A vehicle battery control system, method, vehicle, and storage medium

Abstract

The application discloses a kind of vehicle battery control system, method, vehicle and storage medium, and the system includes: several battery packs and regional controllers;Battery pack is provided with battery monitoring unit and multiple electric cores in;Battery monitoring unit gathers battery monitoring information, and sends to regional controller;Regional controller receives the battery monitoring information sent by battery monitoring unit, and according to battery monitoring information generates the control instruction to each battery pack. The battery control algorithm in the battery control unit originally arranged in the battery pack is arranged in the regional controller in the embodiment, and the running state of the battery pack is controlled using the regional controller, so when the control system of the whole vehicle needs to be developed, since the simplified battery monitoring unit only includes battery monitoring or data acquisition related hardware, when hardware is upgraded, the simplified battery pack can realize fast hardware upgrade, save hardware upgrade resources and improve upgrade efficiency.

Description

Technical Field

[0001] This application relates to the field of automotive control technology, specifically to a vehicle battery control system, method, vehicle, and storage medium. Background Technology

[0002] Based on the development of intelligence in the automotive industry, the battery management system of new energy vehicles remains the core of basic functions, used to meet the basic functions and safety functions of vehicle battery management.

[0003] In existing technologies, the overall design of a battery control system includes a power domain controller and multiple battery packs. Each battery pack contains multiple battery control units (BCUs). Each BCU monitors, checks, and performs BMS algorithm calculations on the cells within its pack. The information obtained from these monitoring, checks, and calculations is transmitted to the power domain controller via CAN bus, enabling information interaction and control. However, because each battery pack contains an MCU (Microcontroller Unit), when the hardware chip in the BCU cannot meet the requirements of the upgraded functionality, the entire hardware needs to be replaced, resulting in wasted hardware resources. Furthermore, since the calculation speed and accuracy of the MCUs in different battery packs vary, if any one MCU makes a calculation error, it can lead to control errors and reduce the accuracy of vehicle control.

[0004] Therefore, existing technologies still need to be improved and enhanced. Summary of the Invention

[0005] This application provides a vehicle battery control system, method, vehicle, and storage medium to solve the problems of wasted hardware resources and low control accuracy in existing vehicle battery control systems.

[0006] To achieve the above objectives, this application adopts the following technical solution:

[0007] The first aspect of this application provides a vehicle battery control system, which includes: a plurality of battery packs and at least one area controller; the battery packs are provided with a battery monitoring unit and a plurality of battery cells;

[0008] The battery monitoring unit is used to collect the cell status parameters of each cell and monitor the operation of the battery pack, and send the cell status parameters and the monitored battery status information to the area controller as battery monitoring information.

[0009] The area controller is used to receive the battery monitoring information sent by the battery monitoring unit, and generate control commands for each battery cell based on the battery monitoring information.

[0010] Based on the aforementioned technical means, the system provided in this application, by relocating the battery control algorithm originally located in the battery control unit of the battery pack to the area controller, utilizes the area controller to control the operating state of the battery pack, thereby overcoming potential safety risks when the battery control unit malfunctions. Furthermore, since the battery control algorithm is relocated to the area controller to control the operating state of the battery pack, when the vehicle's control system needs to be developed, the original battery control unit, which currently only contains hardware related to battery monitoring or data acquisition, can be quickly upgraded to a simplified battery monitoring unit, saving hardware upgrade resources and improving upgrade efficiency.

[0011] Optionally, in one embodiment of this application, the step of generating control instructions for each battery pack based on the battery monitoring information specifically involves: using a battery control algorithm to calculate and process the battery monitoring information to obtain first control parameters, and using the first control parameters to generate control instructions for the battery pack.

[0012] Based on the aforementioned technical means, this embodiment utilizes a battery control algorithm to process the state parameters in the battery monitoring information to achieve control of the battery pack. Under normal circumstances, the battery control algorithm can quickly obtain the corresponding control parameters, thereby achieving real-time and accurate control of the vehicle. Integrating the battery pack control calculations into the area controller improves the integration and compatibility of the algorithm control, enhances software upgrade efficiency, and reduces the failure rate of software upgrades.

[0013] Optionally, in one embodiment of this application, the at least one area controller includes: a first area controller connected to each battery pack.

[0014] Based on the above technical means, in this embodiment of the application, a regional controller is used to calculate and process the battery monitoring information obtained from each battery pack of the vehicle, so as to realize the control operation of each battery pack.

[0015] Optionally, in one embodiment of this application, the at least one area controller includes a first area controller and a second area controller; the first area controller is communicatively connected to the second area controller, and the second area controller is connected to each battery pack.

[0016] Based on the above technical means, in this embodiment of the application, two area controllers are set up, and each area controller is connected to each battery pack to realize the control of the battery pack.

[0017] Optionally, in one embodiment of this application, when the first area controller is in a failed state, the second area controller is used to receive the battery monitoring information sent by the battery monitoring unit and generate control instructions for each battery pack based on the battery monitoring information; when the second area controller is in a failed state, the first area controller is used to receive the battery monitoring information sent by the battery monitoring unit and generate control instructions for each battery pack based on the battery monitoring information.

