Lithium ion battery pack capacity estimation method and device and electronic equipment

Abstract

The invention provides a lithium ion battery pack capacity estimation method and device and electronic equipment, and the method comprises the steps: carrying out the online identification of an open-circuit voltage curve of each single battery in a battery pack in the operation process of the battery pack, extracting an open-circuit voltage feature reflecting the aging state of the battery based on the open-circuit voltage curve and a state-of-charge consistency parameter, and calculating the capacity of the battery pack according to the extracted open-circuit voltage feature. Calculating state-of-charge consistency parameters among the monomers in the battery pack; an electrode open-circuit voltage model is constructed, the electrode open-circuit voltage model represents the nonlinear relation between the battery open-circuit voltage and the state of charge based on the positive electrode potential function and the negative electrode potential function, and potential parameters are updated according to the open-circuit voltage characteristics; performing iterative optimization on model parameters of the electrode open-circuit voltage model based on the currently acquired training data, and calculating a capacity estimation value corresponding to the single battery according to the optimized model parameters; and calculating the available capacity of the whole battery pack according to the capacity estimation value and the charge state consistency parameter of each single battery.

Description

Technical Field

[0001] This application relates to the field of battery management system technology, and in particular to a method, apparatus and electronic device for estimating the capacity of a lithium-ion battery pack. Background Technology

[0002] Existing methods for estimating the capacity of lithium-ion battery packs mainly include direct measurement methods, model-based methods, and data-driven methods. Direct measurement methods require full-charge-discharge tests in a laboratory environment, which is time-consuming and cannot meet the needs of online applications. Model-based methods typically use equivalent circuit models combined with adaptive filtering algorithms for state estimation, but the model accuracy is significantly affected by temperature changes and battery aging, and high-precision models have high computational complexity, making them unsuitable for real-time systems. While data-driven methods can achieve high estimation accuracy through neural networks or machine learning, they rely on large amounts of training data and high computing power, making them unsuitable for embedded battery management systems.

[0003] In addition, existing methods mostly estimate the capacity of individual cells, making it difficult to simultaneously diagnose the consistency of state of charge among cells within a battery pack, thus limiting their practicality under complex operating conditions. Summary of the Invention

[0004] The purpose of this application is to provide a lithium-ion battery pack capacity estimation method, apparatus, and electronic device to alleviate the aforementioned technical problems existing in the prior art.

[0005] In a first aspect, the present invention provides a method for estimating the capacity of a lithium-ion battery pack, comprising: The open-circuit voltage curves of each individual cell in the battery pack during battery pack operation are identified online using the dual-path recursive least squares method. One path is used to identify the battery dynamic model parameters, and the other path is used to estimate the battery's state of charge consistency parameters. Based on the open-circuit voltage curve and state-of-charge consistency parameters, the open-circuit voltage characteristics reflecting the battery aging state are extracted, and the state-of-charge consistency parameters between individual cells in the battery pack are calculated. An electrode open-circuit voltage model is constructed. The electrode open-circuit voltage model is based on the potential functions of the positive and negative electrodes to characterize the nonlinear relationship between the battery open-circuit voltage and the state of charge. The potential parameters are updated according to the open-circuit voltage characteristics. An online gradient descent algorithm is used to iteratively optimize the model parameters of the electrode open-circuit voltage model based on the currently acquired training data, and the capacity estimate corresponding to a single cell is calculated based on the optimized model parameters. The usable capacity of the entire battery pack is calculated based on the estimated capacity of each individual cell and the state-of-charge consistency parameters.

[0006] In an optional implementation, the open-circuit voltage curves of each individual cell within the battery pack during battery pack operation are identified online using a dual-path recursive least squares method, including: During battery pack operation, the terminal voltage and current data of each individual cell are collected in real time. The second-order RC equivalent circuit model parameters of the battery are identified based on the first recursive least squares algorithm. The model parameters include DC internal resistance, polarization internal resistance and polarization capacitance. Calculate the open-circuit voltage curve of the battery based on the model parameters and battery terminal voltage and current data; The second recursive least squares algorithm is used to identify the mapping parameters between the battery's discharge throughput and state of charge. Battery state-of-charge consistency parameters are estimated based on mapping relationship parameters.

[0007] In an optional implementation, based on the open-circuit voltage curve and state-of-charge consistency parameters, open-circuit voltage characteristics reflecting the battery aging state are extracted, including: The voltage plateau characteristics of the battery under different states of charge are extracted based on the open-circuit voltage curve. The voltage plateau characteristics include at least one of the following: voltage inflection point, voltage slope change rate, and voltage plateau width during the SOC change process of the open-circuit voltage curve. Based on the mapping relationship between the battery's cumulative discharge throughput and open-circuit voltage, a voltage-throughput change curve is constructed, and the voltage offset reflecting the capacity decay trend is extracted as an aging feature. The current state of charge of each individual cell is calculated based on the initial discharge throughput and the cumulative discharge throughput during the discharge process of each individual cell in the battery pack. The state of charge (SOC) of each individual cell is compared with the SOC of a preset reference cell, and the difference in SOC between each individual cell is calculated as a parameter for consistency of SOC within the battery pack. Based on voltage platform characteristics, voltage offset, and state of charge consistency parameters, open-circuit voltage characteristics reflecting the battery aging state are generated.

[0008] In an optional implementation, the state-of-charge consistency parameters among the individual cells within the battery pack are calculated, including: Based on the identification results of the dual-path recursive least squares algorithm, the initial discharge throughput and cumulative discharge throughput of each individual cell in the battery pack are obtained. Calculate the state of charge (SOC) value of each individual cell at the current moment based on the initial discharge throughput and the cumulative discharge throughput. Select any cell in the battery pack as a reference cell, and compare the state of charge of other individual cells with the reference cell one by one. Based on the comparison results, the state of charge difference between each individual cell and the reference cell is calculated, and the state of charge difference is determined as the state of charge consistency parameter among the individual cells in the battery pack.

[0009] In an optional implementation, an electrode open-circuit voltage model is constructed, including: Based on the electrochemical characteristics of the battery, an electrode open-circuit voltage model is constructed based on the positive electrode potential function and the negative electrode potential function. Based on the battery's initial calibration data and historical operating data, determine the initial parameters of the positive electrode potential function and the negative electrode potential function. The initial parameters include at least one of the following: the potential inflection point position, the potential plateau range, and the potential slope change rate. Based on the battery's cumulative discharge throughput, the initial state of charge of the positive and negative electrodes is calculated, and the real-time state of charge values ​​of the positive and negative electrodes are calculated based on the initial state of charge and the change in the current state of charge. Substitute the real-time state of charge values ​​of the positive and negative electrodes into the corresponding potential functions to calculate the predicted open-circuit voltage of the current battery. The predicted open-circuit voltage is compared with the open-circuit voltage value identified by the dual-path recursive least squares method to generate the voltage prediction error. Based on the voltage prediction error, the parameters in the positive and negative electrode potential functions are updated to achieve parameter updates for the electrode open-circuit voltage model.

