Methods, devices, electronic equipment and storage media for acquiring lithium battery parameters

Abstract

This disclosure provides a method, apparatus, electronic device, and storage medium for acquiring lithium battery parameters, relating to the field of electronic digital data processing. The method includes: acquiring the current, measured terminal voltage, and a first remaining charge value of a lithium battery cell during the charging period; obtaining the current change in remaining charge based on the range of the remaining charge change; obtaining the physical model terminal voltage based on the current, the first remaining charge value, and the current change in remaining charge; obtaining the cell internal resistance based on the measured terminal voltage, the physical model terminal voltage, and the current; obtaining a predicted terminal voltage based on the current, the physical model terminal voltage, and the cell internal resistance; obtaining an error value based on the predicted terminal voltage and the measured terminal voltage; when the error value meets the convergence condition, correcting the first remaining charge value based on the current change in remaining charge to obtain a second remaining charge value; and determining the second remaining charge value and the corresponding cell internal resistance as the battery parameters of the cell, thus achieving high-precision acquisition of lithium battery cell parameters.

Description

Technical Field

[0001] This disclosure relates to the field of electronic digital data processing, and more particularly to a method, apparatus, electronic device, and storage medium for acquiring lithium battery parameters. Background Technology

[0002] With the explosive growth of new energy vehicles, energy storage power stations, and other fields, the accuracy of performance monitoring and condition assessment of lithium batteries, as core energy storage components, directly affects the safety and reliability of the equipment. As the basic unit of a lithium battery, the real-time and accurate acquisition of its internal parameters (such as internal resistance and remaining capacity) is a key foundation for the Battery Management System (BMS) to achieve efficient control and fault early warning.

[0003] In related technologies, lithium battery parameter acquisition methods mostly rely on empirical models or simplified equivalent circuit models. However, due to fragmented operating data and the lack of long-term static calibration conditions, the initial remaining capacity value is difficult to obtain accurately. At the same time, the coupling effect of polarization and open circuit voltage leads to insufficient accuracy in internal resistance extraction, which in turn affects the reliability of battery aging assessment and remaining capacity prediction. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and storage medium for obtaining lithium battery parameters, in order to solve the technical problems of difficulty in accurately obtaining initial remaining power and insufficient accuracy in internal resistance extraction in the prior art.

[0005] According to one aspect of the present invention, a method for obtaining lithium battery parameters is provided, the method comprising: Obtain the current, measured terminal voltage, and initial first remaining capacity value of the cell in the target lithium battery during a preset charging period, and construct a single-particle model of the cell. A current change in remaining power is obtained based on a preset range of the change in remaining power. The physical model terminal voltage of the battery cell is obtained through the single-particle model based on the current, the first remaining power value, and the current change in remaining power. The internal resistance of the battery cell corresponding to the current change in remaining power is obtained based on the measured terminal voltage, the physical model terminal voltage, and the current. When the internal resistance of the battery cell meets the preset internal resistance condition, the predicted terminal voltage of the battery cell is obtained based on the current, the physical model terminal voltage, and the internal resistance of the battery cell. The objective function is used to obtain the function error value based on the predicted terminal voltage and the measured terminal voltage. When the function error value meets the convergence condition, the first remaining power value is corrected based on the change in the current remaining power to obtain a second remaining power value, and the cell internal resistance corresponding to the second remaining power value and the change in the current remaining power is determined as the battery parameters of the cell.

[0006] According to another aspect of the present invention, a lithium battery parameter acquisition device is provided, the device comprising: The first module is used to obtain the current, measured terminal voltage and initial first remaining capacity value of the cell in the target lithium battery during a preset charging period, and to construct a single-particle model of the cell. The second module is used to obtain a current change in remaining power based on a preset range of the change in remaining power, obtain the physical model terminal voltage of the cell based on the current, the first remaining power value and the current change in remaining power through the single particle model, and obtain the cell internal resistance corresponding to the current change in remaining power based on the measured terminal voltage, the physical model terminal voltage and the current. The third module is used to obtain the predicted terminal voltage of the battery cell based on the current, the physical model terminal voltage, and the battery cell internal resistance when the internal resistance of the battery cell meets the preset internal resistance condition, and to obtain the function error value based on the predicted terminal voltage and the measured terminal voltage using the objective function. The fourth module is used to correct the first remaining power value based on the current remaining power change when the function error value meets the convergence condition, to obtain a second remaining power value, and to determine the cell internal resistance corresponding to the second remaining power value and the current remaining power change as the battery parameters of the cell.

[0007] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: One or more processors; Storage device for storing one or more programs. When one or more programs are executed by one or more processors, the one or more processors implement a lithium battery parameter acquisition method as described in any of the embodiments of this disclosure.

[0008] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute and implement any of the lithium battery parameter acquisition methods of the present invention.