[0018] Based on the above technical means, in this embodiment of the application, two area controllers are connected to the battery pack. When either area controller malfunctions, the other area controller performs monitoring and control of the battery pack. This avoids the problem that the battery pack cannot be controlled when one area controller malfunctions, thereby improving the safety of battery use during driving.

[0019] Optionally, in one embodiment of this application, the battery pack contains a plurality of cell monitoring units;

[0020] The cell monitoring unit is connected to each cell in the battery pack in a one-to-one correspondence, and is used to collect the cell status parameters of the connected cells and send the cell status parameters to the battery monitoring unit; the battery monitoring unit interacts with the cell monitoring unit through communication inside the battery pack.

[0021] Based on the above technical means, the system in this application embodiment uses a battery monitoring unit to obtain cell status parameters and sends the obtained cell status parameters and battery status parameters together to the area controller, thereby obtaining comprehensive battery operating status information and providing a basis for comprehensive control of the battery.

[0022] Optionally, in one embodiment of this application, the vehicle battery control system further includes a central computing unit, and the area controller is connected to the central computing unit;

[0023] The central computing unit receives the battery monitoring information and generates a second control parameter based on the battery monitoring information.

[0024] The area controller is also configured to generate control commands for each battery pack based on the second control parameters.

[0025] Based on the aforementioned technical means, the system in this embodiment connects a central computing unit external to the area controller. This central computing unit receives and processes battery monitoring information to obtain second control parameters. These second control parameters are then used to generate control commands for each battery pack. In this solution, the first and second control parameters are obtained through different methods. In practical implementation, the two sets of control parameters can be compared to generate control commands with more accurate parameters, thereby improving the accuracy of the control commands.

[0026] Optionally, in one embodiment of this application, generating the second control parameter based on battery monitoring information specifically includes:

[0027] The battery monitoring information is input into a preset parameter prediction model to obtain the second control parameter output by the parameter prediction model.

[0028] Based on the aforementioned technical means, the central computing unit in this embodiment of the application processes the battery monitoring information by inputting the battery monitoring information into a pre-trained parameter prediction model. Since using the parameter prediction model yields more accurate control parameters, more precise control can be achieved, thus improving vehicle control safety.

[0029] Optionally, in one embodiment of this application, the area controller is further configured to generate control instructions for each battery pack based on the second control parameter when the battery monitoring information contains state parameters within a preset critical range;

[0030] When the battery monitoring information does not contain any state parameters within a preset critical range, control commands for each battery pack are generated based on the battery monitoring information.

[0031] Based on the above technical means, the system provided in this application embodiment, in order to avoid the problem of inaccurate control parameters calculated when using battery-related algorithms to process battery state parameters within the critical range, proposes to input battery control information into a preset parameter prediction model, use the parameter prediction model to output a second control parameter for the battery, and then realize the control of the battery pack based on the second control parameter, thereby achieving precise control of the battery pack and improving the safety of vehicle driving.

[0032] Optionally, in one embodiment of this application, generating control commands for each battery pack based on the second control parameters specifically includes:

[0033] The second control parameter is weighted and calculated with a preset correction value to obtain a third control parameter corresponding to each battery pack, and control commands for each battery pack are generated based on the third control parameter.

[0034] Based on the above technical means, the system provided in this application embodiment, in order to obtain more accurate control parameters, performs weighted calculation on the second control parameter by setting a preset correction value, and uses the weighted calculation to obtain the third control parameter to generate control commands, so as to obtain more accurate control commands.

[0035] Secondly, this embodiment also discloses a vehicle battery control method, wherein the method is applied to the vehicle battery control system and includes:

[0036] The area controller receives battery monitoring information from the battery packs and generates control commands for each battery pack based on the battery monitoring information.

[0037] Optionally, in one embodiment of this application, the step of generating control commands for each battery pack based on the battery monitoring information includes:

[0038] The area controller uses a battery control algorithm to calculate and process battery monitoring information to obtain first control parameters, and then uses these first control parameters to generate control commands for the battery pack.

[0039] Optionally, in one embodiment of this application, the at least one area controller includes a first area controller and a second area controller; the first area controller is communicatively connected to the second area controller, and the second area controller is connected to each battery pack;

[0040] The method further includes:

[0041] When the first area controller is in a failed state, the second area controller receives the battery monitoring information sent by the battery monitoring unit and generates control commands for each battery pack based on the battery monitoring information.

[0042] Alternatively, when the second area controller is in a failed state, the first area controller is used to receive the battery monitoring information sent by the battery monitoring unit and generate control commands for each battery pack based on the battery monitoring information.

[0043] Optionally, in one embodiment of this application, the vehicle battery control system further includes a central computing unit, and the step of generating control commands for each battery pack from the battery monitoring information includes:

[0044] The central computing unit receives the battery monitoring information and generates a second control parameter based on the battery monitoring information.

[0045] The area controller generates control commands for each battery pack based on the second control parameters.

[0046] Optionally, in one embodiment of this application, the step of generating the second control parameter based on the battery monitoring information includes:

[0047] The central computing unit inputs the battery monitoring information into a preset parameter prediction model to obtain the second control parameter output by the parameter prediction model.

