Lithium battery SOE estimation method and related device

Abstract

The invention discloses a lithium ion battery SOE estimation method and a related device, and the method comprises the steps: building an equivalent circuit model of a target lithium battery, building a state equation and an observation equation based on the equivalent circuit model, and forming a mathematical framework of a self-adaptive unscented Kalman filtering algorithm; a filtering algorithm is executed under a plurality of candidate sliding window lengths, corresponding SOE estimation loss is calculated, the optimal sliding window length is selected as a residual error updating basis, the filtering algorithm is executed on a target lithium battery by utilizing the optimized sliding window length, high-precision and stable estimation of SOE is realized, the method can adapt to different working conditions, and the reliability of the lithium battery is improved. And the response capability of the filter to the dynamic characteristics of the battery and the SOE estimation precision are improved.

Description

Technical Field

[0001] This invention pertains to lithium battery safety monitoring technology, and particularly relates to a lithium battery SOE estimation method and related apparatus. Background Technology

[0002] With the rapid development of electric vehicles, wearable devices, and energy storage systems, the state of energy (SOE) estimation technology for lithium-ion batteries plays a crucial role in battery management systems. SOE reflects the proportion of currently available energy in a battery, and its estimation accuracy directly affects charge / discharge control, safety management, and lifespan prediction. Therefore, researching high-precision and robust SOE estimation algorithms has become a key focus of current battery management technology.

[0003] In existing technologies, Kalman filtering (KF) and its extended forms, such as extended Kalman filtering (EKF) and unscented Kalman filtering (UKF), are widely used for online estimation of battery SOC / SOE. However, due to factors such as time-varying parameters in the battery model, unmodeled errors, and changes in the statistical characteristics of measurement noise, traditional UKF struggles to maintain stable accuracy under many complex operating conditions. Therefore, researchers have introduced adaptive mechanisms to correct the noise covariance matrix, achieving more accurate state estimation. Among these, adaptive unscented Kalman filtering (AUKF) dynamically adjusts the process noise covariance and measurement noise covariance by analyzing the residuals between predicted and actual measurements, thus improving the filter's adaptability to complex operating conditions and noise variations to a certain extent.

[0004] However, the AUKF algorithm generally relies on residual statistics to drive noise covariance updates, and residual statistical properties are typically obtained through a sliding window approach. The sliding window length directly affects the smoothness of the residual sequence, the accuracy of variance estimation, and the sensitivity of the adaptive adjustment strategy. In practical engineering, a sliding window that is too short will lead to excessive fluctuations in residual statistics, causing frequent changes in filter gain and even estimation oscillations; while a window that is too long will make the residual characteristics sluggish, reducing the filter's responsiveness to noise changes. Therefore, the sliding window length M is a key parameter affecting AUKF performance.

[0005] In existing publicly available technologies, the sliding window length M is mostly selected based on experience or set according to empirical models, lacking a direct correlation with the actual operating conditions, dynamic characteristics, and target estimation indicators (such as SOE accuracy) of the battery. Especially when facing diverse application scenarios such as different discharge rates, temperatures, and load fluctuations, a fixed value of M is difficult to guarantee stable and optimal SOE estimation performance. Due to the lack of quantitative optimization methods for M, existing AUKF methods may still suffer from insufficient accuracy and poor adaptability to different operating conditions in SOE estimation. Summary of the Invention

[0006] Based on this, the present invention aims to propose a lithium battery SOE estimation method and related apparatus. By utilizing the loss of the battery SOE value estimated by the AUKF algorithm, the optimal sliding window length in the AUKF algorithm is determined, thereby determining the optimal residual of the AUKF algorithm, so as to overcome the problem of insufficient accuracy of the existing AUKF algorithm in SOE estimation.