[0009] According to another aspect of the present disclosure, a computer program product is provided, which, when executed by a processor, implements a lithium battery parameter acquisition method as described in any of the embodiments of the present disclosure.

[0010] The technical solution of this disclosure, by acquiring the cell's current, measured terminal voltage, and initial first remaining charge value within a preset charging period and constructing a single-particle model, can more accurately simulate the internal dynamic characteristics of the cell and reduce the dependence of empirical models on fragmented data. Based on a preset range of remaining charge change, the current remaining charge change is obtained, and the physical model terminal voltage is calculated. Combined with measured data, the cell's internal resistance is separated, effectively isolating the coupling effect of polarization and open-circuit voltage, thus improving the accuracy of internal resistance extraction. When the internal resistance meets preset conditions, the predicted terminal voltage is calculated using the current, physical model terminal voltage, and internal resistance. The error between prediction and measurement is quantified using an objective function, providing a quantitative basis for remaining charge correction. Subsequently, when the error converges, the initial remaining charge value is corrected, and battery parameters are determined, ensuring the reliability and accuracy of the initial remaining charge and internal resistance. The technical solution of this disclosure solves the technical problems of difficulty in accurately obtaining the initial remaining charge and insufficient accuracy of internal resistance extraction in the prior art, achieving high-precision acquisition of lithium battery cell parameters and further improving the technical effect of reliable battery aging assessment and remaining charge prediction.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart illustrating a method for obtaining lithium battery parameters according to an embodiment of the present disclosure. Figure 2 This is a schematic diagram of the structure of a lithium battery parameter acquisition device provided in an embodiment of the present disclosure; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0015] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0016] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0017] For example, upon receiving a user's active request, a prompt message is sent to the user to explicitly inform them that the requested operation will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the software or hardware, such as the electronic device, application, server, or storage medium performing the operations of this disclosed technical solution, based on the prompt message.

[0018] As an optional but non-limiting implementation, in response to a user's active request, sending a prompt message to the user can be done via a pop-up window, where the prompt message can be presented in text format. Furthermore, the pop-up window can also include a selection control allowing the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0019] It is understood that the above notification and user authorization process are merely illustrative and do not constitute a limitation on the implementation of this disclosure. Other methods that comply with relevant laws and regulations may also be applied to the implementation of this disclosure.

[0020] It is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0021] Figure 1This is a flowchart illustrating a method for acquiring lithium battery parameters according to an embodiment of this disclosure. This embodiment is applicable to situations where lithium battery parameters need to be acquired. The method can be executed by a lithium battery parameter acquisition device, which can be implemented in hardware and / or software and can be configured in electronic devices such as computers or servers. Figure 1 As shown, the method in this embodiment includes: S110. Obtain the current, measured terminal voltage, and initial first remaining charge value of the cell in the target lithium battery during a preset charging period, and construct a single-particle model of the cell.

[0022] The target lithium battery can be understood as a lithium-ion battery pack requiring parameter identification. In this embodiment, the target lithium battery may include multiple cells. The target lithium battery can be a lithium battery for electric vehicles or a lithium battery for energy storage systems. A cell, the smallest functional unit in a lithium battery, consists of positive and negative electrodes, electrolyte, and separator, and its performance directly affects the overall performance of the battery pack. Battery parameters may include the internal resistance of the cells in the target lithium battery and the initial remaining charge value. Remaining charge (State of Charge, SOC) describes the percentage of the battery's current remaining charge relative to its total capacity. For example, SOC=50% means that the battery's remaining charge is half of its total capacity. The preset charging period can be understood as the time period during which the target lithium battery is charged, used to collect real-time data during the charging process. In this embodiment, the preset charging period can be the time period during which the battery is in a preset remaining charge range under charging conditions. For example, 30%≤SOC≤70%. Within this range, the open-circuit voltage curve of the lithium-ion battery exhibits relatively stable linear or monotonic characteristics, and the phase transition interference in the two-phase coexistence region is relatively small. Compared to the nonlinear region where the remaining charge changes drastically at both ends (e.g., SOC < 10% or SOC > 90%), the model fit in this region is easier to converge and better represents the plateau performance of the battery.

[0023] Here, current can be understood as the current flowing through the target lithium battery cell during charging. Measured terminal voltage can be understood as the voltage across the two ends of the cell obtained after measurement, reflecting the current potential difference of the cell. The first remaining charge value can be understood as the remaining charge value of the cell at the start of charging within a preset charging period. In this embodiment, the first remaining charge value is the remaining charge value to be corrected. In this embodiment, a single-particle model is used as the core observer. The single-particle model is a simplified electrochemical model of the battery cell, a reduced-order form of a pseudo-two-dimensional model, ignoring the concentration gradient and potential drop in the electrolyte, and assuming that each electrode consists of a representative spherical particle. It should be noted that the single-particle model includes solid-phase diffusion equations, boundary conditions, and terminal voltage output equations.

