Battery active balancing method and vehicle

Abstract

This application provides a battery active balancing method and a vehicle. The method includes: acquiring travel demand data of a target battery, the travel demand data including at least travel prediction data and cell state data; calculating a target state of charge (SOC) based on the travel prediction data and cell state data in the travel demand data to obtain the target SOC of each cell in the target battery, the target SOC indicating the required SOC of the corresponding cell in the next trip indicated by the travel prediction data; and redistributing cell energy based on the target SOC of each cell in the target battery when the target battery meets preset balancing start conditions, thereby achieving active balancing of the target battery. This solves technical problems in the prior art that affect the overall usable energy output and actual driving range of the battery.

Description

Technical Field

[0001] This application relates to the field of battery technology, specifically to a battery active balancing method and a vehicle. Background Technology

[0002] With the continuous development of power battery technology, the number of cells in battery packs is constantly increasing, and the battery structure is becoming increasingly complex. During long-term use, due to factors such as manufacturing differences among cells, temperature gradient distribution, differences in self-discharge rate, and inconsistent aging, the battery will gradually exhibit a discrete state of charge (SOC) phenomenon, affecting the overall usable energy output and actual driving range of the battery.

[0003] Therefore, there is an urgent need to propose a better active battery balancing scheme. Summary of the Invention

[0004] In view of this, the embodiments of this application aim to provide a battery active balancing method and vehicle, which can solve technical problems in the prior art that affect the overall available energy output and actual driving range of the battery.

[0005] In a first aspect, this application provides a battery active balancing method, the method comprising: Acquire travel demand data for the target battery. The travel demand data includes at least travel prediction data and cell status data. The travel prediction data is the next travel data predicted based on the historical travel data corresponding to the target battery. The cell status data is used to indicate at least the current available capacity and current state of charge of each cell in the target battery. Based on the trip prediction data and cell status data in the trip demand data, the target state of charge is calculated to obtain the target state of charge of each cell in the target battery. The target state of charge is used to indicate the battery state of charge required by the corresponding cell in the next trip indicated by the trip prediction data. When the target battery meets the preset equalization start-up conditions, the cell energy is redistributed based on the target state of charge of each cell in the target battery to achieve active equalization of the target battery.

[0006] In some embodiments, the trip prediction data includes the next trip mileage and a corresponding confidence upper limit value, wherein the confidence upper limit value is used to reflect the upper limit that the predicted next trip mileage is allowed to reach. The step of calculating the target state of charge based on the trip prediction data and cell state data in the trip demand data to obtain the target state of charge of each cell in the target battery includes: Based on the upper confidence limit value corresponding to the next trip mileage, the energy demand is calculated to obtain the corresponding total energy demand for the trip. The total energy demand for the trip is used to reflect the total energy of battery discharge required for the next trip mileage. Based on the cell status data and the total energy requirement of the trip, the target state of charge of each cell is calculated in reverse to obtain the target state of charge of each cell in the target battery.

[0007] In some embodiments, the trip prediction data includes the next trip mileage and a corresponding confidence interval, wherein the confidence interval reflects the upper and lower limits of the predicted next trip mileage. The calculation of the target state of charge (SOC) based on the trip prediction data and cell state data in the trip demand data to obtain the target SOC of each cell in the target battery includes: Based on the next travel mileage and the corresponding confidence interval, a scene is generated to obtain a corresponding generated scene. The generated scene is used to limit the next travel mileage and its required state of charge under the corresponding scene. Based on the generated scenario, a corresponding objective function and constraints are determined. The objective function includes a function that minimizes the variance of the state of charge (SOC) of any two cells in the target battery. The SOC is the SOC of the corresponding cell at the end of the next trip mileage in the generated scenario, and the SOC is related to the target SOC of the corresponding cell. The constraints are at least used to indicate the constraints on the target SOC of any cell in the target battery. The target state of charge of each cell is solved based on the objective function and the constraints to obtain the target state of charge of each cell in the target battery.

[0008] In some embodiments, the trip prediction data includes the next trip mileage and a corresponding confidence interval, wherein the confidence interval reflects the upper and lower limits of the predicted next trip mileage. The calculation of the target state of charge (SOC) based on the trip prediction data and cell state data in the trip demand data to obtain the target SOC of each cell in the target battery includes: Determine reference data, which includes at least one reference state of charge, a reference travel distance corresponding to the reference state of charge, and a reference interval; Based on the reference data, the next mileage, and the confidence interval, multidimensional interpolation is performed to obtain the target state of charge of each cell in the target battery.

[0009] In some embodiments, the cell energy redistribution based on the target state of charge of each cell in the target battery includes: Based on the deviation between the current state of charge and the target state of charge of each cell in the target battery, the deviation value of the state of charge of each cell in the target battery is obtained. The deviation value of the state of charge is used to reflect the difference between the current state of charge and the target state of charge of the corresponding cell. Based on the state of charge deviation value of each cell in the target battery, cell pairing is performed to obtain at least one cell pair. The cell pair includes a first cell and a second cell. The first cell is a cell in the target battery whose state of charge deviation value is positive and whose absolute value of the state of charge deviation value exceeds a preset value. The second cell is a cell in the target battery whose state of charge deviation value is negative and whose absolute value of the state of charge deviation value exceeds a preset value. Energy redistribution between cells is performed based on the first and second cells in at least one of the cell pairs.

[0010] In some embodiments, the inter-cell energy redistribution based on the first and second cells in at least one of the cell pairs includes any of the following: When the absolute value of the state of charge deviation of the first cell is within a preset first range, the cell energy of the first cell is transferred to the second cell based on a preset first equalization current. When the absolute value of the state of charge deviation of the first cell is within a preset second range, the cell energy of the first cell is transferred to the second cell based on the second equalization current. The second equalization current is a current value obtained by proportional control based on the absolute value of the state of charge deviation of the first cell. When the absolute value of the state of charge deviation of the first cell is within a preset third range, the cell energy of the first cell is transferred to the second cell based on a preset third equalization current. Wherein, the lower limit of the preset first range is greater than or equal to the upper limit of the preset second range, the lower limit of the preset second range is greater than or equal to the upper limit of the preset third range, and the first equalizing current is greater than the second equalizing current is greater than the third equalizing current.

[0011] In some embodiments, the travel demand data includes travel forecast data, and the travel demand data for acquiring the target battery includes: Obtain the historical travel data corresponding to the target battery, wherein the historical travel data includes at least one of the following: driving record data, navigation data, charging behavior data, and schedule data of the vehicle where the target battery is located; Based on the historical travel data, feature extraction is performed to obtain the corresponding historical travel features. The historical travel features include at least one of the following: time period features, travel mode features, historical statistical features, and charging-related behavior features. The next trip is predicted based on the historical travel characteristics and the trip prediction model to obtain the trip prediction data. The trip prediction data includes the next trip mileage and the corresponding confidence interval. The confidence interval includes an upper confidence limit and a lower confidence limit. The trip prediction model is pre-trained based on the historical travel data of the target battery.

[0012] In some embodiments, the balanced startup conditions include: The target battery is in a charging state; The average value of the target state of charge of each cell in the target battery exceeds a preset first threshold. The target state of charge of each cell in the target battery is within a preset charge range. The confidence level in the trip prediction data exceeds a preset second threshold; The deviation between the current state of charge and the target state of charge of each cell in the target battery exceeds a preset third threshold; and, The active balancing circuit corresponding to the target battery has successfully completed its self-test.

[0013] In some embodiments, the method further includes: When the target state of charge of any cell in the target battery is not within the preset charge range, an alarm is issued for that cell; and / or, When the target battery meets the preset equalization termination condition, the energy redistribution of the cells is stopped or the active equalization of the target battery is stopped. The equalization termination condition includes that the absolute value of the state of charge deviation of each cell in the target battery is less than a preset fourth threshold, or the target battery ends charging.

[0014] Secondly, this application provides a battery active balancing device, the device comprising: The acquisition module is used to acquire travel demand data of the target battery. The travel demand data includes at least travel prediction data and cell status data. The travel prediction data is the next travel data predicted based on the historical travel data corresponding to the target battery. The cell status data is used to indicate at least the current available capacity and current state of charge of each cell in the target battery. The processing module is used to calculate the target state of charge based on the trip prediction data and cell status data in the trip demand data, and to obtain the target state of charge of each cell in the target battery. The target state of charge is used to indicate the battery state of charge required by the corresponding cell in the next trip indicated by the trip prediction data. The processing module is further configured to redistribute the cell energy based on the target state of charge of each cell in the target battery when the target battery meets the preset equalization start-up conditions, so as to achieve active equalization of the target battery.

