Methods, devices, equipment, and media for observing interference in nonlinear models of lithium-ion batteries

Abstract

This invention discloses a method, apparatus, device, and medium for observing nonlinear model disturbances in lithium-ion batteries, relating to the field of battery management technology. The method includes: establishing a first-order RC linear model of the lithium-ion battery; the first-order RC linear model characterizing the dynamic characteristics of the lithium-ion battery during charging and discharging; converting the first-order RC linear model into a nonlinear model containing nonlinear disturbance terms; designing a nonlinear disturbance observer based on a fast tracking differentiator to construct the dynamic equations of the nonlinear model; wherein, in the dynamic equations, the disturbance estimate of the nonlinear disturbance term is updated using a fast tracking differentiator structure with an inverse hyperbolic sine function. This method enables online observation and state estimation of the nonlinear dynamic characteristics of the battery.

Description

Technical Field

[0001] This invention relates to the field of battery management technology, and in particular to a method, apparatus, equipment, and medium for observing interference in a nonlinear model of a lithium-ion battery. Background Technology

[0002] Lithium-ion batteries, due to their high energy density, long cycle life, and low self-discharge rate, have been widely used in various portable electronic devices, power tools, and new energy vehicles, becoming a key component of modern energy storage systems. However, in actual use, the electrochemical characteristics of lithium-ion batteries are affected by various factors such as temperature changes, charge / discharge rates, and historical usage, exhibiting significant nonlinear behavior. Especially under rapid charge / discharge or complex operating conditions, the battery's voltage response, polarization effect, and internal resistance changes show strong time dependence and nonlinear characteristics. This complex dynamic characteristic poses challenges to monitoring and control based on traditional linear models, limiting the capabilities of Battery Management Systems (BMS) in battery state estimation, performance prediction, and safety protection.

[0003] In related technologies, lithium-ion batteries are typically modeled using equivalent circuit models (ECMs), with the first-order RC model being widely used due to its simple structure and ease of implementation. This model describes the battery's load-side voltage and polarization effects using elements such as resistance, capacitance, and power nodes, and can reflect the battery's dynamic response to some extent. However, during actual charging and discharging, the nonlinear effects within the battery cannot be accurately characterized by the linear parameters of the first-order RC model, leading to increased state estimation errors. This is particularly true under extreme operating conditions or high dynamic loads, where its prediction accuracy and response performance remain insufficient. Furthermore, since battery performance degrades with increasing cycle count, traditional models struggle to effectively reflect the battery's true dynamic characteristics in long-term applications, further limiting the control accuracy and safety performance of the battery management system (BMS). Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in related technologies. Therefore, the purpose of this invention is to propose a method, apparatus, device, and medium for observing interference in a nonlinear model of a lithium-ion battery, so as to achieve online observation and state estimation of the nonlinear dynamic characteristics of the battery.

[0005] To achieve the above objectives, a first aspect of the present invention proposes a method for observing interference in a nonlinear model of a lithium-ion battery, comprising: A first-order RC linear model of a lithium-ion battery is established; the first-order RC linear model is used to characterize the dynamic characteristics of the lithium-ion battery during the charging and discharging process. The first-order RC linear model is transformed into a nonlinear model that includes nonlinear perturbation terms; A nonlinear disturbance observer is designed based on a fast tracking differentiator, and the dynamic equations of the nonlinear model are constructed. In these dynamic equations, the disturbance estimate of the nonlinear disturbance term is expressed using an inverse hyperbolic sine function. The fast tracking differentiator structure is updated.

[0006] In addition, the lithium-ion battery nonlinear model interference observation method of the above embodiments of the present invention may also have the following additional technical features: According to one embodiment of the present invention, the first-order RC linear model includes the load-side voltage. Open circuit voltage Current ,resistance Polarization resistance ,capacitance and battery capacity The first-order RC linear model satisfies:

[0007] in, Indicates open circuit voltage rate of change, Indicates polarization voltage The rate of change.

[0008] According to an embodiment of the present invention, the nonlinear model satisfies:

[0009] in, Indicates the load terminal voltage rate of change, Indicates open circuit voltage rate of change, Indicates polarization voltage rate of change, This represents the linear coupling coefficient or attenuation coefficient between different voltages. This represents the linear effect coefficient of current on various voltages. This represents the nonlinear perturbation term.