[0010] In an optional implementation, an online gradient descent algorithm is used to iteratively optimize the model parameters of the electrode open-circuit voltage model based on the currently acquired training data, including: During battery pack operation, the open-circuit voltage curve extracted by the dual-path recursive least squares algorithm is collected in real time as training data. The open-circuit voltage curve data collected within the current time period are divided into multiple batches of small-batch training datasets; An online gradient descent algorithm is used to update the model parameters of the electrode open-circuit voltage model sequentially using each batch of mini-batch training dataset; During each update, the error between the model output value and the actual measured value of the electrode open-circuit voltage is calculated, and the update direction and adjustment magnitude of the model parameters are calculated based on the error. The parameters of the electrode open-circuit voltage model are adjusted and optimized successively according to the preset learning rate. The optimized model parameters are fed back to the electrode open-circuit voltage model for the next stage of battery state prediction and capacity estimation.

[0011] In an optional implementation, the capacity estimate corresponding to a single cell is calculated based on the optimized model parameters, including: Substitute the updated electrode open-circuit voltage model parameters into the electrode open-circuit voltage model to calculate the predicted open-circuit voltage values ​​of the current battery under different states of charge. Based on the predicted open-circuit voltage, the predicted open-circuit voltage curves of the current battery under different states of charge are obtained; The discharge cutoff voltage of the battery is matched with the open circuit voltage prediction curve to determine the critical state of charge corresponding to the discharge cutoff voltage. Based on the difference between the critical state of charge and the initial state of charge of the battery, and combined with the cumulative discharge throughput of the battery, the capacity estimate of a single cell under the current health state is calculated.

[0012] In a second aspect, the present invention provides a lithium-ion battery pack capacity estimation device, comprising: The online identification module is used to identify the open-circuit voltage curve of each cell in the battery pack during the operation of the battery pack according to the dual-path recursive least squares method. One path is used to identify the dynamic model parameters of the battery, and the other path is used to estimate the state of charge consistency parameters of the battery. The feature extraction module is used to extract open-circuit voltage features that reflect the aging state of the battery based on the open-circuit voltage curve and the state of charge consistency parameters, and to calculate the state of charge consistency parameters between individual cells in the battery pack. The model building module is used to build an electrode open-circuit voltage model. The electrode open-circuit voltage model is based on the positive and negative electrode potential functions to characterize the nonlinear relationship between the battery open-circuit voltage and the state of charge, and updates the potential parameters according to the open-circuit voltage characteristics. The model optimization module is used to iteratively optimize the model parameters of the electrode open-circuit voltage model based on the currently acquired training data using an online gradient descent algorithm, and calculate the capacity estimate corresponding to a single cell based on the optimized model parameters. The capacity estimation module is used to calculate the available capacity of the entire battery pack based on the capacity estimates of each individual cell and the state-of-charge consistency parameters.

[0013] Thirdly, the present invention provides an electronic device including a processor and a memory, the memory storing computer-executable instructions that can be executed by the processor, the processor executing the computer-executable instructions to implement the lithium-ion battery pack capacity estimation method of any of the foregoing embodiments.

[0014] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the lithium-ion battery pack capacity estimation method of any of the foregoing embodiments.

[0015] The lithium-ion battery pack capacity estimation method, apparatus, and electronic equipment provided in this application achieve online identification of the open-circuit voltage curves of each individual cell within the battery pack using a dual-path recursive least squares method. This improves applicability under complex operating conditions and overcomes the implementation difficulties caused by traditional methods relying on offline testing or high-precision models. Aging state information is extracted based on open-circuit voltage characteristics and combined with state-of-charge consistency parameters, effectively diagnosing consistency differences between individual cells within the battery pack and compensating for the shortcomings of existing technologies in simultaneously evaluating capacity and consistency. By constructing an electrode open-circuit voltage model and combining it with an online gradient descent algorithm for parameter updates, not only is the estimation accuracy improved, but computational complexity and data storage requirements are also reduced, making it suitable for embedded battery management systems. This method achieves lightweight, online, and highly adaptable capacity estimation, meeting the needs of battery management systems in practical application scenarios. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating a lithium-ion battery pack capacity estimation method provided in this application embodiment; Figure 2 A flowchart illustrating the overall process of a lithium-ion battery pack capacity estimation method provided in this application embodiment; Figure 3 A structural diagram of a lithium-ion battery pack capacity estimation device provided in an embodiment of this application; Figure 4 This is a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0019] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0020] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0021] This application provides a method for estimating the capacity of a lithium-ion battery pack. (See also...) Figure 1 As shown, the method mainly includes the following steps: S110 uses a dual-path recursive least squares method to identify the open-circuit voltage curves of each individual cell in the battery pack during battery pack operation. One path is used to identify the battery dynamic model parameters, and the other path is used to estimate the battery's state of charge consistency parameters.

[0022] The aforementioned Dual Recursive Least Squares (D-RLS) method includes two parallel recursive least squares processing paths: one path is based on the battery dynamic equivalent circuit model, which uses the recursive least squares algorithm to identify model parameters online, thereby extracting the battery's OCV curve in real time to reflect the battery's aging state; the other path models the relationship between battery discharge throughput and SOC, uses the RLS algorithm to estimate the initial SOC value and capacity, and then diagnoses the SOC consistency differences between individual cells in the battery pack.

[0023] In the specific implementation process, a second-order RC equivalent circuit model suitable for dynamic operating conditions is first constructed and transformed into a linear regression form. The first-path RLS algorithm is used to identify model parameters such as ohmic internal resistance and polarization parameters online, thereby extracting the OCV curve. Simultaneously, the second-path RLS algorithm, based on the linear relationship between battery discharge throughput and SOC, identifies the reciprocal of capacity and initial discharge throughput online, thus estimating the SOC consistency parameters of each individual battery cell. The two algorithms run in parallel without interference, ultimately fusing OCV aging characteristics and SOC consistency characteristics to achieve accurate estimation of the battery pack's capacity state. This method has advantages such as high computational efficiency, low memory usage, and adaptability to complex operating conditions, making it suitable for online learning and state estimation in low-cost embedded battery management systems.