[0007] In a first aspect, the present invention provides a method for estimating the SOE of a lithium battery, comprising:

[0008] Establish an equivalent circuit model of the target lithium battery;

[0009] The state equations and observation equations required for constructing an adaptive unscented Kalman filter algorithm based on an equivalent circuit model;

[0010] The candidate sliding window length for updating the residual is determined according to the set step size. Based on the state equation and the observation equation, the adaptive unscented Kalman filter algorithm is executed under each candidate sliding window length to calculate the SOE estimation loss corresponding to each candidate sliding window length.

[0011] The candidate sliding window length corresponding to the SOE estimation loss that meets the preset optimization conditions is taken as the optimized sliding window length. Based on the optimized sliding window length, an adaptive unscented Kalman filter algorithm is executed on the target lithium battery to complete the SOE estimation.

[0012] Furthermore, based on the state equation and observation equation, an adaptive unscented Kalman filter algorithm is executed for each candidate sliding window length, and the SOE estimation loss for each candidate sliding window length is calculated, including:

[0013] Determine at least one preset operating condition;

[0014] Under at least one preset operating condition, an adaptive unscented Kalman filter algorithm is executed based on the state equation and the observation equation for each candidate sliding window length to obtain the SOE estimate for each candidate sliding window length.

[0015] Calculate the estimated loss for each candidate sliding window length based on the SOE estimate.

[0016] Furthermore, the estimated loss is calculated based on the SOE estimate, including:

[0017] Calculate the first and second errors of the SOE estimate;

[0018] The estimated loss of the SOE estimate is calculated by summing the first error and the second error.

[0019] Furthermore, the first error is the mean absolute error, and the second error is the root mean square error.

[0020] Furthermore, the preset optimization conditions include:

[0021] The candidate sliding window length corresponding to the minimum loss estimated by SOE is the optimal sliding window length.

[0022] Furthermore, establishing the equivalent circuit model of the target lithium battery includes:

[0023] Establish a first-order equivalent circuit model of the target lithium battery;

[0024] The parameters of the equivalent circuit model under dynamic conditions are solved using the least squares method with a variable forgetting factor.

[0025] Update the first-order equivalent circuit model with the parameters of the equivalent circuit model.

[0026] Furthermore, the parameters of the equivalent circuit model under dynamic operating conditions are solved using the least squares method with a variable forgetting factor, including:

[0027] The first-order equivalent circuit model is reconstructed into the following discrete equations:

[0028] ,

[0029] in, This represents the terminal voltage, and k represents the sampling point. Represents current. Indicates equivalent parameters;

[0030] The following transformation is defined:

[0031] ,

[0032] in, and These represent the measurement vector and the parameter vector, respectively.

[0033] The measurement vector and parameter vector are iteratively solved using the least squares method with a variable forgetting factor as follows:

[0034] ;

[0035] Based on parameter vector The solution is as follows: calculate the parameters of the equivalent circuit model.

[0036] ,

[0037] in, , , These represent the time constant, polarization resistance, and internal resistance of the lithium battery in the equivalent circuit model, respectively.

[0038] In a second aspect, the present invention provides a lithium battery SOE estimation device, comprising:

[0039] The equivalent modeling module is used to establish the equivalent circuit model of the target lithium battery.

[0040] The equation building module is used to construct the state equations and observation equations required for the adaptive unscented Kalman filter algorithm based on the equivalent circuit model.

[0041] The sliding window length optimization module is used to determine the candidate sliding window length for updating the residuals according to a set step size. Based on the state equation and observation equation, it executes an adaptive unscented Kalman filter algorithm under each candidate sliding window length to calculate the SOE estimation loss for each candidate sliding window length.

[0042] The SOE estimation module is used to take the candidate sliding window length corresponding to the SOE estimation loss that meets the preset optimization conditions as the optimized sliding window length, and to perform an adaptive unscented Kalman filter algorithm on the target lithium battery based on the optimized sliding window length to complete the SOE estimation.

[0043] Thirdly, the present invention provides an electronic device including a memory storing computer-executable instructions and a processor, wherein when the computer-executable instructions are executed by the processor, the device performs the steps of the lithium battery SOE estimation method provided in the first aspect.