[0048] Optionally, in one embodiment of this application, generating control commands for each battery pack based on the battery monitoring information includes:

[0049] When the area controller determines whether the battery monitoring information contains state parameters within a preset critical range, if the battery monitoring information contains state parameters within the preset critical range, it generates control instructions for each battery pack based on the second control parameter; if the battery monitoring information does not contain state parameters within the preset critical range, it generates control instructions for each battery pack based on the first control parameter.

[0050] Optionally, in one embodiment of this application, the step of generating control commands for each battery pack based on the second control parameters includes:

[0051] The area controller calculates the second control parameter by weighting it with a preset correction value to obtain a third control parameter corresponding to each battery pack, and generates control commands for each battery pack based on the third control parameter.

[0052] Optionally, in one embodiment of this application, there are multiple standard values ​​corresponding to the preset correction value, and each standard value corresponds to a different battery type;

[0053] Before the step of weighting the second control parameter with the preset correction value, the following is also included:

[0054] The area controller determines the standard value of the preset correction value based on the battery type corresponding to the battery pack.

[0055] Optionally, in one embodiment of this application, the method for generating the parameter prediction model includes:

[0056] Collect cell state parameters during battery operation and establish a battery parameter sample set based on the collected cell state parameters;

[0057] The preset network model is trained using battery parameter samples from the battery parameter sample set to obtain the trained preset parameter prediction model.

[0058] A third aspect of this application provides a vehicle comprising: a memory, a processor, and a vehicle battery control program stored in the memory and executable on the processor, wherein when the processor executes the vehicle battery control program, it implements the steps of the vehicle battery control method.

[0059] The fourth aspect of this application provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors to implement the steps of the vehicle battery control method.

[0060] The beneficial effects of this application are:

[0061] The vehicle battery control system, method, vehicle, and storage medium disclosed in this embodiment have the following advantages compared with existing technologies:

[0062] 1. Since the algorithms and other modules originally located in the battery control units of each battery pack have been moved to the area controller, if the hardware chips in the battery control units cannot meet the new upgraded functions during the overall upgrade, there is no need to upgrade the overall hardware, thus saving hardware resources.

[0063] 2. Since the software algorithm for controlling the battery pack is integrated into the area controller, the problem of reduced vehicle control accuracy caused by MCU calculation errors in a battery control unit, which may occur in distributed control, is avoided. Therefore, the system and method in this embodiment have high accuracy of control commands.

[0064] 3. In this embodiment, by using both the first area controller and the second area controller to establish connections with each battery pack, if one area controller malfunctions, the other area controller is used to control each battery pack. Therefore, the control system provided in this embodiment has good robustness.

[0065] 4. The battery control system provided in this embodiment integrates the control algorithms for each battery pack into the area controller. When a software upgrade is required, the upgrade operation is only performed within the area controller. Therefore, the software upgrade efficiency is high and the upgrade speed is fast. Attached Figure Description

[0066] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0067] Figure 1 This is a schematic diagram of the structure of a battery control system in the prior art;

[0068] Figure 2 This is a schematic diagram illustrating the principle of algorithm control in a battery control system in the prior art;

[0069] Figure 3 This is a schematic diagram of the vehicle battery control system in the embodiments of this application;

[0070] Figure 4 This is a schematic diagram illustrating the principle and structure of the vehicle battery control system in a specific application of this application.

[0071] Figure 5 This is a block diagram illustrating the principle structure of redundant area controllers in a specific application embodiment of the vehicle battery control system in this application.

[0072] Figure 6 This is a schematic diagram of the vehicle battery control system embodiment in this application.

[0073] Figure 7 This is a schematic diagram of the structure when this application is specifically applied in a particular embodiment;

[0074] Figure 8 This is a schematic diagram of the layout structure of the algorithm module in the vehicle battery control system described in this application embodiment;

[0075] Figure 9 This is a flowchart illustrating the steps of the vehicle battery control method according to an embodiment of this application;

[0076] Figure 10 This is a schematic diagram of the vehicle in the embodiments of this application. Detailed Implementation

[0077] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0078] Vehicle control and power battery management, as fundamental functions of new energy vehicles, are considered core capabilities. The battery management system of new energy vehicles, as important as the engine management system of gasoline vehicles, has evolved from initially only fulfilling basic vehicle battery management functions to becoming a platform-based and standardized module that meets the diverse functional needs of different vehicle models.

[0079] In traditional distributed architectures, the battery control system is an independent controller, separate from the battery pack. Currently, the hardware of battery control systems has evolved into battery control units integrated into the battery pack. Simultaneously, related battery control algorithms are also integrated into the battery control unit, and each battery control unit utilizes the integrated battery control algorithms to control the battery. For example... Figure 1 and Figure 2As shown, the battery control unit within the battery pack contains an MCU (Microcontroller Unit). The MCU processes battery monitoring information obtained from the cell monitoring unit and returns corresponding control commands to control the battery. However, when developing the control system for the entire vehicle, traditional development methods require separate development and optimization of the battery control system for different vehicle models. When the core hardware chip of the battery control unit cannot meet the new functions, the battery control units for different models need to be replaced, resulting in wasted resources.

