Battery management system, method, individual battery and electric vehicle

Abstract

The embodiment of the application provides a battery management system, method, single battery and electric vehicle. It relates to the technical field of battery. A charging data set of a single battery is acquired, whether the single battery enters a charging end state at a current moment is determined according to the charging data set; if the single battery enters the charging end state, a voltage variation trend of the single battery at the current moment is determined according to the charging data set, and a state of charge variation quantity at the current moment is calculated through an algorithm adapted to the voltage variation trend; a candidate state of charge at the current moment is determined according to the state of charge variation quantity and a state of charge at a previous moment; the candidate state of charge is verified through a preset state of charge threshold, and the candidate state of charge is determined as a target state of charge at the current moment after the verification. In the above scheme, the state of charge of the single battery is dynamically corrected in the process of real-time calculation of the state of charge, so as to adapt to the parameter fluctuation of the single battery, thereby improving the calculation accuracy of the state of charge.

Description

Technical Field

[0001] This application relates to the field of battery technology, and in particular to a battery management system, method, single cell battery, and electric vehicle. Background Technology

[0002] During battery charging, the real-time display of the battery's State of Charge (SOC) is a core indicator for users to perceive the battery's energy status. The accuracy of the SOC directly impacts the user experience.

[0003] In related technologies, the current state of charge is calculated by accumulating the charging current, and the calculation results are displayed.

[0004] However, this method suffers from low accuracy in state of charge. Summary of the Invention

[0005] This application provides a battery management system, method, single-cell battery, and electric vehicle to improve the accuracy of state of charge.

[0006] In a first aspect, embodiments of this application provide a battery management system, comprising: the battery management system being used to calculate the state of charge (SOC) of a single battery cell in real time, the battery management system being configured to perform the following steps: acquiring a charging dataset of the single battery cell; determining, based on the charging dataset, whether the single battery cell has entered the end-of-charge state at the current moment; if it has entered the end-of-charge state, determining, based on the charging dataset, the voltage change trend of the single battery cell at the current moment, and calculating the change in SOC at the current moment using an algorithm adapted to the voltage change trend; determining, based on the change in SOC and the SOC at the previous moment, a candidate SOC at the current moment; verifying the candidate SOC using a preset SOC threshold, and determining, upon successful verification, the candidate SOC as the target SOC at the current moment.

[0007] Secondly, embodiments of this application provide a battery management method, comprising: acquiring a charging dataset of a single battery cell; determining whether the single battery cell has entered the charging end state at the current moment based on the charging dataset; if it has entered the charging end state, determining the voltage change trend of the single battery cell at the current moment based on the charging dataset, and calculating the change in state of charge at the current moment using an algorithm adapted to the voltage change trend; determining a candidate state of charge at the current moment based on the change in state of charge and the state of charge at the previous moment; verifying the candidate state of charge using a preset state of charge threshold, and determining the candidate state of charge as the target state of charge at the current moment after successful verification.

[0008] Thirdly, embodiments of this application provide a single battery cell, which is signal-connected to the battery management system described in the first aspect; the single battery cell is used to send a charging dataset to the battery management system so that the battery management system can calculate the target state of charge of the single battery cell in real time.

[0009] Fourthly, embodiments of this application provide a battery pack comprising at least two individual cells as described in the third aspect, wherein each individual cell is electrically connected to the other.

[0010] Fifthly, embodiments of this application provide a battery pack, including a housing and at least two battery packs as described in the fourth aspect, each battery pack being disposed within the housing and electrically connected to each other.

[0011] Sixthly, embodiments of this application provide an electric vehicle, including at least a battery pack and a display device as described in the fifth aspect, wherein the display device is used to display the state of charge calculated by the battery management system.

[0012] In a seventh aspect, embodiments of this application provide an electrical device, which includes at least a single battery cell as described in the third aspect and a display device, wherein the display device is used to display the state of charge calculated by the battery management system.

[0013] Eighthly, embodiments of this application provide a battery management device, comprising: a judgment module, configured to acquire a charging dataset of a single battery cell, and determine whether the single battery cell has entered a charging end state at the current moment based on the charging dataset; a calculation module, configured to, if the single battery cell has entered a charging end state, determine the voltage change trend of the single battery cell at the current moment based on the charging dataset, and calculate the change in state of charge at the current moment using an algorithm adapted to the voltage change trend; an overlay module, configured to determine a candidate state of charge at the current moment based on the change in state of charge and the state of charge at the previous moment; and a verification module, configured to verify the candidate state of charge using a preset state of charge threshold, and determine the candidate state of charge as the target state of charge at the current moment after successful verification.

[0014] Ninthly, embodiments of this application provide a battery management device, including: a memory and a processor;

[0015] The memory stores computer-executed instructions;

[0016] The processor executes computer execution instructions stored in the memory, causing the processor to perform the implementation method described in the second aspect above.

[0017] In a tenth aspect, embodiments of this application provide a non-volatile computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the embodiments of the second aspect above.

[0018] Eleventhly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the implementation methods described in the second aspect above.

[0019] The battery management system, method, single-cell battery, and electric vehicle provided in this application embodiment are used to calculate the state of charge (SOC) of a single-cell battery in real time. The battery management system is configured to perform the following steps: acquiring a charging dataset of the single-cell battery; determining whether the single-cell battery has entered the end-of-charge state at the current moment based on the charging dataset; if it has entered the end-of-charge state, determining the voltage change trend of the single-cell battery at the current moment based on the charging dataset, and calculating the change in SOC at the current moment using an algorithm adapted to the voltage change trend; determining a candidate SOC at the current moment based on the change in SOC and the SOC at the previous moment; verifying the candidate SOC using a preset SOC threshold, and determining the candidate SOC as the target SOC at the current moment after successful verification. This scheme dynamically corrects the SOC during real-time calculation to adapt to parameter fluctuations in the single-cell battery, thereby improving the accuracy of SOC calculation. Attached Figure Description

[0020] 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.