[0044] Fourthly, the present invention provides a readable storage medium storing a computer-executable program that, when executed, implements the various steps of the lithium battery SOE estimation method provided in the first aspect.

[0045] Compared with existing SOE estimation methods, the present invention has the following advantages:

[0046] This invention proposes a lithium battery SOE estimation method. By using the sliding window length as a key adjustment parameter in the adaptive unscented Kalman filter algorithm, and optimizing this parameter based on the SOE estimation loss under multiple operating conditions, the residual update mechanism can more accurately reflect the true noise characteristics of the battery under different dynamic operating conditions, thereby significantly improving the effectiveness of covariance matrix updates. Compared with the traditional AUKF algorithm with a fixed sliding window length, this invention can adaptively match the changes in residual statistical characteristics under different operating conditions, improve the accuracy of process noise and measurement noise estimation, enhance the rationality of filter gain, and thus improve the robustness and accuracy of SOE estimation. A further embodiment optimizes the sliding window globally by simultaneously evaluating the loss under multiple operating conditions, avoiding the limitations of single-condition parameter tuning, making the SOE estimation method more universal and applicable in engineering. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0048] Figure 1 A flowchart illustrating the implementation of the lithium battery SOE estimation method provided in this embodiment of the invention;

[0049] Figure 2 This is a schematic diagram of the equivalent circuit model of a lithium battery provided in an embodiment of the present invention;

[0050] Figure 3 The curve showing the total loss of the AUKF algorithm as a function of the sliding window length, provided in an embodiment of the present invention.

[0051] Figure 4 This invention provides a comparison of SOE estimation performance between embodiments of the present invention and other filtering algorithms.

[0052] Figure 5 This invention provides a comparison of SOE estimation algorithm errors between embodiments of the present invention and other filtering algorithms.

[0053] Figure 6 This invention provides a comparison of SOE estimation results for battery internal resistance at different orders of magnitude in embodiments of the present invention.

[0054] Figure 7 This invention provides a comparison of the estimation errors for SOE estimation of battery internal resistance at different orders of magnitude in embodiments of the present invention.

[0055] Figure 8 This is a schematic diagram of the structure of the lithium battery SOE estimation device provided in an embodiment of the present invention;

[0056] Figure 9 This is an electronic device architecture diagram provided for an embodiment of the present invention. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] See Figure 1 One embodiment of the present invention proposes a method for estimating the SOE of a lithium battery, comprising the following steps:

[0059] Step S110. Establish the equivalent circuit model of the target lithium battery.

[0060] This step establishes an equivalent circuit model of the target lithium battery to provide a physical basis model that can accurately reflect the dynamic characteristics of the battery for the subsequent adaptive unscented Kalman filter algorithm. This includes at least establishing a basic model, identifying model parameters based on actual operating conditions, and updating the equivalent circuit model using the identified parameters to ensure that the model has good dynamic adaptability and predictive ability.

[0061] First, a first-order equivalent circuit model is established for the target lithium battery. This model typically consists of an open-circuit voltage source, internal resistance, and a resistor-capacitor branch used to characterize the battery polarization effect. It can describe the basic relationship between the battery terminal voltage and the battery's internal state. The first-order equivalent circuit model has the advantages of simple structure, high computational efficiency, and clear physical meaning, making it suitable for real-time state estimation requirements in most vehicle or energy storage operating conditions.

[0062] In a further embodiment, in order to enable the established equivalent circuit model to adapt to the actual operating state under dynamic conditions, this embodiment uses the least squares method with a variable forgetting factor to identify the model parameters online.

[0063] Specifically, based on dynamic voltage and current sampling data under actual operating conditions, the first-order equivalent circuit model is reconstructed into a discrete form, and the terminal voltage, current, and parameters to be identified in the model are reorganized into measurement vectors and parameter vectors. Using a least-squares algorithm with a variable forgetting factor, these measurement vectors and parameter vectors are updated sample-by-sample, enabling the algorithm to retain historically valid information while rapidly responding to the latest changes in battery state. The variable forgetting factor can be automatically adjusted according to changes in operating conditions, maintaining a balance between stability and sensitivity in the parameter update process.