[0010] According to one embodiment of the present invention, the dynamic equation for the disturbance estimate of the nonlinear disturbance term is:

[0011] in, This represents the disturbance estimate of the nonlinear disturbance term. This represents the overall response gain of the nonlinear disturbance observer. This indicates the nonlinear adjustment of the nonlinear disturbance observer.

[0012] According to an embodiment of the present invention, the dynamic equations of the nonlinear model include:

[0013] in, This represents the online estimate of the load terminal voltage. This represents the online estimate of the open-circuit voltage. This represents the online estimate of the polarization voltage. This represents the online estimate of the load terminal voltage. rate of change, This represents the online estimate of the open-circuit voltage. rate of change, Represents the online estimate of polarization voltage rate of change, This represents the linear coupling coefficient or attenuation coefficient between different voltages. This represents the linear influence coefficient of current on each voltage.

[0014] According to one embodiment of the present invention, the inverse hyperbolic sine function "arsh" ( It exhibits nonlinearity when the error exceeds the preset threshold, and linearity when the error is less than or equal to the preset threshold.

[0015] According to one embodiment of the present invention, the method further includes: The state of charge of the lithium-ion battery is corrected based on the estimated value obtained from the dynamic equation of the nonlinear model.

[0016] To achieve the above objectives, a second aspect of the present invention provides a lithium-ion battery nonlinear model interference observation device, comprising: A first-order RC linear model establishment module is used to establish a first-order RC linear model of a lithium-ion battery; the first-order RC linear model is used to characterize the dynamic characteristics of a lithium-ion battery during the charging and discharging process. The nonlinear model building module is used to transform the first-order RC linear model into a nonlinear model containing nonlinear perturbation terms. A nonlinear disturbance observer construction module is used to design a nonlinear disturbance observer based on a fast tracking differentiator and construct the dynamic equations of the nonlinear model; wherein, in the dynamic equations, the disturbance estimate of the nonlinear disturbance term adopts an inverse hyperbolic sine function. The fast tracking differentiator structure is updated.

[0017] To achieve the above objectives, a third aspect of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described nonlinear model interference observation method for lithium-ion batteries.

[0018] To achieve the above objectives, a fourth aspect of the present invention provides a computer-readable storage medium storing a computer program thereon, which, when executed, implements the steps of the above-described method for observing interference with a nonlinear model of a lithium-ion battery.

[0019] The lithium-ion battery nonlinear model disturbance observation method, apparatus, device, and medium of this invention establish a first-order RC linear model of the lithium-ion battery and further transform it into a nonlinear model containing nonlinear disturbance terms. This enables a more accurate characterization of the dynamic characteristics of the lithium-ion battery during actual charging and discharging. Based on this, a nonlinear disturbance observer is designed using a fast tracking differentiator, and a disturbance estimation update method based on an inverse hyperbolic sine function structure is introduced into the dynamic equation. This not only improves the observation accuracy of nonlinear disturbances but also significantly enhances the tracking capability of disturbances under rapidly changing operating conditions. This effectively improves the accuracy of model state estimation and ensures the reliability and stability of the lithium-ion battery under complex operating conditions. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the interference observation method for a nonlinear model of a lithium-ion battery in one embodiment. Figure 2 This is a schematic diagram of a first-order RC linear model of a lithium-ion battery in one embodiment. Figure 3 This is a schematic diagram comparing the inverse hyperbolic sine function and its time response in one embodiment; Figure 4 This is a schematic diagram of the design of a nonlinear disturbance observer in one embodiment; Figure 5 This is a design flowchart of a nonlinear disturbance observer in one embodiment; Figure 6 This is a structural block diagram of a lithium-ion battery nonlinear model interference observation device in one embodiment. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0022] The implementation details of the technical solutions in the embodiments of this application are described in detail below.

[0023] In one embodiment, such as Figure 1 The diagram illustrates a flowchart of a nonlinear model interference observation method for lithium-ion batteries. This method may include the following steps: Step S101: Establish a first-order RC linear model of the lithium-ion battery.

[0024] The first-order RC linear model is a common equivalent circuit model for lithium-ion batteries, which can describe the main dynamic behavior of the battery during charging and discharging through a simplified circuit structure. Specifically, a lithium-ion battery is equivalent to a circuit structure consisting of an open-circuit voltage source, an ohmic internal resistance, and a set of RC branches composed of resistors and capacitors connected in series, such as... Figure 2 As shown, Figure 2 A schematic diagram of a first-order RC linear model of a lithium-ion battery is shown. The open-circuit voltage reflects the static voltage characteristics of the battery under charged conditions, the ohmic internal resistance characterizes the instantaneous voltage drop caused by current changes, and the RC branch describes the polarization effect exhibited by the battery during dynamic charging and discharging.