[0015] For any content not introduced or described in the embodiments of this application, please refer to the relevant descriptions in the foregoing method embodiments; they will not be repeated here.

[0016] Thirdly, this application provides a vehicle, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the executable instructions to implement the steps of the above-described active battery balancing method.

[0017] Fourthly, this application provides a computer-readable storage medium storing computer program instructions thereon, which, when executed by a processor, implement the steps of the above-described active battery balancing method.

[0018] The technical solution provided in this application embodiment may include the following beneficial effects: This application obtains travel demand data of the target battery, the travel demand data including at least travel prediction data and cell status data, the travel prediction data being the next travel data predicted based on the historical travel data corresponding to the target battery, and the cell status data being used to indicate at least the current available capacity and current state of charge of each cell in the target battery; based on the travel prediction data and cell status data in the travel demand data, a target state of charge calculation is performed to obtain the target state of charge of each cell in the target battery, the target state of charge being used to indicate the battery state of charge required by the corresponding cell in the next travel indicated by the travel prediction data; when the target battery meets the preset equalization start-up conditions, cell energy redistribution is performed based on the target state of charge of each cell in the target battery to achieve active equalization of the target battery. In this way, active balancing no longer simply adjusts the state of charge of each cell to a uniform level at the end of charging, but is associated with the predicted discharge demand of the next trip. By adopting a target state of charge and cell energy redistribution method that is adapted to the trip demand, it avoids cells with smaller capacity or faster aging from hitting the state of charge limit protection first, thus limiting the release of the overall available energy of the battery. This improves the output of the overall available energy of the battery and the actual driving range. At the same time, it reduces the problem of insufficient or excessive active balancing caused by the decoupling of the balancing target and the trip demand, thereby improving the practicality and reliability of active battery balancing.

[0019] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0020] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings.

[0021] Figure 1 This is a schematic diagram of the structure of a battery active balancing system provided in an embodiment of this application.

[0022] Figure 2 This is a schematic flowchart of a battery active balancing method provided in an embodiment of this application.

[0023] Figure 3 This is a schematic flowchart illustrating a target state of charge calculation provided in an embodiment of this application.

[0024] Figure 4 This is a schematic diagram illustrating another process for calculating the target state of charge, provided in an embodiment of this application.

[0025] Figure 5 This is a schematic diagram illustrating another process for calculating the target state of charge, provided in an embodiment of this application.

[0026] Figure 6 This is a schematic diagram of a cell energy redistribution process provided in an embodiment of this application.

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

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

[0029] Figure 9 This is a structural schematic diagram of a vehicle provided in an embodiment of this application. Detailed Implementation

[0030] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0031] Unless otherwise defined, the technical or scientific terms used in the embodiments of this specification shall have the ordinary meaning understood by one of ordinary skill in the art to which this specification pertains. The terms "first," "second," and similar terms used in the embodiments of this specification do not indicate any order, quantity, or importance, but are merely used to avoid confusion of constituent elements.

[0032] Unless the context otherwise requires, throughout this specification, "a plurality of" means "at least two," and "including" is interpreted as open-ended or encompassing, that is, "including, but not limited to." In the description of this specification, terms such as "one embodiment," "some embodiments," "exemplary embodiment," "example," "specific example," or "some examples" are intended to indicate that a particular feature, structure, material, or characteristic associated with that embodiment or example is included in at least one embodiment or example of this specification. The illustrative representations of the above terms do not necessarily refer to the same embodiment or example.

[0033] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0034] In the process of filing this application, the applicant also discovered that active balancing technology is widely used for energy transfer and SOC regulation between cells to maintain overall battery performance. In related technologies, current mainstream active balancing schemes generally transfer energy from cells with higher state of charge (SOC) to cells with lower SOC during the final stages of charging or when the vehicle is stationary, thus bringing all cells towards the same SOC level, for example, making all cells reach the same or nearly the same SOC value. However, this scheme aims for consistent SOC across all cells, decoupling it from the actual discharge requirements of the vehicle's subsequent journey. In cases of long journeys or significant differences in cell capacity, cells with smaller capacity or faster aging will reach their SOC limit earlier during discharge, triggering the battery management system's power limiting protection. Meanwhile, other cells with larger capacity still retain some usable energy, limiting the overall energy release of the battery and affecting its energy efficiency.

[0035] Specifically, existing active balancing schemes typically aim for a consistent state of charge (SOC) across all cells at the end of charging, implicitly assuming relatively uniform or uncertain subsequent discharge demand. However, in real-world user scenarios, energy demand for the next trip is often predictable. For example, users may have relatively regular travel patterns: weekday commutes are relatively fixed (e.g., 30km one way), while weekends or holidays may involve longer trips. Based on historical user travel data and time patterns, a probabilistic prediction model for the next trip's mileage and energy demand can be established. The objectives of active balancing should differ depending on the trip length. If the next trip is short (e.g., a 10km commute), balancing all cells to a higher SOC is reasonable because the discharge depth of each cell is shallow during the trip, preventing individual cells from reaching the bottom first. However, if the next trip is long (e.g., 150km), the SOC of each cell will decrease at the same discharge rate due to varying degrees of capacity decay. If all cells have the same state of charge when charging ends, the smaller cells at the end of the trip will reach the state of charge limit first, triggering the power limiting protection of the battery management system, which will limit the power of the whole vehicle, while the larger cells will still have residual energy that has not been utilized.

[0036] The existing active balancing schemes mentioned above are completely decoupled from range requirements. Their balancing objective is fixed at consistent state of charge (SOC), without considering the SOC distribution of each cell after discharge during the expected range. This results in the overall usable energy of the battery not being fully released in long-range scenarios, leading to a lower actual vehicle range than the theoretically calculated value, meaning the battery's energy efficiency cannot be fully utilized. To address these issues, this application proposes an active battery balancing method, device, vehicle, and dielectric.

[0037] Please see Figure 1 This is a schematic diagram of the structure of a battery active balancing system provided in an embodiment of this application. Figure 1 The system 10 shown can be applied to, for example, vehicles or other devices equipped with batteries. The system 10 may include a travel behavior analysis module 101, a trip demand prediction module 102, a cell status assessment module 103, a target state of charge calculation module 104, and an active balancing execution module 105.

[0038] The aforementioned travel behavior analysis module 101 is primarily responsible for collecting users' historical travel data and establishing a trip prediction model. Specifically, this model can be a probabilistic prediction model combining travel time and trip demand. For example, it can collect data such as vehicle driving records, navigation data, charging behavior data, schedule data, or other travel data related to battery usage at fixed or non-fixed sampling frequencies; this application does not impose further limitations on this. The aforementioned travel behavior analysis module 101 can be deployed on a cloud server or in a vehicle telematics box (T-Box) processor. The specific implementation details of this module 101 will be described in detail below and will not be elaborated upon here.

[0039] The aforementioned trip demand prediction module 102 is primarily responsible for predicting the next trip based on the analysis results of the aforementioned travel behavior analysis module 101 (such as travel characteristics and trip prediction models in historical travel data), obtaining corresponding trip prediction data. This trip prediction data may include, but is not limited to, the next trip mileage, confidence interval, confidence level, upper confidence limit, lower confidence limit, or other custom data. Specific implementation details related to module 102 will be described in detail below in this application, and will not be elaborated upon here.

[0040] The aforementioned cell status assessment module 103 can be deployed in the battery management system. It is mainly responsible for maintaining the current available capacity, internal resistance, state of charge, or other cell parameters related to cell status of each cell in the battery in real time. This application does not impose any further limitations on this.

[0041] The target state of charge (SPC) calculation module 104 is primarily responsible for calculating the SPC of each battery cell based on data obtained from the trip demand prediction module 102 and / or the cell state assessment module 103. For example, it can reverse-engineer the optimal SPC of each cell at the end of charging based on predicted next trip mileage and the current available capacity of each cell. Specific implementation details regarding module 104 or the target SPC calculation will be described in detail below and will not be elaborated upon here.

[0042] The aforementioned active balancing execution module 105 is primarily responsible for driving the active balancing circuit to perform energy transfer or redistribution between cells based on the target state of charge of each cell in the battery, thereby achieving the purpose of active battery balancing. The specific implementation details of this module 105 will be described in detail below, and will not be elaborated upon here.

[0043] Please see Figure 2 This is a schematic flowchart of a battery active balancing method provided in an embodiment of this application. Figure 2 The method shown can be applied to Figure 1 In the system shown, the method may include the following implementation steps: S201. Obtain travel demand data for the target battery. The travel demand data includes at least travel prediction data and cell status data. The travel prediction data is the next travel data predicted based on the historical travel data corresponding to the target battery. The cell status data is used to indicate at least the current available capacity and current state of charge of each cell in the target battery.