[0024] S120 extracts the open-circuit voltage characteristics reflecting the battery aging state based on the open-circuit voltage curve and the state-of-charge consistency parameters, and calculates the state-of-charge consistency parameters between individual cells in the battery pack.

[0025] The aforementioned open-circuit voltage characteristics refer to features extracted from the open-circuit voltage (OCV) curves of individual battery cells that reflect the aging state of the battery. These features may include, but are not limited to: the overall shape of the OCV curve, the mapping relationship between OCV and SOC, feature points of the OCV curve, the area of ​​the OCV curve, and the trend of OCV changes with usage time or cycle count. These features can be used for subsequent capacity estimation model training and model parameter updates.

[0026] Open-circuit voltage characteristics (OCV) are used to characterize the battery's capacity decay and internal aging by analyzing the deviation between the OCV curves at different states of charge (SOC) and the initial or standard OCV-SOC relationship. Since the OCV curve is closely related to the state of the battery's positive and negative electrode materials, it undergoes overall shift or local deformation as the battery cycles and ages, especially noticeable in specific SOC ranges. By extracting features such as the shift, slope change, and curvature difference of the curve, the aging trend of the battery can be reflected, facilitating subsequent battery capacity estimation.

[0027] In the specific implementation process, firstly, the OCV curve obtained through online identification using the dual-path recursive least squares method is compared point-by-point with the calibrated standard OCV-SOC curve to calculate the voltage offset at multiple key SOC points, serving as the initial aging characteristics. Further, curve fitting or interpolation methods are used to analyze the overall shape of the OCV curve, extracting higher-order features such as slope change rate and curvature integral to enhance sensitivity to aging conditions. Simultaneously, based on the SOC consistency diagnosis results, combined with the capacity estimate of each individual cell and the initial SOC, the SOC deviation between the individual cell and the reference cell is calculated to obtain the SOC consistency parameters within the battery pack. Finally, the aforementioned OCV aging characteristics and SOC consistency parameters are fused together as the input feature vector for subsequent model parameter updates and capacity estimation based on the online gradient descent (OGD) algorithm.

[0028] S130, Construct an electrode open-circuit voltage model. The electrode open-circuit voltage model is based on the positive and negative electrode potential functions to characterize the nonlinear relationship between the battery open-circuit voltage and the state of charge, and the potential parameters are updated according to the open-circuit voltage characteristics.

[0029] The open-circuit voltage (OCV) model is a high-precision model based on the potential functions of the positive and negative electrode materials in a battery. It describes the nonlinear relationship between the battery's open-circuit voltage and state of charge (SOC). This model represents the overall OCV of the battery as the difference between the positive and negative electrode potentials, and establishes functional relationships with the positive and negative electrode SOCs as variables. This allows it to more accurately reflect changes in the battery's internal electrochemical state, maintaining good modeling accuracy even under conditions of battery aging or uneven SOC distribution. The core technical features of this model are its strong physical interpretability, high nonlinear modeling accuracy, and support for online parameter updates to adapt to changes in battery state.

[0030] In the specific implementation process, firstly, initial positive and negative electrode potential functions are constructed based on the electrode material characteristics, establishing a mapping relationship between the battery's OCV and the positive and negative electrode SOC. Then, the OCV aging characteristics extracted in step S120 are used as input data, and the key parameters in the electrode potential function are updated successively using the online gradient descent (OGD) algorithm, enabling the model to dynamically adapt to the changing trends of battery aging and SOC consistency. Finally, based on the updated electrode OCV model and combined with the estimated SOC consistency parameters, accurate estimation of the single-cell capacity is achieved, which is further used for the overall capacity state assessment of the battery pack. This method has advantages such as high modeling accuracy, low computational complexity, and adaptability to online learning frameworks, making it suitable for real-time applications in embedded battery management systems.

[0031] S140 employs an online gradient descent algorithm to iteratively optimize the model parameters of the electrode open-circuit voltage model based on the currently acquired training data, and calculates the capacity estimate corresponding to a single cell based on the optimized model parameters.

[0032] Online Gradient Descent (OGD) is a parameter optimization method suitable for real-time data streaming scenarios. The key advantage of OGD is that it eliminates the need for batch training data, instead updating model parameters sequentially based on the training samples acquired at the current moment, thus achieving dynamic adaptive optimization of the model. This step primarily utilizes the OGD algorithm to optimize the parameters in the open-circuit voltage model online, enabling the model to respond in real-time to battery aging and changes in SOC consistency. Simultaneously, by feeding back the model's prediction error to the parameter update process, the accuracy and robustness of capacity estimation are improved.

[0033] Specifically, this method first uses the OCV curve features and SOC consistency parameters extracted from the current operating cycle as training inputs to calculate the predicted output of the electrode OCV model, and constructs a loss function with the actual measured OCV values. Then, based on this loss function, gradient calculations are performed on the model parameters, and the parameters are updated using a fixed or adaptive learning rate strategy. Finally, the updated model parameters are used to calculate the current capacity estimate of the single cell. This method significantly reduces the dependence of traditional data-driven methods on large-scale training data and high-computing platforms while ensuring computational efficiency, making it suitable for online learning and state estimation in embedded battery management systems.

[0034] The update object of the electrode OCV model parameters constructed and updated in step S130 above is the parameters of the electrode OCV model, specifically including the parameters of the positive and negative electrode potential functions, such as the nonlinear function form or parameterized coefficients between potential and SOC. This update is mainly to accurately describe the trend of changes in the electrochemical characteristics of the electrode materials inside the battery with aging, thereby improving the modeling accuracy of the OCV-SOC relationship. The parameter update in this step is mainly based on the OCV curve extracted in step 120 as training data, and the model parameters are updated by optimizing the loss function. This process belongs to the online fitting of model structural parameters.

[0035] Step 140 above involves updating the model parameters using Online Gradient Descent (OGD). The updated parameters are the learnable parameters (such as function coefficients and nonlinear mapping parameters) in the electrode OCV model used for capacity estimation. The purpose of this update is to learn online using real-time data, allowing the model to adapt to the current battery aging state and improving the dynamic adaptability of capacity estimation. The update method uses the Online Gradient Descent (OGD) algorithm to progressively optimize the model parameters based on small batches of online-acquired data. This process belongs to online learning and optimization of model parameters.

[0036] Step 130 above emphasizes the construction of a high-precision electrode OCV model and the preliminary parameter fitting based on the extracted OCV curve. Step 140 emphasizes the further use of an online learning mechanism to continuously optimize the model parameters and improve its adaptability and estimation accuracy.

[0037] S150 calculates the usable capacity of the entire battery pack based on the estimated capacity of each individual cell and the state-of-charge consistency parameters.