[0025] Based on the above equivalent structure, the relationship between the battery terminal voltage, open-circuit voltage, internal resistance voltage drop, and polarization voltage can be derived using circuit laws, thus forming the mathematical expression of a first-order RC linear model. The first-order RC linear model can accurately reflect the basic dynamic characteristics of lithium-ion batteries during charging and discharging, providing a reliable foundation for subsequent nonlinear modeling and disturbance observation.

[0026] In one embodiment, the first-order RC linear model includes the load-side voltage. Open circuit voltage Current ,resistance Polarization resistance ,capacitance and battery capacity . refer to Figure 2 As shown, a lithium-ion battery can be equivalently represented as a circuit structure including an open-circuit voltage source, a series resistor, and a parallel RC network. The open-circuit voltage source is used to characterize the battery's open-circuit voltage. The series resistance corresponds to the battery resistance. A parallel RC network is used to characterize the polarization characteristics of the battery, including the polarization resistance. With capacitor In addition, battery capacity is also considered. This describes the effect of changes in the state of charge on the open-circuit voltage.

[0027] Based on this equivalent structure, the mathematical expression for the first-order RC linear model can be obtained:

[0028] in, Indicates polarization voltage The rate of change is determined by the combined effect of the polarization resistance and capacitance, and is also affected by the magnitude of the current. Indicates open circuit voltage The rate of change of reflects the dynamic adjustment of the battery's state of charge under the influence of current.

[0029] The first-order RC linear model described above can accurately characterize the dynamic behavior of lithium-ion batteries in the interference observation method, providing support for subsequent interference estimation and observation based on nonlinear extension.

[0030] Step S102: Transform the first-order RC linear model into a nonlinear model that includes nonlinear perturbation terms.

[0031] A first-order RC linear model can reflect the voltage dynamics of a lithium-ion battery under ideal conditions. However, in actual operation, lithium-ion batteries are affected by various factors such as temperature changes, aging effects, nonlinearity of the relationship between state of charge (SOC) and voltage, changes in charge and discharge rates, and other external disturbances, making it difficult for linear models to accurately describe the battery's true output characteristics.

[0032] To compensate for the dynamic characteristics that the linear model cannot describe, a nonlinear disturbance term can be introduced into the mathematical expression of the first-order RC linear model to reflect the load-side voltage. Polarization voltage and open circuit voltage The dynamic characteristics not accurately described by the linear part. Specifically, in the polarization voltage... In the dynamic equations, nonlinear perturbation terms characterizing the electrode polarization effect as a function of SOC and temperature can be added; at open-circuit voltage In the dynamic equations, nonlinear disturbance terms characterizing voltage hysteresis and other non-idealized characteristics can be added; the load voltage reflects the combined effects of overall nonlinear dynamics and external disturbances by introducing corresponding nonlinear disturbance terms.

[0033] Building upon this foundation, by combining the linear component of the original first-order RC linear model with the nonlinear perturbation term, a nonlinear model of the lithium-ion battery can be obtained. This nonlinear model not only describes the change in voltage response over time during charging and discharging, but also reflects the nonlinear characteristics and uncertainties in the battery's dynamic behavior, such as the hysteresis of polarization effects, the nonlinear relationship between SOC and open-circuit voltage, and transient perturbations caused by temperature or current changes. By modeling these nonlinear dynamics and uncertainties, the nonlinear model can provide a more accurate theoretical basis for subsequent online observation and state estimation, thereby improving the battery management system's ability to accurately monitor battery state under different operating conditions.

[0034] Through the above steps, the linear RC model is successfully extended into a nonlinear model that includes nonlinear perturbations. This not only retains the ability to describe the basic charging and discharging characteristics of the battery, but also compensates for and observes the nonlinear dynamic characteristics, providing theoretical support for battery state monitoring and management.

[0035] In one embodiment, to accurately characterize the nonlinear dynamic characteristics of a lithium-ion battery during charging and discharging, the nonlinear model is further expressed by mathematical dynamic equations as follows:

[0036] in, Indicates the load terminal voltage rate of change, Indicates open circuit voltage rate of change, Indicates polarization voltage The rate of change.

