Method and device for obtaining battery cell OCV and computer readable storage medium
By establishing a fitting extrapolation model of short-time voltage relaxation behavior and steady-state OCV, and using the fitting function and optimization algorithm to solve the parameters, the problem of long OCV testing time for lithium-ion batteries was solved, thereby improving production efficiency and capacity.
Patent Information
- Application Number
- CN202511798294.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-02
- Publication Date
- 2026-02-03
AI Technical Summary
In existing technologies, the voltage stabilization and relaxation process of lithium-ion batteries is slow after switching from charging/discharging to resting state, resulting in long OCV testing time and affecting the turnover efficiency and capacity of the production line.
By establishing a fitting extrapolation model between short-time voltage relaxation behavior and steady-state OCV, and using short-time voltage measurement data to extrapolate near-steady-state OCV values, fitting parameters are solved using fitting functions and optimization algorithms, thereby shortening the OCV test time.
It significantly shortens the OCV testing time, improves the turnover efficiency and capacity of battery production cycles, and reduces the occupation of production space and equipment resources.
Smart Images
Figure CN121454344A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of battery testing technology, and specifically to a method, apparatus, and computer-readable storage medium for obtaining the OCV of a battery cell. Background Technology
[0002] Open circuit voltage (OCV) is the terminal voltage of a battery when it is at rest with no current flowing through it. It is one of the most direct and accurate parameters reflecting the battery's state of charge (SOC). OCV plays a crucial role in battery manufacturing, quality control, sorting and grouping, and in the battery management system (BMS) during use.
[0003] In related technologies, the current state of charge (SOC) of a battery is generally determined by the correspondence between OCV and SOC. Specifically, when measuring OCV, it is necessary to ensure that the battery reaches a fully relaxed state, that is, the polarization phenomenon is completely eliminated. Then, charging, resting, and discharging steps are performed, and the voltage value at the end of the resting period is recorded. The above steps are repeated until the battery is completely discharged, thereby determining the OCV value.
[0004] In the process of implementing the embodiments of this disclosure, at least the following problems were found in the related art:
[0005] In chemical systems such as lithium-ion batteries, the voltage stabilization and relaxation process is very slow after the battery switches from the charging / discharging state to the resting state. Especially during aging and sorting on the production line, tens of hours or even days of resting time are usually required to obtain a high-precision OCV value. This increases the battery production cycle, thereby reducing the turnover efficiency and capacity of the production line, and also occupies a large production space and a lot of equipment resources.
[0006] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention
[0007] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments, but rather as a prelude to the detailed description that follows.
[0008] This disclosure provides a method, apparatus, and computer-readable storage medium for obtaining the OCV of a battery cell. It can determine the near-steady-state OCV value through a short-time relaxation voltage, thereby significantly shortening the OCV testing time, accelerating the battery production cycle, and increasing production capacity.
[0009] In some embodiments, a method for obtaining the OCV of a battery cell is characterized by comprising the following steps:
[0010] Adjust the battery cells to the target SOC;
[0011] During the preset time period of cell resting, the time and voltage data of the cell are acquired at preset intervals to obtain the time and voltage data set of the cell;
[0012] A fitting objective function is established based on the time-voltage data set of the battery cell and the extrapolation fitting formula;
[0013] The fitting parameter values of the extrapolation formula are obtained by solving the fitting objective function, and the fitting parameter values are used as the OCV value of the battery cell.
[0014] In some embodiments, the extrapolation fitting formula includes:
[0015]
[0016] param = [k1,k2,k3,k4,V];
[0017] Among them, U fit (t,param) represents the fitted value of the stationary voltage at time t, k1, k2, k3, and k4 are the coefficients to be fitted, t is the stationary time, and V is the fitted parameter value of the target SOC.