[0064] During the iterative update process, the parameter vector to be identified is updated at each sampling time based on the current measurement data. After obtaining the steady-state convergence value of the parameter vector, it can be converted into the corresponding first-order equivalent circuit model parameters, including physical parameters such as the time constant, polarization resistance, and lithium battery internal resistance used to describe the polarization dynamic characteristics. These parameters reflect the true electrochemical characteristics of the battery under the current temperature, aging level, and discharge rate. The obtained parameter values ​​are then updated into the original first-order equivalent circuit model, thus forming an updated equivalent circuit model based on dynamic operating conditions. This model can more accurately reflect the battery's transient response, polarization behavior, and steady-state output characteristics.

[0065] For example, Figure 2 The diagram illustrates one scenario of the equivalent circuit model of a lithium battery, whose continuous state equations are expressed as follows:

[0066]

[0067] in, Indicates terminal voltage. Represents the open-circuit voltage, which is a function of SOE. For internal resistance, and These are the polarization resistor and polarization capacitor, respectively. Polarization voltage, Indicates to Differential operations.

[0068] Discretizing the above linear state equations yields the following discrete state equations:

[0069]

[0070] By performing a Laplace transform on the state equations and combining it with the response relationship of a first-order linear system in control theory, the original state equations are reconstructed into the following discrete equations in the form of a linear recursive relationship between current input and terminal voltage output:

[0071]

[0072] in, This represents the terminal voltage, and k represents the sampling point. Represents current. Indicates equivalent parameters;

[0073] The following transformation is defined:

[0074]

[0075] in, and These represent the measurement vector and the parameter vector, respectively.

[0076] The equivalent circuit model at each sampling time under actual operating conditions is solved using the least squares method with a variable forgetting factor. The measurement vector and parameter vector are solved iteratively as follows:

[0077]

[0078] Based on parameter vector The solution is as follows: calculate the parameters of the equivalent circuit model.

[0079]

[0080] in, , , These represent the time constant, polarization resistance, and internal resistance of the lithium battery in the equivalent circuit model, respectively.

[0081] Step S120. Construct the state equations and observation equations required for the adaptive unscented Kalman filter algorithm based on the equivalent circuit model.

[0082] This step constructs a mathematical description that reflects the internal electrochemical dynamics of lithium batteries, enabling the filtering algorithm to accurately estimate the SOE of lithium batteries under nonlinear and dynamically changing actual charge and discharge conditions.

[0083] The state equation is used to describe the intrinsic law of SOE evolution over time. Its independent variables usually include current, voltage, and dynamic parameters derived from the equivalent circuit model. The observation equation is used to characterize the relationship between measurable output (e.g., terminal voltage) and state variables. Since the terminal voltage is not only related to the open-circuit voltage of the battery itself, but also affected by the internal resistance voltage drop and polarization voltage change, the observation equation couples these physical quantities to form a voltage prediction model, enabling the filter to correct the state estimation results based on the actual measured terminal voltage.

[0084] In a further embodiment, when constructing the state equation, factors such as temperature, aging degree, and reversible polarization term can be selected to be included in the state vector according to the specific application scenario. When the battery operates under high-rate conditions or large temperature difference environments, temperature has a significant impact on voltage characteristics, internal resistance, and polarization behavior. Therefore, using temperature as an extended state variable can improve the filter's adaptability to unsteady-state scenarios.

[0085] Step S130. Determine the candidate sliding window length for updating the residuals according to the set step size. Based on the state equation and observation equation, execute the adaptive unscented Kalman filter algorithm under each candidate sliding window length to calculate the SOE estimation loss for each candidate sliding window length.

[0086] This step involves finding the optimal window size for SOE estimation by analyzing the estimation loss of SOE estimates under different sliding window lengths. The sliding window length refers to a fixed time interval taken from the residual sequence, within which the residuals are statistically analyzed to update the noise covariance or for performance evaluation. This step requires comparing multiple sliding window lengths of different sizes to find the optimal window size for SOE estimation. The calculated estimation loss is used as a metric to quantify SOE estimation performance; specifically, it can be calculated by combining the error values ​​of the estimates to reflect the estimation bias and jitter.