[0021] Figure 1 This is a schematic diagram illustrating an application scenario of a battery management method provided in an embodiment of this application;

[0022] Figure 2 A schematic flowchart illustrating a battery management method provided in an embodiment of this application;

[0023] Figure 3 A schematic flowchart illustrating another battery management method provided in an embodiment of this application;

[0024] Figure 4 This is a schematic diagram illustrating the determination of the charging end state according to an embodiment of this application;

[0025] Figure 5 A schematic diagram illustrating the determination of changes in state of charge provided in an embodiment of this application;

[0026] Figure 6 A schematic diagram of iterative calculation provided for embodiments of this application;

[0027] Figure 7 This is a schematic diagram of the structure of a battery management device provided in an embodiment of this application;

[0028] Figure 8 This is a schematic diagram of another battery management device provided in an embodiment of this application;

[0029] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0030] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0031] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0032] In this application embodiment, "at least one" refers to one or more, and "more than one" 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, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0033] It should be noted that the phrase "at...time" in the embodiments of this application can refer to the instant at which a certain situation occurs, or to a period of time after the occurrence of a certain situation; the embodiments of this application do not specifically limit this. Furthermore, the display interface provided in the embodiments of this application is merely an example, and the display interface may include more or less content.

[0034] It should be noted that the battery management system, method, single cell, and electric vehicle of this application can be used in the field of battery technology, or in any field other than batteries. The application fields of the battery management system, method, single cell, and electric vehicle of this application are not limited.

[0035] Figure 1 This is a schematic diagram illustrating an application scenario of a battery management method provided in an embodiment of this application. An example is given based on the scenario shown in the diagram: the state of charge of a single battery cell is calculated based on its parameters.

[0036] For example, the state of charge (SOC) represents the percentage of a single battery cell's full capacity that remains uncharged. Unlike parameters such as current, voltage, and temperature, SOC cannot be directly measured. It needs to be calculated or estimated using known parameters.

[0037] In practical applications, state of charge (SCC) is sensitive data for users. When charging a single battery cell, users use SCC to determine whether to stop charging. Therefore, the accuracy of SCC affects the user experience.

[0038] In related technologies, the state of charge is calculated by accumulating the accumulated charging current, based on a measurable charging current.

[0039] However, towards the end of the charging process, when the state of charge (SOC) is about to reach 100%, the charging current gradually decreases and its amplitude becomes lower due to the individual battery's electrochemical characteristics, while the battery polarization effect significantly increases. If only the ampere-hour integral of the cumulative charging current is used to calculate the SOC, errors in small current acquisition and polarization voltage interference can easily lead to falsely high or low SOC values, resulting in low accuracy in SOC estimation. Consequently, the SOC display may show abrupt changes (e.g., suddenly jumping from 95% to 100%), affecting the user experience.

[0040] The battery management method provided in this application aims to solve the above-mentioned technical problems in related technologies.

[0041] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0042] Figure 2 This is a flowchart illustrating a battery management method provided in an embodiment of this application. The method includes the following steps:

[0043] S201. Obtain the charging data set of a single battery cell, and determine whether the single battery cell has entered the end state of charging at the current moment based on the charging data set.

[0044] As an example, the implementing entity of this embodiment can be a battery management system, which is used to calculate the state of charge of individual cells in real time.

[0045] For example, real-time acquisition of charging-related data sets for individual batteries, including directly measurable data such as current charging current, individual battery voltage, and battery temperature, as well as historically calculated data, such as the state of charge at historical moments.

[0046] Optionally, after collecting the charging dataset, abnormal data filtering processing can be performed on the data to filter out interfering data, ensure the accuracy of the data, and provide a reliable data foundation for subsequent state of charge calculation.

[0047] Optionally, the filtering process includes, but is not limited to, at least one of the following: control filtering, normal range filtering (e.g., voltage within the upper and lower limits of the rated voltage, state of charge between 0-100%, charging current within the range of the rated charging current), duplicate data filtering, and abnormal jump data filtering (e.g., filtering data with two consecutive differences greater than or equal to the difference threshold).

[0048] For example, the end-of-charge state refers to the operating state of a single battery cell in the later stages of charging, when the state of charge is close to full charge and / or the electrochemical characteristics of the single battery cell enter a stage of rapid change. Calculating the state of charge using the ampere-hour integration method at the end-of-charge state has a significant error; other high-precision methods should be used to calculate the state of charge.

[0049] For example, the state of a single cell can be characterized based on the charging dataset to determine whether it is similar to the state at the end of the charging process, thereby allowing for timely switching to a high-precision method to calculate the state of charge.

[0050] S202. If the charging end state is entered, the voltage change trend of the individual battery at the current moment is determined according to the charging dataset, and the change in state of charge at the current moment is calculated by the algorithm adapted to the voltage change trend.

[0051] For example, different voltage trends correspond to different battery operating conditions.

[0052] For example, by analyzing the voltage change trend of individual cells in real time, it can be determined whether the current voltage is rising, stable or falling, so as to dynamically select the appropriate calculation algorithm and avoid the calculation deviation of a single algorithm under different operating conditions.

[0053] For example, the change in state of charge is the difference between the state of charge at the current moment and the state of charge at the previous moment.

[0054] S203. Based on the change in state of charge and the state of charge at the previous moment, determine the candidate state of charge at the current moment.

[0055] For example, by combining the previously determined state of charge with the currently calculated change in state of charge, a candidate state of charge for the current moment can be obtained.

[0056] For example, the calculation of the state of charge is a continuous iterative and dynamically updated process. After each candidate state of charge is calculated, it is recorded so that subsequent calculations have a reference basis, thereby improving the accuracy of the state of charge calculation.

[0057] S204. Verify the candidate charge state using a preset charge state threshold, and determine the candidate charge state as the target charge state at the current moment after the verification is successful.

[0058] Optionally, the preset state of charge threshold can be 100% or a value close to 100%.

[0059] For example, the candidate state of charge is calculated, and there may be calculation errors caused by algorithm errors and data acquisition noise.

[0060] For example, if a candidate state of charge exceeds the state of charge threshold and the excess value is unreasonable, such as if the state of charge threshold is 100% and the candidate state of charge is 102%, the candidate state of charge will be automatically corrected to 100% to ensure that the target state of charge matches the actual operating state of the single cell.