[0037] coefficient This represents the linear coupling coefficient or attenuation coefficient between different voltages. Specifically, Characterizes the dynamic coupling degree and voltage decay rate between the load terminal voltage and the open circuit voltage; Characterizes the strength of the linear relationship between open-circuit voltage and load terminal voltage and polarization voltage; Characterizes the attenuation characteristics of the polarization voltage itself.

[0038] coefficient This represents the linear influence coefficient of current on each voltage; specifically, Describe the direct effect of current on load voltage; It describes the direct effect of current on polarization voltage, thus characterizing the linear contribution of current changes to the transient voltage of the battery.

[0039] This represents the nonlinear disturbance term in the corresponding voltage dynamics that is not described by the linear model. Specifically, It is used to compensate for the nonlinear response of the load terminal voltage under different charge and discharge rates, temperature changes and charging states. Used to compensate for the hysteresis effect of open-circuit voltage, the nonlinear relationship of SOC, and temperature dependence; This is used to reflect the nonlinear disturbances in polarization voltage caused by electrode chemical reaction rates, changes in internal resistance, and temperature effects during charging and discharging. Each disturbance term can change dynamically over time to more realistically simulate the actual operating state of the battery.

[0040] In the above nonlinear model, By load voltage With open circuit voltage The difference, current and disturbance terms Together, they determine the transient response of the battery terminal voltage under actual operating conditions; Subject to load terminal voltage Polarization voltage With disturbance term The influence of this is used to characterize the nonlinear relationship between the battery's state of charge and voltage, as well as the hysteresis effect; Then, based on the polarization voltage Attenuation characteristics, current The role and disturbance term The determination is used to reflect the nonlinear change of electrode polarization effect with the charging and discharging process.

[0041] By combining the nonlinear dynamic equations described above, the nonlinear perturbation terms and linear components can be effectively integrated to describe the nonlinear dynamics and uncertainties of lithium-ion batteries during charging and discharging. Furthermore, this nonlinear model is applicable to battery operation under different temperatures, states of charge, and charge / discharge rates, ensuring its applicability and stability under complex operating conditions.

[0042] Step S103: Design a nonlinear disturbance observer based on a fast tracking differentiator and construct the dynamic equations of the nonlinear model.

[0043] In the nonlinear model of lithium-ion batteries, the dynamics of each voltage are affected by nonlinear perturbation terms, which are difficult to measure or predict directly through linear analysis. In order to observe these perturbation terms online, a nonlinear perturbation observer is established to observe and dynamically compensate for the perturbation terms in the nonlinear model online, thereby enabling the tracking of the nonlinear dynamic characteristics of lithium-ion batteries.

[0044] In constructing the nonlinear disturbance observer, a fast-tracking differentiator is used as the core design unit to track the changing trend of the nonlinear disturbance term in real time. This not only reflects the linear part of the battery but also the dynamic characteristics of the disturbance term over time. By dynamically updating the nonlinear disturbance term, the nonlinear dynamic changes of the battery during charging and discharging can be reflected in a timely manner, thereby improving the real-time performance and accuracy of the disturbance estimation. To achieve this dynamic update, an inverse hyperbolic sine function is introduced. As a nonlinear mapping, it is used to adjust the variation amplitude of the nonlinear disturbance term, and to quickly track and dynamically update the error of the nonlinear disturbance term.

[0045] The nonlinear model dynamic equations constructed through this step can effectively combine online observation of nonlinear disturbances with the linear dynamics of the battery, providing a foundation for subsequent online state estimation and nonlinear disturbance compensation. This enables the entire method to more accurately characterize the nonlinear dynamic characteristics and uncertainties of lithium-ion batteries during the charging and discharging process.

[0046] In one embodiment, the nonlinear disturbance term This affects the dynamic behavior of lithium-ion batteries. Therefore, a nonlinear disturbance observer is introduced to compensate for the disturbance in real time by estimating the nonlinear disturbance term online. The disturbance estimate output by the nonlinear disturbance observer is denoted as... ,in, Indicates the corresponding nonlinear disturbance term The online estimation is used to replace the role of the actual disturbance term in the dynamic equation of the nonlinear model, thereby realizing the tracking and compensation of the nonlinear dynamics of the battery.