[0018] In some embodiments, the extrapolation fitting formula includes:
[0019]
[0020] param) = [α,β,V];
[0021] Among them, U fit,t (k) represents the resting voltage fitted value corresponding to the kth voltage data point at that time, U fit,t (k-1) is the stationary voltage fitting value corresponding to the (k-1)th voltage data point, k is the sequence number of the voltage data in the time voltage data set, Δt is the time interval between the kth voltage data point and the (k-1)th voltage data point, α and β are the coefficients to be fitted, and V is the fitting parameter value of the target SOC.
[0022] In some embodiments, the extrapolation fitting formula includes:
[0023]
[0024] param = [α,β,γ,V];
[0025] Among them, U fit,t (k) represents the resting voltage fitted value corresponding to the kth voltage data point at that time, U fit,t (k-1) is the stationary voltage fitting value corresponding to the (k-1)th voltage data point, Δt is the time interval between the kth voltage data point and the (k-1)th voltage data point, α, β, and γ are the coefficients to be fitted, and V is the fitting parameter value of the target SOC.
[0026] In some embodiments, the fitting objective function includes:
[0027]
[0028] Among them, U fit (t,param) represents the fitted value of the stationary voltage at time t, U exp (t) represents the static voltage measurement at time t, and N represents the total number of voltage data in the time-voltage dataset.
[0029] In some embodiments, the fitting objective function includes:
[0030]
[0031] Among them, U fit (t,param) represents the fitted value of the stationary voltage at time t, U exp (t) represents the measured static voltage at time t, mean(U exp (t) represents the average voltage value in the time-voltage data set.
[0032] In some embodiments, adjusting the battery cell to a target SOC includes:
[0033] The nominal capacity Q of the battery cell is obtained through standard charge and discharge methods;
[0034] Based on the nominal capacity Q of the battery cell, the initial SOC0 of the battery cell is adjusted to the target SOC according to a preset rate.
[0035] In some embodiments, the target SOC of the battery cell is determined by the following formula:
[0036]
[0037] Where I is the current of 1C, and dt is the derivative of the time variable t.
[0038] In some embodiments, the preset resting time is greater than 300s, the preset period T is less than 45s, and the preset rate is 0.2C to 10C.
[0039] In some embodiments, the fitting objective function is solved by an optimization algorithm; wherein the optimization algorithm includes the least squares method or the particle swarm optimization algorithm.
[0040] In some embodiments, the apparatus for acquiring cell OCV includes a processor and a memory storing program instructions, the processor being configured to execute the method for acquiring cell OCV as described in the foregoing embodiments when running the program instructions.
[0041] In some embodiments, the computer-readable storage medium stores program instructions that, when executed, cause a computer to perform the method for obtaining cell OCV as described in the foregoing embodiments.
[0042] The method, apparatus, and computer-readable storage medium for obtaining the OCV of a battery cell provided in this disclosure can achieve the following technical effects:
[0043] Based on theoretical derivations of electrochemistry and transport processes, this application establishes a fitting extrapolation model between short-time voltage relaxation behavior and steady-state open-circuit voltage (OCV). By using short-time relaxation voltage measurement data to identify the parameters of the derived fitting function, a near-steady-state OCV can be extrapolated from the short-term voltage response. Therefore, this method does not rely on long-term open-circuit or complete charge-discharge curves, thus significantly shortening the OCV testing time, thereby shortening the battery production cycle, improving production line turnover efficiency and capacity, and avoiding the occupation of large amounts of production space and equipment resources.