[0061] The battery management method provided in this application acquires a charging dataset of individual batteries, determines whether the individual battery has entered the final charging state at the current moment based on the charging dataset, and if it has entered the final charging state, determines the voltage change trend of the individual battery at the current moment based on the charging dataset, and calculates the change in state of charge (SOC) at the current moment using an algorithm adapted to the voltage change trend. Based on the change in SOC and the SOC of the previous moment, a candidate SOC is determined for the current moment. The candidate SOC is verified using a preset SOC threshold, and if the verification is successful, the candidate SOC is determined as the target SOC for the current moment. This scheme dynamically corrects the SOC during real-time calculation of the individual battery's SOC to adapt to parameter fluctuations in the individual battery, thereby improving the accuracy of SOC calculation.

[0062] Based on any of the above embodiments, the following, in conjunction with Figure 3 This section provides a detailed explanation of the battery management process.

[0063] Figure 3 This is a schematic flowchart illustrating another battery management method provided in an embodiment of this application. Figure 3 As shown, the method includes:

[0064] S301. Determine the real-time parameters of a single battery cell at the current moment from the charging data set.

[0065] For example, real-time parameters characterizing the operating condition of a single battery cell are extracted and parsed from the charging dataset. These parameters are physical quantities that can be directly collected from the battery. Real-time parameters are the direct basis for determining whether a single battery cell has entered the final stage of charging.

[0066] Optional real-time parameters include, but are not limited to, at least one of the following: voltage, charging current, battery temperature, number of cycles, and usage time.

[0067] S302. Based on real-time parameters, a first state determination is performed on a single battery cell to obtain a first determination result. The first determination result is either pass or fail. The first state determination is used to determine whether the real-time state of the single battery cell conforms to the charging end state.

[0068] For example, the first state determination is a single, instantaneous operating condition matching determination, used to determine whether the instantaneous operating state of a single battery cell at the current moment meets the basic operating condition characteristics of the charging end state.

[0069] With the help of scenario examples, the basic operating conditions include, for example, the voltage is close to the full charge voltage, the charging current enters the low current range, and the battery temperature is within the normal range.

[0070] For example, the result of the first state determination is either pass or fail. If the real-time parameters meet the instantaneous characteristics of the charging end, it is determined to pass; otherwise, it is determined to fail, thus completing the initial screening of individual batteries.

[0071] One feasible implementation method is to determine the first state by: determining a parameter threshold baseline value; determining the battery temperature from real-time parameters and determining a correction value based on the battery temperature; determining the parameter threshold based on the parameter threshold baseline value and the correction value; if the real-time parameter is greater than the parameter threshold, then the first determination result is determined to be passed; if the real-time parameter is less than or equal to the parameter threshold, then the first determination result is determined to be failed.

[0072] For example, the real-time parameters include multiple parameters. If each parameter is greater than its corresponding threshold, the first determination result is determined to be "pass". If any parameter is less than or equal to its corresponding threshold, the first determination result is determined to be "fail". By determining multiple parameters, the error introduced by determining a single parameter can be reduced, thereby improving the accuracy of the first state determination.

[0073] Optionally, the first state determination can be represented by the following formula:

[0074]

[0075] Where t represents the current time, This indicates the state of charge of the previous time step. Indicates the state of charge threshold. This indicates the temperature at the current moment. Indicates the temperature threshold. This represents the voltage at the current moment. Indicates the voltage threshold. This indicates the charging current at the current moment. This indicates the current threshold.

[0076] For example, during the charging process of a single battery cell, the operating conditions change constantly. If a fixed parameter threshold is used, it cannot match the changing operating conditions, reducing the accuracy of the state of charge calculation.

[0077] With the example of the scenario, as the battery temperature rises, the actual parameter threshold corresponding to the end state of charging changes accordingly, and the fixed parameter threshold cannot match the changing actual parameter threshold.

[0078] For example, each parameter threshold is corrected by dynamically determined correction values ​​so that the state determination result matches the actual state of the individual battery cell.

[0079] In this feasible implementation, using multiple parameters for determination can reduce the error introduced by a single parameter determination, thereby improving the accuracy of determining the charging end state and thus improving the accuracy of the state of charge calculation.

[0080] One feasible implementation method is to determine the correction value by: determining a correction value prediction model, which is obtained by training the model based on multiple historical battery temperatures and the historical correction value corresponding to each historical battery temperature; inputting the battery temperature into the correction value prediction model to obtain the correction value output by the correction value prediction model.

[0081] For example, the correction value prediction model represents the mapping relationship between battery temperature and correction values.

[0082] For example, the corrected value prediction model is a lightweight prediction model that has been pre-trained offline and can be adapted to the embedded operating environment of the battery management system.

[0083] For example, the training process of the correction value prediction model is based on a large amount of measured data: historical battery temperatures under multiple different operating conditions are collected as model input features, and historical correction values ​​that are matched one-to-one with each historical battery temperature and are actually calibrated are collected as model output targets. The model learns the inherent mapping law between battery temperature and correction value through model training, and finally obtains a stable and reliable correction value prediction model.

[0084] For example, the real-time battery temperature at the current moment is used as input to the trained correction value prediction model for model inference. Based on the learned mapping relationship between battery temperature and correction value, the correction value prediction model quickly performs forward inference calculation and directly outputs the correction value that matches the current battery temperature.

[0085] In this feasible implementation, the correction value prediction model can work adaptively throughout the entire battery life cycle after a one-time offline training, without the need for repeated manual adjustments and calibrations, thus reducing the error introduced by manual calibration and improving the accuracy of state of charge calculation.

[0086] S303. If the first determination result is passed, the cumulative number of individual battery cells that have passed the determination is determined based on the charging dataset.

[0087] For example, the cumulative number is the number of consecutive first judgment results that pass, starting from the current time and counting forward according to a preset step size.

[0088] S304. Based on the cumulative quantity, a second state determination is performed on the individual battery to obtain a second determination result. The second determination result is either pass or fail. The second state determination is used to determine whether the cumulative state of the individual battery meets the charging end state.

[0089] Below, in conjunction with Figure 4 The method for determining the state at the end of the charging process is explained.