[0044] The target battery mentioned above in this application can be broadly defined as a battery system capable of providing driving power. It may include a battery pack composed of several cells, such as a battery pack composed of N cells connected in series, where N is a positive integer custom-set according to the actual needs of the system; this application does not impose any further limitations on this. The aforementioned travel demand data can be broadly defined as data related to influencing battery active balancing, and may include travel prediction data, cell status data, or other custom data. The aforementioned travel prediction data can be broadly defined as data related to the next travel demand obtained through prediction, and may include, but is not limited to, next travel mileage, confidence interval, upper confidence limit, lower confidence limit, confidence level, or other custom data. The aforementioned confidence interval may include both an upper confidence limit and a lower confidence limit. The aforementioned upper confidence limit reflects the upper limit of the predicted next travel mileage that is allowed to be reached, and the aforementioned lower confidence limit reflects the lower limit of the predicted next travel mileage that is allowed to be reached. The aforementioned confidence level reflects the degree of confidence in the predicted next travel mileage, such as 80%, etc.; this application does not impose any further limitations on this. The aforementioned historical travel data can broadly refer to relevant travel / usage data of the target battery, which may include, but is not limited to, any combination of one or more of the following: driving record data, navigation data, charging behavior data, schedule data, or other relevant data that affects active battery balancing. The aforementioned current available capacity can broadly refer to the effective capacity that the battery cell can release under current aging, temperature, and usage history conditions. This capacity can be pre-calibrated using the cell's historical charge and discharge data, or it can be calculated and updated online using traditional cell capacity estimation algorithms. This application does not impose any limitations on this. The aforementioned state of charge can refer to the ratio between the remaining charge and available capacity of the corresponding cell, usually expressed as a percentage, such as 10%, 50%, or 95%. This application does not impose any limitations on this.

[0045] S202. Calculate the target state of charge based on the trip prediction data and cell status data in the trip demand data to obtain the target state of charge of each cell in the target battery. The target state of charge is used to indicate the battery state of charge required by the corresponding cell in the next trip indicated by the trip prediction data.

[0046] This application can calculate the target state of charge (SOC) of each cell at the end of charging based on predicted data for the next trip and cell SOC data, ensuring that each cell approaches the same lower limit at the end of the expected trip. This method shifts the goal of active balancing from uniform adjustment at the end of charging to differentiated adjustment based on the SOC at the end of the expected trip, thereby reducing the likelihood of smaller cells reaching their lower limit prematurely and affecting the overall energy release of the battery. "Approaching the same lower limit" can mean that the SOC of multiple cells converges to a preset lower limit at the end of the trip, for example, to the range of 10% to 15%, or that the deviation of each cell from the preset lower limit is small, for example, not exceeding 0.5% or 1%. This application does not impose further limitations on this.

[0047] The target state of charge mentioned above in this application can refer to the target value of the state of charge pre-configured for the corresponding cell at the end of charging. The target value here usually refers to the state of charge value required by the corresponding cell during the expected discharge process. The state of charge value can be a single value or a target range that is allowed to fluctuate. This application does not impose any restrictions on this.

[0048] S203. When the target battery meets the preset equalization start-up conditions, the cell energy is redistributed based on the target state of charge of each cell in the target battery to achieve active equalization of the target battery.

[0049] When the system meets the equalization start-up conditions, this application can perform energy transfer and redistribution between cells based on the target state of charge of each cell in the target battery, thereby achieving active equalization of the target battery. The aforementioned equalization start-up conditions may refer to the judgment conditions that are pre-defined by the system to determine whether active battery equalization can be performed. The specific content of these conditions will be detailed below in this application and will not be elaborated on here.

[0050] By implementing the embodiments of this application, this application obtains travel demand data of a target battery. The travel demand data includes at least travel prediction data and cell status data. The travel prediction data is the next travel data predicted based on the historical travel data corresponding to the target battery. The cell status data is used to indicate at least the current available capacity and current state of charge of each cell in the target battery. Based on the travel prediction data and cell status data in the travel demand data, a target state of charge calculation is performed to obtain the target state of charge of each cell in the target battery. The target state of charge is used to indicate the battery state of charge required by the corresponding cell in the next travel indicated by the travel prediction data. When the target battery meets the preset equalization start-up conditions, cell energy redistribution is performed based on the target state of charge of each cell in the target battery to achieve active equalization of the target battery. In this way, active balancing no longer simply adjusts the state of charge of each cell to a uniform level at the end of charging, but is associated with the predicted discharge demand of the next trip. By adopting a target state of charge and cell energy redistribution method that is adapted to the trip demand, it avoids cells with smaller capacity or faster aging from hitting the state of charge limit protection first, thus limiting the release of the overall available energy of the battery. This improves the output of the overall available energy of the battery and the actual driving range. At the same time, it reduces the problem of insufficient or excessive active balancing caused by the decoupling of the balancing target and the trip demand, thereby improving the practicality and reliability of active battery balancing.

[0051] The following describes specific and optional embodiments related to this application.

[0052] In step S201, this application does not limit the specific implementation method for obtaining the above-mentioned travel demand data. For example, this application can obtain the above-mentioned travel demand data from the vehicle's local terminal or cloud server through the network.

[0053] For example, taking the trip prediction data in the aforementioned trip demand data as an example, when the vehicle plugs in to charge or the battery management system detects a charging start event, the trip prediction data acquisition process can be triggered. Specifically, this application can first acquire the historical travel data corresponding to the target battery, or it can be the user's historical travel data. This historical travel data can include any combination of one or more of the following: driving record data, navigation data, charging behavior data, schedule data, or other custom data used to influence battery active balancing of the vehicle where the target battery is located. The aforementioned driving record data can refer to the relevant record data of each trip of the vehicle where the target battery is located, such as the departure time, arrival time, mileage, average energy consumption (kWh / 100km), or other trip-related data of each trip. This driving record data can be automatically recorded and uploaded by the vehicle communication terminal, and this application does not impose any restrictions on this. The aforementioned navigation data can refer to navigation destination data, such as the navigation destination information set by the user, estimated route mileage, and other information data. The aforementioned charging behavior data can refer to the relevant data of each charge of the target battery, such as the start time, charging duration, charging amount, or other data used to infer the user's charging and travel rhythm. The aforementioned schedule data may refer to the user's schedule obtained through internet devices such as mobile phones (e.g., meeting schedules with locations marked), or other calendar data used to assist in trip prediction, etc., and this application does not impose any further limitations on it.

[0054] Subsequently, this application can extract features based on the aforementioned historical travel data to obtain corresponding historical travel features. These historical travel features may include combinations of one or more of the following features: time period features, travel pattern features, historical statistical features, charging-related behavior features, and other custom dimension features. Specifically, the aforementioned time period features can encode departure time into a combination of day of the week and time period, such as weekday morning rush hour or weekend morning travel categories, and can use one-hot encoding or cyclic encoding to express time periodicity, etc., without further limitation in this application. The aforementioned travel pattern features may include, for example, the average daily mileage over the past 7 days, the maximum single mileage over the past 7 days, and the number of trips over the past 7 days, or other features reflecting the user's recent travel activity and intensity, etc., without further limitation in this application. The aforementioned historical statistical features may include feature data such as the average mileage, mileage standard deviation, and maximum mileage value over the past 12 weeks for the same day of the week and time period, without further limitation in this application. The aforementioned charging-related behavior features may include feature data such as the state of charge at the start of the current charging, the interval since the last trip, and the mileage of the last trip, without further limitation in this application.

[0055] Furthermore, this application can input the aforementioned historical travel features and a pre-trained trip prediction model. Specifically, the historical travel features can be input into the pre-trained trip prediction model to output trip prediction data including the next trip mileage and the corresponding confidence interval. This limitation aims to establish a mapping relationship between user travel patterns and next trip needs, ensuring reliable input for the target calculation. This application does not limit the aforementioned trip prediction model; for example, a gradient boosting decision tree model can be used, such as XGBoost or LightGBM, with an input dimension of approximately 15 to 20 dimensions. The aforementioned historical travel features are input into the model for prediction, and the output includes, for example, the next trip mileage Rpred and its confidence interval [R]. low R high The above-mentioned confidence interval corresponds to an 80% confidence level, meaning that there is an 80% probability that the actual mileage of the next trip will fall within the above confidence interval. The trip prediction model is typically pre-trained, for example, using travel records from the past 90 days as a training set, with the goal of minimizing the mean squared error of the trip mileage, and combining quantile regression models to obtain the upper and lower bounds of the confidence interval. Optionally, this application can optimize and update the model parameters of the above-mentioned trip prediction model on a weekly incremental basis; this application does not impose many limitations on this. This application also does not limit the specific deployment method of the above-mentioned trip prediction model; for example, it can be directly deployed to the cloud, where the cloud can directly distribute the corresponding trip prediction data after model inference; or it can be deployed locally in the vehicle, such as in the in-vehicle T-Box, where the above-mentioned trip prediction data can be directly calculated through local inference, etc., this application does not impose many limitations on this. When the above-mentioned model is deployed locally, its model size is usually less than 500KB; this application does not impose many limitations on this. By adopting the above scheme, the trip prediction data can simultaneously reflect user travel patterns and the range of uncertainty, thereby improving the accuracy of trip demand prediction and providing a more reliable data foundation for subsequent target state of charge calculation. Optionally, the above trip prediction model can also be replaced by models that can output a prediction distribution, such as Gaussian process regression, or other models that can obtain the next trip mileage and its confidence interval information. This application does not impose any further limitations on this.