[0024] The solid-phase diffusion equation is expressed by the following formula: ; in, Indicates the concentration of lithium ions in the solid phase; Indicates the solid-phase diffusion coefficient; Indicates the particle radius.

[0025] The boundary conditions are expressed by the following formula: ; in, This can be expressed as the battery cell charging within a preset charging period. Current at a given moment; It can be expressed as the Faraday constant; It can be expressed as specific surface area; This can be expressed as electrode thickness; It can be expressed as the radius of the active particles.

[0026] The terminal voltage output equation can be expressed by the following formula: ; in This can represent the first remaining power value, that is, the initial remaining power value of the battery cell during the preset charging period; This can indicate the battery cell's charging time within a preset period. The terminal voltage of the physical model at that time; This can be represented as the positive open-circuit potential; It can represent the percentage of the current remaining charge of the cathode material relative to its rated capacity; This can be represented as the negative open-circuit potential; It can represent the percentage of the current remaining charge of the negative electrode material relative to its rated capacity; It can be expressed as the reaction overpotential; that is, the additional voltage loss / gain generated by the electrochemical reaction when current flows through it. It can represent the current flowing through the battery cell.

[0027] In this embodiment of the disclosure, in order to accurately obtain battery parameters, the terminal voltage output equation is explicitly split into a physical voltage part and an ohmic voltage drop part, which can be expressed by the following formula: ; in, This can be expressed as the battery cell charging within a preset charging period. Predicted terminal voltage at time 1; This can be expressed as the internal resistance of the battery cell; This can be expressed as the battery cell's charge within a preset charging period. The physical model terminal voltage at that moment; This can be expressed as the battery cell charging within a preset charging period. Current at a given moment; It can represent the first remaining battery level.

[0028] In this embodiment of the disclosure, It is only related to the cell's chemical thermodynamic state (i.e., remaining charge) and kinetic polarization. And... Only related to current and ohmic internal resistance related.

[0029] Specifically, a preset charging period for the target lithium battery is obtained. If the target lithium battery has one or more cells, then for each cell, the current, measured terminal voltage, and initial remaining charge value for that cell within the preset charging period are obtained. Furthermore, a single-particle model of the cell is constructed.

[0030] In this embodiment, after acquiring the current or measured terminal voltage of the battery cell, the acquired information can be smoothed using a preset filter. For example, the preset filter can be a Savitzky-Golay filter. It should be noted that the Savitzky-Golay filter utilizes local polynomial least squares fitting, which can smooth high-frequency noise (such as glitches caused by electromagnetic interference) while preserving the transient characteristics of the signal (such as voltage jumps caused by current steps), thereby ensuring the accuracy of subsequent internal resistance identification. The window length is dynamically set to 11-21 sampling points, and the polynomial order is 2nd order.

[0031] In this embodiment, the preset charging period can be determined by scanning the historical data stream of the target lithium battery and slicing it based on current direction and duration logic. Data points with current less than a current threshold (e.g., -1A, defined according to the current sensor direction) are considered charging segments. Data points that continuously meet the charging conditions are clustered into "charging sessions." If a current interruption or fluctuation occurs in the middle that is shorter than a specific threshold (e.g., 30 seconds), it is considered a brief disturbance within the same session and is retained; if the interruption is too long, it is divided into different sessions. Fragmented data with a duration less than a preset minimum duration (e.g., 5 minutes) or with too little accumulated charging power are removed, thereby ensuring that the segments contain sufficient dynamic stimulus information.

[0032] S120. Obtain a current change in remaining power based on a preset range of remaining power change. Obtain the physical model terminal voltage of the battery cell using the single-particle model based on the current, the first remaining power value, and the current change in remaining power. Obtain the internal resistance of the battery cell corresponding to the current change in remaining power based on the measured terminal voltage, the physical model terminal voltage, and the current.

[0033] The remaining charge change can be understood as the increment or decrement used to correct the initial remaining charge value, aiming to make the estimated remaining charge of the battery cell closer to the true value through iterative adjustments. The preset value range can be understood as the pre-defined range of values ​​for the remaining charge change. For example, the preset value range could be [-0.15, +0.15]. The current remaining charge change can be understood as the change in the initial remaining charge value within the preset value range at the current moment, such as -0.1, 0.01, or 0.1. The physical model terminal voltage can be understood as the theoretical voltage of the battery cell calculated based on the single-particle model, reflecting the electrochemical behavior of the battery cell under specific current and remaining charge. The battery cell internal resistance, i.e., the ohmic internal resistance inside the battery cell, reflects the energy loss during charging and discharging.