[0080] To address the aforementioned issues, this embodiment discloses a vehicle battery control system, method, vehicle, and storage medium. It utilizes a region controller to process battery monitoring information acquired from the battery pack, generating control commands for each battery pack to achieve battery pack control. The system and method provided in this embodiment, by relocating the battery control algorithm originally located in the battery control unit of the battery pack to the region controller, and using the region controller to control the battery pack, allows for rapid hardware upgrades when developing the overall vehicle control system. Since the battery control unit only contains hardware related to battery monitoring or data acquisition functions, hardware upgrades can be performed quickly, saving resources and improving upgrade efficiency.

[0081] The following description, in conjunction with the accompanying drawings, provides a more detailed account of a vehicle battery control system, method, vehicle, and storage medium provided in this embodiment.

[0082] The first aspect of this application provides a vehicle battery control system, such as... Figure 3 As shown, it includes:

[0083] A battery pack 200 and at least one area controller 100 connected to the battery pack 200; the battery pack 200 is provided with a battery monitoring unit 210 and a plurality of battery cells 220;

[0084] The battery monitoring unit 210 is used to collect the cell status parameters of each cell 220 and monitor the operation of the battery pack, and send the cell status parameters and the monitored battery status information to the area controller 100 as battery monitoring information.

[0085] The area controller 100 is used to receive the battery monitoring information sent by the battery monitoring unit, and generate control instructions for each battery pack based on the battery monitoring information.

[0086] In this embodiment, the battery pack contains multiple battery cells, each of which is connected to a battery monitoring unit. The battery monitoring unit acquires the operating status parameters of the battery cells to obtain cell status parameters. Additionally, the battery monitoring unit also collects data on the overall battery operating status and sends the collected battery operating status information, along with the cell status parameters, to the area controller as battery monitoring information.

[0087] Specifically, battery monitoring information includes: individual cell voltage acquisition, cell / module temperature acquisition, total current acquisition, total voltage acquisition, insulation detection, etc. The cell monitoring unit in the power battery pack completes the acquisition of individual cell voltage and cell / module temperature for each cell, and synchronizes the acquired battery monitoring information to the battery monitoring unit via a daisy-chain communication link. The area controller receives the battery monitoring information sent by the battery monitoring unit through CANFD communication, processes the battery monitoring information using different algorithms, and then feeds back control signals to the battery monitoring unit.

[0088] The battery monitoring unit provided in this embodiment only has data acquisition and driving functions, and does not have data processing or the function of generating corresponding battery control commands based on the processed results. Therefore, the battery monitoring unit in this embodiment only retains the necessary hardware circuits and materials, thereby reducing costs. In one implementation, combined with Figure 4 As shown, the battery monitoring unit's hardware does not contain an MCU; it only retains the digital control hardware circuitry for high-voltage acquisition, high-voltage bridging, internal communication (daisy-chain, SPI), external communication (CANFD), insulation detection, contactor drive, and explosive fuse. While receiving cell information from the cell monitoring unit, the battery monitoring unit's high-voltage acquisition, insulation detection, and high-voltage bridging sections collect the total current and total voltage of the power battery and perform insulation detection. This information is then transmitted to the area controller via CANFD. The battery monitoring unit does not perform any algorithmic processing on this data.

[0089] When the area controller receives the battery monitoring information transmitted by the battery monitoring unit, it processes the data in the battery monitoring information to obtain control commands for each battery cell.

[0090] In the method disclosed in this embodiment, the control algorithms for each battery pack are integrated into a regional controller. The regional controller performs corresponding calculations on the received battery monitoring information of the battery packs to generate control commands for the battery packs. Because the software algorithms contained in the original battery control unit of the battery pack are removed, the simplified battery pack retains only the cells, cell monitoring units, and battery monitoring units. The battery monitoring units are only used to collect relevant operating status information of the batteries and do not have the function of performing calculations based on algorithms. Therefore, the simplified battery pack has low manufacturing costs and high upgrade efficiency during software upgrades. On the other hand, the regional controller integrates the control algorithms for each battery pack, thereby improving the software integration and compatibility. This avoids the problems of high upgrade failure rates and slow upgrades that may occur when software algorithms are distributed in each battery pack, thus ensuring vehicle driving safety.

[0091] In one implementation, the battery monitoring unit and the area controller interact via CANFD. The area controller receives real-time voltage, temperature, and current data from the power battery using the high-bandwidth CANFD to achieve timely data acquisition and real-time processing. It is conceivable that the battery monitoring unit and the area controller can also interact via LIN bus, FlexRay bus system, LonWorks communication system, or Profibus bus.

[0092] Furthermore, such as Figure 5 As shown, there can be multiple area controllers. Two area controllers are configured, including a redundant first area controller and a second area controller. Both the first and second area controllers communicate with the central computing unit, communicate with each other, and communicate data with each of the battery packs. When the first area controller is in a failed state, the second area controller receives battery monitoring information sent by the battery monitoring unit and generates control commands for each battery pack based on the battery monitoring information. When the second area controller is in a failed state, the first area controller receives battery monitoring information sent by the battery monitoring unit and generates control commands for each battery pack based on the battery monitoring information.