[0056] For example, taking the cell status data in the aforementioned travel demand data as an example, this application can obtain the current available capacity, current state of charge, internal resistance, or other custom state parameters of each cell in the target battery through the battery management system or cell status evaluation module. This application will not elaborate further on this. For ease of description, taking the i-th cell as an example, the current available capacity of the i-th cell can be expressed as C. i The current state of charge (SOC) of the i-th cell can be represented as SOC. i, where i is a positive integer.

[0057] In step S202, this application provides specific implementation methods for the above-mentioned target state of charge calculation and limits them. Two possible implementation methods are described below by example.

[0058] In one implementation, it is obtained through reverse calculation based on deterministic trip mileage prediction. See also... Figure 3 This is a schematic diagram illustrating a possible target state of charge calculation method provided in an embodiment of this application. For example... Figure 3 The process shown may include the following implementation steps: S301. Calculate the energy demand based on the upper confidence limit value corresponding to the next trip mileage to obtain the corresponding total trip energy demand. The total trip energy demand is used to reflect the total energy of battery discharge required for the next trip mileage.

[0059] This application does not limit the specific calculation method of the above energy demand calculation. For example, this application can calculate the total energy demand of the trip based on the predicted next trip mileage and the historical average energy consumption in historical travel data. The specific calculation can be shown in the following formula (1): Q trip =R pred ×E avg / 100 / V pack_nominal Formula (1) Among them, Q trip R represents the total energy requirement for the trip. pred This indicates the mileage for the next leg of the journey. E avg This represents the historical average energy consumption. V pack_nominal This indicates the nominal voltage of the battery pack, which can be pre-calibrated or set by the system.

[0060] Preferably, considering the confidence interval, this application can select the most conservative maximum energy demand for the trip as the final total energy demand for the trip. Specifically, for example, this application can calculate the maximum energy demand for the trip based on the upper confidence limit corresponding to the next trip mileage and the historical average energy consumption in historical travel data, so as to use it as the final total energy demand for the trip. The specific calculation can be performed using the following formula (2): Q trip_max =R high ×E avg / 100 / V pack_nominal Formula (2) Among them, R high This represents the upper confidence limit for the next trip mileage mentioned above.

[0061] Understandably, the above Q trip_maxThe aim is to cover the discharge demand corresponding to the upper boundary of the predicted confidence interval, reducing the possibility of insufficient target state of charge configuration due to a single low predicted value. The principle is that the series-connected cells carry the same current during the journey, so the discharge demand corresponding to each cell is approximately the same. By first determining the conservative discharge amount and then performing reverse calculations in combination with the available capacity of each cell, the target allocation can be made closer to long-journey operating conditions.

[0062] S302. Based on the cell state data and the total energy requirement of the trip, the target state of charge of each cell is calculated in reverse to obtain the target state of charge of each cell in the target battery.

[0063] This application does not limit the specific implementation method of the reverse calculation of the target state of charge. For example, in order to make full use of the capacity of each cell, this application gives priority to ensuring that the state of charge of each cell reaches the preset state of charge limit value (SOC) at the end of the expected journey. min_allow For example, 10%-15%. Taking the i-th cell as an example, to ensure safe driving during the expected trip (i.e., the predicted next trip mileage), the state of charge (SOC) of the i-th cell at the end of the trip can be the lower limit of SOC. min_allow Therefore, the target state of charge of the i-th cell at the start of the trip (i.e., the end of charging) can be specifically represented by the following formula (3): SOC target_i =SOC min_allow +Q trip_max / C i Formula (3) Among them, SOC target_i This represents the target state of charge of the i-th cell mentioned above. C i This represents the current available capacity of the i-th cell mentioned above.

[0064] In practical applications, the above-mentioned SOC target_i There are corresponding constraints, such as the SOC mentioned above. target_i It must be less than or equal to the preset upper limit of the state of charge (SOC). max For example, 90%-100%. Optionally, if the target state of charge (e.g., SOC) of any cell... target_i () Exceeding or exceeding the above-mentioned upper limit value of state of charge (SOC) max This indicates that the capacity of any given battery cell is insufficient to support the next journey up to the State of Charge (SOC) limit. min_allowAt this point, corresponding alarm prompts can be automatically issued to remind the user to reduce or decrease the expected mileage of the next trip. This application does not impose further limitations on this. It is understood that by adopting the above limitations, the target state of charge configuration can cover a high-confidence upper limit of the predicted mileage, thereby reducing the risk of insufficient energy at the end of a long trip. This further helps to improve the conservatism of target allocation and the adaptability to trip fluctuations.

[0065] This application addresses the aforementioned upper limit of the state of charge (SOC). max and the above-mentioned State of Charge (SOC) limit min_allow There are no restrictions; it can be an empirical value set based on user experience, or it can be statistical data obtained from a series of experiments, such as the aforementioned upper limit of state of charge (SOC). max It can be 400%, the above-mentioned state of charge limit (SOC) min_allow It can be 10%, etc., and this application does not impose any further restrictions on this.

[0066] In another implementation, the solution is obtained through stochastic optimization based on multiple scenarios. Please refer to [link / reference]. Figure 4 This is a schematic diagram illustrating another possible calculation process for the target state of charge provided in an embodiment of this application. For example... Figure 4 The process shown may include the following implementation steps: S401. Based on the next travel mileage and the corresponding confidence interval, a scenario is generated to obtain a corresponding generated scenario. The generated scenario is used to limit the next travel mileage and its required state of charge under the corresponding scenario.

[0067] This application can select multiple representative points within the confidence interval corresponding to the next trip mileage to form generated scenarios, such as short trip scenarios, medium trip scenarios, and long trip scenarios. This application does not limit the number of scenarios; for example, it can have 50, 100, or more. In specific implementations, for example, this application can treat the next trip mileage as a random variable, use a trip prediction model to output a fitted distribution, for example, truncate the overall distribution, and arbitrarily select the next trip mileage R based on the aforementioned confidence interval. pred Standard deviation σ≈(R high -R low ) / 1.28, corresponding to an 80% confidence level, uses the Monte Carlo method to extract S scenes (e.g., S=500) to obtain the above generated scenes, which can be specifically represented as, for example, a scene set {R1, ..., R...} s For a more detailed introduction to the Monte Carlo method, please refer to the introduction of the traditional Monte Carlo algorithm; this application will not provide further limitations or details.

[0068] S402. Based on the generated scenario, determine the corresponding objective function and constraints. The objective function includes a function that minimizes the variance of the state of charge (SOC) of any two cells in the target battery. The SOC is the SOC of the corresponding cell at the end of the next trip in the generated scenario, and the SOC is related to the target SOC of the corresponding cell. The constraints are at least used to indicate the constraints on the target SOC of any cell in the target battery.

[0069] This application introduces the uncertainty of the next journey mileage as a random variable, and uses minimizing the variance between the target states of charge of each cell as the objective function to establish a stochastic programming problem. Specifically, this application can design an objective function and constraint functions to establish a stochastic programming problem. The above objective function is an optimization objective that the system pre-defined according to the actual situation. For example, it can be to minimize the variance between any two cell states of charge at the end of the journey in all scenarios, or it can be equivalent to a function that minimizes the dispersion of the end state of all cells in each scenario. For example, the above objective function can be expressed as shown in the following formula (4): Objective function: E[(SOC) i (end)-SOC j (end))²]; SOC i (end)=SOC target_i -Q(R s ) / C i ) Formula (4) Among them, SOC i (end) indicates that the i-th cell is in the generation scenario R s The state of charge of the battery cell at the end of the next journey. Q(R) s ) indicates that in the generated scene R s The total energy requirement for the journey can be found in the aforementioned introduction on the calculation of total energy requirement for the journey, which will not be repeated here.

