Method and device for determining state of charge of battery of electric vehicle, medium and electronic equipment

Abstract

The invention provides a method and device for determining the state of charge of a battery of an electric vehicle, a medium and electronic equipment. The method comprises the steps that the current open-circuit voltage of a target battery in the electric vehicle and a SOC-OCV curve calibrated when the target battery leaves a factory are obtained, and the SOC-OCV curve is used for representing the corresponding relation between the residual electric quantity of the target battery and the open-circuit voltage; obtaining a current performance influence parameter set of the target battery, and correcting the SOC-OCV curve based on the current performance influence parameter set to obtain a target SOC-OCV curve; and on the basis of the current open-circuit voltage, the current residual electric quantity of the target battery is determined through the target SOC-OCV curve, and the current residual electric quantity is used for representing the current state of charge of the target battery. The accuracy of determining the state of charge of the battery of the electric vehicle can be improved.

Description

Technical Field

[0001] This application relates to the field of electric vehicles and their battery management technology, and more specifically, to a method, apparatus, medium and electronic equipment for determining the state of charge of an electric vehicle battery. Background Technology

[0002] With the rapid development of the global new energy vehicle sector, electric vehicles, as an important carrier of green and low-carbon travel, are gradually becoming the mainstream direction of transportation innovation. Among the core components of electric vehicles, the management of the power battery system is crucial. Accurate estimation of the battery's State of Charge (SOC) is the foundation of the vehicle's energy management system and plays a decisive role in achieving the safe, reliable, and efficient operation of electric vehicles. SOC represents the percentage of the battery's remaining charge compared to its fully charged state, and is a key parameter for determining the battery's remaining range, health status, and formulating energy recovery control strategies.

[0003] Currently, the open-circuit voltage method utilizes the relationship between the battery's open-circuit voltage (OCV) and state of charge (SOC) to estimate the current SOC by detecting the OCV, making it one of the most widely used and convenient methods. In existing solutions, the SOC-OCV curve (the standard open-circuit voltage curve for a specific battery model at different SOCs) initially calibrated at the battery's factory is typically used to estimate the state of charge (SOC) of electric vehicle batteries. However, in practical applications, the accuracy of SOC estimation for electric vehicle batteries decreases, thus affecting the reliability of electric vehicle energy management, range prediction, and safety strategy execution. Therefore, improving the accuracy of determining the SOC of electric vehicle batteries has become a pressing technical problem in the field of power battery management systems. Summary of the Invention

[0004] The embodiments of this application provide a method, apparatus, computer program product or computer program, computer-readable storage medium, or electronic device for determining the state of charge of an electric vehicle battery, thereby improving the accuracy of determining the state of charge of an electric vehicle battery to at least a certain extent.

[0005] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0006] According to one aspect of the embodiments of this application, a method for determining the state of charge (SOC) of an electric vehicle battery is provided. The method includes: acquiring the current open-circuit voltage of a target battery in an electric vehicle and the SOC-OCV curve calibrated at the factory of the target battery, wherein the SOC-OCV curve is used to characterize the correspondence between the remaining charge of the target battery and the open-circuit voltage; acquiring a current performance influence parameter set of the target battery and correcting the SOC-OCV curve based on the current performance influence parameter set to obtain a target SOC-OCV curve; and determining the current remaining charge of the target battery based on the current open-circuit voltage and the target SOC-OCV curve, wherein the current remaining charge is used to characterize the current SOC of the target battery.

[0007] In some embodiments of this application, based on the foregoing scheme, the step of correcting the SOC-OCV curve based on the current performance impact parameter set includes: obtaining test SOC-OCV curves of multiple test batteries under different test performance impact parameter sets, wherein the test batteries are of the same model as the target battery; and correcting the SOC-OCV curve based on the current performance impact parameter set and the test SOC-OCV curves of the test batteries under multiple test performance impact parameter sets.

[0008] In some embodiments of this application, based on the aforementioned scheme, the performance impact parameter set includes battery storage parameters and charge / discharge cycle count. The storage parameters include storage time and storage ambient temperature. Obtaining the test SOC-OCV curves of multiple test batteries under different test performance impact parameter sets includes: performing cyclic charge and discharge on each test battery and recording the correspondence between the remaining charge and open-circuit voltage of each test battery during each discharge process, wherein the storage parameters of each test battery are different; and fitting the test SOC-OCV curve of the test battery under the corresponding test performance impact parameter set based on the correspondence between the remaining charge and open-circuit voltage of each test battery during any discharge process.

[0009] In some embodiments of this application, based on the foregoing scheme, the step of correcting the SOC-OCV curve based on the current performance impact parameter set and the test SOC-OCV curve of the test battery under multiple test performance impact parameter sets includes: calculating a first similarity value between the multiple test performance impact parameter sets and the current performance impact parameter set; determining the test performance impact parameter set with the smallest first similarity value as the target performance impact parameter set; determining the test SOC-OCV curve under the target performance impact parameter set as the reference SOC-OCV curve, and correcting the SOC-OCV curve based on the reference SOC-OCV curve.

[0010] In some embodiments of this application, based on the foregoing scheme, calculating the first similarity value between the plurality of test performance influence parameter sets and the current performance influence parameter set includes: normalizing the number of charge-discharge cycles, storage environment temperature, and storage time for each test performance influence parameter set and the current performance influence parameter set, respectively, to obtain the first normalized value corresponding to the number of charge-discharge cycles, the first normalized value corresponding to the storage environment temperature, and the first normalized value corresponding to the storage time for each test performance influence parameter set, and the second normalized value corresponding to the number of charge-discharge cycles, the second normalized value corresponding to the storage environment temperature, and the second normalized value corresponding to the storage time for the current performance influence parameter set; and calculating the number of charge-discharge cycles respectively. The first absolute value of the difference between the first normalized value and the second normalized value is stored; the second absolute value of the difference between the first normalized value and the second normalized value corresponding to the ambient temperature is stored; and the third absolute value of the difference between the first normalized value and the second normalized value corresponding to the time is stored. Based on the first weight of the first absolute value of the difference, the second weight of the second absolute value of the difference, and the third weight of the third absolute value of the difference, the weighted average of the first absolute value of the difference, the second absolute value of the difference, and the third absolute value of the difference is calculated as the first similarity value between each set of test performance influence parameters and the current set of performance influence parameters, wherein the first weight is greater than or equal to the second weight, and the second weight is greater than or equal to the third weight.