[0090] Figure 4 This is a schematic diagram illustrating the determination of the charging end state according to an embodiment of this application. Figure 4 The image shows the real-time parameters collected by the battery management system. Parameter thresholds are obtained through dynamic adjustments based on real-time operating conditions. By comparing the real-time parameters with the parameter thresholds, it is determined whether a single battery cell meets the instantaneous operating condition characteristics of the charging end at the current moment. If so, starting from the current moment, the cumulative number of consecutive "pass" results is accumulated to reflect the continuity of the operating condition. By comparing the cumulative number with the threshold number, it is determined whether the operating condition is stable. If the operating condition is stable, the single battery cell is determined to have entered the charging end state.

[0091] One feasible implementation method is to determine the cumulative quantity by: starting from the current time, continuously accumulating the first judgment result that has passed according to a preset step size to obtain the cumulative quantity.

[0092] The second state determination can be performed using the following methods: if the cumulative quantity is greater than or equal to a preset quantity threshold, the second determination result is determined to be passed; if the cumulative quantity is less than the quantity threshold, the second determination result is determined to be failed.

[0093] Optionally, the preset step size can be a dynamically adjustable value. For example, the preset step size can be dynamically adjusted according to the number of cycles and / or usage time of a single battery cell to adapt to the actual operating conditions of the single battery cell.

[0094] The preset step size can be the same as the data acquisition step size of the charging dataset, or the preset step size can be an integer multiple of the data acquisition step size.

[0095] For example, the cumulative quantity obtained by continuous accumulation is compared with a preset quantity threshold.

[0096] If the cumulative number is greater than or equal to the preset number threshold, it means that the individual battery has met the working condition characteristics at the end of the charging process for multiple consecutive cycles and the state is stable. Therefore, the second judgment result is passed.

[0097] If the cumulative number is less than the preset threshold, it means that the individual battery only temporarily and occasionally meets the end-of-charge characteristics and has not formed a continuous and stable state. Therefore, the second judgment result is that it fails.

[0098] In this feasible implementation, the second state determination avoids misidentification of the charging end state due to single sampling anomalies, instantaneous voltage or current jumps, and improves the accuracy of state determination, thereby enhancing the accuracy of the state of charge calculation.

[0099] S305. If the second determination result is passed, it is determined that the single cell has entered the charging end state.

[0100] For example, the second state determination is only performed on the basis that the first determination result is passed. Only when both the first determination result and the second determination result are passed is it determined that the single cell has entered the charging end state.

[0101] Based on the above implementation method, by combining the first state determination and the second state determination, the misidentification of the charging end state caused by single sampling abnormalities, instantaneous voltage or current jumps is avoided, the accuracy of state determination is improved, and thus the accuracy of the charge state calculation is improved.

[0102] S306. If the charging end state is entered, the voltage change trend of the individual battery at the current moment is determined according to the charging dataset, and the change in state of charge at the current moment is calculated by the voltage change trend adaptation algorithm.

[0103] One feasible implementation method, when the voltage change trend is upward, is to calculate the change in state of charge at the current moment using the following method: From the charging data set, determine the initial voltage and initial state of charge corresponding to the starting moment of the voltage change trend, and the current voltage at the current moment; determine the full-charge voltage of a single cell; determine a first voltage difference based on the initial voltage and the current voltage using non-zero constraints; determine a second voltage difference based on the initial voltage and the full-charge voltage using non-zero constraints; determine the state of charge difference based on the initial state of charge and the state of charge threshold; and determine the change in state of charge based on the proportional relationship between the first voltage difference and the second voltage difference, and the state of charge difference.

[0104] For example, if the voltage at the current moment is greater than the voltage at the previous moment, then the voltage change trend is determined to be an upward trend.

[0105] For example, the change in state of charge at the current moment represents the change in state of charge at the current moment relative to the previous moment.

[0106] For example, from the charging dataset, the initial voltage and initial state of charge corresponding to the starting moment of this voltage rise trend, as well as the current voltage corresponding to the current moment, are extracted. Simultaneously, the full charge voltage of the individual battery (determined by the material properties of the individual battery) and a preset state of charge threshold are determined.

[0107] For example, the difference between the initial voltage and the current voltage is calculated, and a first voltage difference is obtained under the constraint that the difference is not zero. The non-zero constraint is used to avoid the subsequent calculations being invalid due to the difference being zero.

[0108] For example, the difference between the initial voltage and the full charge voltage is calculated, and a second voltage difference is obtained under the constraint that the difference is not zero.

[0109] For example, the difference between the initial state of charge and the state of charge threshold is calculated to obtain the state of charge difference, which represents the total state of charge difference from the trend start point to the fully charged state.

[0110] For example, the ratio of the first voltage difference to the second voltage difference is used as the voltage rise ratio. The voltage rise ratio is multiplied by the state of charge difference to obtain the change in state of charge at the current moment.

[0111] Optionally, the change in state of charge can be calculated using the following method:

[0112]

[0113] in, It represents the change in the state of charge. Indicates the state of charge threshold. Indicates the initial state of charge. This indicates the full charge voltage. Indicates the initial voltage. This indicates the current voltage.

[0114] In this feasible implementation, during the voltage rise phase at the end of charging, the battery polarization is large, the charging current is small, and it is easily disturbed. Voltage proportional mapping calculation is adopted, which does not depend on the charging current, and eliminates the interference of small current and polarization on the state of charge calculation, thereby improving the accuracy of the state of charge.

[0115] One feasible implementation method, when the voltage change trend is decreasing or the voltage is stable, is to calculate the change in state of charge (SOC) at the current moment using the following method: determining the charging current and a preset deceleration coefficient model, where the deceleration coefficient model represents the mapping relationship between battery temperature, SOC, and deceleration coefficient, and the deceleration coefficient is used to correct the change in SOC; inputting the current battery temperature and the previous SOC into the deceleration coefficient model to obtain the target deceleration coefficient; calculating candidate SOC changes using the ampere-hour integral method based on the charging current; and correcting the candidate SOC changes based on the target deceleration coefficient to obtain the change in SOC at the current moment.

[0116] For example, the charging current of a single battery cell at the current moment is obtained, and the system's preset deceleration coefficient model is called.