[0034] In this embodiment, there are multiple ways to obtain a current change in remaining power based on a preset range of values ​​for the change in remaining power, and no specific limitation is made here. As an optional implementation in this embodiment, obtaining a current change in remaining power based on a preset range of values ​​for the change in remaining power may include: randomly selecting a change in remaining power within the preset range of values ​​for the change in remaining power as the current change in remaining power; or, obtaining multiple candidate changes in remaining power based on the preset range of values ​​for the change in remaining power, and selecting the change in remaining power with the largest value among the multiple candidate changes in remaining power as the current change in remaining power; or, selecting the change in remaining power with the smallest value among the multiple candidate changes in remaining power as the current change in remaining power.

[0035] In this embodiment of the disclosure, obtaining the physical model terminal voltage of the battery cell based on the current, the first remaining charge value, and the current change in remaining charge using the single-event model may include: summing the first remaining charge value and the current change in remaining charge to obtain the summation result, i.e., the third remaining charge value. The physical model terminal voltage of the battery cell is then obtained using the terminal voltage output equation of the single-event model, based on the current and the third remaining charge value.

[0036] In this embodiment of the disclosure, obtaining the cell internal resistance corresponding to the change in the current remaining charge based on the measured terminal voltage, the physical model terminal voltage, and the current may include: calculating the difference between the measured terminal voltage and the physical model terminal voltage; obtaining the cell internal resistance corresponding to the change in the current remaining charge based on the difference calculation result and the current. It is understood that the difference calculation is the larger value minus the smaller value. Specifically, obtaining the cell internal resistance corresponding to the change in the current remaining charge based on the difference calculation result and the current involves performing a division operation between the difference calculation and the current to obtain the division result, i.e., the cell internal resistance corresponding to the change in the current remaining charge.

[0037] S130. When the internal resistance of the battery cell meets the preset internal resistance condition, the predicted terminal voltage of the battery cell is obtained based on the current, the physical model terminal voltage and the internal resistance of the battery cell, and the function error value is obtained based on the predicted terminal voltage and the measured terminal voltage using the objective function.

[0038] The preset internal resistance condition can be understood as a pre-defined effective range of internal resistance, such as 10μΩ < <10mΩ. Setting the internal resistance condition aims to: screen for internal resistance values ​​that conform to physical meaning and avoid interference from abnormal data. The predicted terminal voltage can be understood as the voltage obtained based on the cell's internal resistance voltage and the physical model terminal voltage. Specifically, the predicted terminal voltage is the result of adding the cell's internal resistance voltage and the physical model terminal voltage. The objective function can be understood as a function that quantifies the difference between the predicted terminal voltage and the measured terminal voltage to obtain the required battery parameters. The function error value can be understood as the calculation result of the objective function. In this embodiment, the smaller the function error value, the closer the predicted terminal voltage is to the measured terminal voltage.

[0039] In this embodiment, obtaining the predicted terminal voltage of the battery cell based on the current, the physical model terminal voltage, and the cell's internal resistance may include: obtaining the internal resistance voltage of the battery cell based on the cell's internal resistance and the current, and summing the internal resistance voltage and the physical model terminal voltage to obtain the predicted terminal voltage of the battery cell. Specifically, obtaining the internal resistance voltage of the battery cell based on the cell's internal resistance and the current involves multiplying the cell's internal resistance and the current to obtain the result of the multiplication operation, i.e., the cell's internal resistance voltage.

[0040] In this embodiment of the disclosure, the expression for the objective function can be: ; in, ; ; in, This indicates the change in the current remaining battery power; This represents the objective function value with the change in the current remaining battery power as the independent variable; A weight vector of the same length as the time series of the preset charging period; This represents the total number of time points in the time series; This indicates that the battery cell is charged during the preset charging period. The measured terminal voltage at that time. This indicates that the battery cell is charged during the preset charging period. Predicted terminal voltage at time 1; This indicates the internal resistance of the battery cell; This indicates that the battery cell is within the preset charging period. The physical model terminal voltage at that moment; This indicates that the battery cell is charged during the preset charging period. Current at a given moment; This represents the first remaining battery level.

[0041] In this embodiment of the disclosure, for the constructed weight vector, at time t, if ,but ;like ,but .in, This is a threshold coefficient, such as 0.8; This indicates the maximum current flowing through the battery cell. This indicates that the system has entered the constant voltage or trickle-current region. At this point, the ohmic voltage drop accounts for a very small percentage of the total terminal voltage and is easily overwhelmed by the concentration polarization voltage. Therefore, setting... This is to avoid noise in the low current region interfering with the identification of internal resistance.

[0042] S140. When the function error value meets the convergence condition, the first remaining power value is corrected based on the change in the current remaining power to obtain a second remaining power value, and the cell internal resistance corresponding to the second remaining power value and the change in the current remaining power is determined as the battery parameters of the cell.