[0011] In some embodiments of this application, based on the foregoing scheme, the step of correcting the SOC-OCV curve based on the current performance impact parameter set and the test SOC-OCV curves of the test battery under multiple test performance impact parameter sets includes: calculating a second similarity value between the parameters stored in the multiple test performance impact parameter sets and the parameters stored in the current performance impact parameter set; determining multiple test SOC-OCV curves corresponding to the test performance impact parameter set with the smallest second similarity value; determining a test SOC-OCV curve from the multiple test SOC-OCV curves that has the same number of charge-discharge cycles as the current performance impact parameter set, as a reference SOC-OCV curve, and correcting the SOC-OCV curve based on the reference SOC-OCV curve.

[0012] In some embodiments of this application, based on the foregoing scheme, the step of correcting the SOC-OCV curve based on the reference SOC-OCV curve includes: determining a target remaining power range, wherein the open-circuit voltage deviation between the reference SOC-OCV curve and the SOC-OCV curve in the target remaining power range exceeds a preset deviation value; and replacing the curve segment of the SOC-OCV curve in the target remaining power range with the curve segment of the reference SOC-OCV curve in the target remaining power range.

[0013] According to one aspect of the embodiments of this application, an apparatus for determining the state of charge (SOC) of an electric vehicle battery is provided. The apparatus includes: a first acquisition unit, configured to acquire the current open-circuit voltage of a target battery in an electric vehicle and the SOC-OCV curve calibrated at the factory of the target battery, wherein the SOC-OCV curve is used to characterize the correspondence between the remaining charge of the target battery and the open-circuit voltage; a second acquisition unit, configured to acquire a current performance influence parameter set of the target battery and correct the SOC-OCV curve based on the current performance influence parameter set to obtain a target SOC-OCV curve; and a determination unit, configured to determine the current remaining charge of the target battery based on the current open-circuit voltage and the target SOC-OCV curve, wherein the current remaining charge is used to characterize the current SOC of the target battery.

[0014] According to one aspect of the embodiments of this application, a computer program product or computer program is provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods described in the above embodiments.

[0015] According to one aspect of the embodiments of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the method described in the above embodiments.

[0016] According to one aspect of the embodiments of this application, an electronic device is provided, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the one or more processors to perform the method described in the above embodiments.

[0017] Based on the technical solution proposed in this application, the accuracy and adaptability of SOC estimation can be effectively improved by introducing a multi-parameter dynamically corrected SOC-OCV curve to determine the state of charge (SOC) of electric vehicle batteries. Firstly, this application not only relies on the SOC-OCV curve calibrated at the battery's factory as a foundation but also collects the open-circuit voltage of the battery in real time during actual use, providing timely and objective basic data for SOC determination. More importantly, this application comprehensively considers a series of parameters that actually affect performance, such as battery aging, temperature fluctuations, and historical usage, forming a current performance-influencing parameter set. Based on this, the original SOC-OCV curve is dynamically corrected to obtain a target SOC-OCV curve that more closely reflects the current state of the battery, effectively compensating for the inaccuracy decline of traditional SOC estimation methods after long-term use. By combining the corrected target SOC-OCV curve with the current open-circuit voltage, the true remaining capacity of the battery can be accurately determined, thereby avoiding SOC estimation deviations caused by environmental changes and cyclic aging. Ultimately, the SOC obtained by this solution not only reflects the battery's true state of charge in a timely manner, but also provides highly reliable data support for the battery management system's energy management, safety control, and range prediction, greatly improving the safety of electric vehicle operation and user experience.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0020] Figure 1 A flowchart is shown for a method for determining the state of charge of an electric vehicle battery according to an embodiment of this application;

[0021] Figure 2 A comparison diagram of SOC-OCV curves according to an embodiment of this application is shown;

[0022] Figure 3 A schematic diagram showing the open-circuit voltage values ​​of different remaining battery capacity at various charge-discharge cycles according to an embodiment of this application is provided.

[0023] Figure 4 A block diagram of an apparatus for determining the state of charge of an electric vehicle battery according to an embodiment of this application is shown;

[0024] Figure 5 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0025] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0026] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0027] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0028] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0029] It should be noted that "multiple" in this article refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such uses of these terms can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described.

[0031] With the rapid development of the global new energy vehicle industry, electric vehicles, as an important carrier of green and low-carbon travel, have made battery management and maintenance a core issue of concern in the industry. Battery state of charge (SOC) is a key parameter for measuring the remaining capacity of a battery and is fundamental information for modules such as energy management systems, safety controls, and range prediction. Accurately estimating battery SOC directly affects the prediction of electric vehicle range, the implementation of safety strategies, and the optimization of battery life.

[0032] Currently, battery state-of-charge (SOC) estimation methods based on open-circuit voltage measurement are widely used due to their simplicity and low implementation cost. Traditionally, the SOC-OCV standard curve obtained during initial calibration at the battery's factory is used as the basis for subsequent SOC estimation. This involves measuring the battery's open-circuit voltage and finding the corresponding SOC value on the SOC-OCV standard curve to estimate the remaining capacity. However, the inventors have found that battery performance is affected by various factors during long-term use, causing the actual SOC-OCV characteristic curve to drift, thus reducing the accuracy of SOC estimation based on the initial calibration curve. Therefore, this application proposes a scheme for determining the SOC of electric vehicle batteries by dynamically correcting the SOC-OCV curve to improve the accuracy of determining the SOC of electric vehicle batteries.

[0033] The implementation details of the technical solutions in the embodiments of this application are described below:

[0034] See Figure 1 The diagram shows a flowchart of a method for determining the state of charge of an electric vehicle battery according to an embodiment of the present application, the method being executed by a computing device in the electric vehicle.

[0035] like Figure 1 As shown, the method for determining the state of charge of the electric vehicle battery includes at least steps 110 to 130, which are detailed below:

[0036] In step 110, the current open-circuit voltage of the target battery in the electric vehicle and the SOC-OCV curve calibrated by the target battery at the factory are obtained. The SOC-OCV curve is used to characterize the correspondence between the remaining charge of the target battery and the open-circuit voltage.