[0070] This application does not limit the above constraints, which can be pre-defined and set by the system according to actual conditions. For example, they may specifically include: SOC min_allow ≤SOC target_i ≤SOC max Optionally, risk constraints such as P(SOC) can also be added. i (end)≥0)≥1-ε, where P represents the risk function pre-defined by the system for risk calculation, which is not further limited or detailed in this application. ε is a parameter value pre-defined by the system according to the actual situation, which can be pre-calibrated by the system, and is also not further limited or detailed in this application.

[0071] S403. Solve for the target state of charge of each cell based on the objective function and the constraints to obtain the target state of charge of each cell in the target battery.

[0072] This application can optimize the solution based on the objective function and constraints corresponding to the generated scenarios. Specifically, this application can use the scenario method to approximate the expected value of the objective function as the sample mean of S generated scenarios, thereby transforming stochastic programming into a deterministic convex optimization problem (such as quadratic programming / second-order cone programming, etc.), and then solve for the current optimal SOC. target_i This application will not impose further limitations or elaborate on these matters.

[0073] In some embodiments, the above-mentioned stochastic optimization solution can be completed offline, and a corresponding look-up table (LUT) can be generated for vehicle-side access, thereby reducing real-time computational overhead. Optionally, in addition to stochastic optimization, robust optimization, sub-brush robust optimization, or scenario-weighted programming, etc., can also be used, and this application will not limit or elaborate on these methods. By adopting the above scheme, the impact of single-point prediction deviation on target state of charge allocation can be reduced, making the cell end states under multiple possible travel scenarios more coordinated, thereby improving the robustness of the allocation results. This scheme can also be combined with the multi-dimensional interpolation scheme described later to further transform the results obtained from offline solution into a data table that can be quickly accessed by the vehicle-side.

[0074] In another implementation, the result is obtained through a lookup and interpolation method based on pre-calculated results. See also... Figure 5 This is a schematic diagram illustrating another possible calculation process for the target state of charge provided in an embodiment of this application. For example... Figure 5 The process shown may include the following implementation steps: S501. Determine reference data, the reference data including at least one reference state of charge, the reference travel mileage corresponding to the reference state of charge, and the reference interval.

[0075] This application does not limit the implementation method for determining the above-mentioned reference data. For example, this application can retrieve several reference data or reference nodes from the pre-stored lookup table (LUT). The lookup table can be pre-established offline with target state of charge mapping relationships under different combinations of reference states of charge, different travel distances, and different confidence intervals. That is, the lookup table includes different reference states of charge, reference travel distances corresponding to any one reference state of charge, and reference confidence intervals (which can be simply referred to as reference intervals). The method of obtaining the lookup table can be referred to the relevant description in the previous embodiment, and will not be repeated here.

[0076] S502. Based on the reference data, the next mileage, and the confidence interval, perform multidimensional interpolation to obtain the target state of charge of each cell in the target battery.

[0077] This application can perform multidimensional interpolation based on the aforementioned reference data, the aforementioned next travel mileage, and the aforementioned confidence interval to obtain the target state of charge (SOC) of each cell in the aforementioned target battery. Specifically, during online operation, this application can perform interpolation calculations, such as two-dimensional, three-dimensional, or higher-dimensional calculations, based on the currently predicted next travel mileage and the aforementioned confidence interval, and in conjunction with each reference SOC in the aforementioned reference data, the reference travel mileage corresponding to the reference SOC, and the reference interval, to obtain the target SOC of each cell in the aforementioned target battery. Optionally, this application does not limit the specific implementation method of the aforementioned interpolation; for example, it can include linear interpolation, spline interpolation, piecewise polynomial interpolation, or lookup table fitting, etc. Using this method, the calculation speed of the target SOC can be improved without significantly sacrificing target accuracy.

[0078] This application, through the above-described solution, can reduce the online computational load for calculating the target state of charge and improve the onboard real-time processing capability. Furthermore, the multi-dimensional interpolation method in this embodiment is combined with the aforementioned offline solution method based on multi-scenario optimization. That is, multiple sets of robust targets are first obtained offline, and then the target state of charge of each cell under the current operating condition is obtained online through interpolation, thereby simultaneously achieving good scenario adaptability and computational efficiency.

[0079] In step S203, this application does not limit the above-mentioned equalization start conditions. These conditions can be pre-set by the system based on actual conditions to determine whether to initiate active battery equalization. For example, they may include: the target battery is in a charging state; the average value of the target state of charge (SOC) of each cell in the target battery exceeds a preset first threshold; the target SOC of each cell is within a preset charge range; the confidence level in the aforementioned travel prediction data exceeds a preset second threshold; the deviation between the current SOC and the target SOC of each cell exceeds a preset third threshold; and the active equalization circuit corresponding to the target battery successfully completes its self-test. The above limitations aim to constrain the equalization start timing from multiple perspectives, including system state, prediction reliability, target feasibility, equalization necessity, and hardware availability.

[0080] Specifically, the charging status of the vehicle containing the target battery can be confirmed by combining the charging connection signal and the charging current detection result; this application will not elaborate further on this. The average target state of charge (SOC) exceeds a preset first threshold, preferably corresponding to the final stage of charging when the average SOC of the battery pack is greater than 85%. The target SOC of each cell is within a preset range, which can be understood as not lower than a set lower limit and not higher than the SOC. maxFor example, the upper bound of 95% to 100%. The confidence level in the above-mentioned trip prediction data exceeding a preset second threshold can be used to ensure the reliability of the prediction results. This application does not limit the above-mentioned preset first threshold and preset second threshold; for example, the above-mentioned preset first threshold can be 85%, and the above-mentioned preset second threshold can be 0.6, etc. The deviation value of each cell exceeding a preset third threshold can be used to determine whether there is an actual balancing requirement. The above-mentioned preset third threshold can be set to, for example, 1%. Successful self-testing of the active balancing circuit can include normal self-testing of switching devices, drive circuits, inductors or capacitors, sampling links, and temperature monitoring channels. Specifically, the above-mentioned active balancing circuit self-testing can include, for example, power transistor / drive circuit detection, such as normal MOS transistor gate drive signal, no open circuit, short circuit faults, etc.; sampling channel detection, such as detecting no faults in current and voltage sampling channels, and sampling values ​​within a reasonable range, etc.; energy storage element detection, such as detecting no open circuits or short circuits in inductors or capacitors, no abnormal leakage, etc.; communication interlock detection, such as normal status reporting channels between the balancing controller and the battery management system, no activation fault codes, etc. Once all the above self-test items pass, the system can report a "self-test normal" status flag to the battery management system via the communication bus. The battery management system uses this flag to determine whether the active balancing circuit has successfully completed the self-test. If any item is abnormal, the status flag can be set to "fault," indicating that the balancing start conditions are not met. Optionally, the system can also report and record fault codes, etc. This application does not impose further limitations or details on this.

[0081] By employing the above combination of conditions, false starts and invalid balances can be reduced, improving the security and targeted nature of proactive balance execution. The aforementioned start-up criteria also provide a unified basis for subsequent balance termination and anomaly alerts.

[0082] When the system meets the above-mentioned equalization start-up conditions, this application can perform cell energy redistribution based on the target state of charge of each cell in the target battery to achieve active equalization of the target battery. This application does not limit the specific implementation method of the above-mentioned cell energy redistribution; for example, please refer to Figure 6, which is a schematic flowchart of a cell energy redistribution provided by an embodiment of this application. Figure 6 The process shown may include the following implementation steps: S601. Based on the deviation between the current state of charge and the target state of charge of each cell in the target battery, the deviation value of the state of charge of each cell in the target battery is calculated to obtain the deviation value of the state of charge of each cell in the target battery. The deviation value of the state of charge is used to reflect the difference between the current state of charge and the target state of charge of the corresponding cell.

[0083] This application does not limit the specific implementation method of the above deviation calculation. Taking the i-th cell as an example, this application can use the current state of charge of the i-th cell and the target state of charge of the i-th cell to calculate the difference, thereby obtaining the state of charge deviation value of the i-th cell. Specifically, it can be expressed as shown in the following formula (5): ΔSOC i =SOC i_current -SOC target_i Formula (5) Wherein, ΔSOC i This represents the state-of-charge (SOC) deviation value of the i-th cell mentioned above. i_current This indicates the current state of charge of the i-th cell mentioned above.

[0084] S602. Based on the state-of-charge deviation value of each cell in the target battery, perform cell pairing to obtain at least one cell pair. The cell pair includes a first cell and a second cell. The first cell is a cell in the target battery whose state-of-charge deviation value is positive and whose absolute value exceeds a preset value. The second cell is a cell in the target battery whose state-of-charge deviation value is negative and whose absolute value exceeds a preset value.