[0093] Since both the first and second area controllers establish communication connections with each battery monitoring unit, each area controller can independently control each battery monitoring unit. Furthermore, the first and second area controllers can be configured to be redundant with each other. If any one of them fails and cannot output control commands to the battery monitoring unit, the other area controller will control the battery monitoring unit based on the received battery monitoring information, thereby improving battery control safety during vehicle operation.

[0094] In one embodiment, the central computing unit, the first area controller, and the second area controller form a ring communication link, and both the first area controller and the second area controller communicate with each of the battery monitoring units.

[0095] Furthermore, since a first area controller and a second area controller are deployed in this embodiment, a baseline version can be deployed for different vehicle models. During subsequent OTA upgrades, the software on the first area controller and the second area controller can be updated first and then last to ensure the normal use of the vehicle.

[0096] Furthermore, in one implementation, combined with Figure 5 As shown, the area controller is used to calculate and process the battery monitoring information using a battery control algorithm when the battery monitoring information does not contain state parameters within a preset critical range, to obtain a first control parameter, and to generate control commands for the battery pack using the first control parameter.

[0097] Specifically, the area controller has multiple preset battery control algorithms, including one or more of the following: SOX algorithm, equalization algorithm, thermal management algorithm, and charging time algorithm; the first control unit includes one or more of the following: SOX algorithm module, equalization algorithm module, thermal management algorithm module, and charging time algorithm module.

[0098] The SOX algorithm module is used to determine the target SOX index using the SOX algorithm after receiving battery monitoring information sent by the battery monitoring unit, and to generate corresponding control commands based on the target SOX index.

[0099] The equalization algorithm module is used to calculate and process battery monitoring information using a preset equalization algorithm to determine battery equalization control parameters.

[0100] The thermal management algorithm module is used to calculate and process battery monitoring information using a preset thermal management algorithm to obtain battery thermal management parameters.

[0101] The charging time algorithm module is used to calculate and process battery monitoring information using a charging time algorithm to obtain battery charging time control parameters.

[0102] The area controller processes the battery SOC value received from battery monitoring information using the SOX algorithm model to obtain corresponding control commands for displaying battery capacity. The SOX algorithm includes algorithms corresponding to SOC (State of Charge), SOH (State of Health), and SOE (State of Energy). This SOX algorithm module will be invoked according to the specific usage scenario.

[0103] In this embodiment, the corresponding control settings of the SOX algorithm are set in the area controller, and the area controller is used to control the battery SOX. This avoids the shortcomings of the prior art, which integrates the algorithm into each battery control unit, develops and optimizes the algorithm separately for the battery management system of different vehicle models, and requires hardware replacement according to the optimized algorithm when the software algorithm needs to be upgraded, thus causing waste of resources.

[0104] Specifically, the SOX algorithm module includes: a SOC algorithm unit;

[0105] The SOC algorithm unit is used to update and display the battery SOC based on the real-time battery SOC and real-time current value in the battery monitoring information.

[0106] In practical implementation, the SOC algorithm integrated into the SOC algorithm unit is used to calculate the battery's SOC and control each battery monitoring unit. The process of SOC algorithm control by the area controller includes: when the battery SOC value is lower than the preset minimum SOC range, the absolute SOC is obtained by combining small current correction; when the battery SOC is in the normal operating range, the SOC calculation is completed by time-integration, and the temperature SOC is obtained by combining temperature; based on this, the SOC is updated by combining current; when the battery SOC is close to about 5% of the maximum value, a lookup table is used to perform mapping and matching based on the measured data to obtain the final SOC.

[0107] In practical applications, when the power battery is charging, the SOX algorithm module, equalization algorithm module, thermal management algorithm module, and charging time algorithm module of the area controller all perform dynamic charging control of the battery based on the received battery monitoring information, and perform corresponding equalization and thermal management control of the battery based on the temperature.

[0108] Since the system provided in this embodiment has a complete SOX algorithm, equalization algorithm, thermal management algorithm and charging time algorithm deployed in the area controller, when software upgrades are required for the algorithms corresponding to battery control, they are all implemented in the area controller. This avoids the potential risk that the battery control algorithm is integrated into the battery control unit in traditional battery control systems, and the power battery information cannot be received normally during software upgrades.

[0109] Combination Figure 6 As shown, the vehicle battery control system also includes a central computing unit 120, and the area controller 100 is connected to the central computing unit 120.

[0110] The central computing unit 120 is used to receive the battery monitoring information and generate second control parameters based on the battery monitoring information.

[0111] The area controller 100 generates control commands for each battery pack based on the second control parameters.

[0112] Furthermore, the central computing unit processes the battery monitoring information to obtain the second control parameter, specifically by inputting the battery monitoring information into a preset parameter prediction model to obtain the second control parameter output by the parameter prediction model.

[0113] In this step, the central computing unit uses the trained parameter prediction model to process the battery monitoring information, thereby obtaining more accurate control parameters and improving the accuracy of battery pack control.

[0114] Furthermore, the area controller also determines whether the received battery monitoring information contains state parameters within a preset critical range. When the battery monitoring information contains state parameters within a preset critical range, it obtains the second control parameters calculated by the central computing unit and generates control commands for the battery pack based on the second control parameters.

[0115] In this embodiment, the system stores a trained parameter prediction model in the central computing unit, processes battery monitoring information containing state parameters within a preset critical range using the parameter prediction model, and obtains a more stable and accurate second control parameter. The second control parameter is then used to control the battery pack.