[0043] The convergence condition can be understood as a pre-set error threshold (e.g., 15mV). When the function error value is lower than this threshold, the objective function can be determined to have converged. At this point, the predicted terminal voltage is closer to the measured terminal voltage. The second remaining capacity value can be understood as the corrected remaining capacity value of the battery cell. Battery parameters, i.e., the cell parameters in the target lithium battery, can include the corrected initial remaining capacity value and the corresponding internal resistance of the cell within a preset charging period. In this embodiment, the second remaining capacity value is obtained by correcting the first remaining capacity value based on the current change in remaining capacity. Specifically, this can include: performing an addition operation on the first remaining capacity value and the current change in remaining capacity to obtain the addition result, i.e., the second remaining capacity value.

[0044] Based on the above embodiments, the method may further include: when the cell internal resistance does not meet a preset internal resistance condition, updating the current change in remaining charge based on the preset value range, and re-executing the operation of obtaining the physical model terminal voltage of the cell based on the current, the first remaining charge value, and the current change in remaining charge using the single-particle model, and obtaining the cell internal resistance corresponding to the current change in remaining charge based on the measured terminal voltage, the physical model terminal voltage, and the current. For example, the preset internal resistance condition is a preset internal resistance range. The cell internal resistance not meeting the preset internal resistance condition can be either the cell internal resistance exceeding the upper limit of the preset internal resistance range, or the cell internal resistance being lower than the upper limit of the preset internal resistance range.

[0045] Based on the above embodiments, the method may further include: when the function error value does not meet the convergence condition, updating the current remaining power change based on the preset value range, and re-executing the operation of obtaining the physical model terminal voltage of the cell based on the current, the first remaining power value and the current remaining power change through the single-particle model, and obtaining the cell internal resistance corresponding to the current remaining power change based on the measured terminal voltage, the physical model terminal voltage and the current.

[0046] In this embodiment, there are multiple ways to update the current remaining power change based on the preset value range, and no specific limitation is made here. For example, updating the current remaining power change based on the preset value range may include: determining the next remaining power change within the preset value range that is within the current remaining power change, and updating the next remaining power change to the current remaining power change. It should be noted that the next remaining power change is an unused remaining power change during the iteration process to avoid reusing the same remaining power change. In one case, the difference between the current remaining power change and its next remaining power change may be the same. In another case, the difference between the current remaining power change and its next remaining power change may be different.

[0047] Based on the above embodiments, the method further includes: if the function error value calculated based on all remaining power changes within the preset value range fails to converge, it can be determined that the target lithium battery has failed. In this embodiment, if the identified cell internal resistance exceeds the upper limit of the preset internal resistance range; or is lower than the lower limit of the preset internal resistance range; or the corrected remaining power exceeds the range of [0, 1], a warning mechanism can be triggered, such as generating a prompt message. In addition, at the battery pack level, the identification results of each cell in each battery pack can be compared. If the internal resistance of a certain cell deviates from the group mean by more than 3σ, it is marked as an outlier, indicating that there may be loose connection or abnormal cell aging.

[0048] It should be noted that the technical solution of this disclosure constructs a two-layer decoupled architecture of "nonlinear state-linear parameter", which transforms the high-dimensional and non-convex optimization problem in the traditional electrochemical model parameter identification into a nested problem that combines an outer one-dimensional search with an inner linear analytical solution, thereby achieving an exponential improvement in computational efficiency while ensuring physical accuracy.

[0049] In this embodiment, the target lithium battery contains multiple cells, and the parameter identification tasks for these multiple cells are executed in parallel. Specifically, to address the real-time analysis needs of large-scale battery clusters (such as energy storage containers containing thousands of cells), this embodiment utilizes a process-parallel computing architecture. Each cell's analysis request is encapsulated as an independent atomic task object, containing the cell's voltage sequence, current sequence, temperature, and metadata. Furthermore, due to Python's global interpreter lock limitation, this embodiment can employ the startup of multiple independent processes to avoid repeatedly passing a large chemical parameter library to each child process. This embodiment can use a strategy of "parameter name passing + child process lazy loading" or "shared memory dictionary." In this case, the main process only passes the name of the parameter set, and the child process loads the parameters once during initialization, thereby reducing the serialization overhead of inter-process communication.

[0050] In this embodiment of the disclosure, the method may further include: obtaining the standard capacity of the battery cell and the actual capacity during the preset charging period; scaling the current based on the standard capacity and the actual capacity; correspondingly, obtaining the physical model terminal voltage of the battery cell based on the current, the first remaining charge value, and the current change in remaining charge using the single-event model includes: obtaining the physical model terminal voltage of the battery cell based on the scaled current, the first remaining charge value, and the current change in remaining charge using the single-event model; correspondingly, obtaining the internal resistance of the battery cell corresponding to the current change in remaining charge based on the measured terminal voltage, the physical model terminal voltage, and the current using an objective function includes: obtaining the internal resistance of the battery cell corresponding to the current change in remaining charge based on the measured terminal voltage, the physical model terminal voltage, and the scaled current using an objective function.