[0087] In filtering algorithms, the update residual refers to the difference between the actual measured value and the filtered predicted value. For example, for terminal voltage measurement, the residual represents the error between the filter's predicted voltage and the actual acquired terminal voltage, reflecting the degree to which the current filtering model describes the actual battery state. The smaller the residual, the closer the current model and noise settings are to the actual operating conditions; the larger the residual, the greater the model error or noise setting mismatch.

[0088] The sliding window length is closely related to the residual statistical characteristics. If the window is too short, the noise estimation will be overly sensitive; if the window is too long, the response will be lagy and it will be difficult to reflect dynamic operating conditions. Therefore, this step runs the filter under a series of candidate window lengths and calculates their SOE estimation accuracy to select the optimal window length, so as to achieve a balance between filter performance and response speed.

[0089] Specifically, firstly, multiple candidate sliding window lengths are generated according to a set step size, for example, increasing from the minimum window length to the maximum window length at fixed intervals. Under each candidate window length, an adaptive unscented Kalman filter algorithm is executed based on the state equation and observation equation constructed in step S120, using the corresponding window length to update the noise estimate and output the corresponding SOE estimate, and then calculating the corresponding estimated loss value.

[0090] In a further embodiment, the AUKF algorithm is executed under at least one preset operating condition to obtain SOE estimation results corresponding to different candidate sliding window lengths under different operating conditions. The preset operating conditions may include typical battery application scenarios such as normal temperature conditions, low temperature conditions, and high load conditions, to comprehensively characterize the adaptability of the sliding window length.

[0091] In a more preferred embodiment, the mean absolute error and root mean square error of the SOE estimate are calculated separately, and the sum of the two is used as the estimated loss of the SOE estimate. This loss calculation is performed for each candidate sliding window length to obtain the estimated loss for each candidate sliding window length. In a further preferred implementation, different errors can be weighted and combined according to actual needs. For example, for operating conditions more sensitive to dynamic errors, the root mean square error weight can be increased to improve the constraint ability on large errors. In another more preferred embodiment, statistical characteristics such as residual mean, variance, and skewness can be added as auxiliary indicators. When the errors of two sliding window lengths are close, the optimal window length is further selected by judging the statistical stability of the residuals.

[0092] For example, two preset working conditions are selected, and N candidate sliding window lengths are generated according to the set step size. For each preset working condition, the AUKF algorithm is executed once under each candidate sliding window length, and the mean absolute error and root mean square error of the corresponding SOE estimate are calculated, thus obtaining N rows and 4 columns of error data.

[0093] Step S140. Take the candidate sliding window length corresponding to the SOE estimation loss that meets the preset optimization conditions as the optimized sliding window length, and perform an adaptive unscented Kalman filter algorithm on the target lithium battery based on the optimized sliding window length to complete the SOE estimation.

[0094] The preset optimization criteria in this step refer to the standards used to judge the merits of candidate sliding window lengths. In some embodiments, the window length corresponding to the minimum loss estimated by SOE is used as the optimized sliding window length.

[0095] Specifically, based on all candidate sliding window lengths and their corresponding SOE estimated losses obtained in the preceding steps, a total loss curve is generated, representing the mapping relationship between window length and loss value. The candidate sliding window length corresponding to the minimum value is found on the total loss curve, and this length is defined as the optimal sliding window length. This optimal sliding window length is then input into the AUKF algorithm to control residual statistics and covariance updates. During the actual operation or experimental sampling of the target lithium battery, a complete AUKF algorithm iteration is performed based on the optimal sliding window length to generate the final SOE estimation result.

[0096] In a further embodiment, the initially selected optimal window length can be locally fine-tuned, for example, by searching with a smaller step size near the minimum loss point, in order to further improve the SOE estimation accuracy and filtering stability.