[0117] For example, the deceleration coefficient model is a pre-trained mapping model used to characterize the relationship between battery temperature, state of charge, and deceleration coefficient.

[0118] The deceleration coefficient is used to correct the deviation in the state of charge calculation caused by the ampere-hour integration method under low current and polarized conditions at the end of charging.

[0119] For example, the battery temperature and state of charge determined at the previous moment are used as inputs into the deceleration coefficient model to obtain the target deceleration coefficient corresponding to the current operating condition.

[0120] Optionally, the deceleration coefficient model can be represented by the following formula:

[0121]

[0122] in, Indicates the reference deceleration coefficient. , Represents the proportionality coefficient. Indicates the reference temperature.

[0123] For example, based on the real-time collected charging current, a preliminary calculation is performed using the ampere-hour integration method to obtain an uncorrected candidate change in state of charge. This candidate value is the original estimate and does not take into account distortion caused by polarization at the charging end and insufficient current.

[0124] Optionally, the change in state of charge can be calculated using the ampere-hour integral method using the following formula:

[0125]

[0126] in, This indicates the rated capacity of a single battery cell (unit: Ah). This indicates the preset step size.

[0127] Below, in conjunction with Figure 5 The determination of the change in state of charge is explained.

[0128] Figure 5 This is a schematic diagram illustrating the determination of changes in state of charge as provided in an embodiment of this application. Figure 5 As shown, the voltage change trend is determined based on voltage data, and different calculation methods are used for different voltage change trends. If the trend is upward, the voltage difference and the change in state of charge (SOC) are determined, and the SOC change is calculated based on the voltage ratio mapping relationship. If the trend is downward or the voltage is stable, the target detection coefficient is determined through a deceleration coefficient model. The candidate SOC change is calculated using the ampere-hour integration method. The candidate SOC change is corrected using the target deceleration coefficient to obtain the current SOC change.

[0129] In this feasible implementation, the target deceleration coefficient is used to correct the change in candidate state of charge, which offsets the cumulative error of the ampere-hour integral method under stable or decreasing voltage conditions, and finally obtains the accurate change in state of charge at the current moment, thereby improving the accuracy of the calculation of state of charge.

[0130] S307. Based on the change in state of charge and the state of charge at the previous moment, determine the candidate state of charge at the current moment.

[0131] It should be noted that the execution process of S307 is the same as that of S203, and will not be repeated here.

[0132] S308. Verify the candidate charge state using a preset charge state threshold, and determine the candidate charge state as the target charge state at the current moment after the verification is passed.

[0133] One feasible implementation method for verifying candidate states of charge using a preset state of charge threshold may include: if the candidate state of charge is less than or equal to the state of charge threshold, the verification result is determined to be successful; if the candidate state of charge is greater than the state of charge threshold, the verification result is determined to be unsuccessful, and the candidate state of charge is modified to the state of charge threshold.

[0134] For example, the candidate states of charge obtained through previous calculations are numerically compared with the state of charge threshold, and the corresponding operation is performed based on the comparison result:

[0135] If the candidate state of charge is less than or equal to the state of charge threshold, it means that the candidate state of charge is within the reasonable state of charge range of the single cell and has not exceeded the physical upper limit. Therefore, the verification is passed and the candidate state of charge is directly used as the final target state of charge at the current moment.

[0136] If the candidate state of charge is greater than the state of charge threshold, it indicates that the candidate state of charge is excessively high due to factors such as polarization at the end of charging, small current acquisition error, and algorithm calculation deviation. This exceeds the upper limit of the actual state of charge that a large single cell can achieve. Therefore, the verification is deemed unsuccessful, and the abnormal candidate state of charge is directly and forcibly corrected to the state of charge threshold. The corrected value is then used as the final target state of charge at the current moment.

[0137] In this feasible implementation, a state of charge threshold is used as a constraint to ensure that the final state of charge output by the system does not exceed the actual physical upper limit of the individual battery, thus ensuring that the state of charge value matches the actual remaining capacity of the individual battery and improving the accuracy of the state of charge calculation.

[0138] In one feasible implementation, if the candidate state of charge is less than or equal to the state of charge threshold, the battery management method may further include: performing a preset operation until the updated state of charge is greater than or equal to the state of charge threshold; wherein the preset operation includes: obtaining the charging dataset at the next time step according to a preset step size, calculating the change in state of charge at the next time step according to the charging dataset at the next time step, and determining the updated state of charge at the next time step according to the change in state of charge at the next time step; wherein the preset condition is that the judgment result is that the verification is passed.

[0139] For example, if the candidate state of charge (SOC) is determined to be less than the SOC threshold, it means that the current individual battery cell has not yet reached a fully charged state, and the SOC calculation for subsequent charging cycles needs to continue. That is, the SOC calculation continues in real time during subsequent charging processes. This process is repeated iteratively until the SOC of the individual battery cell is greater than or equal to the SOC threshold.

[0140] For example, the iterative loop operation includes: collecting and obtaining the charging dataset for the next moment according to a preset step size (consistent with the sampling period and calculation period of the battery management system); calculating the change in state of charge for the next moment according to the aforementioned method based on the charging dataset for the next moment; and recursively determining the updated state of charge for the next moment based on the change in state of charge.

[0141] For example, the iteration termination condition is that the updated state of charge is greater than or equal to the state of charge threshold, that is, the current charging has been terminated.

[0142] Below, in conjunction with Figure 6 The iterative calculation is explained.

[0143] Figure 6 This is a schematic diagram of iterative calculation provided for an embodiment of this application. For example... Figure 6 As shown, after a single battery cell enters the charging state, real-time charging data is determined. After data preprocessing, it is determined whether the charging process has reached the end-of-charge stage. Once the end-of-charge stage is reached, real-time parameters are updated, and voltage and state of charge (SOC) are recorded. An adaptive algorithm is used to calculate the change in SOC based on the voltage trend. The updated SOC is then calculated. The updated SOC is used to determine whether charging has ended. If not, the updated SOC is calculated repeatedly using the same method as for the end-of-charge stage.