[0038] The above capacity estimates refer to the estimation results of the current actual capacity of each individual cell in the battery pack based on the updated electrode open-circuit voltage model. The state-of-charge (SOC) consistency parameter is used to characterize the degree of difference in SOC distribution among the individual cells within the battery pack. The usable capacity of the entire battery pack refers to the maximum amount of electricity that the battery pack can effectively release under the current state, considering the differences in individual cell capacity and the impact of SOC inconsistency.

[0039] In practice, the capacity estimates of each individual battery cell are first integrated according to their physical connection relationship, and combined with the SOC consistency parameter extracted in step S120, it is determined whether there is an uneven distribution of SOC in the current battery pack. Then, a consistency correction coefficient is introduced into the calculation of available capacity to adjust the minimum capacity of each individual cell, and finally the available capacity of the battery pack under the current operating conditions is determined.

[0040] This method enables online, high-precision assessment of the available capacity of a battery pack without relying on additional testing, and is suitable for real-time capacity display and lifespan prediction in battery management systems.

[0041] This invention achieves online identification of OCV curves and SOC consistency diagnosis under dynamic operating conditions through a dual-path recursive least squares algorithm. It combines an electrode OCV model with an online gradient descent algorithm to achieve real-time updating of model parameters and capacity estimation. Finally, it comprehensively evaluates the usable capacity of the battery pack through individual cell capacity and SOC consistency parameters. Using the OCV curve as an aging characteristic, it is insensitive to temperature changes and suitable for complex operating environments. Employing recursive algorithms such as RLS and OGD results in low computational load and low memory requirements per cycle, making it suitable for embedded system deployment. Online model parameter updates eliminate the need for pre-collecting large amounts of training data, overcoming the limitations of traditional data-driven methods.

[0042] For ease of understanding, see [link to relevant documentation]. Figure 2 As shown below, the specific implementation details of this solution will be explained in detail.

[0043] In one embodiment, the online identification of the open-circuit voltage curve of each individual cell in the battery pack during battery pack operation using the dual-path recursive least squares method includes the following steps 1-1 to 1-5: Step 1-1: During the operation of the battery pack, collect the battery terminal voltage and current data of each individual battery cell in real time.

[0044] Steps 1-2: Identify the parameters of the second-order RC equivalent circuit model of the battery according to the first recursive least squares algorithm. The model parameters include DC internal resistance, polarization internal resistance and polarization capacitance.

[0045] The battery dynamic model uses a second-order RC equivalent circuit model to describe the battery's dynamic characteristics. For any i-th cell in the battery pack, the relationship between its terminal voltage, current, OCV, and internal state is as follows: (1) in, Indicates terminal voltage. k Indicates the sampling time. Indicates the open-circuit voltage of a single cell. R represents current. s Indicates DC internal resistance. a 1 、a 2 、b 1 、b 2 is the dynamic coefficient, which is defined as follows: (2) Where Δt represents the sampling time interval, The polarization resistance of the rapid polarization stage, For the polarization capacitor of the fast polarization stage, This represents the polarization resistance of the slow polarization stage. This represents the polarization capacitance of the slow polarization stage. The above formula (1) can be transformed into a regression form as follows: (3) in Let represent the regression vector at time k. Let represent the parameter vector at time k, and its specific definition is as follows: (4) (5) Steps 1-3: Calculate the open-circuit voltage curve of the battery based on the model parameters and battery terminal voltage and current data.

[0046] During battery operation, the recursive least squares (RLS) algorithm is used to process the parameter vector. The online identification process is as follows: (6) (7) (8) (9) in, To update the gain, This is the system output, i.e., the battery pack terminal voltage. For the parameters to be identified, For the regression vector, For prediction error, For error covariance, It is a forgetting factor.

[0047] Based on the identified parameter vector The battery OCV can be extracted according to the following formula: (10) in, , , Representing parameter vectors respectively Items 1, 2, and 6 in the list.

[0048] Steps 1-4: Use the second recursive least squares algorithm to identify the mapping relationship parameters between the battery's discharge throughput and state of charge.

[0049] In the SOC consistency diagnostic path, based on the relationship between discharge throughput and battery SOC, discharge throughput and SOC are regarded as an input-output pair, and the RLS algorithm is also used to identify the relevant parameters online.

[0050] Steps 1-5 estimate the battery's state of charge consistency parameters based on the mapping relationship parameters.

[0051] Furthermore, the above-mentioned extraction of open-circuit voltage characteristics reflecting the battery aging state based on the open-circuit voltage curve and state-of-charge consistency parameters can, in specific implementation, include the following steps 2-1 to 2-5: Step 2-1: Extract the voltage plateau features of the battery under different states of charge based on the open-circuit voltage curve. The voltage plateau features include at least one of the following: voltage inflection point, voltage slope change rate, and voltage plateau width during the SOC change process of the open-circuit voltage curve.

[0052] Specifically, this includes extracting features such as the voltage inflection point, voltage slope change rate, and voltage plateau width of the battery within different SOC ranges based on the OCV-SOC curves obtained through online identification. These features reflect the changes in the electrochemical behavior of the battery's positive and negative electrode materials at different aging stages. Especially during capacity decay, the voltage plateau tends to flatten or the inflection point shifts, demonstrating good aging sensitivity. The OCV curve is less affected by temperature and clearly shows characteristics of capacity reduction and SOC differences.

[0053] Step 2-2: Based on the mapping relationship between the battery's cumulative discharge throughput and open-circuit voltage, construct a voltage-throughput change curve and extract the voltage offset reflecting the capacity decay trend as an aging feature.

[0054] Specifically, based on the mapping relationship between the battery's cumulative discharge throughput and OCV, a voltage-throughput curve is constructed. By comparing this curve with the initial voltage-throughput curve, the voltage offset is extracted as a characteristic indicator of the capacity degradation trend. This method can achieve aging assessment without relying on full charge-discharge cycles and is suitable for online operating conditions.

[0055] Steps 2-3: Calculate the current state of charge of each individual cell based on its initial discharge throughput and cumulative discharge throughput during the discharge process.

[0056] The current State of Charge (SOC) value is calculated based on the initial discharge throughput and the cumulative discharge throughput of each individual cell, combined with its capacity estimate. This calculation method avoids high dependence on the initial SOC and enhances the robustness of the algorithm.

[0057] Steps 2-4 compare the state of charge (SOC) of each individual cell with the SOC of a preset reference cell, and calculate the SOC difference between each individual cell as a parameter for consistency of SOC within the battery pack.