[0044] The above general description and the description below are exemplary and illustrative only and are not intended to limit this application. Attached Figure Description
[0045] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations and drawings do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are shown as similar elements. The drawings are not to be scaled. And wherein:
[0046] Figure 1 This is a flowchart of a method for obtaining the OCV of a battery cell provided in an embodiment of this disclosure;
[0047] Figure 2 These are static voltage data curves for Examples 1 to 4;
[0048] Figure 3 This is a graph showing the fitting results of Example 1;
[0049] Figure 4 This is a graph showing the fitting results of Example 2;
[0050] Figure 5 This is a graph showing the fitting results of Example 3;
[0051] Figure 6 This is a graph showing the fitting results of Example 4;
[0052] Figure 7 This is a graph of the static voltage data from Example 5;
[0053] Figure 8 This is a graph showing the fitting results of Example 5;
[0054] Figure 9 This is a graph of the static voltage data from Example 6;
[0055] Figure 10 This is a graph showing the fitting results of Example 6;
[0056] Figure 11 This is a schematic diagram of an apparatus for obtaining the OCV of a battery cell, provided in an embodiment of this disclosure. Detailed Implementation
[0057] To provide a more detailed understanding of the features and technical content of the embodiments of this disclosure, the implementation of the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this disclosure. In the following technical description, for ease of explanation, several details are used to provide a full understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be simplified in their depiction to simplify the drawings.
[0058] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure 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 for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.
[0059] Unless otherwise stated, the term "multiple" means two or more.
[0060] In this embodiment of the disclosure, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.
[0061] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.
[0062] The term "correspondence" can refer to an association or binding relationship. The correspondence between A and B means that there is an association or binding relationship between A and B.
[0063] Combination Figure 1 As shown in the figure, this disclosure provides a method for obtaining the OCV of a battery cell, including the following steps:
[0064] S101, Adjust the battery cell to the target SOC;
[0065] S102. During the preset time period of cell resting, acquire the time and voltage data of the cell according to a preset cycle to obtain the time and voltage data set of the cell.
[0066] S103. Establish the fitting objective function based on the time-voltage data set of the battery cell and the extrapolation fitting formula;
[0067] S104. Obtain the fitting parameter values of the extrapolation formula by solving the fitting objective function, and use the fitting parameter values as the OCV value of the battery cell.
[0068] The method for obtaining the cell OCV provided in this disclosure establishes a fitting extrapolation model between short-time voltage relaxation behavior and steady-state OCV based on theoretical derivation of electrochemical and transport processes. By using short-time relaxation voltage measurement data to identify the parameters of the derived fitting function, a near-steady-state OCV can be extrapolated from the short-term voltage response. Therefore, this method does not rely on long-term open-circuit or complete charge-discharge curves, thus significantly shortening the OCV testing time, thereby shortening the battery production cycle, improving production line turnover efficiency and capacity, and avoiding the occupation of large amounts of production space and equipment resources.
[0069] In some embodiments, adjusting the battery cell to a target SOC includes:
[0070] The nominal capacity Q of the battery cell is obtained through standard charge and discharge methods;
[0071] Based on the nominal capacity Q of the battery cell, the initial SOC0 of the battery cell is adjusted to the target SOC according to a preset rate.
[0072] In some embodiments, the target SOC of the battery cell is determined by the following formula:
[0073]
[0074] Where I is the 1C current with a value of Q[Ah] / 1[h], and dt is the derivative of the time variable t.
[0075] In some embodiments, the extrapolation fitting formula includes a first formula:
[0076]
[0077] param = [k1,k2,k3,k4,V];
[0078] Among them, U fit (t,param) represents the fitted value of the stationary voltage at time t, k1, k2, k3, and k4 are the coefficients to be fitted, t is the stationary time, and V is the fitted parameter value of the target SOC.
[0079] In this embodiment of the disclosure, the first formula is derived based on the concentration potential theory.
[0080] In some embodiments, the extrapolation fitting formula includes a second formula:
[0081]
[0082] param = [α,β,V];
[0083] Among them, U fit,t (k) represents the resting voltage fitted value corresponding to the kth voltage data point at that time, U fit,t (k)-1) represents the stationary voltage fitting value corresponding to the (k-1)th voltage data point, k is the sequence number of the voltage data in the time-voltage data set, Δt is the time interval between the kth voltage data point and the (k-1)th voltage data point, α and β are the coefficients to be fitted, and V is the fitting parameter value of the target SOC.
[0084] In this embodiment of the disclosure, the second formula is derived through a diffusion equivalent circuit, wherein the time constant of the RC loop in the equivalent circuit is treated as a linear change αt+β.
