as The components of the first row; Represents the component index, specifically as The components in the second row; and Positive constants pre-defined for the thermal model; for The first derivative of the estimate with respect to time; for The first derivative with respect to time; for The first derivative with respect to time; for The second derivative of the estimate with respect to time; and Positive constants pre-defined for the thermal model; This is a preset thermal model sliding mode switching function. Indicates according to Defined symbolic functions; Positive definite functions are preset for the thermal model.

[0049] S30: Two adaptive inversion sliding mode observers interact with each other in real time. The electrical parameters output by the electrical observer are fed into the thermal model, and the temperature parameters output by the thermal observer are used to correct the electrical model. Based on the real-time updated electrothermal bidirectional coupling model, the online adaptive identification of lithium battery electrical and thermal parameters, as well as the joint estimation of battery state of charge and core temperature, are realized simultaneously.

[0050] Specifically, the real-time bidirectional data exchange between the two observers includes: The electrical and thermal observers use the same sampling period, and complete a bidirectional data exchange and synchronous update of parameters and states within each sampling period to ensure the real-time and synchronous nature of the electrothermal coupling estimation.

[0051] Specifically, the joint estimation of the state of charge is as follows: The electrical model has a pre-defined nonlinear mapping relationship between the battery open-circuit voltage and the state of charge. Based on the identified electrical parameters, the terminal voltage estimation results and the open-circuit voltage mapping relationship, the real-time high-precision estimation of the battery state of charge is realized simultaneously.

[0052] In the specific application of this embodiment, the battery joint estimation test platform is used to conduct charge-discharge tests on the battery under dynamic cyclic operating conditions of an automobile. The acquired lithium battery terminal voltage, current, and temperature data are applied to the proposed joint estimation method for lithium battery electrothermal parameters and state based on adaptive inversion sliding mode observation. The effectiveness of the proposed method is analyzed by comparing the lithium battery electrothermal parameter identification results with the electrothermal state estimation accuracy.

[0053] Reference Figures 7 to 10 ; Figure 7 This application provides a parameter estimation curve for the battery electrical model in an embodiment of the present application. Figure 8 This is a battery open-circuit voltage estimation curve provided in an embodiment of this application; Figure 9 This application provides a parameter estimation curve for the battery thermal model in an embodiment of the present application. Figure 10 A battery core temperature estimation curve provided for an embodiment of this application.

[0054] like Figures 7 to 10As shown in the summary, the lithium battery parameter and state estimation method based on adaptive inversion sliding mode observation in this embodiment, in practical applications, first initializes the initial values ​​of the internal state of the battery electrothermal coupling model. Then, the active disturbance rejection battery power optimization controller adjusts the input power of the coupling model in real time according to the difference between the terminal voltage and surface temperature output by the coupling model and the measured actual battery terminal voltage and surface temperature, and makes the output of the coupling model always track the actual battery state. Finally, the energy state and core temperature estimate of the lithium battery can be obtained based on the internal state of the coupling model. It achieves good online tracking of actual battery parameters and dynamic response, and has high electrothermal state estimation accuracy and low estimation algorithm complexity.

[0055] In summary, the lithium battery parameter and state estimation method based on adaptive inversion sliding mode observation in this embodiment achieves joint estimation of state of charge and core temperature while ensuring high accuracy in identifying the electrothermal equivalent parameters of the lithium battery. This reduces the cost of state estimation and improves the complexity of joint estimation of battery state and parameters in industrial applications.

[0056] In the embodiments provided in this application, it should be understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code, or any suitable combination thereof. For hardware implementation, the processor may be implemented in one or more of the following: application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, other electronic units designed to implement the functions described herein, or combinations thereof. For software implementation, some or all of the processes of the embodiments may be performed by a computer program instructing the associated hardware. During implementation, the program may be stored in a computer-readable storage medium or transmitted as one or more instructions or code on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of a computer program from one place to another. Storage media may be any available medium accessible to a computer. Computer-readable storage media may include, but are not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code having the form of instructions or data structures and accessible to a computer.