[0058] The SOC value of each individual battery cell is compared with the SOC value of a preset reference cell, and the SOC difference is calculated to form the SOC consistency parameter within the battery pack. This parameter reflects the state differences between the individual cells within the battery pack and is an important factor affecting the usable capacity of the battery pack.

[0059] Steps 2-5: Based on voltage platform characteristics, voltage offset, and state of charge consistency parameters, generate open-circuit voltage characteristics that reflect the battery aging state.

[0060] The voltage plateau features, voltage offset, and SOC consistency parameters extracted in the above steps are fused into a comprehensive feature vector, which serves as the open-circuit voltage feature reflecting the battery aging state. This vector is used to drive subsequent electrode OCV model updates and capacity estimation based on the online gradient descent (OGD) algorithm.

[0061] Furthermore, the above calculation of the state-of-charge consistency parameters among the individual cells within the battery pack can, in practice, include the following steps 3-1 to 3-4: Step 3-1: Based on the identification results of the dual-path recursive least squares algorithm, obtain the initial discharge throughput and cumulative discharge throughput of each individual cell in the battery pack.

[0062] Based on the SOC consistency diagnostic path in the dual-path recursive least squares algorithm, the initial discharge throughput and the cumulative discharge throughput during operation of each individual cell are extracted. These throughput data are obtained by current integration and serve as the basis for SOC estimation.

[0063] pass Q ah This represents the discharge throughput of a battery at any given moment, defined as the cumulative amount of electricity discharged from a fully charged state to the current state. For any single cell within the battery pack, a superscript is used. i Note that if it is not in a fully charged state when it begins to discharge, its discharge throughput at any time can be expressed by the following formula: (11) in This represents the initial discharge throughput at the moment discharge begins, which is related to the battery's state of charge (SOC). The cumulative throughput during the discharge process is calculated by integrating the current: (12) Step 3-2: Calculate the state of charge (SOC) value of each individual cell at the current moment based on the initial discharge throughput and the cumulative discharge throughput.

[0064] The battery's state of charge (SOC) at any given time is expressed by the following formula: (13) in Indicates battery capacity, Initial SOC value: (14) Will and As the system input and output, formula (13) can be expressed in the following discrete regression form: (15) in and Let be the regression vector and parameter vector for consistency diagnosis, respectively, defined as follows: (16) Step 3-3: Select any cell in the battery pack as a reference cell, and compare the state of charge (SOC) values ​​of the other individual cells with the reference cell one by one.

[0065] During battery operation, based on the above regression vector and parameter vector, the RLS algorithm shown in formulas (6)-(9) is used again to process the position parameter vector. Online identification.

[0066] Based on the identification results, the initial discharge throughput information of the battery can be extracted. The initial battery throughput is extracted by the following formula: (17) Steps 3-4: Based on the comparison results, calculate the state of charge difference between each individual cell and the reference cell, and determine the state of charge difference as the state of charge consistency parameter among the individual cells in the battery pack.

[0067] Furthermore, the state-of-charge consistency parameters among the individual cells within the battery pack can be calculated using the following formula: (18) in, The reference cell is arbitrarily selected.

[0068] Furthermore, the above-mentioned construction of the electrode open-circuit voltage model can, in specific implementation, include the following steps 4-1 to 4-6: Step 4-1: Based on the electrochemical characteristics of the battery, construct an electrode open-circuit voltage model based on the positive electrode potential function and the negative electrode potential function.

[0069] Specifically, the electrode open-circuit voltage model (electrode OCV model) is a high-precision battery model, and its specific form is as follows: (19) The physical meaning of the electrode open-circuit voltage model is that the open-circuit voltage of the battery is equal to the difference between the positive and negative electrode potentials. Here, the positive and negative electrode potentials are expressed as nonlinear functions of the state of charge (SOC). and , and These represent the SOC of the positive and negative electrodes, respectively.

[0070] Step 4-2: Based on the battery's initial calibration data and historical operating data, determine the initial parameters of the positive electrode potential function and the negative electrode potential function. The initial parameters include at least one of the following: the potential inflection point position, the potential plateau range, and the potential slope change rate.

[0071] Step 4-3: Calculate the initial state of charge (SOC) of the positive and negative electrodes based on the cumulative discharge throughput of the battery, and calculate the real-time SOC values ​​of the positive and negative electrodes based on the initial SOC and the change in the current SOC.

[0072] The calculation of the real-time state of charge (SOC) values ​​for the positive and negative electrodes is based on the current initial SOC value, capacity, and cumulative charge / discharge capacity. (20) in, and Indicates the initial state of charge (SOC) of the positive and negative electrodes. and These represent the capacities of the positive and negative electrodes, respectively. This model can simultaneously reflect the impact of capacity decay and SOC inconsistency on the OCV curve, and is suitable for online modeling and updating.

[0073] Step 4-4: Substitute the real-time state of charge values ​​of the positive and negative electrodes into the corresponding potential functions to calculate the predicted open-circuit voltage of the current battery.

[0074] Calculated in real time according to formula (11) Based on the value and electrode OCV model, the current OCV value of the battery can be predicted: (twenty one) Steps 4-5 compare the predicted open-circuit voltage with the open-circuit voltage value identified by the dual-path recursive least squares method to generate the voltage prediction error.

[0075] Steps 4-6: Based on the voltage prediction error, update the parameters in the positive electrode potential function and the negative electrode potential function to realize the parameter update of the electrode open circuit voltage model.

[0076] The above-mentioned real-time acquisition The characteristic curve data, used as training data, can be used to obtain updated electrode model parameters by optimizing and solving the following loss function. : (twenty two) To solve equation (22), the online gradient descent (OGD) algorithm is used to update the model parameters in batches using the training data: (twenty three) in Let L represent the learning rate, and L represent the loss function expressed in formula (22). This represents a dataset of length b used for model training. , This indicates that, given the current input training dataset, the loss function modulates the parameters. The average gradient is calculated by the following formula: (twenty four) Furthermore, the above-mentioned online gradient descent algorithm is used to iteratively optimize the model parameters of the electrode open-circuit voltage model based on the currently acquired training data. In specific implementation, this may include the following steps 5-1 to 5-6: Step 5-1: During battery pack operation, the open-circuit voltage curve extracted by the dual-path recursive least squares algorithm is collected in real time as training data. Specifically, this includes: during battery pack operation, the open-circuit voltage (OCV) curve identified by the dual-path recursive least squares algorithm is collected in real time as training data for online learning. This OCV curve reflects the aging state and SOC change characteristics of the battery under actual operating conditions.