[0085] In some embodiments, the extrapolation fitting formula includes a third formula:
[0086]
[0087] param = [α,β,γ,V];
[0088] Among them, U fit,t (k) represents the resting voltage fitted value corresponding to the kth voltage data point at that time, U fit,t(k-1) is the stationary voltage fitting value corresponding to the (k-1)th voltage data point, Δt is the time interval between the kth voltage data point and the (k-1)th voltage data point, α, β, and γ are the coefficients to be fitted, and V is the fitting parameter value of the target SOC.
[0089] In this embodiment, the third formula is also derived through a diffusion equivalent circuit; the difference between the third and second formulas lies in the fact that the time constant of the RC loop in the equivalent circuit is handled as a nonlinear change αt. γ +β.
[0090] In some embodiments, the fitting objective function includes a first function:
[0091]
[0092] Among them, U fit (t,param) represents the fitted value of the stationary voltage at time t, U exp (t) represents the static voltage measurement at time t, and N represents the total number of voltage data in the time-voltage dataset.
[0093] In this embodiment of the disclosure, the first function determines the error by the mean square error between the fitted value of the stationary voltage and the measured value of the stationary voltage.
[0094] In some embodiments, the fitting objective function includes a second function:
[0095]
[0096] Among them, U fit (t,param) represents the fitted value of the stationary voltage at time t, U exp (t) represents the measured static voltage at time t, mean(U exp (t) represents the average voltage value in the time-voltage data set.
[0097] In this embodiment of the disclosure, the first function determines the error by the goodness of fit between the fitted value of the stationary voltage and the measured value of the stationary voltage.
[0098] In some embodiments, the preset resting time is greater than 300s, the preset period T is less than 45s, and the preset rate is 0.2C to 10C.
[0099] In the above embodiment, in the fitting parameter param in the extrapolation fitting formula, the fitting is achieved by adjusting the value of param through an optimization algorithm to minimize the error between the calculated static voltage fitting value and the static voltage measurement value; wherein, the error is calculated through the objective function object, and when the error is minimized, the final value of param can be obtained, that is, V in param is the fitting parameter value of the target SOC.
[0100] In some embodiments, the fitting objective function is solved by an optimization algorithm; wherein the optimization algorithm includes the least squares method or the particle swarm optimization algorithm.
[0101] The present invention will be further explained and illustrated below with reference to embodiments.
[0102] Example 1:
[0103] The lithium iron phosphate (LiFePO4) cell was selected for testing, and its battery capacity was 2Ah.
[0104] The preset rate is 2C, the target state of charge (SOC) is 0.6, the preset period (T) is 30s, and the preset resting time is 7170s. The time and voltage data set of the battery cell during the resting process is as follows: Figure 1 As shown;
[0105] Here, the extrapolation fitting formula is:
[0106]
[0107] param = [k1,k2,k3,k4,V];
[0108] The fitting objective function is:
[0109]
[0110] The fitting result obtained by solving the objective function using the particle swarm optimization algorithm is as follows: Figure 3 As shown, through Figure 3 As can be seen, the fitting effect is good, the mean square error of the fit is 0.019mV, and the final OCV value is 3.3237V.
[0111] Example 2:
[0112] The lithium iron phosphate (LiFePO4) cell was selected for testing, and its battery capacity was 2Ah.
[0113] The preset rate is 2C, the target state of charge (SOC) is 0.6, the preset period (T) is 30s, and the preset resting time is 7170s. The time and voltage data set of the battery cell during the resting process is as follows: Figure 1 As shown;
[0114] Here, the extrapolation fitting formula is:
[0115]
[0116] param = [k1, α2, k3, k4, V];
[0117] The fitting objective function is:
[0118]
[0119] The fitting result obtained by solving the objective function using the particle swarm optimization algorithm is as follows: Figure 4 As shown, through Figure 4 As can be seen, the fitting effect is good, the mean square error of the fit is 0.019mV, and the final OCV value is 3.3237V.