[0047] Specifically, the disturbance estimate of the nonlinear disturbance term can be calculated using the following dynamic equation:

[0048] in, This represents the overall response gain of the nonlinear disturbance observer, used to adjust the response speed of the disturbance estimate; This is a nonlinear adjustment parameter for the nonlinear disturbance observer, used to control the amplitude of the nonlinear mapping during the disturbance estimation process.

[0049] by For example, in the dynamic equation, This represents the error between the estimated load-side voltage and the actual load-side voltage, used to reflect the current disturbance deviation; inverse hyperbolic sine function. and , Nonlinear adjustment parameters are used to adjust the nonlinear response characteristics of the disturbance estimation; The overall response gain determines the convergence speed and sensitivity of the disturbance estimation. This equation enables a fast tracking differentiator to handle the load-side voltage disturbance term. Real-time observation and dynamic updates accurately reflect the nonlinear dynamics of the battery under actual charging and discharging conditions.

[0050] Similarly, and The structure of dynamic equations and Similarly, they correspond to open-circuit voltages respectively. and polarization voltage The nonlinear perturbation term is estimated online to achieve comprehensive tracking of the nonlinear behavior of the battery.

[0051] In one embodiment, the estimation process of the load-side voltage not only relies on the basic electrical model but also considers the impact of external disturbances on the voltage dynamics. These disturbances mainly manifest as changes in the voltage's evolution over time. Therefore, by modeling disturbances at the rate of change level using a nonlinear model, the impact of disturbances on voltage dynamics can be more accurately characterized, thereby improving the stability and real-time performance of the estimation results.

[0052] The dynamic equations of the nonlinear model further include:

[0053] in, , , These represent the online estimates of the load terminal voltage, open-circuit voltage, and polarization voltage, respectively. , , These represent the rates of change of the corresponding online voltage estimates; coefficients Indicates linear coupling or attenuation effect between voltages; coefficient Represents current The degree of linear influence on different voltage components; The disturbance term is estimated based on the nonlinear disturbance observer, i.e., the disturbance estimate of the nonlinear disturbance term, and is used to dynamically compensate for nonlinear effects and uncertainties not covered by the linear model.

[0054] In the dynamic equation of the load-side voltage, the estimated value of the disturbance is superimposed. This allows for a more accurate reflection of the dynamic changes in battery load-side voltage under actual operating conditions; the dynamic equation for open-circuit voltage incorporates load-side voltage, polarization voltage, and disturbance estimates. This better characterizes the nonlinear relationship between the state of charge and voltage; in the dynamic equation of polarization voltage, a perturbation estimate is introduced. This effectively compensates for the polarization effect and its changes over time.

[0055] In one embodiment, an inverse hyperbolic sine function is used in the nonlinear disturbance observer. As a nonlinear mapping function, it is used to dynamically update the estimate of the disturbance term. Inverse hyperbolic sine function. The characteristic is that when the disturbance estimation error is greater than the preset threshold, the function exhibits a significant nonlinear response, which enables the disturbance estimation to quickly track the dynamic change trend of voltage, thereby accelerating the convergence speed; while when the disturbance estimation error is less than or equal to the preset threshold, that is, close to zero, the function is approximately linear, which is beneficial to improve the stability and accuracy of the disturbance estimation and avoid the estimation value from oscillating or being overcorrected.

[0056] By introducing this inverse hyperbolic sine function into the nonlinear disturbance observer, it is possible to achieve rapid tracking and accurate compensation of nonlinear disturbances in lithium-ion batteries, thereby improving the dynamic response capability and online estimation reliability of the entire nonlinear model during the charging and discharging process.

[0057] It should be noted that, compared with the commonly used hyperbolic sine function, using the inverse hyperbolic sine function as the tracking function, while maintaining system stability, enables the system state variables to achieve faster convergence and improves transient response performance. Figure 3 For illustration, it can be clearly observed that, under the same system parameters, using the inverse hyperbolic sine function... The designed observer can improve the dynamic performance and response speed of the nonlinear perturbation estimation process. Figure 3 The left figure shows the inverse hyperbolic sine function. The function curve shows that it is approximately linear near the origin, but exhibits suppressed nonlinear growth when the deviation is large, which is beneficial for suppressing overshoot and high-frequency oscillation. Figure 3 The right figure shows a comparison of time-domain responses. and This indicates the use of the inverse hyperbolic sine function. Two-way estimation, and This is the corresponding quantity using the hyperbolic sine function. It can be seen that using the inverse hyperbolic sine function... The residual error decays faster and approaches steady state more smoothly in the initial transient phase, and decays more rapidly and with less fluctuation in the middle and late stages. Therefore, an inverse hyperbolic sine function is used. The designed nonlinear disturbance observer not only improves the dynamic performance of disturbance estimation, but also helps to improve the online estimation accuracy and stability of lithium-ion battery nonlinear models.