[0057] Finally, it should be noted that the above description is only a preferred embodiment of this application and is not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for estimating lithium battery parameters and state based on adaptive inversion sliding mode observation, characterized in that, include: A bidirectional electrothermal coupling model for lithium batteries was constructed. The first-order RC Thevenin model was used to characterize the battery's electrical dynamics as the electrical model, and the second-order Cauer thermal model was used to characterize the battery's thermal dynamics as the thermal model. A bidirectional closed-loop coupling link was established, in which electrical parameters are input to the thermal model in the positive direction and thermal temperature parameters are used to correct the electrical model in the negative direction. The standardized electrical and thermal equations of state were derived. A closed loop for observation error is constructed using the measured terminal voltage and surface temperature of the battery as feedback quantities. An adaptive inversion sliding mode observer with a unified architecture is designed for both the electrical and thermal state equations, integrating inversion dual-loop error control and sliding mode disturbance rejection control. The corresponding parameter adaptive law is derived based on the Lyapunov stability criterion. The adaptive inversion sliding mode observer designed for the electrical state equation is the electrical observer. Specifically, the design of this electrical observer involves: constructing a first-order terminal voltage observation error closed loop using the difference between the measured terminal voltage of the lithium battery and the estimated terminal voltage output by the electrical observer; defining a second-order terminal voltage observation error based on the inversion design method; and introducing a positive gain coefficient for the electrical model to dynamically control the first-order terminal voltage observation error, thus constructing an inversion dual-loop error control architecture for the electrical model. Define the sliding mode switching function of the electrical model and introduce the sliding mode control law to suppress the uncertainty and external disturbances caused by electrical parameter drift, measurement noise, and operating condition fluctuations; A positive definite Lyapunov function for the electrical model is constructed, and an adaptive law for electrical parameters to ensure system stability is derived based on the Lyapunov stability criterion. An adaptive inversion sliding mode observer is designed for the thermal equation of state, adopting an architecture completely unified with the adaptive inversion sliding mode observer for the electrical model. Specifically, a first-order surface temperature observation error closed loop is constructed using the difference between the measured surface temperature of the lithium battery and the surface temperature estimate output by the thermal observer. A second-order surface temperature observation error is defined based on the inversion design method, and a positive gain coefficient is introduced for the thermal model to dynamically control the first-order surface temperature observation error, thus constructing an inversion dual-loop error control architecture for the thermal model. Define a sliding mode switching function for the thermal model, introduce a sliding mode control law to suppress uncertainties and external disturbances caused by ambient temperature fluctuations and thermal parameter aging drift; construct a positive definite Lyapunov function for the thermal model, and derive an adaptive law for thermal parameters to ensure system stability based on the Lyapunov stability criterion. Two adaptive inversion sliding mode observers interact with each other in real time. The electrical parameters output by the electrical observer are fed into the thermal model, and the temperature parameters output by the thermal observer are used to correct the electrical model. Based on the real-time updated electrothermal bidirectional coupling model, the online adaptive identification of the electrical and thermal parameters of the lithium battery and the joint estimation of the battery state of charge and core temperature are realized simultaneously.

2. The method for estimating lithium battery parameters and state based on adaptive inversion sliding mode observation according to claim 1, characterized in that, The establishment of a bidirectional closed-loop coupling link, in which electrical parameters are input to the thermal model in the forward direction and thermal temperature parameters are used to correct the electrical model in the reverse direction, is specifically as follows: The working current and electrical parameters output in real time by the electrical model are used as the calculation input for the heat generation rate of the thermal model, with irreversible Joule heating as the core calculation item. The core temperature and surface temperature output in real time from the thermal model are used as temperature correction inputs for the electrical parameters of the electrical model, forming a bidirectional closed-loop coupling between electrothermal and thermal systems.