[0077] Step 5-2 involves dividing the open-circuit voltage curve data collected within the current time period into multiple batches of mini-training datasets. Specifically, the OCV curve data collected within the current time period is divided into multiple batches of mini-datasets according to time windows to support batch updates of the model. This method reduces the amount of data and computational burden per training session, adapting to the resource constraints of embedded platforms.

[0078] Step 5-3 employs an online gradient descent algorithm to update the model parameters of the electrode open-circuit voltage model using mini-batch training datasets for each batch. This model represents the battery OCV as the difference between the potential functions of the positive and negative electrodes, demonstrating good nonlinear modeling capabilities.

[0079] Step 5-4: During each update, calculate the error between the output value of the electrode open-circuit voltage model and the actual measured value, and calculate the update direction and adjustment range of the model parameters based on the error.

[0080] Step 5-5: Adjust and optimize the parameters of the electrode open-circuit voltage model successively according to the preset learning rate, so that the model can dynamically adapt to changes in battery aging and operating conditions.

[0081] Steps 5-6 involve feeding the optimized model parameters back into the electrode open-circuit voltage model for the next stage of battery state prediction and capacity estimation. Specifically, this includes feeding the updated model parameters back into the electrode OCV model for the next stage of OCV prediction, capacity estimation, and SOC consistency evaluation, thus achieving continuous online optimization and performance improvement of the model.

[0082] Furthermore, the above-mentioned calculation of the capacity estimate corresponding to a single cell based on the optimized model parameters may include the following steps 6-1 to 6-4 in a specific implementation: Step 6-1: Substitute the updated electrode open-circuit voltage model parameters into the electrode open-circuit voltage model to calculate the predicted open-circuit voltage values ​​of the current battery under different states of charge (SOC). Specifically, this includes substituting the updated electrode open-circuit voltage model parameters, obtained through an online gradient descent algorithm, into the model to calculate the predicted open-circuit voltage (OCV) values ​​of the current battery under different SOC states. This model is built based on the potential functions of the positive and negative electrodes and has good nonlinear modeling capabilities.

[0083] Step 6-2: Based on the predicted open-circuit voltage (OCV) value, obtain the predicted OCV curves for the current battery under different states of charge (SOC). That is, based on the predicted OCV values ​​at different SOC points, generate a complete OCV-SOC prediction curve. This curve reflects the current aging state and capacity change trend of the battery, and is an important basis for subsequent capacity estimation.

[0084] Step 6-3: Match the battery's discharge cutoff voltage with the open-circuit voltage prediction curve to determine the state of charge (SOC) critical point corresponding to the discharge cutoff voltage. This critical point represents the maximum SOC range that the battery can release under its current healthy state.

[0085] Step 6-4: Based on the difference between the critical state of charge and the initial state of charge of the battery, and combined with the cumulative discharge throughput of the battery, calculate the estimated capacity of a single cell in its current healthy state.

[0086] In one specific implementation, the capacity of a single battery cell is determined based on the model parameters learned online, using the following formula: (25) in, This indicates the voltage at the battery discharge cutoff terminal.

[0087] Based on individual cell capacity and battery SOC consistency parameters, the capacity of the battery pack Calculated by the following formula: (26) In summary, this application employs a dual-path recursive least squares algorithm to identify battery dynamic model parameters and SOC consistency-related parameters, enabling online extraction of OCV curves and SOC consistency diagnosis under complex operating conditions. This effectively addresses the issues of low accuracy and poor adaptability of existing methods in dynamic environments. Aging features are extracted based on the OCV curves, and a comprehensive feature vector is constructed by combining SOC consistency parameters, improving the robustness of capacity estimation to temperature changes and battery aging. Furthermore, an online gradient descent algorithm is used to update the electrode OCV model parameters, avoiding the dependence on large amounts of training data and high computational resources required by traditional data-driven methods. This reduces algorithm complexity and memory consumption, making it suitable for embedded battery management systems. Finally, the overall usable capacity of the battery pack is calculated by combining individual cell capacity estimation and SOC consistency parameters, improving the accuracy and practicality of battery pack state estimation and effectively addressing the problems of stringent testing conditions, high computational load, and inability to simultaneously assess individual cell and pack-level state in existing technologies.

[0088] Based on the above method embodiments, this application also provides a lithium-ion battery pack capacity estimation device, see [link to relevant documentation]. Figure 3 As shown, the device mainly includes the following parts: The online identification module 310 is used to identify the open-circuit voltage curve of each cell in the battery pack during the operation of the battery pack according to the dual-path recursive least squares method. One path is used to identify the battery dynamic model parameters, and the other path is used to estimate the battery state of charge consistency parameters. The feature extraction module 320 is used to extract the open-circuit voltage features reflecting the aging state of the battery based on the open-circuit voltage curve and the state of charge consistency parameters, and to calculate the state of charge consistency parameters between individual cells in the battery pack. The model building module 330 is used to build an electrode open-circuit voltage model. The electrode open-circuit voltage model is based on the positive and negative electrode potential functions to characterize the nonlinear relationship between the battery open-circuit voltage and the state of charge, and the potential parameters are updated according to the open-circuit voltage characteristics. The model optimization module 340 is used to iteratively optimize the model parameters of the electrode open circuit voltage model based on the currently acquired training data using an online gradient descent algorithm, and calculate the capacity estimate corresponding to the single cell based on the optimized model parameters. The capacity estimation module 350 is used to calculate the available capacity of the entire battery pack based on the capacity estimates of each individual cell and the state-of-charge consistency parameters.

[0089] In one feasible implementation, the online identification module 310 described above is used for: During battery pack operation, the terminal voltage and current data of each individual cell are collected in real time. The second-order RC equivalent circuit model parameters of the battery are identified based on the first recursive least squares algorithm. The model parameters include DC internal resistance, polarization internal resistance and polarization capacitance. Calculate the open-circuit voltage curve of the battery based on the model parameters and battery terminal voltage and current data; The second recursive least squares algorithm is used to identify the mapping parameters between the battery's discharge throughput and state of charge. Battery state-of-charge consistency parameters are estimated based on mapping relationship parameters.

[0090] In one feasible implementation, the feature extraction module 320 is used for: The voltage plateau characteristics of the battery under different states of charge are extracted based on the open-circuit voltage curve. The voltage plateau characteristics include at least one of the following: voltage inflection point, voltage slope change rate, and voltage plateau width during the SOC change process of the open-circuit voltage curve. Based on the mapping relationship between the battery's cumulative discharge throughput and open-circuit voltage, a voltage-throughput change curve is constructed, and the voltage offset reflecting the capacity decay trend is extracted as an aging feature. The current state of charge of each individual cell is calculated based on the initial discharge throughput and the cumulative discharge throughput during the discharge process of each individual cell in the battery pack. The state of charge (SOC) of each individual cell is compared with the SOC of a preset reference cell, and the difference in SOC between each individual cell is calculated as a parameter for consistency of SOC within the battery pack. Based on voltage platform characteristics, voltage offset, and state of charge consistency parameters, open-circuit voltage characteristics reflecting the battery aging state are generated.