[0120] Example 3:
[0121] The lithium iron phosphate (LiFePO4) cell was selected for testing, and its battery capacity was 2Ah.
[0122] The preset rate is 2C, the target state of charge (SOC) is 0.6, the preset period (T) is 30s, and the preset resting time is 7170s. The time and voltage data set of the battery cell during the resting process is as follows: Figure 1 As shown;
[0123] Here, the extrapolation fitting formula is:
[0124]
[0125] param = [α,β,γ,V];
[0126] The fitting objective function is:
[0127]
[0128] The fitting result obtained by solving the objective function using the least squares method is as follows: Figure 5 As shown, through Figure 5 As can be seen, the fitting effect is good, the mean square error of the fit is 0.02mV, and the final OCV value is 3.2962V.
[0129] Example 4:
[0130] The lithium iron phosphate (LiFePO4) cell was selected for testing, and its battery capacity was 2Ah.
[0131] The preset rate is 2C, the target state of charge (SOC) is 0.6, the preset period (T) is 30s, and the preset resting time is 7170s. The time and voltage data set of the battery cell during the resting process is as follows: Figure 1 As shown;
[0132] Here, the extrapolation fitting formula is:
[0133]
[0134] param = [α,β,V];
[0135] The fitting objective function is:
[0136]
[0137] The fitting result obtained by solving the objective function using the least squares method is as follows: Figure 6 As shown, through Figure 6 As can be seen, the fitting effect is good, the mean square error of the fit is 0.05mV, and the final OCV value is 3.2156V.
[0138] Example 5:
[0139] A ternary lithium battery cell was selected for testing, and its battery capacity was 5Ah.
[0140] The preset rate is 1C, the target state of charge (SOC) is 0.9, the preset period (T) is 5s, and the preset resting time is 1200s. The time and voltage data set of the battery cell during the resting process is as follows: Figure 7 As shown;
[0141] Here, the extrapolation fitting formula is:
[0142]
[0143] param = [α,β,γ,V];
[0144] The fitting objective function is:
[0145]
[0146] The fitting result obtained by solving the objective function using the least squares method is as follows: Figure 8 As shown, through Figure 8 As can be seen, the fitting effect is good, the mean square error of the fit is 0.024mV, and the final OCV value is 2.6590V.
[0147] Example 6:
[0148] A ternary lithium battery cell was selected for testing, and its battery capacity was 5Ah.
[0149] The preset rate is 1C, the target state of charge (SOC) is 0.9, the preset period (T) is 5s, and the preset resting time is 1200s. The time and voltage data set of the battery cell during the resting process is as follows: Figure 9 As shown;
[0150] Here, the extrapolation fitting formula is:
[0151]
[0152] param = [α,β,V];
[0153] The fitting objective function is:
[0154]
[0155] The fitting result obtained by solving the objective function using the particle swarm optimization algorithm is as follows: Figure 10 As shown, through Figure 10 As can be seen, the fitting effect is good, the mean square error of the fit is 0.3416mV, and the final OCV value is 4.0795V.
[0156] Combination Figure 11 As shown, this disclosure provides an apparatus 20 for acquiring the OCV of a battery cell, including a processor 200 and a memory 201. Optionally, the apparatus 20 may further include a communication interface 202 and a bus 203. The processor 200, communication interface 202, and memory 201 can communicate with each other via the bus 203. The communication interface 202 can be used for information transmission. The processor 200 can call logical instructions in the memory 201 to execute the method for acquiring the OCV of a battery cell described in the above embodiment.
[0157] Furthermore, the logic instructions in the aforementioned memory 201 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.
[0158] The memory 201, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this disclosure. The processor 200 executes functional applications and data processing by running the program instructions / modules stored in the memory 201, that is, it implements the method for obtaining the OCV of the battery cell in the above embodiments.
[0159] The memory 201 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 201 may include high-speed random access memory and may also include non-volatile memory.