[0144] In this feasible implementation, the calculation of the state of charge is synchronized with the physical process of charging a single battery cell. The state of charge is updated cycle by cycle to follow the charging progress, avoiding mismatch with the actual charging progress caused by premature termination after a single calculation, thereby improving the accuracy of the state of charge calculation.

[0145] One feasible implementation of the battery management method further includes: generating a display instruction for the state of charge calculated at each state of charge calculation time; and sending the display instruction to the electric vehicle or electrical equipment to instruct the display device of the electric vehicle or electrical equipment to update the calculated state of charge.

[0146] For example, the display instruction is used to instruct the display device to display the calculated state of charge.

[0147] For example, a display device is used to show a user the state of charge so that the user is aware of the current charging status.

[0148] With the help of scenario examples, users can determine whether they need to charge only based on the state of charge displayed on the device and their own needs. For example, a user can determine whether to continue charging based on the electric vehicle's displayed state of charge and their own range requirements.

[0149] In this feasible implementation, the accurately calculated state of charge is displayed on a display device, thereby accurately assisting the user in judging the charging process and improving the user experience.

[0150] This application provides a single battery cell that is signal-connected to a battery management system; the single battery cell is used to send charging data sets to the battery management system so that the battery management system can calculate the target state of charge of the single battery cell in real time.

[0151] Based on the above implementation methods, during the real-time calculation of the state of charge (SOC) of a single battery cell, the SOC is dynamically corrected to adapt to the parameter fluctuations of the single battery cell, thereby improving the accuracy of the SOC calculation.

[0152] This application provides a battery pack comprising at least two of the above-described individual cells, each of which is electrically connected to the other.

[0153] Based on the above implementation methods, during the real-time calculation of the state of charge (SOC) of a single battery cell, the SOC is dynamically corrected to adapt to the parameter fluctuations of the single battery cell, thereby improving the accuracy of the SOC calculation.

[0154] This application provides a battery pack, including a housing and at least two battery packs as described above, each battery pack being disposed within the housing and electrically connected to each other.

[0155] Based on the above implementation methods, during the real-time calculation of the state of charge (SOC) of a single battery cell, the SOC is dynamically corrected to adapt to the parameter fluctuations of the single battery cell, thereby improving the accuracy of the SOC calculation.

[0156] This application provides an electric vehicle that includes at least the aforementioned battery pack and a display device, wherein the display device is used to display the state of charge calculated by the battery management system.

[0157] Based on the above implementation methods, during the real-time calculation of the state of charge (SOC) of a single battery cell, the SOC is dynamically corrected to adapt to the parameter fluctuations of the single battery cell, thereby improving the accuracy of the SOC calculation.

[0158] This application provides an electrical device, which includes at least the aforementioned single battery cell and a display device. The display device is used to display the state of charge calculated by the battery management system.

[0159] Based on the above implementation methods, during the real-time calculation of the state of charge (SOC) of a single battery cell, the SOC is dynamically corrected to adapt to the parameter fluctuations of the single battery cell, thereby improving the accuracy of the SOC calculation.

[0160] Figure 7 This is a schematic diagram of a battery management device provided in an embodiment of this application. Figure 7As shown, the battery management device 70 may include: a judgment module 71, a calculation module 72, an overlay module 73, and a verification module 74.

[0161] The judgment module 71 is used to obtain the charging data set of a single battery cell and determine whether the single battery cell has entered the end state of charging at the current moment based on the charging data set.

[0162] The calculation module 72 is used to determine the voltage change trend of a single battery cell at the current moment based on the charging dataset if the charging end state is entered, and to calculate the change in state of charge at the current moment through an algorithm adapted to the voltage change trend.

[0163] The superposition module 73 is used to determine the candidate state of charge at the current moment based on the change in state of charge and the state of charge at the previous moment.

[0164] The verification module 74 is used to verify the candidate charge state through a preset charge state threshold, and after the verification is passed, the candidate charge state is determined as the target charge state at the current time.

[0165] Optionally, the judgment module 71 can execute... Figure 2 S201 in the embodiment.

[0166] Optionally, the calculation module 72 can perform... Figure 2 S202 in the embodiment.

[0167] Optionally, the overlay module 73 can execute Figure 2 S203 in the embodiment.

[0168] Optionally, the verification module 74 can be executed. Figure 2 S204 in the embodiment.

[0169] It should be noted that the battery management device shown in the embodiments of this application can execute the technical solution shown in the above method embodiments, and its implementation principle and beneficial effects are similar, so they will not be described again here.

[0170] Based on the above implementation methods, during the real-time calculation of the state of charge (SOC) of a single battery cell, the SOC is dynamically corrected to adapt to the parameter fluctuations of the single battery cell, thereby improving the accuracy of the SOC calculation.

[0171] In one possible implementation, the determination module 71 is specifically used for:

[0172] Determine the real-time parameters of a single battery cell at the current moment from the charging data set;

[0173] Based on real-time parameters, a first state determination is performed on a single battery cell to obtain a first determination result. The first determination result is either pass or fail. The first state determination is used to determine whether the real-time state of a single battery cell conforms to the charging end state.

[0174] If the first judgment result is passed, the cumulative number of individual batteries that have passed the judgment is determined based on the charging dataset;

[0175] Based on the cumulative quantity, a second state determination is performed on the individual battery to obtain a second determination result, which is either pass or fail. The second state determination is used to determine whether the cumulative state of the individual battery meets the charging end state.

[0176] If the second determination result is passed, it is determined that the single battery cell has entered the end of the charging state.

[0177] In one possible implementation, the determination module 71 is specifically used for:

[0178] Determine the baseline value for the parameter threshold;

[0179] The battery temperature is determined from real-time parameters, and a correction value is determined based on the battery temperature.

[0180] The parameter threshold is determined based on the baseline and correction values.

[0181] If the real-time parameter is greater than the parameter threshold, then the first judgment result is determined to be passed;

[0182] If the real-time parameter is less than or equal to the parameter threshold, the first judgment result is determined to be unsuccessful.

[0183] In one possible implementation, the determination module 71 is specifically used for:

[0184] The correction value prediction model is determined by training the model based on multiple historical battery temperatures and the corresponding historical correction values ​​for each historical battery temperature.