[0116] Furthermore, there are multiple standard values ​​corresponding to the preset correction value, and each standard value corresponds to a different battery type.

[0117] The weighted calculation unit is also used to: determine the standard value of the preset correction value according to the battery type corresponding to the battery pack.

[0118] In one implementation, the physical core parameter of the central computing unit is set to the AI ​​(artificial intelligence) computing power of the SOC, with the computing power ranging from 254 AI TOPS (Tera Operations Per Second) to 508 AI TOPS, so as to enable it to process the received battery monitoring information.

[0119] The central computing unit is connected to each area controller, and the central computing unit is equipped with a parameter prediction model that iteratively optimizes the battery control algorithm in the area controller. When the battery monitoring information contains state parameters within a critical range (e.g., the voltage is below a certain critical value), the battery control algorithm deployed in the area controller would cause anomalies. In this case, the parameter prediction model can be used to process the received battery monitoring information to compensate for the inaccuracy of the control parameters calculated by the battery control algorithm in the area controller. In addition, the preset parameter prediction model can also take into account the performance differences between different power battery platforms, thereby achieving more precise control of the battery.

[0120] Based on the above technical means, in this embodiment of the application, a central computing unit is used to compensate for the differences in battery management algorithms of different power battery packs, thereby improving efficiency.

[0121] Since the accuracy of the predictive parameter model in the central computing unit is higher than the processing efficiency of the battery control algorithm in the area controller, in this embodiment, when the acquired battery monitoring information contains state parameters within the critical range, the area controller obtains the second control parameter from the central computing unit, performs weighted calculation using the second control parameter, and outputs the third control parameter for battery control in a more timely manner, thereby generating control commands for the battery pack.

[0122] In one implementation, combined with Figure 7 As shown, the battery monitoring unit interacts with other units within the battery pack via daisy-chain communication. The area controllers interact with the battery monitoring unit via CANFD; data interaction occurs between the area controllers and between the area controllers and the central computing unit via Ethernet. This ensures that the area controllers and the central computing unit synchronously receive real-time monitoring data of the battery pack transmitted by the battery monitoring unit.

[0123] Combination Figure 8As shown, the battery control system provided in this embodiment deploys equalization algorithm modules, thermal management algorithm modules, and charging time modules, which have equalization algorithms, thermal management algorithms, and charging time algorithms, to the area controller. The area controller is used to control the battery's equalization algorithm, thermal management, and charging time, thereby realizing the equalization, thermal management, and charging time control of the battery using the area controller. In addition, by deploying a parameter prediction model in the central computing unit, it can compensate for the control anomalies that may be caused by the use of the battery control algorithm to determine the control parameters when the state parameters contained in the battery monitoring information are within the critical range, thereby achieving more precise control of the battery.

[0124] Secondly, this embodiment also provides a vehicle battery control method, such as... Figure 9 As shown, the method includes:

[0125] Step S1: Receive battery monitoring information from the battery pack.

[0126] This step involves monitoring the operational status of each battery pack to obtain battery monitoring information. Specifically, this information includes: overall operational status parameters of the battery pack and operational status parameters related to each cell within the battery pack. Cell-specific information includes: individual cell voltage acquisition, cell / module temperature acquisition, total current acquisition, total voltage acquisition, insulation detection, etc.

[0127] To monitor the operating status of each battery pack, each battery pack establishes a communication connection with the area controller. The battery monitoring unit in the battery pack is connected to the cell monitoring unit and receives the operating status parameters of each cell collected by the cell monitoring unit. The battery monitoring unit sends the obtained operating status parameters of each cell and the result information issued based on logic processing to the area controller, so that the area controller obtains the battery monitoring information of the battery pack.

[0128] Step S2: Generate control commands for each battery pack based on the battery monitoring information.

[0129] Specifically, the steps for generating control commands for each battery pack based on the battery monitoring information include:

[0130] The area controller uses a battery control algorithm to calculate and process battery monitoring information to obtain first control parameters, and then uses these first control parameters to generate control commands for the battery pack.

[0131] Furthermore, the at least one area controller includes a first area controller and a second area controller; the first area controller is communicatively connected to the second area controller, and the second area controller is connected to each battery pack;

[0132] The method further includes:

[0133] When the first area controller is in a failed state, the second area controller receives the battery monitoring information sent by the battery monitoring unit and generates control commands for each battery pack based on the battery monitoring information.

[0134] Alternatively, when the second area controller is in a failed state, the first area controller is used to receive the battery monitoring information sent by the battery monitoring unit and generate control commands for each battery pack based on the battery monitoring information.

[0135] Furthermore, the battery pack contains multiple battery monitoring units, which are connected to each battery cell to receive relevant status information from the cells. The area controllers interact via Ethernet and use CANFD to receive internal battery pack information from the battery monitoring units. Simultaneously, based on the results of logic processing, they send relevant thermal management and equalization management signals to the battery monitoring units.