[0037] In a power battery management system, open-circuit voltage (OCV) refers to the voltage between the positive and negative terminals of the battery after the load is disconnected. Because batteries are affected by dynamic factors such as polarization and electrolyte distribution during charging and discharging, the terminal voltage can only accurately reflect their true electrochemical characteristics and remaining capacity after the battery has been in a static state for a certain period. Therefore, accurate measurement of OCV is crucial for SOC estimation.

[0038] For example, when an electric vehicle is stopped or the battery system is in standby or dormant mode, the control system disconnects all loads from the battery to ensure it remains idle. After a period of time, the internal polarization voltage of the battery is eliminated, and the terminal voltage measured at this time is the battery's open-circuit voltage. A high-precision voltage acquisition module can be used to collect and record the current OCV value of the target battery in real time, ensuring that the measurement error is within the millivolt level.

[0039] In this application, the power battery in the electric vehicle can have its own SOC-OCV standard curve established through experimental calibration at the factory. This curve describes the theoretical open-circuit voltage value of the battery at different SOCs (e.g., 0% to 100%), and is an important basic parameter for subsequent SOC estimation.

[0040] Specifically, during the experimental calibration process, newly manufactured power batteries can undergo full charge-discharge cycles to activate their electrochemical characteristics and ensure the representativeness and stability of the SOC-OCV curve. Then, under standard temperature conditions (e.g., 25°C), the battery is slowly discharged with a constant small current, and at regular intervals (e.g., a 1% SOC gradient), the battery is allowed to stand, and its open-circuit voltage is measured. Afterward, a series of corresponding SOC and OCV points are collected, and a complete SOC-OCV characteristic curve is plotted using polynomial or piecewise linear fitting algorithms. Finally, this curve data is burned into the control chip of the battery management system (e.g., EEPROM or FLASH memory) in tabular or functional form as a standard basis for subsequent battery SOC estimation.

[0041] Continue to refer to Figure 1 In step 120, the current performance impact parameter set of the target battery is obtained, and the SOC-OCV curve is corrected based on the current performance impact parameter set to obtain the target SOC-OCV curve.

[0042] During actual use, the inventors discovered that the SOC-OCV curve of a battery is affected by various objective factors. In this application, these factors are defined as a "performance influencing parameter set." In this application, the performance influencing parameter set may include the number of charge-discharge cycles and storage parameters of the battery. The storage parameters may further include the storage environment temperature and storage time of the battery.

[0043] In this application, the charge-discharge cycle count refers to the number of complete charge-discharge cycles the battery has undergone since it left the factory. A complete cycle can be the process of discharging the battery from a fully charged state (SOC≈100%) to a specified lower limit (such as SOC≈0% or a set cutoff voltage) and then recharging it to full charge. Partial discharge followed by recharging can be calculated as an equivalent complete cycle by summing the actual total capacity discharged / charged.

[0044] In this application, the storage ambient temperature refers to the ambient temperature during which the battery is stored in a static, unused environment (neither charging nor discharging). The average temperature value can be used, or the highest, lowest, and average temperatures during the storage period can be emphasized.

[0045] In this application, storage time refers to the continuous period of time during which the battery is left unused (without charging or discharging operations). It is typically the length of time from the end of the last charge / discharge operation (i.e., after the last use) to the current measurement.

[0046] The inventors also discovered that differences in charge-discharge cycle count, storage temperature, and storage time can lead to irreversible capacity loss in the battery cells. For example, the cyclic aging characteristics of a battery affect its electrochemical performance; the more cycles, the more significant the drift in the SOC-OCV curve. Furthermore, prolonged storage can cause self-discharge and polarization, affecting the accuracy of the OCV. Additionally, the battery's electrochemical reaction rate and polarization characteristics change with temperature; higher or lower temperatures may cause a certain degree of shift in the SOC-OCV curve.

[0047] In this application, the current performance-influencing parameter set of the target battery refers to the set of the target battery's charge-discharge cycle count, storage ambient temperature, and storage time up to the present. The charge-discharge cycle count can be continuously recorded by the battery management system. The storage ambient temperature can be monitored in real time using a temperature sensor built into the battery pack, taking into account both current and historical ambient temperatures. The storage time can be determined based on the battery's manufacturing date.

[0048] In this application, considering the impact of the aforementioned performance influence parameter set on the SOC-OCV curve, this application adopts a dynamic correction method to make personalized adjustments to the SOC-OCV curve initially calibrated at the factory, thereby obtaining a target SOC-OCV curve that is more in line with the current actual situation.

[0049] In this application, the correction of the SOC-OCV curve based on the current performance impact parameter set can be performed according to the following steps 121 to 122:

[0050] In step 121, the test SOC-OCV curves of multiple test batteries under different test performance influence parameter sets are obtained, and the test batteries are of the same model as the target battery.

[0051] In this application, the test battery refers to a battery with the exact same model as the target battery in actual application, which can be systematically tested in a laboratory or verification site using experimental methods to target the combination of key parameters affecting the SOC-OCV curve. The purpose of step 121 is to establish a detailed and representative basic database for subsequent correction of the target battery's SOC-OCV curve.

[0052] In this application, the set of parameters affecting test performance may include the number of charge-discharge cycles, storage ambient temperature, and storage time. These parameters can be set in stages (e.g., 0-2500 charge-discharge cycles, storage ambient temperatures of 45℃, 60℃, 80℃, ..., and storage times of 10 days, 20 days, 30 days, ...).

[0053] In one embodiment of this application, test batteries can be prepared in advance, and then the test SOC-OCV curves of these test batteries at different charge-discharge cycles can be determined through testing.

[0054] Test battery 1, stored at an ambient temperature of 45℃ for 10 days;

[0055] Test battery 2, stored at an ambient temperature of 45℃ for 20 days;

[0056] Test battery 3, stored at an ambient temperature of 45℃ for 30 days;

[0057] Test battery 4, stored at an ambient temperature of 60℃ for 10 days;

[0058] Test battery 5, stored at an ambient temperature of 60℃ for 20 days;

[0059] Test battery 6, stored at an ambient temperature of 60℃ for 30 days;

[0060] Test battery 7, stored at an ambient temperature of 80℃ for 10 days;

[0061] Test battery 8, stored at an ambient temperature of 80℃ for 20 days;

[0062] Test battery 9, stored at an ambient temperature of 80℃ for 30 days.