[0058] In practical applications, Figure 4 A schematic diagram of the nonlinear disturbance observer is shown. Figure 5 A flowchart illustrating the design of a nonlinear disturbance observer is shown.

[0059] exist Figure 4 First, a first-order RC equivalent circuit for engineering modeling is derived from the electrochemical model of a lithium-ion battery. This equivalent circuit includes the open-circuit voltage. ,capacitance Polarization resistance and resistance Components, current The external excitation is used. The design of the nonlinear observer takes this first-order RC linear model as the observed object, where the linear part of the model and the circuit topology are explicitly known, while the nonlinear part is mainly reflected in the open-circuit voltage. The nonlinear relationship with certain dynamic terms and the nonlinear term caused by current injection. Current measured through the acquisition port. With load terminal voltage Using measurable quantities and the linear equations of the RC model, an observer input is constructed to estimate the nonlinear terms and internal states. Figure 4 The nonlinear disturbance observer shown implements this mapping. Based on the model inverse operation and estimation mechanism, the nonlinear disturbance observer feeds back the measurement signal and model prediction error into the observer, thereby estimating the nonlinear disturbance and unmeasurable state online, and finally outputting the estimated value.

[0060] exist Figure 5 In the process, the lithium-ion battery is first represented as an equivalent first-order RC linear model, and its mathematical expression is given. Then, common algebraic and differential transformations are performed on the mathematical expression of the first-order RC linear model, and the state equations are written as state equations in parallel with voltage derivatives:

[0061] To facilitate observer design and introduce nonlinear perturbation terms, the above equations are rewritten in quasi-linear form, so that the nonlinear perturbation terms to be estimated and the measurable quantities are expressed in the same set of observation equations, thus obtaining the corresponding nonlinear model.

[0062] To improve estimation accuracy, a fast tracking differentiator is introduced for high-precision derivative and perturbation estimation. The basic form of the fast tracking differentiator is as follows:

[0063] In the above formula, and These are the estimators of the state and the disturbance, respectively. For the nonlinear mapping function of the perturbation estimation, To design gain.

[0064] Based on the structure and nonlinear model of this fast tracking differentiator, the dynamic equation of the nonlinear disturbance observer is further constructed, namely:

[0065] Through this closed-loop design involving linear RC model transformation, differentiator estimation, and error feedback, the nonlinear disturbance observer can rapidly track nonlinear disturbances under stable system conditions, quickly reducing the disturbance estimation error to a small order of magnitude. This effectively improves the accuracy and real-time performance of battery nonlinear dynamic state estimation. The design also ensures tight coupling between voltage and disturbance estimates, enabling online observation of the dynamic characteristics and uncertainties of lithium-ion batteries.

[0066] It should be noted that the nonlinear disturbance observer can not only estimate the nonlinear disturbance term online, but also simultaneously output the critical states of the battery. , , The online estimates are used to compensate for unmodeled nonlinear characteristics in the first-order RC linear model, enabling the model to adaptively correct itself during operation. State estimates are used to reconstruct internal variables such as open-circuit voltage and polarization voltage, which cannot be directly measured. To ensure consistency between model predictions and actual battery behavior, the perturbation estimates output by the nonlinear disturbance observer are fed back to the nonlinear model in real time, thereby eliminating dynamic errors caused by non-ideal battery characteristics at the model level. Simultaneously, the state estimates can serve as approximations of the battery's true internal state, directly applicable to subsequent SOC estimation, energy management, and lifetime prediction. Through this feedback mechanism, the nonlinear model and the nonlinear disturbance observer form a closed-loop collaboration, making the estimation of the battery's dynamic state more accurate and reliable.

[0067] In one embodiment, based on the dynamic equations of a nonlinear model, online estimates of the load-side voltage, open-circuit voltage, and polarization voltage can be obtained. By comparing these estimates with the actual measured voltage data, a voltage deviation signal can be acquired in real time. This deviation signal is then mapped to a State of Charge (SOC) correction value, and the battery's SOC value is updated through accumulation or filtering, thereby dynamically correcting the SOC. The corrected SOC not only reflects the battery's state of charge under actual operating conditions but can also be used in subsequent battery management and control strategies to achieve precise monitoring and management of the lithium-ion battery's state of charge.