[0085] This application does not limit the specific implementation method of the above-mentioned cell pairing. For example, this application can first calculate the absolute value of the state of charge deviation of each cell |ΔSOC. i The cells can be sorted, for example, from largest to smallest. The first cell can be selected from the positive deviation cells with the largest deviation, and the second cell from the negative deviation cells with the largest deviation, forming at least one cell pair. Multiple cell pairs can be processed sequentially or in parallel; this application does not impose further limitations on this. That is, this application can pair the two cells with the largest absolute difference in their state-of-charge (SOC) deviation values, thus obtaining at least one cell pair. For example, taking four cells with SOC deviation values ​​of ΔSOC1 = +3, ΔSOC2 = -1, ΔSOC3 = +1, and ΔSOC4 = -2, pairing them according to the largest absolute difference in their deviation values ​​is possible. Since +3 and -2 are the furthest apart, followed by +1 and -1, cell 1 and cell 4 can be paired, and cell 3 and cell 2 can be paired. This application does not impose further limitations or details on this.

[0086] Understandably, when ΔSOC i Positive value (ΔSOC) i When ΔSOC > 0), the corresponding cell can be identified as the first cell and used as the energy release terminal; when ΔSOC i Negative value (ΔSOC) iWhen the charge level is less than 0, the corresponding cell can be identified as the second cell, serving as the energy receiver. This limitation aims to clearly distinguish between the energy source and the energy target, establishing an executable migration relationship. The principle is that the direction of deviation of the cell's current state from the target state directly indicates whether the cell should decrease or increase its state of charge.

[0087] By employing the specific constraints described above, the energy redistribution of battery cells can be made more directional, prioritizing cell pairs with greater balancing needs. This further helps improve the efficiency and operability of balancing operations. Furthermore, the aforementioned cell pairing scheme can be combined with the aforementioned target state of charge calculation scheme, ensuring a clear mapping of the calculated target state during the execution phase.

[0088] This application does not limit the aforementioned preset values; for example, they can be 0.5%, 1%, or higher, to avoid invalid migration for minor deviations. This application also does not limit the aforementioned active balancing circuit; for example, it can employ an inductor-based energy storage topology, a capacitor-switching topology, a transformer-based multi-winding balancing topology, or other topologies that support bidirectional energy transfer between adjacent and non-adjacent cells. This application does not impose further limitations or provide detailed descriptions in these regard.

[0089] S603. Perform energy redistribution between cells based on the first and second cells in at least one of the cell pairs.

[0090] This application does not limit the specific implementation of the above-mentioned energy redistribution between battery cells. Several possible implementation methods are described below as examples.

[0091] In one embodiment, when the absolute value of the state of charge deviation of the first cell is |ΔSOC i |Within the preset first range, for example, |ΔSOC i When the energy level is greater than 3%, this application can transfer the energy from the first battery cell to the second battery cell based on a preset first equalization current. This application does not limit the preset first range or the first equalization current; both can be pre-calibrated by the system according to actual conditions. For example, the preset first range could be a range exceeding or greater than 3%. The first equalization current could be the maximum equalization current that the active equalization circuit can support, etc., which this application will not limit or elaborate on further.

[0092] In another embodiment, when the absolute value of the state of charge deviation of the first cell is |ΔSOC i | Within the preset second range, for example, 0.5% ≤ |ΔSOC iWhen | ≤ 3%, this application can transfer energy from the first cell to the second cell based on a second equalization current. The second equalization current is the absolute value of the state-of-charge deviation (SOC) of the first cell. i The current value obtained through proportional control, for example, the second equalization current mentioned above, can specifically be Kp × |ΔSOC i | Kp is a pre-calibrated proportional coefficient of the system based on actual conditions, which will not be further limited or detailed in this application. This application does not limit the aforementioned preset second range. Generally, the upper limit of the aforementioned preset second range must be less than or equal to the lower limit of the aforementioned preset first range. For example, the aforementioned preset second range could be 0.5% ≤ |ΔSOC i |≤3%, etc., this application will not impose further restrictions or details on this.

[0093] In another embodiment, when the absolute value of the state of charge deviation of the first cell is |ΔSOC i |In the preset third range, for example|ΔSOC i When |<0.5%, this application can transfer energy from the first cell to the second cell based on a preset third equalization current. This application does not limit the preset third range or the third equalization current; both can be pre-calibrated by the system according to actual conditions. Typically, the upper limit of the preset third range must be less than or equal to the lower limit of the preset second range, for example, the preset third range is less than 0.5%. The third equalization current can be a small current pre-calibrated by the system. Typically, the first equalization current is greater than the second equalization current, which is greater than the third equalization current; this application does not impose further limitations or details in this regard.

[0094] By adopting the above scheme, a faster balancing progress can be achieved in the large deviation range, while higher control accuracy and lower energy loss can be achieved in the small deviation range, thus balancing migration efficiency and stability. Those skilled in the art can also combine this scheme with the aforementioned deviation sorting and pairing scheme, that is, prioritize processing cell pairs with larger deviations and automatically switch to a smaller balancing current when approaching the target, to further improve the overall balancing effect. Optionally, during battery charging, the system can continuously monitor the progress of each cell approaching the target state of charge and dynamically adjust the direction and magnitude of the balancing current; this application does not impose further limitations or details on this.

[0095] In some optional embodiments, this application can issue an alarm for any cell in the cedar target battery when the target state of charge (SOC) is not within a preset range. This application does not limit the preset SOC range; it can be pre-calibrated by the system based on actual conditions, for example, it can be less than or equal to the lower limit of SOC. maxEtc. This application calculates the target state of charge (SOC) of any of the aforementioned battery cells. target_i Then, it can be determined one by one whether it meets the SOC (State of Contents) requirement. target_i ≤SOC max If the target state of charge (SOC) of a certain cell, such as the first cell, exceeds the SOC... max If the system detects that the battery capacity is insufficient to support the expected trip, it can generate a warning message and issue an alarm via the instrument panel, vehicle infotainment system, or mobile terminal. At this point, the system can prompt the user to reduce the expected trip duration, or adjust the target state of charge of any of the aforementioned battery cells, etc. This application does not impose further limitations on these adjustments.

[0096] In some alternative embodiments, when the target battery meets the preset balancing termination condition, this application can automatically stop the cell energy redistribution or stop the above-mentioned active balancing. This application does not limit the above-mentioned balancing termination condition; it can be a judgment condition pre-defined by the system according to actual conditions to determine whether to end the above-mentioned active battery balancing. Specifically, it may include the absolute value of the state-of-charge deviation of each cell being less than a preset fourth threshold, or the target battery ending charging, or other custom conditions. The above limitations aim to avoid risks caused by exceeding the target limit and to avoid continuing energy migration after balancing is basically completed or the charging process has ended.

[0097] In specific implementations, for example, this application can determine that equalization is complete when the absolute value of the state-of-charge deviation of all cells satisfies |SOCi-SOCtarget_i| < the preset fourth threshold of 0.5%; or it can immediately stop equalization when the charging end time is reached, the charging gun is disconnected, or the charging current terminates. After stopping, the system can also record the unfinished deviation data for continued execution in the next charging cycle. This application does not limit the aforementioned preset fourth threshold, which can be a deviation judgment threshold pre-calibrated by the system according to actual conditions. For example, in addition to the 0.5% threshold, the aforementioned preset fourth threshold can also be set to 0.3%, 0.8%, or other values ​​according to sampling accuracy, execution efficiency requirements, and circuit capability, etc., and this application does not impose further limitations on this.

[0098] By employing the specific limitations described above, risks of exceeding target limits can be identified in a timely manner, and unnecessary balancing operations can be terminated, thereby reducing the risks of overcharging, over-discharging, and over-balancing. This further helps to ensure the safety of the active balancing process and reduce losses caused by ineffective energy migration.

[0099] As can be seen, this application's solution couples trip mileage prediction with the calculation of the target state of charge (SOC) for cell balancing, realizing a paradigm shift in balancing strategies from post-charging consistency to end-of-trip consistency. The balancing target is no longer uniform SOC at the end of charging, but rather that all cells simultaneously reach the preset SOC limit at the end of the trip, maximizing the available energy at the package level. This application also constructs a trip mileage prediction model based on user travel patterns. Utilizing time period characteristics and historical statistical features, the prediction accuracy can reach within ±15% at an 80% confidence level, providing reliable demand input for balancing target calculation. This application directly correlates the actual available capacity differences of each cell through a reverse target SOC calculation method, allocating higher initial SOC to cells with smaller capacity and allowing lower initial SOC to cells with larger capacity, achieving fair energy utilization under capacity difference conditions. This application supports parallel equalization and charging processes without adding extra waiting time for the user, resulting in a seamless user experience. Thus, under the condition of a 5% difference in cell capacity, compared to traditional post-charge equalization strategies, this application's solution can increase the available energy per unit area by 3% to 8% in long-range scenarios (e.g., greater than 100km), corresponding to an increase in driving range of 5 to 15km. In the later stages of battery aging (capacity difference > 8%), the improvement is even more significant, reaching 8% to 12%, etc., but this application does not elaborate further on these aspects.