[0097] In another embodiment, the optimization process can be carried out synchronously with actual operating data. That is, based on the initially selected window length, the window length is dynamically adjusted and the residuals and estimated losses are recalculated to adapt to the dynamic changes in battery characteristics caused by aging, temperature or operating conditions.

[0098] The following is based on Figure 2 The present invention will be further explained using an illustrative equivalent circuit model as an example.

[0099] The state equations and observation equations are established based on the solved equivalent circuit model as follows:

[0100]

[0101] Indicates the sampling interval.

[0102] The following 2n+1 σ points X and their corresponding weights are obtained through unscented transformation. Where n is the state dimension:

[0103]

[0104]

[0105] The i-th column represents the square root of the matrix. The weight subscript m represents the mean, c represents the covariance, and the superscript i represents the i-th sampling point.

[0106] A nonlinear system is defined as follows:

[0107]

[0108] For random vectors, is the observation vector; f is the nonlinear state equation, h is the nonlinear measurement equation; W(k) and V(k) are the process noise and measurement noise, respectively, and their corresponding covariance matrices are Q and R, respectively.

[0109] The sampling points are obtained according to the above formulas as follows:

[0110]

[0111] Calculate the one-step prediction of 2n+1 σ points.

[0112]

[0113] The one-step prediction and covariance matrix of the system state variables are calculated as follows:

[0114]

[0115] Based on the one-step prediction, the UT transformation is applied again, as shown in the following equation:

[0116]

[0117] Substituting the above predicted σ-point set into the measurement equation, we obtain a one-step prediction of the measurement vector:

[0118]

[0119] Based on the above steps, the measurement prediction values ​​of the new σ-point set are obtained, and the measurement mean and variance of the system are obtained by weighted summation:

[0120]

[0121] Calculate the Kalman gain matrix:

[0122]

[0123] The system state update and covariance update are calculated using the following formula:

[0124]

[0125] The adaptive rule for the covariance matrices R and Q is shown in the following equation:

[0126]

[0127] in The value is the residual, and M represents the length of the sliding window.

[0128] Two operating conditions are selected, and N candidate sliding window lengths are generated. The AUKF algorithm is executed under each candidate sliding window length, and the corresponding mean absolute error and root mean square error are calculated, resulting in N rows and 4 columns of data. For each candidate sliding window length, the estimation errors for the two operating conditions are summed to obtain the total loss, resulting in N rows and 1 column of data. The curve showing the total loss changing with the sliding window length is shown below. Figure 3 As shown.

[0129] The optimal sliding window length is selected based on the minimum total loss, and then the optimal residual is calculated. Figure 3 As shown, the sliding window length corresponding to the minimum total loss is 24.

[0130] Figure 4 and Figure 5 The illustrations show the effects and errors of the AUKF algorithm with residual optimization by optimizing the sliding window length compared with other filtering algorithms. The AUKF algorithm with optimized sliding window length has more stable error and better convergence.

[0131] To demonstrate the robustness of the estimation method of this invention, the initial value of the battery internal resistance R0 was set to different orders of magnitude. The SOE estimation results and estimation error curves of this invention are shown below. Figure 6 and 7 As shown, the optimized AUKF algorithm does not exhibit unstable estimations due to the order-of-magnitude difference in input parameters.

[0132] The disclosed method can be implemented using various types of devices. Therefore, the present invention also discloses an apparatus corresponding to the above method, and specific embodiments are given below for detailed description.

[0133] like Figure 8 As shown, one embodiment of the present invention provides a lithium battery SOE estimation device, comprising:

[0134] Equivalent modeling module 802 is used to establish the equivalent circuit model of the target lithium battery;

[0135] Equation building module 804 is used to build the state equations and observation equations required for the adaptive unscented Kalman filter algorithm based on the equivalent circuit model;

[0136] The sliding window length optimization module 806 is used to determine the candidate sliding window length for updating the residuals according to a set step size. Based on the state equation and observation equation, it performs an adaptive unscented Kalman filter algorithm under each candidate sliding window length to calculate the SOE estimation loss corresponding to each candidate sliding window length.