[0160] This disclosure provides a computer-readable storage medium storing computer-executable instructions configured to perform the above-described method for obtaining the OCV of a battery cell.
[0161] The technical solutions of this disclosure can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes one or more 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 method described in this disclosure. The aforementioned storage medium can be a non-transitory storage medium, such as a USB flash drive, external hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, etc., and other media capable of storing program code.
[0162] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0163] The methods and products (including but not limited to devices and equipment) disclosed in the embodiments herein can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may be merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed units may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to implement this embodiment according to actual needs. Furthermore, the functional units in the embodiments of this disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
[0164] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.
Claims
1. A method for obtaining the OCV of a battery cell, characterized in that, Includes the following steps: Adjust the battery cells to the target SOC; During the preset time period of cell resting, the time and voltage data of the cell are acquired at preset intervals to obtain the time and voltage data set of the cell; A fitting objective function is established based on the time-voltage data set of the battery cell and the extrapolation fitting formula; The fitting parameter values of the extrapolation formula are obtained by solving the fitting objective function, and the fitting parameter values are used as the OCV value of the battery cell.
2. The method according to claim 1, characterized in that, The extrapolation fitting formula includes: param = [k1,k2,k3,k4,V]; Among them, U fit (t,param) represents the fitted value of the stationary voltage at time t, k1, k2, k3, and k4 are the coefficients to be fitted, t is the stationary time, and V is the fitted parameter value of the target SOC.
3. The method according to claim 1, characterized in that, The extrapolation fitting formula includes: param = [α,β,V]; Among them, U fit,t (k) represents the resting voltage fitted value corresponding to the kth voltage data point at that time, U fit,t (k-1) is the stationary voltage fitting value corresponding to the (k-1)th voltage data point, k is the sequence number of the voltage data in the time voltage data set, Δt is the time interval between the kth voltage data point and the (k-1)th voltage data point, α and β are the coefficients to be fitted, and V is the fitting parameter value of the target SOC.
4. The method according to claim 1, characterized in that, The extrapolation fitting formula includes: param = [α,β,γ,V]; Among them, U fit,t (k) represents the resting voltage fitted value corresponding to the kth voltage data point at that time, U fit,t (k-1) is the stationary voltage fitting value corresponding to the (k-1)th voltage data point, Δt is the time interval between the kth voltage data point and the (k-1)th voltage data point, α, β, and γ are the coefficients to be fitted, and V is the fitting parameter value of the target SOC.
5. The method according to any one of claims 1 to 4, characterized in that, The fitting objective function includes: Among them, U fit (t,param) represents the fitted value of the stationary voltage at time t, U exp (t) represents the static voltage measurement at time t, and N represents the total number of voltage data in the time-voltage dataset.
6. The method according to any one of claims 1 to 4, characterized in that, The fitting objective function includes: Among them, U fit (t,param) represents the fitted value of the stationary voltage at time t, U exp (t) represents the measured static voltage at time t, mean(U exp (t) represents the average voltage value in the time-voltage data set.
7. The method according to any one of claims 1 to 4, characterized in that, Adjusting the battery cells to the target SOC includes: The nominal capacity Q of the battery cell is obtained through standard charge and discharge methods; Based on the nominal capacity Q of the battery cell, the initial SOC0 of the battery cell is adjusted to the target SOC according to a preset rate.
8. The method according to claim 7, characterized in that, The target SOC of the battery cell is determined by the following formula: Where I is the current of 1C, and dt is the derivative of the time variable t.
9. An apparatus for acquiring the OCV of a battery cell, comprising a processor and a memory storing program instructions, characterized in that, The processor is configured to, when running the program instructions, execute the method for obtaining cell OCV as described in any one of claims 1 to 8.
10. A computer-readable storage medium storing program instructions, characterized in that, When the program instructions are executed, they cause the computer to perform the method for obtaining the OCV of a battery cell as described in any one of claims 1 to 8.