[0091] In one feasible implementation, the feature extraction module 320 is used for: Based on the identification results of the dual-path recursive least squares algorithm, the initial discharge throughput and cumulative discharge throughput of each individual cell in the battery pack are obtained. Calculate the state of charge (SOC) value of each individual cell at the current moment based on the initial discharge throughput and the cumulative discharge throughput. Select any cell in the battery pack as a reference cell, and compare the state of charge of other individual cells with the reference cell one by one. Based on the comparison results, the state of charge difference between each individual cell and the reference cell is calculated, and the state of charge difference is determined as the state of charge consistency parameter among the individual cells in the battery pack.

[0092] In one feasible implementation, the model building module 330 described above is used for: Based on the electrochemical characteristics of the battery, an electrode open-circuit voltage model is constructed based on the positive electrode potential function and the negative electrode potential function. Based on the battery's initial calibration data and historical operating data, determine the initial parameters of the positive electrode potential function and the negative electrode potential function. The initial parameters include at least one of the following: the potential inflection point position, the potential plateau range, and the potential slope change rate. Based on the battery's cumulative discharge throughput, the initial state of charge of the positive and negative electrodes is calculated, and the real-time state of charge values ​​of the positive and negative electrodes are calculated based on the initial state of charge and the change in the current state of charge. Substitute the real-time state of charge values ​​of the positive and negative electrodes into the corresponding potential functions to calculate the predicted open-circuit voltage of the current battery. The predicted open-circuit voltage is compared with the open-circuit voltage value identified by the dual-path recursive least squares method to generate the voltage prediction error. Based on the voltage prediction error, the parameters in the positive and negative electrode potential functions are updated to achieve parameter updates for the electrode open-circuit voltage model.

[0093] In one feasible implementation, the model optimization module 340 is used for: During battery pack operation, the open-circuit voltage curve extracted by the dual-path recursive least squares algorithm is collected in real time as training data. The open-circuit voltage curve data collected within the current time period are divided into multiple batches of small-batch training datasets; An online gradient descent algorithm is used to update the model parameters of the electrode open-circuit voltage model sequentially using each batch of mini-batch training dataset; During each update, the error between the model output value and the actual measured value of the electrode open-circuit voltage is calculated, and the update direction and adjustment magnitude of the model parameters are calculated based on the error. The parameters of the electrode open-circuit voltage model are adjusted and optimized successively according to the preset learning rate. The optimized model parameters are fed back to the electrode open-circuit voltage model for the next stage of battery state prediction and capacity estimation.

[0094] In one feasible implementation, the capacity estimation module 350 is used for: Substitute the updated electrode open-circuit voltage model parameters into the electrode open-circuit voltage model to calculate the predicted open-circuit voltage values ​​of the current battery under different states of charge. Based on the predicted open-circuit voltage, the predicted open-circuit voltage curves of the current battery under different states of charge are obtained; The discharge cutoff voltage of the battery is matched with the open circuit voltage prediction curve to determine the critical state of charge corresponding to the discharge cutoff voltage. Based on the difference between the critical state of charge and the initial state of charge of the battery, and combined with the cumulative discharge throughput of the battery, the capacity estimate of a single cell under the current health state is calculated.

[0095] The lithium-ion battery pack capacity estimation device provided in this application has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the embodiment of the lithium-ion battery pack capacity estimation device can be referred to the corresponding content in the aforementioned lithium-ion battery pack capacity estimation method embodiment.

[0096] This application also provides an electronic device, such as... Figure 4 The diagram shows the structure of the electronic device 100, which includes a processor 41 and a memory 40. The memory 40 stores computer-executable instructions that can be executed by the processor 41. The processor 41 executes the computer-executable instructions to implement any of the above-mentioned lithium-ion battery pack capacity estimation methods.

[0097] exist Figure 4 In the illustrated embodiment, the electronic device further includes a bus 42 and a communication interface 43, wherein the processor 41, the communication interface 43, and the memory 40 are connected via the bus 42.

[0098] The memory 40 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 43 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc. The bus 42 may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus 42 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0099] Processor 41 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 41 or by instructions in software form. Processor 41 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in the memory. The processor 41 reads the information in the memory and, in conjunction with its hardware, completes the steps of the lithium-ion battery pack capacity estimation method of the aforementioned embodiment.

[0100] This application also provides a computer-readable storage medium storing computer-executable instructions. When these computer-executable instructions are called and executed by a processor, they cause the processor to implement the above-described lithium-ion battery pack capacity estimation method. For specific implementation details, please refer to the foregoing method embodiments, which will not be repeated here.

[0101] The computer program product of the lithium-ion battery pack capacity estimation method, apparatus and electronic device provided in the embodiments of this application includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0102] Unless otherwise specifically stated, the relative steps, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of this application.

[0103] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for estimating the capacity of a lithium-ion battery pack, characterized in that, include: The open-circuit voltage curves of each individual cell in the battery pack during battery pack operation are identified online using the dual-path recursive least squares method. One path is used to identify the battery dynamic model parameters, and the other path is used to estimate the battery's state of charge consistency parameters. Based on the open-circuit voltage curve and the state-of-charge consistency parameter, the open-circuit voltage characteristics reflecting the battery aging state are extracted, and the state-of-charge consistency parameter between each cell in the battery pack is calculated. An electrode open-circuit voltage model is constructed. The electrode open-circuit voltage model is based on the potential functions of the positive and negative electrodes to characterize the nonlinear relationship between the battery open-circuit voltage and the state of charge. The potential parameters are updated according to the open-circuit voltage characteristics. An online gradient descent algorithm is used to iteratively optimize the model parameters of the electrode open-circuit voltage model based on the currently acquired training data, and the capacity estimate corresponding to a single cell is calculated based on the optimized model parameters. The available capacity of the entire battery pack is calculated based on the estimated capacity of each individual cell and the state-of-charge consistency parameters.