[0063] In step 121 above, obtaining the test SOC-OCV curves of multiple test batteries under different sets of test performance influencing parameters can be performed according to the following steps 1211 to 1212:

[0064] Step 1211: Perform cyclic charging and discharging on each test battery, and record the relationship between the remaining charge and open circuit voltage of each test battery during each discharge process. The storage parameters of each test battery are different.

[0065] Step 1212: Based on the relationship between the remaining charge and open circuit voltage of each test battery during any discharge process, fit the test SOC-OCV curve of the test battery under the corresponding set of test performance influencing parameters.

[0066] In this application, raw data points of SOC and OCV can be systematically obtained through experimental methods, laying the foundation for subsequent curve fitting and database establishment. Specifically, each test battery can be subjected to a complete charge-discharge cycle according to a standard protocol (such as 1C / 0.5C charge-discharge). During each discharge process, the SOC of the corresponding test battery is periodically collected and recorded, which can be obtained through ampere-hour integration or calculation using standard discharge capacity. At the same time, the OCV of the test battery at this time is recorded, usually using the voltage measured after a certain period of rest to eliminate the influence of polarization.

[0067] In this application, for each test battery, the retention rate and recovery rate of the battery cell capacity can be collected during each discharge process, so as to facilitate more in-depth analysis of the battery in the future.

[0068] In this application, the data points obtained in step 1211 above can be used to fit and generate a continuous, smooth, and standardized SOC-OCV curve based on the remaining power and open-circuit voltage corresponding to each performance impact parameter set, providing a basic model for database inclusion and subsequent algorithm calls.

[0069] In the specific operation process, the SOC and OCV data collected during each discharge process are first mapped one-to-one to form a serialized discrete point array. Then, an appropriate mathematical fitting method (such as polynomial fitting, piecewise linear fitting, or spline curve interpolation) is used to fit the discrete points to a continuous SOC-OCV function curve. Simultaneously, the actual physical characteristics of the battery must be considered to avoid non-physical inflection points, fluctuations, or abnormal intervals. Piecewise fitting or boundary constraints can be used at capacity critical points (such as SOC = 0% or SOC = 100%) to improve fitting accuracy. After curve fitting, each SOC-OCV fitting curve needs to be mapped to its corresponding set of test condition parameters (such as cycle count, temperature, storage time, etc.) to facilitate subsequent database retrieval and algorithm calls. Finally, the SOC-OCV fitting curves under each parameter set will serve as standard template curves for SOC correction, uniformly collected and stored for subsequent analysis and application.

[0070] In this application, it is understood that by summarizing experimental data into a standard curve model, the availability of data and the accuracy and real-time performance of the algorithm can be improved.

[0071] Following step 121 above, step 122 can be further performed. In step 122, the SOC-OCV curve is corrected based on the current set of performance impact parameters and the test SOC-OCV curves of the test battery under multiple sets of test performance impact parameters.

[0072] In step 122 above, the SOC-OCV curve is corrected based on the current set of performance impact parameters and the test SOC-OCV curves of the test battery under multiple sets of test performance impact parameters. This can be performed according to steps 1221 to 1223 as follows:

[0073] Step 1221: Calculate the first similarity value between the plurality of test performance impact parameter sets and the current performance impact parameter set.

[0074] Step 1222: Determine the set of test performance impact parameters with the smallest first similarity value as the target performance impact parameter set.

[0075] Step 1223: Determine the test SOC-OCV curve under the target performance influence parameter set as the reference SOC-OCV curve, and correct the SOC-OCV curve based on the reference SOC-OCV curve.

[0076] In this application, the first similarity value between each set of test performance influence parameters and the current performance influence parameter set is quantitatively measured in the already collected test performance influence parameter set. The purpose is to find the test performance influence parameter that is closest to the current state of the battery so as to select the most suitable reference SOC-OCV curve in the future.

[0077] Specifically, in step 1221 above, calculating the first similarity value between the plurality of test performance impact parameter sets and the current performance impact parameter set can be performed according to steps 12211 to 12213 as follows:

[0078] Step 12211: Normalize the number of charge / discharge cycles, storage environment temperature, and storage time for each set of test performance influence parameters and the current set of test performance influence parameters to obtain the first normalized value corresponding to the number of charge / discharge cycles, the first normalized value corresponding to the storage environment temperature, and the first normalized value corresponding to the storage time for each set of test performance influence parameters, as well as the second normalized value corresponding to the number of charge / discharge cycles, the second normalized value corresponding to the storage environment temperature, and the second normalized value corresponding to the storage time for the current set of test performance influence parameters.

[0079] Step 12212: Calculate the absolute value of the first difference between the first normalized value and the second normalized value corresponding to the number of charge-discharge cycles, the absolute value of the second difference between the first normalized value and the second normalized value corresponding to the storage environment temperature, and the absolute value of the third difference between the first normalized value and the second normalized value corresponding to the storage time.

[0080] Step 12213: Based on the first weight of the absolute value of the first difference, the second weight of the absolute value of the second difference, and the third weight of the absolute value of the third difference, calculate the weighted average of the absolute value of the first difference, the absolute value of the second difference, and the absolute value of the third difference as the first similarity value between each set of test performance influence parameters and the current set of performance influence parameters, wherein the first weight is greater than or equal to the second weight, and the second weight is greater than or equal to the third weight.

[0081] To enable those skilled in the art to better understand steps 12211 to 12213 above, a specific embodiment will be described below.

[0082] For example, a certain set of test performance-affecting parameters is: number of charge-discharge cycles M. 测 =500 times, storage environment temperature N 测 =40℃, storage time S 测 =200 days. The current set of performance-affecting parameters is: number of charge / discharge cycles M. 当 =600 times, storage environment temperature N 当 =35℃, storage time S 当 = 250 days.

[0083] Furthermore, a decimal scaling normalization method (or other normalization methods, such as maximum value normalization, quantile normalization, Log normalization, etc., which are not limited in this application) can be used to normalize the charge-discharge cycle count, storage temperature, and storage time in the test performance influence parameter set and the current performance influence parameter set. For example, for M... 测 =500 times and M 当 After normalizing 600 times, we obtain the first normalized value M1 = 0.5 and the second normalized value M2 = 0.6; for N 测 =40℃ and N 当 Normalizing at 35℃ yields a first normalized value N1 = 0.4 and a second normalized value N2 = 0.35; for S 测 =200 days and S 当 After normalizing for 250 days, we can obtain the first normalized value S1 = 0.2 and the second normalized value S2 = 0.25.