3. The method for estimating lithium battery parameters and state based on adaptive inversion sliding mode observation according to claim 2, characterized in that, The derivation yields standardized electrical and thermal equations of state, which are used to achieve decoupled standardized expressions, specifically: Based on the electric model, the dynamic variation law corresponding to the battery polarization voltage and terminal voltage is extracted, and the electric state equation is constructed; matrix processing is performed to clarify the electric regression matrix and electric parameter vector of the electric model; Based on the thermal model, the dynamic change patterns of the battery core temperature and surface temperature are extracted, and a thermal equation of state is constructed. The model is then matrixed to clarify the thermal regression matrix and thermal parameter vector of the thermal model.

4. The method for estimating lithium battery parameters and state based on adaptive inversion sliding mode observation according to claim 1, characterized in that, The mathematical expression for the adaptive law of the electrical parameters is: in: , For the ohmic resistance of the electrical model, For the polarization resistance of the electrical model, The polarization capacitance is used in the electrical model. , The operating current of the electrical model, for The first derivative with respect to time, The open-circuit voltage of the electrical model is... The terminal voltage of the electrical model. for The corresponding estimated value output by the electrical model, , for The corresponding estimation error, for The first derivative with respect to time; for The second derivative with respect to time; Represents the component index, specifically as The components in the 4th row; and Positive constants are preset for the electric model. For the output of the electric model The first derivative of the estimate with respect to time; for The second derivative with respect to time; Represents the component index, specifically as The components of the first row; Represents the component index, specifically as The components in the second row; for The first derivative with respect to time; Represents the component index, specifically as The components in the third row; for The first derivative with respect to time; For the output of the electric model The second derivative of the estimate with respect to time; and Positive constants pre-defined for the electric model; This is a preset sliding mode switching function for the electric model. Indicates according to Defined symbolic functions; for The first derivative with respect to time; Positive definite functions are preset for the electric model; The first-order terminal voltage observation error is defined for the electrical model.

5. The method for estimating lithium battery parameters and state based on adaptive inversion sliding mode observation according to claim 1, characterized in that, The mathematical expression for the adaptive law of the thermal parameters is: in: , , This represents the core temperature of the lithium battery in the thermal model. This represents the surface temperature of the lithium battery in the thermal model. The ambient temperature of the lithium battery in the thermal model. The heat generation rate of the lithium battery in the thermal model is given by [the relevant parameter]. , The operating current of the electrical model, For the ohmic resistance of the electrical model, The polarization voltage of the electrical model; , The thermal conductivity impedance of the thermal model. For the surface hot melting of the thermal model, The thermal convection impedance of the thermal model. The core of the thermal model is thermal melting; for The estimated value; Represents the component index, specifically as The components of the first row; Represents the component index, specifically as The components in the second row; and Positive constants pre-defined for the thermal model; for The first derivative of the estimate with respect to time; for The first derivative with respect to time; for The first derivative with respect to time; for The second derivative of the estimate with respect to time; and Positive constants pre-defined for the thermal model; This is a preset thermal model sliding mode switching function. Indicates according to Defined symbolic functions; Positive definite functions are preset for the thermal model.

6. The method for estimating lithium battery parameters and state based on adaptive inversion sliding mode observation according to claim 1, characterized in that, The two adaptive inversion sliding mode observers interact with each other in real time, specifically: The electrical and thermal observers use the same sampling period, and complete a bidirectional data exchange and synchronous update of parameters and states within each sampling period to ensure the real-time and synchronous nature of the electrothermal coupling estimation.

7. The method for estimating lithium battery parameters and state based on adaptive inversion sliding mode observation according to claim 1, characterized in that, The joint estimation of the state of charge is specifically as follows: The electrical model has a pre-defined nonlinear mapping relationship between the battery open-circuit voltage and the state of charge. Based on the identified electrical parameters, the terminal voltage estimation results and the open-circuit voltage mapping relationship, the real-time high-precision estimation of the battery state of charge is realized simultaneously.

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