[0137] The SOE estimation module 808 is used to take the candidate sliding window length corresponding to the SOE estimation loss that meets the preset optimization conditions as the optimized sliding window length, and perform an adaptive unscented Kalman filter algorithm on the target lithium battery based on the optimized sliding window length to complete the SOE estimation.

[0138] The device provided in this application embodiment has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0139] The methods and related apparatuses mentioned in the above embodiments are described with reference to the method flowcharts and / or structural diagrams provided in the embodiments of this application. Specifically, each block of the method flowchart and / or structural diagram, as well as combinations of blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the process. Figure 1 A schematic diagram of one or more processes and / or structures. Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 A schematic diagram of one or more processes and / or structures. Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 A process or multiple processes and / or structures illustrate the steps of the functions specified in one or more boxes.

[0140] The following embodiments illustrate the application of this method to a computer device. It is understood that the computer device can be any device with computing and processing capabilities, including but not limited to servers or personal laptops. In one embodiment, the computer device can be an application server, which can be a server used to run the application under test.

[0141] See Figure 9This document illustrates a hardware block diagram of an electronic device intended to represent various forms of digital computers, such as laptops, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present application described and / or claimed herein.

[0142] like Figure 9 As shown, the electronic device includes: at least one processor 1, at least one communication interface 2, at least one memory 3, and at least one communication bus 4;

[0143] In this embodiment, the number of processor 1, communication interface 2, memory 3, and communication bus 4 is at least one, and processor 1, communication interface 2, and memory 3 communicate with each other through communication bus 4.

[0144] Processor 1 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention.

[0145] Memory 3 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device;

[0146] The memory stores a program, which the processor can call. The program is used to implement the various processing steps of the aforementioned lithium battery SOE estimation scheme.

[0147] This invention also provides a readable storage medium storing a computer program thereon, which, when executed by a processor, implements various processing flows of the lithium battery SOE estimation scheme provided in any possible implementation of the above embodiments and / or in combination with the embodiments.

[0148] The invention has been described in particular detail above with respect to possible scenarios, and those skilled in the art will recognize that the invention can be practiced through other embodiments. Specific naming of components, capitalization of terms, attributes, data structures, or any other programming or structural aspects are not mandatory or important, and the mechanisms or features of implementing the invention may have different names, forms, or procedures. The system can be implemented through a combination of hardware and software (as described), entirely through hardware elements, or entirely through software elements. The specific division of functions among the various system components described herein is merely exemplary and not mandatory; rather, the functions performed by a single system component can be performed by multiple components, or the functions performed by multiple components can be performed by a single component.

[0149] Those skilled in the art should understand that the various steps of the disclosed methods can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using device-executable program code, which can then be stored in a storage device for execution by the computing device. Alternatively, they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Therefore, the embodiments disclosed in this invention are not limited to any specific hardware and software combination.

[0150] The programs (also referred to as programs, software, software applications, or code) executable by these computing devices include machine instructions of a programmable processor and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms “machine-readable medium” and “computer-readable medium” refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) used to provide machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term “machine-readable signal” refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0151] Certain aspects of this invention include the process steps and instructions described herein in algorithmic form. It should be noted that the process steps and instructions of this invention can be implemented in software, firmware, and / or hardware, and when implemented in software, they can be downloaded, stored on various operating systems and operated from said platforms.

[0152] Those skilled in the art will understand that the structures shown in the figures are merely block diagrams of some structures related to the present application and do not constitute a limitation on the terminal device to which the present application is applied. Specific terminal devices may include more or fewer components than those shown in the figures, or combine certain components, or have different component arrangements.