[0084] Furthermore, the absolute value of the first difference between the first and second normalized values ​​corresponding to the number of charge / discharge cycles, the absolute value of the second difference between the first and second normalized values ​​corresponding to the ambient temperature, and the absolute value of the third difference between the first and second normalized values ​​corresponding to the storage time can be calculated separately. For example, the absolute value of the first difference ΔM = 0.1, the absolute value of the second difference ΔN = 0.05, and the absolute value of the third difference ΔS = 0.05.

[0085] Furthermore, based on the first weight of the absolute value of the first difference, the second weight of the absolute value of the second difference, and the third weight of the absolute value of the third difference, a weighted average of the absolute values ​​of the first, second, and third differences is calculated as the first similarity value. For example, if the first weight is 0.5, the second weight is 0.3, and the third weight is 0.2, then the first similarity value is 0.5 × 0.1 + 0.3 × 0.05 + 0.2 × 0.05 = 0.075.

[0086] It should be noted that the first similarity value can be used to characterize the similarity between the test performance impact parameter set and the current performance impact parameter set. It can be understood that the smaller the similarity value, the greater the similarity; and the larger the similarity value, the smaller the similarity.

[0087] Based on the technical solutions in steps 12211 to 12213 above, the advantages are as follows: Firstly, by normalizing the parameters in the test performance influence parameter set and the current performance influence parameter set, the influence of differences in physical dimensions and magnitudes is effectively eliminated, enabling all parameters to be objectively and accurately compared under the same standard. This avoids the problem of imbalance in the overall similarity calculation results due to a large value of a certain parameter, thereby improving the scientificity and rationality of multi-source data fusion. Secondly, by calculating the difference between the normalized values ​​of each parameter and assigning different weights according to the actual influence of each parameter on the SOC-OCV curve, the contribution of each parameter to the overall performance similarity can be more accurately reflected. This not only highlights the dominant role of the most important parameters affecting battery performance (such as the number of charge-discharge cycles) but also takes into account the actual situation of the synergistic effect of other factors such as ambient temperature and storage time, ensuring the accuracy of the similarity evaluation results and the pertinence of engineering applications. Furthermore, through the aforementioned normalization and weighted difference technical process, a scientifically quantified distance measurement can be achieved between the current battery's actual usage environment and state and historical test sample conditions, making the selection of subsequent reference SOC-OCV curves more representative. Ultimately, dynamic personalized correction of the SOC-OCV curve can be realized, improving the accuracy of SOC estimation and the algorithm's adaptive capability, thereby effectively enhancing the intelligence level and overall operational safety of the battery management system.

[0088] In step 1222 above, the set of test performance influence parameters with the smallest first similarity value is selected as the target performance influence parameter set. This ensures that the test SOC-OCV curve in the database that best reflects the current actual operating conditions of the battery is selected, providing the optimal reference for SOC-OCV curve correction. This globally optimal selection method avoids biases caused by subjective judgment and experience-based selection, improving the accuracy and scientific rigor of curve correction. In step 1223 above, by calling the test SOC-OCV curve under the target performance influence parameter set as a reference, the SOC-OCV relationship of the current battery is corrected, which can significantly improve the accuracy and reliability of SOC estimation, making the estimation results more realistically reflect the current actual characteristics and aging state of the battery. In addition, the corrected curve can also provide more accurate data support for battery energy management and safety strategies, effectively improving the management level of the battery's entire life cycle.

[0089] In step 122 above, the SOC-OCV curve is corrected based on the current set of performance impact parameters and the test SOC-OCV curves of the test battery under multiple sets of test performance impact parameters. Alternatively, steps 1224 to 1226 can be performed as follows:

[0090] Step 1224: Calculate the second similarity value between the multiple test performance impact parameter storage parameters and the current performance impact parameter storage parameters.

[0091] Step 1225: Determine multiple test SOC-OCV curves corresponding to the test performance influence parameter set with the smallest second similarity value.

[0092] Step 1226: Determine the test SOC-OCV curve with the same number of charge-discharge cycles as the current performance influencing parameter set from the plurality of test SOC-OCV curves, and use it as the reference SOC-OCV curve, and correct the SOC-OCV curve based on the reference SOC-OCV curve.

[0093] In this application, step 1224 first focuses on the impact of battery storage parameters on the SOC-OCV curve. In practical applications, battery storage conditions (such as storage time and temperature) significantly affect their electrochemical characteristics and the shape of the SOC-OCV curve. Therefore, this step extracts storage-related parameters from multiple sets of test performance influencing parameters, and normalizes and calculates distances (such as weighted distance or Euclidean distance) with the current actual battery storage parameters to obtain a second similarity value. The smaller the second similarity value, the closer the corresponding test conditions are to the current conditions. This process can accurately measure which samples in historical test conditions are closest to the current battery state in terms of storage, laying the foundation for accurate correction.

[0094] Secondly, step 1225 uses the minimum second similarity value as the selection criterion to filter out the set of test performance influence parameters that best match the current battery storage state from the historical database. This parameter set typically contains SOC-OCV test curves collected under multiple different operating conditions. By locking onto the test group with the most similar storage conditions, curve deviations caused by differences in storage history can be effectively eliminated, further improving the scientific rigor and accuracy of subsequent corrections.

[0095] Finally, step 1226 further refines the matching of charge-discharge cycle count with the current actual battery state under the established test parameter set. Specifically, from all stored SOC-OCV curves with matched parameters, the curve with the same cycle count as the current one is selected as the final reference SOC-OCV curve. If no identical cycle count is found, interpolation or the nearest curve can be used. In this way, the corrected reference not only matches the storage conditions but also closely reflects the current actual battery aging state, achieving dual parameter protection.

[0096] Through the above-mentioned hierarchical screening and dual matching process, this solution can effectively improve the accuracy and adaptability of SOC-OCV curve correction, ensure that the estimation results are more consistent with the complex changes in the actual use and storage history of batteries, provide a more reliable data foundation for battery management systems, and help realize intelligent and personalized battery management.