[0068] Using the above method, the estimation results of the nonlinear model can simultaneously reflect the battery voltage behavior and correct the SOC, thereby significantly improving the SOC estimation accuracy and the reliability of the battery management system.

[0069] In the above embodiments, by establishing a first-order RC linear model and introducing a nonlinear disturbance term, combined with a nonlinear disturbance observer designed using a fast tracking differentiator, the nonlinear dynamic characteristics of the battery during charging and discharging can be accurately characterized. This method can estimate the internal state of the battery in real time, effectively capturing nonlinear behavior caused by factors such as temperature changes, aging effects, changes in state of charge, and fluctuations in charge and discharge rates, thereby significantly improving the battery management system's understanding and accuracy of the battery's current state. Simultaneously, the nonlinear disturbance observer can suppress the influence of external disturbances on state estimation, ensuring the stability and reliability of the battery system. Based on this accurate state observation, the battery management system can obtain more reliable feedback information, providing support for the charge and discharge control algorithm, thereby optimizing charging efficiency, extending battery life, and improving overall control performance. Furthermore, compared to traditional physical model-based observation methods, this method has a simpler structure and higher computational efficiency, reducing the complexity of system design and implementation, and improving the feasibility and scalability of the method. Therefore, the nonlinear model disturbance observation method provided by this invention can comprehensively improve the performance and reliability of the battery management system, providing strong technical support for the widespread application of lithium-ion batteries in various application scenarios.

[0070] In one embodiment, a lithium-ion battery nonlinear model interference observation device is provided, referencing... Figure 6 As shown, the lithium-ion battery nonlinear model interference observation device 600 may include: a first-order RC linear model establishment module 601, a nonlinear model establishment module 602, a nonlinear interference observer construction module 603, and a correction module 604.

[0071] Among them, the first-order RC linear model establishment module 601 is used to establish a first-order RC linear model of lithium-ion battery; the first-order RC linear model is used to characterize the dynamic characteristics of lithium-ion battery during charging and discharging. The nonlinear model building module 602 is used to transform a first-order RC linear model into a nonlinear model containing nonlinear perturbation terms. The nonlinear disturbance observer construction module 603 is used to design a nonlinear disturbance observer based on a fast tracking differentiator and construct the dynamic equations of the nonlinear model; wherein, in the dynamic equations, the disturbance estimate of the nonlinear disturbance term adopts an inverse hyperbolic sine function. The fast tracking differentiator structure is updated.

[0072] In the above embodiments, the first-order RC linear model includes the load-side voltage. Open circuit voltage Current ,resistance Polarization resistance ,capacitance and battery capacity The first-order RC linear model satisfies:

[0073] in, Indicates open circuit voltage rate of change, Indicates polarization voltage The rate of change.

[0074] In the above embodiments, the nonlinear model satisfies:

[0075] in, Indicates the load terminal voltage rate of change, Indicates open circuit voltage rate of change, Indicates polarization voltage rate of change, This represents the linear coupling coefficient or attenuation coefficient between different voltages. This represents the linear effect coefficient of current on various voltages. This represents the nonlinear perturbation term.

[0076] In the above embodiment, the dynamic equation for the disturbance estimate of the nonlinear disturbance term is:

[0077] in, This represents the disturbance estimate of the nonlinear disturbance term. This represents the overall response gain of the nonlinear disturbance observer. This indicates the nonlinear adjustment of the nonlinear disturbance observer.

[0078] In the above embodiments, the dynamic equations of the nonlinear model include:

[0079] Among them, among them, This represents the online estimate of the load terminal voltage. This represents the online estimate of the open-circuit voltage. This represents the online estimate of the polarization voltage. This represents the online estimate of the load terminal voltage. rate of change, This represents the online estimate of the open-circuit voltage. rate of change, Represents the online estimate of polarization voltage rate of change, This represents the linear coupling coefficient or attenuation coefficient between different voltages. This represents the linear influence coefficient of current on each voltage.

[0080] In the above embodiments, the inverse hyperbolic sine function It exhibits non-linearity when the error exceeds a preset threshold, and linearity when the error is less than or equal to the preset threshold.

[0081] In the above embodiments, the lithium-ion battery nonlinear model interference observation device 600 further includes a correction module 604, which is used to correct the state of charge of the lithium-ion battery based on the estimated value obtained from the dynamic equation of the nonlinear model.