[0136] Battery control algorithms include one or more of the following: SOX algorithm, equalization algorithm, thermal management algorithm, and charging time algorithm;

[0137] Specifically, the steps for calculating the first control parameter by processing battery monitoring information using a battery control algorithm include:

[0138] The SOX algorithm is used to calculate and process battery monitoring information to determine the SOX control parameters;

[0139] And / or use a preset equalization algorithm to calculate and process the battery monitoring information to determine the battery equalization control parameters;

[0140] And / or use a preset thermal management algorithm to calculate and process battery monitoring information to obtain battery thermal management parameters;

[0141] And / or use a charging time algorithm to calculate and process the battery monitoring information to obtain battery charging time control parameters.

[0142] Specifically, the algorithm for controlling the battery pack can be divided into four parts: SOX algorithm, equalization algorithm, thermal management algorithm, and charging time algorithm. The battery pack is controlled from these four directions to achieve comprehensive control of the battery pack.

[0143] Furthermore, since the SOX algorithm includes: SOC algorithm, SOH algorithm, SOP algorithm, and SOE algorithm; and the battery monitoring information includes: real-time battery SOC, real-time current value, battery capacity, and temperature; the step of using the SOX algorithm to calculate and process the battery monitoring information to determine the SOX control parameters includes:

[0144] The SOC algorithm is used to calculate and process the real-time battery SOC and real-time current values ​​in the battery monitoring information to obtain the SOC control parameters.

[0145] The SOH algorithm is used to calculate and process the battery capacity in the battery monitoring information to obtain the SOH control parameters;

[0146] The SOP algorithm is used to calculate and process the real-time current, voltage, and temperature in the battery monitoring information to obtain the SOP control parameters;

[0147] The SOE algorithm is used to calculate and process the real-time current, voltage, and temperature in the battery monitoring information to obtain the SOE control parameters.

[0148] The vehicle battery control method provided in this embodiment involves a battery monitoring unit located inside the battery pack interacting with a cell monitoring unit via daisy-chain communication to obtain information such as current, voltage, and temperature of the cells monitored by the battery monitoring unit. This information is then transmitted to the area controller as battery monitoring information. The area controller processes the battery monitoring data to obtain control parameters that best match the current battery operating state, thereby improving the accuracy of battery control and ensuring the safe operation of the vehicle.

[0149] Furthermore, in combination Figure 7 and Figure 8 As shown, each area controller is also connected to a central computing unit. The central computing unit is connected to each area controller and simultaneously receives battery monitoring information at the same time as the area controller receives the battery monitoring information, so as to process the received battery monitoring information when certain conditions are met.

[0150] After obtaining battery monitoring information in step S1, the system performs corresponding calculations based on whether there are any state parameters within the preset critical range in the battery monitoring information, in order to generate different control parameters. For example, the SOC calculation of general power batteries uses the open-circuit voltage method and the ampere-hour integration method. The open-circuit voltage method is only applicable to scenarios where the battery is not working and SOC is estimated after a certain period of time. The ampere-hour integration method mainly addresses situations where the charge changes over a period of time. If the initial SOC of the battery is inaccurate, it will lead to errors in the overall estimation. Therefore, when the state parameters contained in the battery monitoring information are within the critical range, the SOC calculated using the SOC algorithm may be inaccurate.

[0151] To overcome the above problems and improve the accuracy of control parameters, the vehicle battery control system also includes a central computing unit. The steps for generating control commands for each battery pack from the battery monitoring information include:

[0152] The central computing unit receives the battery monitoring information and generates a second control parameter based on the battery monitoring information.

[0153] The area controller generates control commands for each battery pack based on the second control parameters.

[0154] The step of generating the second control parameter based on battery monitoring information includes:

[0155] The central computing unit inputs the battery monitoring information into a preset parameter prediction model to obtain the second control parameter output by the parameter prediction model.

[0156] Specifically, when the battery monitoring information contains state parameters within a preset critical range, the battery monitoring information is input into a preset parameter prediction model, which then generates a second control parameter. Since the preset parameter prediction model is trained using data from a battery pack that has been in use for a long time, it can output relatively accurate control parameters.

[0157] Because the central computing unit uses a parameter prediction model to process data, its processing speed is slower than that of the area controller which uses a battery control algorithm. Therefore, in order to achieve real-time control of the vehicle battery while also ensuring control accuracy, the step of generating control commands for each battery pack based on the battery monitoring information includes:

[0158] When the area controller determines whether the battery monitoring information contains state parameters within a preset critical range, if the battery monitoring information contains state parameters within the preset critical range, it generates control instructions for each battery pack based on the second control parameter; if the battery monitoring information does not contain state parameters within the preset critical range, it generates control instructions for each battery pack based on the first control parameter.

[0159] The area controller determines whether to generate control commands using the first control parameter or the second control parameter based on whether the status parameters in the battery monitoring information are within a preset critical range, thereby achieving a balance between real-time performance and accuracy.

[0160] Specifically, the method for generating the parameter prediction model includes:

[0161] Collect cell state parameters during battery operation and establish a battery parameter sample set based on the collected cell state parameters;

[0162] The preset network model is trained using battery parameter samples from the battery parameter sample set to obtain the preset parameter prediction model.