[0051] The technical solution of this disclosure, by acquiring the cell's current, measured terminal voltage, and initial first remaining charge value within a preset charging period and constructing a single-particle model, can more accurately simulate the internal dynamic characteristics of the cell and reduce the dependence of empirical models on fragmented data. Based on a preset range of remaining charge change, the current remaining charge change is obtained, and the physical model terminal voltage is calculated. Combined with measured data, the cell's internal resistance is separated, effectively isolating the coupling effect of polarization and open-circuit voltage, thus improving the accuracy of internal resistance extraction. When the internal resistance meets preset conditions, the predicted terminal voltage is calculated using the current, physical model terminal voltage, and internal resistance. The error between prediction and measurement is quantified using an objective function, providing a quantitative basis for remaining charge correction. Subsequently, when the error converges, the initial remaining charge value is corrected, and battery parameters are determined, ensuring the reliability and accuracy of the initial remaining charge and internal resistance. The technical solution of this disclosure solves the technical problems of difficulty in accurately obtaining the initial remaining charge and insufficient accuracy of internal resistance extraction in the prior art, achieving high-precision acquisition of lithium battery cell parameters and further improving the technical effect of reliable battery aging assessment and remaining charge prediction.

[0052] Figure 2 This is a schematic diagram of a lithium battery parameter acquisition device provided in an embodiment of this disclosure. Figure 2 As shown, the lithium battery parameter acquisition device includes: a first module 210, a second module 220, a third module 230, and a fourth module 240. The first module 210 is used to acquire the current, measured terminal voltage, and initial first remaining charge value of a cell in a target lithium battery during a preset charging period, and to construct a single-particle model of the cell. The second module 220 is used to obtain a current change in remaining charge based on a preset range of remaining charge change values, and to obtain the physical model terminal voltage of the cell based on the current, the first remaining charge value, and the current change in remaining charge using the single-particle model. Based on the measured terminal voltage, the physical model terminal voltage, and the current, it obtains the cell internal resistance corresponding to the current change in remaining charge. The third module 230 is used to obtain the predicted terminal voltage of the battery cell based on the current, the physical model terminal voltage, and the battery cell internal resistance when the cell internal resistance meets the preset internal resistance condition, and to obtain a function error value based on the predicted terminal voltage and the measured terminal voltage using an objective function; the fourth module 240 is used to correct the first remaining power value based on the current remaining power change to obtain a second remaining power value when the function error value meets the convergence condition, and to determine the battery parameters of the battery cell as the second remaining power value and the cell internal resistance corresponding to the current remaining power change.

[0053] The technical solution of this disclosure, by acquiring the cell's current, measured terminal voltage, and initial first remaining charge value within a preset charging period and constructing a single-particle model, can more accurately simulate the internal dynamic characteristics of the cell and reduce the dependence of empirical models on fragmented data. Based on a preset range of remaining charge change, the current remaining charge change is obtained, and the physical model terminal voltage is calculated. Combined with measured data, the cell's internal resistance is separated, effectively isolating the coupling effect of polarization and open-circuit voltage, thus improving the accuracy of internal resistance extraction. When the internal resistance meets preset conditions, the predicted terminal voltage is calculated using the current, physical model terminal voltage, and internal resistance. The error between prediction and measurement is quantified using an objective function, providing a quantitative basis for remaining charge correction. Subsequently, when the error converges, the initial remaining charge value is corrected, and battery parameters are determined, ensuring the reliability and accuracy of the initial remaining charge and internal resistance. The technical solution of this disclosure solves the technical problems of difficulty in accurately obtaining the initial remaining charge and insufficient accuracy of internal resistance extraction in the prior art, achieving high-precision acquisition of lithium battery cell parameters and further improving the technical effect of reliable battery aging assessment and remaining charge prediction.

[0054] In some embodiments of this disclosure, optionally, the third module 230 is used to obtain the internal resistance voltage of the battery cell based on the internal resistance and current of the battery cell, and to sum the internal resistance voltage and the physical model terminal voltage to obtain the predicted terminal voltage of the battery cell.

[0055] In some embodiments of this disclosure, optionally, the lithium battery parameter acquisition device further includes a fifth module; the fifth module is used to update the current remaining power change based on the preset value range when the cell internal resistance does not meet the preset internal resistance condition, and re-execute the operation of obtaining the physical model terminal voltage of the cell based on the current, the first remaining power value and the current remaining power change through the single particle model, and obtaining the cell internal resistance corresponding to the current remaining power change based on the measured terminal voltage, the physical model terminal voltage and the current.