[0082] Specific limitations regarding the lithium-ion battery nonlinear model interference observation device 600 can be found in the limitations of the lithium-ion battery nonlinear model interference observation method described above, and will not be repeated here. Each module in the aforementioned lithium-ion battery nonlinear model interference observation device 600 can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0083] In one embodiment, an electronic device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement a nonlinear model interference observation method for lithium-ion batteries.

[0084] In one embodiment, a computer storage medium is provided on which a computer program is stored, which, when executed by a processor, implements a method for observing interference from a nonlinear model of a lithium-ion battery.

[0085] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0086] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0087] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0088] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0089] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for observing interference in a nonlinear model of a lithium-ion battery, characterized in that, include: Establish a first-order RC linear model for lithium-ion batteries; The first-order RC linear model is used to characterize the dynamic characteristics of lithium-ion batteries during the charging and discharging process. The first-order RC linear model is transformed into a nonlinear model that includes nonlinear perturbation terms; A nonlinear disturbance observer is designed based on a fast tracking differentiator, and the dynamic equations of the nonlinear model are constructed. In these dynamic equations, the disturbance estimate of the nonlinear disturbance term is expressed using an inverse hyperbolic sine function. The fast tracking differentiator structure is updated.

2. The method for observing interference in a nonlinear model of a lithium-ion battery according to claim 1, characterized in that, The first-order RC linear model includes the load-side voltage. Open circuit voltage Current ,resistance Polarization resistance ,capacitance and battery capacity The first-order RC linear model satisfies: in, Indicates open circuit voltage rate of change, Indicates polarization voltage The rate of change.

3. The method for observing interference in a nonlinear model of a lithium-ion battery according to claim 1, characterized in that, The nonlinear model satisfies: in, Indicates the load terminal voltage rate of change, Indicates open circuit voltage rate of change, Indicates polarization voltage rate of change, This represents the linear coupling coefficient or attenuation coefficient between different voltages. This represents the linear effect coefficient of current on various voltages. This represents the nonlinear perturbation term.

4. A method for observing interference in a nonlinear model of a lithium-ion battery according to claim 1 or 3, characterized in that, The dynamic equation for the disturbance estimate of the nonlinear disturbance term is: in, This represents the disturbance estimate of the nonlinear disturbance term. This represents the overall response gain of the nonlinear disturbance observer. This indicates the nonlinear adjustment of the nonlinear disturbance observer.

5. The method for observing interference in a nonlinear model of a lithium-ion battery according to claim 4, characterized in that, The dynamic equations of the nonlinear model include: Among them, among them, This represents the online estimate of the load terminal voltage. This represents the online estimate of the open-circuit voltage. This represents the online estimate of the polarization voltage. This represents the online estimate of the load terminal voltage. rate of change, Represents the online estimate of the open-circuit voltage. rate of change, Represents the online estimate of polarization voltage rate of change, This represents the linear coupling coefficient or attenuation coefficient between different voltages. This represents the linear influence coefficient of current on each voltage.

6. The method for observing interference in a nonlinear model of a lithium-ion battery according to claim 1, characterized in that, The inverse hyperbolic sine function It exhibits non-linearity when the error exceeds a preset threshold, and linearity when the error is less than or equal to the preset threshold.

7. The method for observing interference in a nonlinear model of a lithium-ion battery according to claim 1, characterized in that, The method further includes: The state of charge of the lithium-ion battery is corrected based on the estimated value obtained from the dynamic equation of the nonlinear model.

8. A device for observing interference in a nonlinear model of a lithium-ion battery, characterized in that, include: The first-order RC linear model building module is used to build a first-order RC linear model of lithium-ion batteries; The first-order RC linear model is used to characterize the dynamic characteristics of lithium-ion batteries during the charging and discharging process. The nonlinear model building module is used to transform the first-order RC linear model into a nonlinear model containing nonlinear perturbation terms. A nonlinear disturbance observer construction module is used to design a nonlinear disturbance observer based on a fast tracking differentiator and construct the dynamic equations of the nonlinear model; wherein, in the dynamic equations, the disturbance estimate of the nonlinear disturbance term adopts an inverse hyperbolic sine function. The fast tracking differentiator structure is updated.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the nonlinear model interference observation method for lithium-ion batteries according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the lithium-ion battery nonlinear model interference observation method according to any one of claims 1 to 7.

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