[0185] Input the battery temperature into the correction value prediction model to obtain the correction value output by the correction value prediction model.

[0186] In one possible implementation, the determination module 71 is specifically used for:

[0187] Starting from the current time, the first judgment result that has passed is continuously accumulated according to the preset step size to obtain the cumulative number;

[0188] Based on the cumulative quantity, a second state determination is performed on the individual battery cells to obtain a second determination result, including:

[0189] If the cumulative quantity is greater than or equal to the preset quantity threshold, the second determination result is determined to be passed;

[0190] If the cumulative number is less than the quantity threshold, the second judgment result is determined to be unsuccessful.

[0191] In one possible implementation, the voltage change trend is upward; the calculation module 72 is specifically used for:

[0192] From the charging dataset, determine the initial voltage and initial state of charge corresponding to the starting moment of the voltage change trend, as well as the current voltage at the current moment;

[0193] Determine the full charge voltage of a single cell;

[0194] The first voltage difference is determined based on the initial voltage and the current voltage using non-zero constraints.

[0195] The second voltage difference is determined based on the initial voltage and the full charge voltage using non-zero constraints.

[0196] The charge state difference is determined based on the initial charge state and the charge state threshold.

[0197] The change in state of charge is determined based on the proportional relationship between the first voltage difference and the second voltage difference, as well as the difference in state of charge.

[0198] In one possible implementation, the voltage change trend is either a decreasing trend or the voltage is stable; the calculation module 72 is specifically used for:

[0199] Determine the charging current and the preset deceleration coefficient model. The deceleration coefficient model represents the mapping relationship between battery temperature, state of charge and deceleration coefficient. The deceleration coefficient is used to correct the change in state of charge.

[0200] Input the current battery temperature and the previous state of charge into the deceleration coefficient model to obtain the target deceleration coefficient;

[0201] Based on the charging current, the change in candidate state of charge is calculated using the ampere-hour integral method.

[0202] The change in the candidate state of charge is corrected based on the target deceleration coefficient to obtain the change in the state of charge at the current moment.

[0203] Figure 8 This is a schematic diagram of another battery management device provided in an embodiment of this application. Figure 7 Based on the illustrated embodiments, as Figure 8 As shown, the battery management device 70 also includes a processing module 75, an iteration module 76, and a display module 77.

[0204] Processing module 75 is used for:

[0205] If the candidate state of charge is less than or equal to the state of charge threshold, the verification result is determined to be successful.

[0206] If the candidate state of charge is greater than the state of charge threshold, the verification result is determined to be a failure, and the candidate state of charge is modified to the state of charge threshold.

[0207] Iteration module 76 is used for:

[0208] Perform the preset operation until the updated state of charge is greater than or equal to the state of charge threshold.

[0209] The preset operations include: obtaining the charging dataset for the next moment according to a preset step size, calculating the change in state of charge for the next moment according to the charging dataset for the next moment, and determining the updated state of charge for the next moment according to the change in state of charge for the next moment.

[0210] Display module 77, used for:

[0211] For each state of charge calculated at each time step, a display command is generated.

[0212] Send display commands to electric vehicles or electrical equipment to instruct the display devices of electric vehicles or electrical equipment to update the calculated state of charge.

[0213] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 9 As shown, the electronic device includes:

[0214] The electronic device includes a processor 291 and a memory 292; it may also include a communication interface 293 and a bus 294. The processor 291, memory 292, and communication interface 293 can communicate with each other via the bus 294. The communication interface 293 can be used for information transmission. The processor 291 can invoke logical instructions stored in the memory 292 to execute the methods of the above embodiments.

[0215] Furthermore, the logic instructions in the aforementioned memory 292 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0216] The memory 292, as a non-volatile 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 application. The processor 291 executes functional applications and data processing by running the software programs, instructions, and modules stored in the memory 292, that is, it implements the methods in the above-described method embodiments.

[0217] The memory 292 may include a program storage area and a data storage area. The program storage area may store the operating system and application programs 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 292 may include high-speed random access memory and may also include non-volatile memory.

[0218] Based on the above implementation methods, during the real-time calculation of the state of charge (SOC) of a single battery cell, the SOC is dynamically corrected to adapt to the parameter fluctuations of the single battery cell, thereby improving the accuracy of the SOC calculation.

[0219] This application provides a non-volatile computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method as described in the foregoing embodiments.

[0220] Based on the above implementation methods, during the real-time calculation of the state of charge (SOC) of a single battery cell, the SOC is dynamically corrected to adapt to the parameter fluctuations of the single battery cell, thereby improving the accuracy of the SOC calculation.

[0221] This application provides a computer program product, including a computer program that, when executed by a processor, implements the method as described in the foregoing embodiments.

[0222] Based on the above implementation methods, during the real-time calculation of the state of charge (SOC) of a single battery cell, the SOC is dynamically corrected to adapt to the parameter fluctuations of the single battery cell, thereby improving the accuracy of the SOC calculation.

[0223] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0224] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps; they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages, which do not necessarily complete at the same time but can be executed at different times. The execution order of these sub-steps or stages is also not necessarily sequential but can be alternated or carried out in turn with other steps or at least some of the sub-steps or stages of other steps.

[0225] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0226] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0227] When integrated units / modules are implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. The processor can be any suitable hardware processor, such as CPU, GPU, FPGA, DSP, and ASIC. The storage unit can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc.

[0228] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to related technologies, 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 memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0229] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0230] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the claims.

[0231] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A battery management system, characterized in that, The battery management system is used to calculate the state of charge of individual cells in real time, and the battery management system is configured to perform the following steps: Obtain the charging dataset of the individual battery, and determine whether the individual battery has entered the charging end state at the current time based on the charging dataset; If the charging end state is reached, the voltage change trend of the single battery cell at the current moment is determined according to the charging dataset, and the change in state of charge at the current moment is calculated by the algorithm adapted to the voltage change trend. Based on the change in state of charge and the state of charge at the previous moment, determine the candidate state of charge at the current moment; The candidate state of charge is verified by a preset state of charge threshold, and the candidate state of charge is determined as the target state of charge at the current moment after the verification is successful.