[0097] In step 1223 or step 1226 above, the correction of the SOC-OCV curve based on the reference SOC-OCV curve can be performed according to the following steps 1231 to 1232:

[0098] Step 1231: Determine the target remaining power range, wherein the open-circuit voltage deviation between the reference SOC-OCV curve and the SOC-OCV curve in the target remaining power range exceeds a preset deviation value.

[0099] Step 1232: Replace the curve segment of the SOC-OCV curve in the target remaining power range with the curve segment of the reference SOC-OCV curve in the target remaining power range.

[0100] In this application, the core of step 1231 lies in accurately identifying the SOC range that needs correction. In practical applications, the deviation of the SOC-OCV curve is not always global; it often only shows a large error in a specific SOC range (remaining charge range). For example, when aging, "memory effect," or drastic environmental changes occur, the OCV prediction in some SOC ranges will deviate from the true value. This step calculates the open-circuit voltage deviation in each SOC range by comparing the current SOC-OCV curve with a selected reference SOC-OCV curve. If the deviation in a target SOC range exceeds a preset threshold, it is determined to be a range that needs correction. The reasonable setting of the preset deviation value is crucial. It is usually set according to the application's accuracy requirements and safety requirements, providing a scientific basis for subsequent local corrections and thus avoiding unnecessary changes to the overall curve.

[0101] Secondly, step 1232 performs segmented replacement correction on the previously identified target SOC intervals. This involves directly replacing the corresponding data on the current SOC-OCV curve with the curve segment of the reference SOC-OCV curve within that target interval. The advantages of this approach are twofold: firstly, it accurately corrects deviations that only exist within abnormal intervals, avoiding the introduction of new errors through global correction; secondly, it preserves valid information from other intervals of the current curve, achieving targeted and efficient correction. The segmented replacement operation can be performed by indexing the endpoints of the SOC interval, extracting the corresponding data segments from the reference curve and the current curve, and then concatenating them.

[0102] Based on the technical solutions described in steps 1231 to 1232 above, the advantage lies in achieving high-precision dynamic correction of the SOC-OCV curve locally. It can focus on key regions affecting actual performance, improve the local accuracy of SOC estimation, reduce the risk of electric vehicles being misjudged in specific regions, and achieve refined and adaptive updates of the SOC-OCV relationship. This also provides a solid foundation for the precise charge and discharge control strategies and safety management of intelligent battery management systems under actual operating conditions.

[0103] It should be noted that in other schemes, the SOC-OCV curve can also be corrected based on the reference SOC-OCV curve through incremental correction or multi-curve fusion interpolation. These schemes can significantly improve the accuracy and reliability of SOC estimation.

[0104] To enable those skilled in the art to better understand this application, the following is combined with Figure 2 The following is an illustration using a specific example.

[0105] See Figure 2 The diagram shows a comparison of SOC-OCV curves according to an embodiment of this application.

[0106] like Figure 2 As shown. Curve BOL is the SOC-OCV curve calibrated at the factory for the target battery, and curve EOL is the determined reference SOC-OCV curve (its corresponding set of test performance influencing parameters includes: 2500 charge-discharge cycles, storage temperature of 40℃, and storage time of 10 days). From Figure 2 As can be seen, the target battery only has a large open-circuit voltage deviation in the 0%-30% SOC range and the 50%-70% SOC range during the entire discharge process. Therefore, only the curve segments of the SOC-OCV curve in the two remaining charge ranges need to be corrected.

[0107] In step 122 above, the SOC-OCV curve is corrected based on the current set of performance impact parameters and the test SOC-OCV curves of the test battery under multiple sets of test performance impact parameters. Alternatively, based on the test SOC-OCV curves of the test battery under multiple sets of test performance impact parameters, the open-circuit voltage value of different remaining battery capacity for each stored parameter at each charge-discharge cycle number can be determined and stored in a database.

[0108] In practice, the test battery that is closest to the target battery in storage state can be determined based on the current performance impact parameter set of the target battery. Then, based on the target battery's current charge-discharge cycle count, the open circuit voltage value of the remaining charge of different batteries of the target battery at each charge-discharge cycle count can be found in the database, and the SOC-OCV curve can be corrected based on the found data.

[0109] To enable those skilled in the art to better understand this application, the following is combined with Figure 3 The following is an illustration using a specific example.

[0110] Reference Figure 3 The diagram illustrates the open-circuit voltage values ​​of different remaining battery charge levels at various charge-discharge cycles according to an embodiment of this application.

[0111] like Figure 3 As shown, for example, when the target battery has completed 50 charge-discharge cycles, and the battery management system detects an open-circuit voltage of 3.273V, the electric vehicle displays a remaining charge level of 52% SOC. However, according to... Figure 3 If the actual remaining charge is determined to be 50% SOC, the battery management system can automatically correct the remaining charge to 50% SOC.

[0112] Continue to refer to Figure 1 In step 130, based on the current open-circuit voltage, the current remaining charge of the target battery is determined by the target SOC-OCV curve, and the current remaining charge is used to characterize the current state of charge of the target battery.

[0113] In this application, the SOC value can be found in the target SOC-OCV curve based on the current open-circuit voltage value through methods such as table lookup, interpolation, or numerical calculation, thus obtaining the current remaining battery capacity. Ultimately, this SOC value not only intuitively represents the battery's state of charge but also provides important basic data for the battery management system's energy management, charging and discharging strategies, and remaining range functional modules.

[0114] Based on the technical solution proposed in this application, the accuracy and adaptability of SOC estimation can be effectively improved by introducing a multi-parameter dynamically corrected SOC-OCV curve to determine the state of charge (SOC) of electric vehicle batteries. Firstly, this application not only relies on the SOC-OCV curve calibrated at the battery's factory as a foundation but also collects the open-circuit voltage of the battery in real time during actual use, providing timely and objective basic data for SOC determination. More importantly, this application comprehensively considers a series of parameters that actually affect performance, such as battery aging, temperature fluctuations, and historical usage, forming a current performance-influencing parameter set. Based on this, the original SOC-OCV curve is dynamically corrected to obtain a target SOC-OCV curve that more closely reflects the current state of the battery, effectively compensating for the inaccuracy decline of traditional SOC estimation methods after long-term use. By combining the corrected target SOC-OCV curve with the current open-circuit voltage, the true remaining capacity of the battery can be accurately determined, thereby avoiding SOC estimation deviations caused by environmental changes and cyclic aging. Ultimately, the SOC obtained by this solution not only reflects the battery's true state of charge in a timely manner, but also provides highly reliable data support for the battery management system's energy management, safety control, and range prediction, greatly improving the safety of electric vehicle operation and user experience.