2. The lithium-ion battery pack capacity estimation method according to claim 1, characterized in that, The open-circuit voltage curves of each individual cell in the battery pack during battery pack operation are identified online using the dual-path recursive least squares method, including: During battery pack operation, the battery terminal voltage and current data of each individual cell are collected in real time. The second-order RC equivalent circuit model parameters of the battery are identified according to the first recursive least squares algorithm. The model parameters include DC internal resistance, polarization internal resistance and polarization capacitance. Calculate the open-circuit voltage curve of the battery based on the model parameters, the battery terminal voltage, and the current data; The second recursive least squares algorithm is used to identify the mapping parameters between the battery's discharge throughput and state of charge. The battery's state-of-charge consistency parameters are estimated based on the mapping relationship parameters.

3. The lithium-ion battery pack capacity estimation method according to claim 1, characterized in that, Based on the open-circuit voltage curve and the state-of-charge consistency parameter, open-circuit voltage characteristics reflecting the battery aging state are extracted, including: Based on the open-circuit voltage curve, voltage plateau characteristics of the battery under different states of charge are extracted. The voltage plateau characteristics include at least one of the following: voltage inflection point, voltage slope change rate, and voltage plateau width during the SOC change process of the open-circuit voltage curve. Based on the mapping relationship between the battery's cumulative discharge throughput and open-circuit voltage, a voltage-throughput change curve is constructed, and the voltage offset reflecting the capacity decay trend is extracted as an aging feature. The current state of charge of each individual cell is calculated based on the initial discharge throughput and the cumulative discharge throughput during the discharge process of each individual cell in the battery pack. The state of charge (SOC) of each individual cell is compared with that of a preset reference cell, and the difference in SOC between the individual cells is calculated as a parameter for consistency of SOC within the battery pack. Based on the voltage platform characteristics, voltage offset, and state of charge consistency parameters, an open-circuit voltage characteristic reflecting the battery aging state is generated.

4. The lithium-ion battery pack capacity estimation method according to claim 3, characterized in that, Calculate the state-of-charge consistency parameters among the individual cells within the battery pack, including: Based on the identification results of the dual-path recursive least squares algorithm, the initial discharge throughput and cumulative discharge throughput of each individual cell in the battery pack are obtained. Based on the initial discharge throughput and cumulative discharge throughput, calculate the state of charge value of each individual battery cell at the current moment; Select any cell in the battery pack as a reference cell, and compare the state of charge of other individual cells with the reference cell one by one. Based on the comparison results, the state of charge difference between each individual cell and the reference cell is calculated, and the state of charge difference is determined as the state of charge consistency parameter among the individual cells in the battery pack.

5. The lithium-ion battery pack capacity estimation method according to claim 1, characterized in that, Constructing an open-circuit voltage model for the electrodes includes: Based on the electrochemical characteristics of the battery, an electrode open-circuit voltage model is constructed based on the positive electrode potential function and the negative electrode potential function. Based on the battery's initial calibration data and historical operating data, the initial parameters of the positive electrode potential function and the negative electrode potential function are determined. The initial parameters include at least one of the following: the potential inflection point position, the potential plateau range, and the potential slope change rate. Based on the battery's cumulative discharge throughput, the initial state of charge of the positive and negative electrodes is calculated, and the real-time state of charge values ​​of the positive and negative electrodes are calculated based on the initial state of charge and the change in the current state of charge. Substitute the real-time state of charge values ​​of the positive and negative electrodes into the corresponding potential functions to calculate the predicted open-circuit voltage of the current battery. The predicted open-circuit voltage value is compared with the open-circuit voltage value identified by the dual-path recursive least squares method to generate a voltage prediction error. Based on the voltage prediction error, the parameters in the positive electrode potential function and the negative electrode potential function are updated to realize the parameter update of the electrode open circuit voltage model.

6. The lithium-ion battery pack capacity estimation method according to claim 1, characterized in that, An online gradient descent algorithm is used to iteratively optimize the model parameters of the electrode open-circuit voltage model based on the currently acquired training data, including: During battery pack operation, the open-circuit voltage curve extracted by the dual-path recursive least squares algorithm is collected in real time as training data. The open-circuit voltage curve data collected within the current time period are divided into multiple batches of small-batch training datasets; An online gradient descent algorithm is used to update the model parameters of the electrode open-circuit voltage model sequentially using each batch of mini-batch training dataset; During each update, the error between the model output value and the actual measured value of the electrode open-circuit voltage is calculated, and the update direction and adjustment magnitude of the model parameters are calculated based on the error. The parameters of the electrode open-circuit voltage model are adjusted and optimized successively according to the preset learning rate. The optimized model parameters are fed back to the electrode open-circuit voltage model for the next stage of battery state prediction and capacity estimation.

7. The lithium-ion battery pack capacity estimation method according to claim 1, characterized in that, The capacity estimate for a single cell is calculated based on the optimized model parameters, including: Substitute the updated electrode open-circuit voltage model parameters into the electrode open-circuit voltage model to calculate the predicted open-circuit voltage values ​​of the current battery under different states of charge. Based on the predicted open-circuit voltage value, the predicted open-circuit voltage curves of the current battery under different states of charge are obtained. The discharge cutoff voltage of the battery is matched with the open-circuit voltage prediction curve to determine the critical point of state of charge corresponding to the discharge cutoff voltage. Based on the difference between the critical state of charge and the initial state of charge of the battery, and combined with the cumulative discharge throughput of the battery, the estimated capacity of a single cell in its current healthy state is calculated.

8. A lithium-ion battery pack capacity estimation device, characterized in that, include: The online identification module is used to identify the open-circuit voltage curve of each cell in the battery pack during the operation of the battery pack according to the dual-path recursive least squares method. One path is used to identify the battery dynamic model parameters, and the other path is used to estimate the battery's state of charge consistency parameters. The feature extraction module is used to extract open-circuit voltage features reflecting the aging state of the battery based on the open-circuit voltage curve and the state of charge consistency parameters, and to calculate the state of charge consistency parameters between individual cells in the battery pack. The model building module is used to build an electrode open-circuit voltage model. The electrode open-circuit voltage model is based on the positive and negative electrode potential functions to characterize the nonlinear relationship between the battery open-circuit voltage and the state of charge, and the potential parameters are updated according to the open-circuit voltage characteristics. The model optimization module is used to iteratively optimize the model parameters of the electrode open-circuit voltage model based on the currently acquired training data using an online gradient descent algorithm, and calculate the capacity estimate corresponding to the single cell based on the optimized model parameters. The capacity estimation module is used to calculate the available capacity of the entire battery pack based on the capacity estimation value of each individual battery cell and the state of charge consistency parameter.

9. An electronic device, characterized in that, The device includes a processor and a memory, the memory storing computer-executable instructions that can be executed by the processor, the processor executing the computer-executable instructions to implement the lithium-ion battery pack capacity estimation method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the lithium-ion battery pack capacity estimation method according to any one of claims 1 to 7.

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