2. The system according to claim 1, characterized in that, The step of determining whether the individual battery has entered the final charging state at the current moment based on the charging dataset specifically includes: Determine the real-time parameters of the individual battery at the current moment from the charging dataset; Based on the real-time parameters, a first state determination is performed on the individual battery to obtain a first determination result. The first determination result is either pass or fail. The first state determination is used to determine whether the real-time state of the individual battery conforms to the charging end state. If the first determination result is passed, then the cumulative number of the single battery cells that have passed the determination is determined based on the charging dataset; Based on the cumulative quantity, a second state determination is performed on the individual battery to obtain a second determination result. The second determination result is either pass or fail. The second state determination is used to determine whether the cumulative state of the individual battery meets the charging end state. If the second determination result is passed, then it is determined that the single battery cell has entered the charging end state.

3. The system according to claim 2, characterized in that, The step of determining the first state of the single battery cell based on the real-time parameters to obtain a first determination result specifically includes: Determine the baseline value for the parameter threshold; The battery temperature is determined from the real-time parameters, and a correction value is determined based on the battery temperature; The parameter threshold is determined based on the parameter threshold baseline value and the correction value; If the real-time parameter is greater than the parameter threshold, then the first determination result is determined to be passed; If the real-time parameter is less than or equal to the parameter threshold, then the first determination result is determined to be unsuccessful.

4. The system according to claim 3, characterized in that, The step of determining the correction value based on the battery temperature specifically includes: A correction value prediction model is determined, which is obtained by training the model based on multiple historical battery temperatures and the historical correction values ​​corresponding to each historical battery temperature. Input the battery temperature into the correction value prediction model to obtain the correction value output by the correction value prediction model.

5. The system according to claim 2, characterized in that, The step of determining the cumulative number of individual batteries that have passed the determination based on the charging dataset specifically includes: Starting from the current time, the first judgment result that has passed is continuously accumulated according to a preset step size to obtain the accumulated number; Based on the cumulative quantity, a second state determination is performed on the individual battery cell to obtain a second determination result, including: If the cumulative quantity is greater than or equal to a preset quantity threshold, then the second determination result is determined to be passed; If the cumulative number is less than the number threshold, then the second determination result is determined to be unsuccessful.

6. The system according to claim 1, characterized in that, The voltage change trend is upward; the step of calculating the change in state of charge at the current moment using an algorithm adapted to the voltage change trend specifically includes: From the charging dataset, determine the initial voltage and initial state of charge corresponding to the starting time of the voltage change trend, as well as the current voltage at the current time; Determine the full charge voltage of the individual battery cell; Based on the initial voltage and the current voltage, a first voltage difference is determined by a non-zero constraint. The second voltage difference is determined based on the initial voltage and the full charge voltage using a non-zero constraint. The charge state difference is determined based on the initial charge state and the charge state threshold. The change in state of charge is determined based on the proportional relationship between the first voltage difference and the second voltage difference, as well as the state of charge difference.

7. The system according to claim 1, characterized in that, The voltage change trend is either decreasing or stable; the step of calculating the change in state of charge at the current moment using an algorithm adapted to the voltage change trend specifically includes: Determine the charging current and the preset deceleration coefficient model, wherein the deceleration coefficient model represents the mapping relationship between battery temperature, state of charge and deceleration coefficient, and the deceleration coefficient is used to correct the change in state of charge; The current battery temperature and the previous state of charge are input into the deceleration coefficient model to obtain the target deceleration coefficient. Based on the charging current, the candidate state of charge change is calculated using the ampere-hour integration method. The candidate state of charge change is corrected based on the target deceleration coefficient to obtain the state of charge change at the current moment.

8. The system according to any one of claims 1-7, characterized in that, The step of verifying the candidate state of charge using a preset state of charge threshold specifically includes: If the candidate state of charge is less than or equal to the state of charge threshold, the verification result is determined to be successful. If the candidate state of charge is greater than the state of charge threshold, the verification result is determined to be a verification failure, and the candidate state of charge is modified to the state of charge threshold.

9. The system according to claim 8, characterized in that, If the candidate state of charge is less than the state of charge threshold, the battery management system is further configured to perform the following steps: Perform the preset operation until the updated state of charge is greater than or equal to the state of charge threshold. The preset operation includes: obtaining the charging dataset for the next moment according to a preset step size, calculating the change in state of charge for the next moment according to the charging dataset for the next moment, and determining the updated state of charge for the next moment according to the change in state of charge for the next moment.

10. The system according to any one of claims 1-7, characterized in that, The battery management system is also configured to perform the following steps: For each state of charge calculated at each time step, a display command is generated. The display command is sent to the electric vehicle or electrical equipment to instruct the display device of the electric vehicle or electrical equipment to update the calculated state of charge.

11. A battery management method, characterized in that, include: Obtain the charging dataset of the individual battery, and determine whether the individual battery has entered the charging end state at the current time based on the charging dataset; If the charging end state is reached, the voltage change trend of the single battery cell at the current moment is determined according to the charging dataset, and the change in state of charge at the current moment is calculated by the algorithm adapted to the voltage change trend. Based on the change in state of charge and the state of charge at the previous moment, determine the candidate state of charge at the current moment; The candidate state of charge is verified by a preset state of charge threshold, and the candidate state of charge is determined as the target state of charge at the current moment after the verification is successful.

12. A single-cell battery, characterized in that, The individual battery cell is signal-connected to the battery management system according to any one of claims 1-10; The individual battery cell is used to send a charging dataset to the battery management system so that the battery management system can calculate the target state of charge of the individual battery cell in real time.

13. A battery pack, characterized in that, It includes at least two individual cells as described in claim 12, each of which is electrically connected to the other.

14. A battery pack, characterized in that, It includes a housing and at least two battery packs as described in claim 13, each of the battery packs being disposed within the housing and electrically connected to each other.

15. An electric vehicle, characterized in that, It includes at least the battery pack and display device as described in claim 14, wherein the display device is used to display the state of charge calculated by the battery management system.

16. An electrical appliance, characterized in that, It includes at least the single-cell battery as described in claim 12 and a display device, wherein the display device is used to display the state of charge calculated by the battery management system.

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