[0115] The following describes an embodiment of the apparatus described in this application, which can be used to execute the method for determining the state of charge of an electric vehicle battery as described in the above embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the method for determining the state of charge of an electric vehicle battery described above in this application.

[0116] Figure 4 A block diagram of an apparatus for determining the state of charge of an electric vehicle battery according to an embodiment of this application is shown.

[0117] Reference Figure 4 As shown, an electric vehicle battery state of charge determination device 400 according to an embodiment of this application includes a first acquisition unit 401, a second acquisition unit 402, and a determination unit 403.

[0118] The first acquisition unit 401 is used to acquire the current open-circuit voltage of the target battery in the electric vehicle and the SOC-OCV curve calibrated by the target battery at the factory. The SOC-OCV curve is used to characterize the correspondence between the remaining charge of the target battery and the open-circuit voltage. The second acquisition unit 402 is used to acquire the current performance influence parameter set of the target battery and correct the SOC-OCV curve based on the current performance influence parameter set to obtain the target SOC-OCV curve. The determination unit 403 is used to determine the current remaining charge of the target battery based on the current open-circuit voltage and the target SOC-OCV curve. The current remaining charge is used to characterize the current state of charge of the target battery.

[0119] In some embodiments of this application, based on the foregoing scheme, the second acquisition unit 402 is configured to: acquire test SOC-OCV curves of multiple test batteries under different test performance influence parameter sets, wherein the test batteries are of the same model as the target battery; and correct the SOC-OCV curves based on the current performance influence parameter set and the test SOC-OCV curves of the test batteries under multiple test performance influence parameter sets.

[0120] In some embodiments of this application, based on the aforementioned scheme, the performance impact parameter set includes battery storage parameters and charge / discharge cycle count. The storage parameters include storage time and storage ambient temperature. The second acquisition unit 402 is configured to: perform cyclic charge / discharge on each test battery and record the correspondence between the remaining charge and open-circuit voltage of each test battery during each discharge process. The storage parameters of each test battery are different. Based on the correspondence between the remaining charge and open-circuit voltage of each test battery during any discharge process, the test SOC-OCV curve of the test battery under the corresponding test performance impact parameter set is fitted.

[0121] In some embodiments of this application, based on the foregoing scheme, the second acquisition unit 402 is configured to: calculate a first similarity value between the plurality of test performance impact parameter sets and the current performance impact parameter set; determine the test performance impact parameter set with the smallest first similarity value as the target performance impact parameter set; determine the test SOC-OCV curve under the target performance impact parameter set as the reference SOC-OCV curve, and correct the SOC-OCV curve based on the reference SOC-OCV curve.

[0122] In some embodiments of this application, based on the foregoing scheme, the second acquisition unit 402 is configured to: normalize the number of charge-discharge cycles, storage environment temperature, and storage time for each set of test performance influence parameters and the current set of performance influence parameters, respectively, to obtain the first normalized value corresponding to the number of charge-discharge cycles, the first normalized value corresponding to the storage environment temperature, and the first normalized value corresponding to the storage time for each set of test performance influence parameters, and the second normalized value corresponding to the number of charge-discharge cycles, the second normalized value corresponding to the storage environment temperature, and the second normalized value corresponding to the storage time for the current set of performance influence parameters; and calculate the first normalized value and the second normalized value corresponding to the number of charge-discharge cycles respectively. The first absolute value of the difference between the normalized values, the second absolute value of the difference between the first normalized value and the second normalized value corresponding to the ambient temperature, and the third absolute value of the difference between the first normalized value and the second normalized value corresponding to the storage time; based on the first weight of the first absolute value of the difference, the second weight of the second absolute value of the difference, and the third weight of the third absolute value of the difference, the weighted average of the first absolute value of the difference, the second absolute value of the difference, and the third absolute value of the difference is calculated as the first similarity value between each set of test performance influence parameters and the current set of performance influence parameters, wherein the first weight is greater than or equal to the second weight, and the second weight is greater than or equal to the third weight.

[0123] In some embodiments of this application, based on the foregoing scheme, the second acquisition unit 402 is configured to: calculate a second similarity value between the parameters stored in the plurality of test performance influence parameter sets and the parameters stored in the current performance influence parameter set; determine a plurality of test SOC-OCV curves corresponding to the test performance influence parameter set with the smallest second similarity value; determine a test SOC-OCV curve from the plurality of test SOC-OCV curves that has the same number of charge-discharge cycles as the current performance influence parameter set, as a reference SOC-OCV curve, and correct the SOC-OCV curve based on the reference SOC-OCV curve.

[0124] In some embodiments of this application, based on the foregoing scheme, the second acquisition unit 402 is configured to: determine a target remaining power range, wherein the open-circuit voltage deviation between the reference SOC-OCV curve and the SOC-OCV curve in the target remaining power range exceeds a preset deviation value; and replace the curve segment of the SOC-OCV curve in the target remaining power range with the curve segment of the reference SOC-OCV curve in the target remaining power range.

[0125] As another embodiment of this application, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods described in the above embodiments.

[0126] As another embodiment of this application, a computer-readable storage medium is also provided. This computer-readable storage medium may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the methods described in the above embodiments.

[0127] Based on the same inventive concept, embodiments of this application also provide an electronic device. (Reference) Figure 5 The diagram illustrates a structural schematic of a computer system suitable for implementing an electronic device according to embodiments of the present application. The electronic device includes one or more memories 504, one or more processors 502, and at least one computer program (program code) stored in the memories 504 and executable on the processors 502. When the processors 502 execute the computer program, they implement the methods described above.

[0128] Among them, Figure 5 In this document, a bus architecture (represented by bus 500) is used. Bus 500 may include any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 502 and memory represented by memory 504. Bus 500 may also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 505 provides an interface between bus 500 and receiver 501 and transmitter 503. Receiver 501 and transmitter 503 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 502 is responsible for managing bus 500 and general processing, while memory 504 can be used to store data used by processor 502 during operation.