[0100] Based on the above embodiments, please refer to Figure 7 This is a schematic diagram of the structure of a battery active balancing device provided in an embodiment of this application. Figure 7 The apparatus shown may include an acquisition module 701 and a processing module 702, wherein: The acquisition module 701 is used to acquire travel demand data of the target battery. The travel demand data includes at least travel prediction data and cell status data. The travel prediction data is the next travel data predicted based on the historical travel data corresponding to the target battery. The cell status data is used to indicate at least the current available capacity and current state of charge of each cell in the target battery. The processing module 702 is used to calculate the target state of charge based on the trip prediction data and cell status data in the trip demand data, and to obtain the target state of charge of each cell in the target battery. The target state of charge is used to indicate the battery state of charge required by the corresponding cell in the next trip indicated by the trip prediction data. The processing module 702 is further configured to redistribute the cell energy based on the target state of charge of each cell in the target battery when the target battery meets the preset equalization start-up conditions, so as to achieve active equalization of the target battery.

[0101] In some embodiments, the trip prediction data includes the next trip mileage and a corresponding confidence upper limit value, wherein the confidence upper limit value is used to reflect the upper limit that the predicted next trip mileage is allowed to reach, and the processing module 702 is specifically used for: Based on the travel forecast data and cell status data in the travel demand data, the target state of charge is calculated to obtain the target state of charge of each cell in the target battery, including: Based on the upper confidence limit value corresponding to the next trip mileage, the energy demand is calculated to obtain the corresponding total energy demand for the trip. The total energy demand for the trip is used to reflect the total energy of battery discharge required for the next trip mileage. Based on the cell status data and the total energy requirement of the trip, the target state of charge of each cell is calculated in reverse to obtain the target state of charge of each cell in the target battery.

[0102] In some embodiments, the trip prediction data includes the next trip mileage and a corresponding confidence interval, wherein the confidence interval reflects the upper and lower limits of the predicted next trip mileage, and the processing module 702 is specifically used for: Based on the next travel mileage and the corresponding confidence interval, a scene is generated to obtain a corresponding generated scene. The generated scene is used to limit the next travel mileage and its required state of charge under the corresponding scene. Based on the generated scenario, a corresponding objective function and constraints are determined. The objective function includes a function that minimizes the variance of the state of charge (SOC) of any two cells in the target battery. The SOC is the SOC of the corresponding cell at the end of the next trip mileage in the generated scenario, and the SOC is related to the target SOC of the corresponding cell. The constraints are at least used to indicate the constraints on the target SOC of any cell in the target battery. The target state of charge of each cell is solved based on the objective function and the constraints to obtain the target state of charge of each cell in the target battery.

[0103] In some embodiments, the trip prediction data includes the next trip mileage and a corresponding confidence interval, wherein the confidence interval reflects the upper and lower limits of the predicted next trip mileage, and the processing module 702 is specifically used for: Determine at least one reference data, the reference data including a reference state of charge, a reference travel distance corresponding to the reference state of charge, and a reference interval; Based on the reference data, the next mileage, and the confidence interval, multidimensional interpolation is performed to obtain the target state of charge of each cell in the target battery.

[0104] In some embodiments, the processing module 702 is specifically used for: Based on the deviation between the current state of charge and the target state of charge of each cell in the target battery, the deviation value of the state of charge of each cell in the target battery is obtained. The deviation value of the state of charge is used to reflect the difference between the current state of charge and the target state of charge of the corresponding cell. Based on the state of charge deviation value of each cell in the target battery, cell pairing is performed to obtain at least one cell pair. The cell pair includes a first cell and a second cell. The first cell is a cell in the target battery whose state of charge deviation value is positive and whose absolute value of the state of charge deviation value exceeds a preset value. The second cell is a cell in the target battery whose state of charge deviation value is negative and whose absolute value of the state of charge deviation value exceeds a preset value. Energy redistribution between cells is performed based on the first and second cells in at least one of the cell pairs.

[0105] In some embodiments, the processing module 702 is specifically configured to perform any of the following: When the absolute value of the state of charge deviation of the first cell is within a preset first range, the cell energy of the first cell is transferred to the second cell based on a preset first equalization current. When the absolute value of the state of charge deviation of the first cell is within a preset second range, the cell energy of the first cell is transferred to the second cell based on the second equalization current. The second equalization current is a current value obtained by proportional control based on the absolute value of the state of charge deviation of the first cell. When the absolute value of the state of charge deviation of the first cell is within a preset third range, the cell energy of the first cell is transferred to the second cell based on a preset third equalization current. Wherein, the lower limit of the preset first range is greater than or equal to the upper limit of the preset second range, the lower limit of the preset second range is greater than or equal to the upper limit of the preset third range, and the first equalizing current is greater than the second equalizing current is greater than the third equalizing current.

[0106] In some embodiments, the travel demand data includes travel forecast data, and the acquisition module 701 is specifically used for: Obtain the historical travel data corresponding to the target battery, wherein the historical travel data includes at least one of the following: driving record data, navigation data, charging behavior data, and schedule data of the vehicle where the target battery is located; Based on the historical travel data, feature extraction is performed to obtain the corresponding historical travel features. The historical travel features include at least one of the following: time period features, travel mode features, historical statistical features, and charging-related behavior features. The next trip is predicted based on the historical travel characteristics and the trip prediction model to obtain the trip prediction data. The trip prediction data includes the next trip mileage and the corresponding confidence interval. The confidence interval includes an upper confidence limit and a lower confidence limit. The trip prediction model is pre-trained based on the historical travel data of the target battery.

[0107] In some embodiments, the balanced startup conditions include: The target battery is in a charging state; The average value of the target state of charge of each cell in the target battery exceeds a preset first threshold. The target state of charge of each cell in the target battery is within a preset charge range. The confidence level in the trip prediction data exceeds a preset second threshold; The deviation between the current state of charge and the target state of charge of each cell in the target battery exceeds a preset third threshold; and, The active balancing circuit corresponding to the target battery has successfully completed its self-test.

[0108] In some embodiments, the processing module 702 is further configured to: When the target state of charge of any cell in the target battery is not within the preset charge range, an alarm is issued for that cell; and / or, When the target battery meets the preset equalization termination condition, the energy redistribution of the cells is stopped or the active equalization of the target battery is stopped. The equalization termination condition includes that the absolute value of the state of charge deviation of each cell in the target battery is less than a preset fourth threshold, or the target battery ends charging.

[0109] For any content not introduced or described in the embodiments of this application, please refer to the relevant descriptions in the foregoing method embodiments; they will not be repeated here.

[0110] Please see Figure 8 This is a schematic diagram of another battery active balancing device provided in an embodiment of this application. Figure 8 The device shown can be a vehicle, mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc. This device can be applied to any device or terminal equipped with a battery.

[0111] Reference Figure 8The device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output interface 812, a sensor component 814, and a communication component 816.

[0112] Processing component 802 typically controls the overall operation of device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the aforementioned active battery balancing method. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0113] Memory 804 is configured to store various types of data to support the operation of device 800. Examples of this data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0114] Power supply component 806 provides power to various components of device 800. Power supply component 806 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power to device 800.

[0115] Multimedia component 808 includes a screen that provides an output interface between the device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0116] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0117] Input / output interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0118] Sensor assembly 814 includes one or more sensors for providing status assessments of various aspects of device 800. For example, sensor assembly 814 can detect the on / off state of device 800, the relative positioning of components such as the display and keypad of device 800, and can also detect changes in the position of device 800 or a component of device 800, the presence or absence of user contact with device 800, the orientation or acceleration / deceleration of device 800, and temperature changes of device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0119] Communication component 816 is configured to facilitate wired or wireless communication between device 800 and other devices. Device 800 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0120] In an exemplary embodiment, the device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described active battery balancing method.