[0056] In some embodiments of this disclosure, optionally, the lithium battery parameter acquisition device further includes a sixth module; the sixth module is used to update the current remaining power change based on the preset value range when the function error value does not meet the convergence judgment condition, and re-execute the operation of obtaining the physical model terminal voltage of the cell based on the current, the first remaining power value and the current remaining power change through the single particle model, and obtaining the cell internal resistance corresponding to the current remaining power change based on the measured terminal voltage, the physical model terminal voltage and the current.

[0057] In some embodiments of this disclosure, optionally, ; in, ; ; in, This indicates the change in the current remaining battery power; This represents the objective function value with the change in the current remaining battery power as the independent variable; A weight vector of the same length as the time series of the preset charging period; This represents the total number of time points in the time series; This indicates that the battery cell is charged during the preset charging period. The measured terminal voltage at that time. This indicates that the battery cell is charged during the preset charging period. Predicted terminal voltage at time 1; This indicates the internal resistance of the battery cell; This indicates that the battery cell is within the preset charging period. The physical model terminal voltage at that moment; This indicates that the battery cell is charged during the preset charging period. Current at a given moment; This represents the first remaining battery level.

[0058] In some embodiments of this disclosure, optionally, the number of battery cells is multiple, and the parameter identification tasks of multiple battery cells in the target lithium battery are executed in parallel.

[0059] In some embodiments of this disclosure, the lithium battery parameter acquisition device may optionally include a seventh module; the seventh module is used to determine that the target lithium battery has failed if the function error value calculated based on all remaining charge changes within the preset value range has not converged.

[0060] The lithium battery parameter acquisition device provided in this disclosure can execute the lithium battery parameter acquisition method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0061] It is worth noting that the various units and modules included in the above-mentioned lithium battery parameter acquisition device are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the protection scope of the embodiments of this disclosure.

[0062] Figure 3This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, 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 (such as helmets, glasses, watches, etc.), 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 disclosure described and / or claimed herein.

[0063] like Figure 3 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0064] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0065] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the lithium battery parameter acquisition method.

[0066] In some embodiments, the lithium battery parameter acquisition method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via read-only memory (ROM) 12 and / or communication unit 19. When the computer program is loaded into random access memory (RAM) 13 and executed by processor 11, one or more steps of the lithium battery parameter acquisition method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the lithium battery parameter acquisition method by any other suitable means (e.g., by means of firmware).

[0067] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (remaining power), load-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0068] Computer programs used to implement the lithium battery parameter acquisition method of this disclosure can be written in any combination of one or more programming languages. These computer programs can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0069] This disclosure provides a computer-readable storage medium storing computer instructions for causing a processor to execute a lithium battery parameter acquisition method, including: acquiring the current, measured terminal voltage, and initial first remaining charge value of a cell in a target lithium battery during a preset charging period; constructing a single-particle model of the cell; obtaining a current remaining charge change based on a preset range of remaining charge change; obtaining the physical model terminal voltage of the cell based on the current, the first remaining charge value, and the current remaining charge change using the single-particle model; and obtaining the measured terminal voltage and the physical model terminal voltage based on the measured terminal voltage. The cell internal resistance corresponding to the change in the current remaining power is obtained by using the terminal voltage and the current. When the cell internal resistance meets a preset internal resistance condition, the predicted terminal voltage of the cell is obtained based on the current, the physical model terminal voltage, and the cell internal resistance. A function error value is obtained using an objective function based on the predicted terminal voltage and the measured terminal voltage. When the function error value meets the convergence condition, the first remaining power value is corrected based on the change in the current remaining power to obtain a second remaining power value. The second remaining power value and the cell internal resistance corresponding to the change in the current remaining power are determined as the battery parameters of the cell.

[0070] In the context of this disclosure, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0071] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0072] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0073] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0074] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication unit 19, or installed from storage unit 18, or installed from read-only memory (ROM) 12. When the computer program is executed by processor 11, it performs the functions defined in the methods of embodiments of this disclosure.

[0075] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements a lithium battery parameter acquisition method according to any embodiment of this disclosure.

[0076] In implementing a computer program product, computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0077] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this disclosure can be achieved, and this is not limited herein.