[0129] The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored as one or more instructions or codes on or transmitted via a computer-readable medium. Other examples and embodiments are within the scope and spirit of this application and the appended claims. For example, due to the nature of software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hardwired, or any combination thereof. Furthermore, the functional units may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit.

[0130] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0131] The units described as separate components may or may not be physically separate. Similarly, the components of the control device may or may not be physical units; they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0132] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium, including instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0133] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method of determining the state of charge of an electric vehicle battery, characterized by, The method comprises: obtaining a current open-circuit voltage of a target battery in an electric vehicle, and a SOC-OCV curve calibrated at a factory for the target battery, the SOC-OCV curve being used to represent a correspondence between a remaining capacity and an open-circuit voltage of the target battery; obtaining a current performance influence parameter set of the target battery, and correcting the SOC-OCV curve based on the current performance influence parameter set to obtain a target SOC-OCV curve; determining a current remaining capacity of the target battery based on the target SOC-OCV curve and the current open-circuit voltage, the current remaining capacity being used to represent a current state of charge of the target battery.

2. The method of claim 1, wherein, The correcting the SOC-OCV curve based on the current performance influence parameter set comprises: obtaining a plurality of test SOC-OCV curves of a plurality of test batteries respectively under different test performance influence parameter sets, the test batteries being of the same type as the target battery; correcting the SOC-OCV curve based on the current performance influence parameter set and the test SOC-OCV curves of the test batteries under the plurality of test performance influence parameter sets.

3. The method of claim 2, wherein, The performance influence parameter set comprises a storage parameter and a number of charge-discharge cycles of the battery, the storage parameter comprises a storage time and a storage ambient temperature, and the obtaining the plurality of test SOC-OCV curves of the plurality of test batteries respectively under different test performance influence parameter sets comprises: performing cyclic charge-discharge on each test battery, and recording a correspondence between a remaining capacity and an open-circuit voltage of each test battery in each discharge process, the storage parameters of the test batteries being different from each other; fitting a test SOC-OCV curve of each test battery under a corresponding test performance influence parameter set based on the correspondence between the remaining capacity and the open-circuit voltage of each test battery in any discharge process.

4. The method of claim 3, wherein, The correcting the SOC-OCV curve based on the current performance influence parameter set and the test SOC-OCV curves of the test batteries under the plurality of test performance influence parameter sets comprises: calculating a first similarity value between each of the plurality of test performance influence parameter sets and the current performance influence parameter set; determining a test performance influence parameter set with a minimum first similarity value as a target performance influence parameter set; determining a test SOC-OCV curve under the target performance influence parameter set as a reference SOC-OCV curve, and correcting the SOC-OCV curve based on the reference SOC-OCV curve.

5. The method of claim 4, wherein, The calculating the first similarity value between each of the plurality of test performance influence parameter sets and the current performance influence parameter set comprises: The number of charge-discharge cycles, the storage environment temperature, and the storage time in each test performance influence parameter set and the current performance influence parameter set are normalized respectively to obtain a first normalized value corresponding to the number of charge-discharge cycles, a first normalized value corresponding to the storage environment temperature, and a first normalized value corresponding to the storage time in each test performance influence parameter set, and a second normalized value corresponding to the number of charge-discharge cycles, a second normalized value corresponding to the storage environment temperature, and a second normalized value corresponding to the storage time in the current performance influence parameter set; The first difference absolute value between the first normalized value and the second normalized value corresponding to the number of charge-discharge cycles, the second difference absolute value between the first normalized value and the second normalized value corresponding to the storage environment temperature, and the third difference absolute value between the first normalized value and the second normalized value corresponding to the storage time are calculated respectively; The weighted average value of the first difference absolute value, the second difference absolute value, and the third difference absolute value is calculated as the first similarity value between each test performance influence parameter set and the current performance influence parameter set based on the first weight of the first difference absolute value, the second weight of the second difference absolute value, and the third weight of the third difference absolute value, wherein the first weight is greater than or equal to the second weight, and the second weight is greater than or equal to the third weight.

6. The method of claim 3, wherein, The correction of the SOC-OCV curve based on the current performance influence parameter set and the test SOC-OCV curve of the test battery under multiple test performance influence parameters includes: The second similarity value between the storage parameters in the multiple test performance influence parameter sets and the storage parameters in the current performance influence parameter set is calculated; The multiple test SOC-OCV curves corresponding to the test performance influence parameter set with the minimum second similarity value are determined; The test SOC-OCV curve with the same number of charge-discharge cycles as the current performance influence parameter set is determined as a reference SOC-OCV curve from the multiple test SOC-OCV curves, and the SOC-OCV curve is corrected based on the reference SOC-OCV curve.

7. The method according to claim 4 or 6, characterized in that, The correction of the SOC-OCV curve based on the reference SOC-OCV curve includes: A target remaining capacity interval is determined, and the open-circuit voltage deviation value of the reference SOC-OCV curve and the SOC-OCV curve in the target remaining capacity interval exceeds a preset deviation value; The curve segment of the SOC-OCV curve in the target remaining capacity interval is replaced by the curve segment of the reference SOC-OCV curve in the target remaining capacity interval.

8. An apparatus for determining the state of charge of an electric vehicle battery, characterized by The device includes: A first acquisition unit is configured to acquire a current open-circuit voltage of a target battery in an electric vehicle and a SOC-OCV curve calibrated when the target battery is manufactured, wherein the SOC-OCV curve is used to represent a corresponding relationship between a remaining capacity of the target battery and an open-circuit voltage; A second obtaining unit is configured to obtain a current performance influence parameter set of the target battery, and correct the SOC-OCV curve based on the current performance influence parameter set to obtain a target SOC-OCV curve. A determining unit is configured to determine a current remaining power of the target battery by the target SOC-OCV curve based on the current open circuit voltage, where the current remaining power is used to represent a current state of charge of the target battery.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores at least one program code, and the at least one program code is loaded and executed by the processor to implement the operations performed by the method according to any one of claims 1 to 7.

10. An electronic device, comprising: The electronic device includes one or more processors and one or more memories, and the one or more memories store at least one program code, and the at least one program code is loaded and executed by the one or more processors to implement the method according to any one of claims 1 to 7.

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