[0121] Understandably, the processor 820 in this application embodiment can be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method embodiment can be completed by integrated logic circuits in the processor's hardware or by software instructions. The processor described above can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0122] Understandably, the memory 804 in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memory used in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0123] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, which can be executed by a processor 820 of the device 800 to complete the above-described active battery balancing method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0124] The aforementioned device can be a standalone electronic device or a part of a standalone electronic device. For example, in one embodiment, the device can be an integrated circuit (IC) or a chip, wherein the integrated circuit can be a single IC or a collection of multiple ICs. The chip can include, but is not limited to, the following types: GPU (Graphics Processing Unit), CPU (Central Processing Unit), FPGA (Field Programmable Gate Array), DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), and SoC (System on Chip). The aforementioned integrated circuit or chip can be used to execute executable instructions (or code) to implement the aforementioned active battery balancing method. These executable instructions can be stored in the integrated circuit or chip or obtained from other devices or equipment. For example, the integrated circuit or chip includes a processor, memory, and an interface for communicating with other devices. The executable instruction can be stored in the memory, and when the executable instruction is executed by the processor, it implements the above-mentioned active battery balancing method; or, the integrated circuit or chip can receive the executable instruction through the interface and transmit it to the processor for execution to implement the above-mentioned active battery balancing method.

[0125] Please see Figure 9 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application. For example, as shown... Figure 9 As shown, the vehicle 900 includes a memory 901 and a processor 902. The memory 901 stores executable program code 9011, and the processor 902 is used to call and execute the executable program code 9011 to perform an active battery balancing method.

[0126] This application embodiment can divide the vehicle into functional modules according to the above method embodiment. For example, each function can be assigned to a separate module, or two or more functions can be integrated into a processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods. When dividing each functional module according to its corresponding function, the vehicle may include a processing module and a communication module, etc.

[0127] It should be noted that all relevant content of each step involved in the above method embodiments can be referenced from the functional description of the corresponding functional module, and will not be repeated here. The vehicle provided in this embodiment is used to execute the above-described active battery balancing method, and therefore can achieve the same effect as the above implementation method.

[0128] In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable device, the computer program having a code portion for performing the above-described active battery balancing method when executed by the programmable device.

[0129] It should be noted that the descriptions of the above embodiments of storage media, devices, and equipment are similar to the descriptions of the above method embodiments, and have similar beneficial effects. For technical details not disclosed in the embodiments of storage media, devices, and equipment of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0130] Other embodiments of this application will readily conceive of by those skilled in the art upon consideration of the specification and practice of this application. 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 in this application.

[0131] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications or equivalent substitutions made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A battery active balancing method, characterized in that, include: Acquire travel demand data for the target battery. The travel demand data includes at least travel prediction data and cell status data. The travel prediction data is the next travel data predicted based on the historical travel data corresponding to the target battery. The cell status data is used to indicate at least the current available capacity and current state of charge of each cell in the target battery. Based on the trip prediction data and cell status data in the trip demand data, the target state of charge is calculated to obtain the target state of charge of each cell in the target battery. The target state of charge is used to indicate the battery state of charge required by the corresponding cell in the next trip indicated by the trip prediction data. When the target battery meets the preset equalization start-up conditions, the cell energy is redistributed based on the target state of charge of each cell in the target battery to achieve active equalization of the target battery.

2. The method according to claim 1, characterized in that, The trip prediction data includes the next trip mileage and a corresponding confidence upper limit value. The confidence upper limit value reflects the upper limit that the predicted next trip mileage is allowed to reach. The target state of charge calculation based on the trip prediction data and cell state data in the trip demand data, to obtain the target state of charge of each cell in the target battery, includes: Based on the upper confidence limit value corresponding to the next trip mileage, the energy demand is calculated to obtain the corresponding total energy demand for the trip. The total energy demand for the trip is used to reflect the total energy of battery discharge required for the next trip mileage. Based on the cell state data and the total energy requirement of the trip, the target state of charge of each cell is calculated in reverse to obtain the target state of charge of each cell in the target battery.

3. The method according to claim 1, characterized in that, The trip prediction data includes the next trip mileage and the corresponding confidence interval. The confidence interval reflects the upper and lower limits of the predicted next trip mileage. The target state of charge calculation based on the trip prediction data and cell state data in the trip demand data, to obtain the target state of charge of each cell in the target battery, includes: Based on the next travel mileage and the corresponding confidence interval, a scene is generated to obtain a corresponding generated scene. The generated scene is used to limit the next travel mileage and its required state of charge under the corresponding scene. Based on the generated scenario, a corresponding objective function and constraints are determined. The objective function includes a function that minimizes the variance of the state of charge (SOC) of any two cells in the target battery. The SOC is the SOC of the corresponding cell at the end of the next trip mileage in the generated scenario, and the SOC is related to the target SOC of the corresponding cell. The constraints are at least used to indicate the constraints on the target SOC of any cell in the target battery. The target state of charge of each cell is solved based on the objective function and the constraints to obtain the target state of charge of each cell in the target battery.

4. The method according to claim 1, characterized in that, The trip prediction data includes the next trip mileage and the corresponding confidence interval. The confidence interval reflects the upper and lower limits of the predicted next trip mileage. The target state of charge calculation based on the trip prediction data and cell state data in the trip demand data, to obtain the target state of charge of each cell in the target battery, includes: Determine reference data, which includes at least one reference state of charge, a reference travel distance corresponding to the reference state of charge, and a reference interval; Based on the reference data, the next mileage, and the confidence interval, multidimensional interpolation is performed to obtain the target state of charge of each cell in the target battery.

5. The method according to claim 1, characterized in that, The cell energy redistribution based on the target state of charge of each cell in the target battery includes: Based on the deviation between the current state of charge and the target state of charge of each cell in the target battery, the deviation value of the state of charge of each cell in the target battery is obtained. The deviation value of the state of charge is used to reflect the difference between the current state of charge and the target state of charge of the corresponding cell. Based on the state of charge deviation value of each cell in the target battery, cell pairing is performed to obtain at least one cell pair. The cell pair includes a first cell and a second cell. The first cell is a cell in the target battery whose state of charge deviation value is positive and whose absolute value of the state of charge deviation value exceeds a preset value. The second cell is a cell in the target battery whose state of charge deviation value is negative and whose absolute value of the state of charge deviation value exceeds a preset value. Energy redistribution between cells is performed based on the first and second cells in at least one of the cell pairs.

6. The method according to claim 5, characterized in that, The energy redistribution between cells based on the first and second cells in at least one of the cell pairs includes any one of the following: When the absolute value of the state of charge deviation of the first cell is within a preset first range, the cell energy of the first cell is transferred to the second cell based on a preset first equalization current. When the absolute value of the state of charge deviation of the first cell is within a preset second range, the cell energy of the first cell is transferred to the second cell based on the second equalization current. The second equalization current is a current value obtained by proportional control based on the absolute value of the state of charge deviation of the first cell. When the absolute value of the state of charge deviation of the first cell is within a preset third range, the cell energy of the first cell is transferred to the second cell based on a preset third equalization current. Wherein, the lower limit of the preset first range is greater than or equal to the upper limit of the preset second range, the lower limit of the preset second range is greater than or equal to the upper limit of the preset third range, and the first equalizing current is greater than the second equalizing current is greater than the third equalizing current.

7. The method according to claim 1, characterized in that, The travel demand data includes travel prediction data, and the travel demand data for acquiring the target battery includes: Obtain the historical travel data corresponding to the target battery, wherein the historical travel data includes at least one of the following: driving record data, navigation data, charging behavior data, and schedule data of the vehicle where the target battery is located; Based on the historical travel data, feature extraction is performed to obtain the corresponding historical travel features. The historical travel features include at least one of the following: time period features, travel mode features, historical statistical features, and charging-related behavior features. The next trip is predicted based on the historical travel characteristics and the trip prediction model to obtain the trip prediction data. The trip prediction data includes the next trip mileage and the corresponding confidence interval. The confidence interval includes an upper confidence limit and a lower confidence limit. The trip prediction model is pre-trained based on the historical travel data of the target battery.

8. The method according to claim 1, characterized in that, The balanced startup conditions include: The target battery is in a charging state; The average value of the target state of charge of each cell in the target battery exceeds a preset first threshold. The target state of charge of each cell in the target battery is within a preset charge range. The confidence level in the trip prediction data exceeds a preset second threshold; The deviation between the current state of charge and the target state of charge of each cell in the target battery exceeds a preset third threshold; and, The active balancing circuit corresponding to the target battery has successfully completed its self-test.

9. The method according to any one of claims 1-8, characterized in that, The method further includes: When the target state of charge of any cell in the target battery is not within the preset charge range, an alarm is issued for that cell; and / or, When the target battery meets the preset equalization termination condition, the energy redistribution of the cells is stopped or the active equalization of the target battery is stopped. The equalization termination condition includes that the absolute value of the state of charge deviation of each cell in the target battery is less than a preset fourth threshold, or the target battery ends charging.

10. A vehicle, characterized in that, include: processor; A memory for storing processor-executable instructions; wherein the processor is configured to execute the executable instructions to implement the steps of the method according to any one of claims 1 to 9.

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