[0153] In the description of this specification, the use of terms such as "one embodiment," "some embodiments," "example," "specific example," or "possible design," etc., refers to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of this application. 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 a suitable manner in any one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0154] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover 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 limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0155] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for estimating SOE in lithium batteries, characterized in that, include: Establish an equivalent circuit model of the target lithium battery; Based on the equivalent circuit model, construct the state equations and observation equations required for the adaptive unscented Kalman filter algorithm; The candidate sliding window length for updating the residual is determined according to the set step size. Based on the state equation and observation equation, an adaptive unscented Kalman filter algorithm is executed under each candidate sliding window length to calculate the SOE estimation loss corresponding to each candidate sliding window length. The candidate sliding window length corresponding to the SOE estimation loss that meets the preset optimization conditions is taken as the optimized sliding window length. Based on the optimized sliding window length, an adaptive unscented Kalman filter algorithm is executed on the target lithium battery to complete the SOE estimation.

2. The method according to claim 1, characterized in that, The adaptive unscented Kalman filter algorithm, based on the state equation and observation equation, is executed for each candidate sliding window length. The calculation of the SOE estimation loss for each candidate sliding window length includes: Determine at least one preset operating condition; Under at least one preset operating condition, an adaptive unscented Kalman filter algorithm is executed based on the state equation and observation equation for each candidate sliding window length to obtain the SOE estimate corresponding to each candidate sliding window length; Calculate the estimated loss for each candidate sliding window length based on the SOE estimate.

3. The method according to claim 2, characterized in that, The estimated loss calculated based on the SOE estimate includes: Calculate the first and second errors of the SOE estimate; The estimated loss of the SOE estimate is calculated by summing the first error and the second error.

4. The method according to claim 3, characterized in that, The first error is the mean absolute error, and the second error is the root mean square error.

5. The method according to claim 1, characterized in that, The preset optimization conditions include: The candidate sliding window length corresponding to the minimum loss estimated by SOE is the optimal sliding window length.

6. The method according to claim 1, characterized in that, The establishment of the equivalent circuit model of the target lithium battery includes: Establish a first-order equivalent circuit model of the target lithium battery; The parameters of the equivalent circuit model under dynamic conditions are solved using the least squares method with a variable forgetting factor. The first-order equivalent circuit model is updated with the parameters of the equivalent circuit model.

7. The method according to claim 6, characterized in that, The method of using a variable forgetting factor to solve for the parameters of the equivalent circuit model under dynamic operating conditions includes: The first-order equivalent circuit model is reconstructed into the following discrete equations: ， in, This represents the terminal voltage, and k represents the sampling point. Represents current. Indicates equivalent parameters; The following transformation is defined: ， in, and These represent the measurement vector and the parameter vector, respectively. The measurement vector and parameter vector are iteratively solved using the least squares method with a variable forgetting factor as follows: ； Based on the parameter vector The solution is as follows: calculate the parameters of the equivalent circuit model. ， in, , , These represent the time constant, polarization resistance, and internal resistance of the lithium battery in the equivalent circuit model, respectively.

8. A lithium battery SOE estimation device, characterized in that, include: The equivalent modeling module is used to establish the equivalent circuit model of the target lithium battery. The equation building module is used to construct the state equations and observation equations required for the adaptive unscented Kalman filter algorithm based on the equivalent circuit model. The sliding window length optimization module is used to determine the candidate sliding window length for updating the residuals according to a set step size. Based on the state equation and observation equation, it executes an adaptive unscented Kalman filter algorithm under each candidate sliding window length to calculate the SOE estimation loss for each candidate sliding window length. The SOE estimation module is used to take the candidate sliding window length corresponding to the SOE estimation loss that meets the preset optimization conditions as the optimized sliding window length, and to perform an adaptive unscented Kalman filter algorithm on the target lithium battery based on the optimized sliding window length to complete the SOE estimation.

9. An electronic device, characterized in that, It includes a memory storing computer-executable instructions and a processor, which, when executed by the processor, causes the device to perform the lithium battery SOE estimation method as described in any one of claims 1 to 7.

10. A readable storage medium, characterized in that, It stores a computer-executable program that, when executed, implements the lithium battery SOE estimation method as described in any one of claims 1 to 7.

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