[0078] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for obtaining lithium battery parameters, characterized in that, The method includes: Obtain the current, measured terminal voltage, and initial first remaining capacity value of the cell in the target lithium battery during a preset charging period, and construct a single-particle model of the cell. A current change in remaining power is obtained based on a preset range of the change in remaining power. The physical model terminal voltage of the battery cell is obtained through the single-particle model based on the current, the first remaining power value, and the current change in remaining power. The internal resistance of the battery cell corresponding to the current change in remaining power is obtained based on the measured terminal voltage, the physical model terminal voltage, and the current. When the internal resistance of the battery cell meets the preset internal resistance condition, the predicted terminal voltage of the battery cell is obtained based on the current, the physical model terminal voltage, and the internal resistance of the battery cell. The objective function is used to obtain the function error value based on the predicted terminal voltage and the measured terminal voltage. When the function error value meets the convergence condition, the first remaining power value is corrected based on the change in the current remaining power to obtain the second remaining power value, and the cell internal resistance corresponding to the second remaining power value and the change in the current remaining power is determined as the battery parameters of the cell; It also includes: when the cell internal resistance does not meet the preset internal resistance condition, updating the current remaining power change based on the preset value range, and re-executing the operation of obtaining the physical model terminal voltage of the cell based on the current, the first remaining power value and the current remaining power change through the single particle model, and obtaining the cell internal resistance corresponding to the current remaining power change based on the measured terminal voltage, the physical model terminal voltage and the current; It also includes: when the function error value does not meet the convergence condition, updating the current remaining power change based on the preset value range, and re-executing the operation of obtaining the physical model terminal voltage of the cell based on the current, the first remaining power value and the current remaining power change through the single particle model, and obtaining the cell internal resistance corresponding to the current remaining power change based on the measured terminal voltage, the physical model terminal voltage and the current.

2. The method for obtaining lithium battery parameters according to claim 1, characterized in that, The method of obtaining the predicted terminal voltage of the battery cell based on the current, the physical model terminal voltage, and the cell internal resistance includes: The internal resistance voltage of the battery cell is obtained based on the internal resistance and current of the battery cell. The internal resistance voltage and the physical model terminal voltage are summed to obtain the predicted terminal voltage of the battery cell.

3. The method for obtaining lithium battery parameters according to claim 1, characterized in that, The expression for the objective function is: ； in, ; ; in, This indicates the change in the current remaining battery power; This represents the objective function value with the change in the current remaining battery power as the independent variable; A weight vector of the same length as the time series of the preset charging period; This represents the total number of time points in the time series; This indicates that the battery cell is charged during the preset charging period. The measured terminal voltage at that time. This indicates that the battery cell is charged during the preset charging period. Predicted terminal voltage at time 1; This indicates the internal resistance of the battery cell; This indicates that the battery cell is within the preset charging period. The physical model terminal voltage at that moment; This indicates that the battery cell is charged during the preset charging period. Current at a given moment; This represents the first remaining battery level.

4. The method for obtaining lithium battery parameters according to claim 1, characterized in that, The number of battery cells is multiple, and the parameter identification tasks of multiple battery cells in the target lithium battery are executed in parallel.

5. The method for obtaining lithium battery parameters according to claim 1, characterized in that, The method further includes: If the function error value calculated based on all remaining charge changes within the preset range fails to converge, it is determined that the target lithium battery has malfunctioned.

6. A lithium battery parameter acquisition device, characterized in that, The device includes: The first module is used to obtain the current, measured terminal voltage and initial first remaining capacity value of the cell in the target lithium battery during a preset charging period, and to construct a single-particle model of the cell. The second module is used to obtain a current change in remaining power based on a preset range of the change in remaining power, obtain the physical model terminal voltage of the cell based on the current, the first remaining power value and the current change in remaining power through the single particle model, and obtain the cell internal resistance corresponding to the current change in remaining power based on the measured terminal voltage, the physical model terminal voltage and the current. The third module is used to obtain the predicted terminal voltage of the battery cell based on the current, the physical model terminal voltage, and the battery cell internal resistance when the internal resistance of the battery cell meets the preset internal resistance condition, and to obtain the function error value based on the predicted terminal voltage and the measured terminal voltage using the objective function. The fourth module is used to correct the first remaining power value based on the current remaining power change when the function error value meets the convergence condition, to obtain a second remaining power value, and to determine the cell internal resistance corresponding to the second remaining power value and the current remaining power change as the battery parameters of the cell. The fifth module is used to update the current change in remaining charge based on the preset value range when the cell internal resistance does not meet the preset internal resistance condition, and to re-execute the operation of obtaining the physical model terminal voltage of the cell based on the current, the first remaining charge value and the current change in remaining charge through the single particle model, and obtaining the cell internal resistance corresponding to the current change in remaining charge based on the measured terminal voltage, the physical model terminal voltage and the current. The sixth module is used to update the current remaining power change based on the preset value range when the function error value does not meet the convergence condition, and to re-execute the operation of obtaining the physical model terminal voltage of the cell based on the current, the first remaining power value and the current remaining power change through the single particle model, and obtaining the cell internal resistance corresponding to the current remaining power change based on the measured terminal voltage, the physical model terminal voltage and the current.

7. An electronic device, characterized in that, The electronic device includes: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the lithium battery parameter acquisition method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the lithium battery parameter acquisition method according to any one of claims 1-5.

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