[0163] To obtain accurate control parameters by processing battery monitoring information, the method includes, before processing the battery monitoring information using a preset parameter prediction model, a step of training a preset neural network model to obtain the preset parameter prediction model. In one implementation, cell state parameters during battery operation are first collected. A battery parameter sample set for training is constructed based on the collected cell state parameters. The preset neural network model is then trained using the sample data from the battery parameter sample set to obtain the trained parameter prediction model. In specific applications, the preset neural network model can be: BP (Back Propagation), CNN (Convolutional Neural Networks), RBF neural network or deep feedforward neural network, or RNN (Recurrent Neural Networks).

[0164] Specifically, when training a prediction model with preset parameters, the prediction model training module first establishes a training sample set. The sample data in this set comes from cell data provided by the cell manufacturer or standard data found online showing normal operation of the power supply. After acquiring the cell data or standard data, the data is normalized to eliminate magnitude differences and improve accuracy. The normalized cell data is then input into the preset network model to obtain its prediction results. Based on the difference between the prediction results and the actual values, the preset network model parameters are adjusted, and the cell data is input into the adjusted preset network model to obtain its prediction results. The difference between the prediction results and the actual values ​​is then assessed, and the preset network model parameters are adjusted accordingly. This process is repeated until the difference between the preset structure output by the adjusted preset network model and the actual value is within a preset range, or until the preset number of repetitions is reached. Training is then complete, and the trained parameter prediction model is obtained. Alternatively, the trained parameter prediction model can be tested using the actual performance curve parameters of the actual power battery pack cells to determine if its performance meets the requirements.

[0165] Specifically, the cell data input into the preset network model in the training sample set is the battery operating status data, and the output is the target battery control parameters that match the current operating status of the battery. After the parameter prediction model is trained, it is saved to the central computing unit. The parameter prediction model is used to process the battery monitoring information obtained in the central computing unit to obtain the first control parameters.

[0166] Since the battery control information contains different state parameters, in specific implementation, the preset neural network can be trained according to different state parameter types to obtain the trained parameter prediction model. When in use, the state parameter type in the battery monitoring information is input into the corresponding parameter prediction model to obtain the corresponding predictive control parameters, and all the predicted control parameters are combined to form the second control parameters.

[0167] For example, real-time battery current and voltage data contained in battery monitoring information are input into a preset prediction parameter model to obtain the SOC value output by the prediction parameter model, thereby realizing the control of battery SOC.

[0168] Once the second control parameter is obtained, control commands for each battery pack are generated based on the second control parameter to achieve control over each battery pack. Specifically, the second control parameter may include one or more control parameters that control the state parameters of the battery, thus correspondingly generating control commands for the battery pack to achieve control over battery capacity, battery charging time, thermal management, etc. The control commands include control signals related to SOX, thermal management, equalization management, or charging time. Thermal management related signals include signals related to heating, cooling, and external temperature; equalization management related signals include signals such as individual cell equalization status, transformer demand voltage, equalization detection, and equalization calculation.

[0169] To achieve precise battery control, this embodiment employs a weighted calculation method that combines the second control parameter with a preset correction value to obtain the third control parameter. Furthermore, in the specific implementation process, multiple standard values ​​for the preset correction values ​​are custom-defined based on different battery types. During the weighted calculation, the standard value for the preset correction value is first determined according to the battery type corresponding to the battery pack used in the current vehicle. Then, the corresponding preset correction value is determined based on the type of battery pack to perform the weighted calculation and obtain the corresponding third control parameter.

[0170] In this step, the second control parameter is weighted using a preset correction value to obtain a third control parameter that is more suitable for the vehicle being controlled, thus achieving further control.

[0171] Once the third control parameter is obtained through weighted calculation, control commands for each battery pack are generated based on this parameter. It's conceivable that this second control parameter contains parameters for controlling various aspects of the battery pack's operating state, such as SOC control parameters for controlling the battery pack's State of Charge (SOC) value and battery charging time control parameters for controlling the battery pack's charging time, thus achieving comprehensive control of the battery pack.

[0172] The method and system provided by the present invention will be further described below with specific application examples. In specific applications, the following steps are included:

[0173] First, the cell monitoring unit in the battery pack collects the individual cell voltage and cell / module temperature of each cell in the battery pack, and synchronizes the information to the battery monitoring unit through the daisy-chain communication link.

[0174] Secondly, while the battery monitoring unit receives the cell-related information sent by the cell monitoring unit, its high-voltage acquisition, insulation detection, and high-voltage bridging components will collect the total current and total voltage of the battery pack and perform insulation detection. This information, along with the cell-related information, will be transmitted to the area controller and central computing unit via CANFD. The battery monitoring unit itself will not perform algorithmic processing on the aforementioned information.

[0175] Furthermore, after receiving battery monitoring information, if the battery monitoring information does not contain state parameters within a preset critical range, the area controller uses various battery control algorithms, such as the SOX algorithm, equalization algorithm, thermal management algorithm, and charging time algorithm, to process the battery monitoring information to obtain the first control parameter. The first control parameter is then used to generate control commands for the battery. Otherwise, the second control parameter output by the parameter prediction model in the central computing unit is obtained, and control commands for the battery are generated based on the second control parameter.

[0176] The vehicle battery control system provided in this embodiment is equipped with a central computing unit and at least one regional controller. The regional controllers are connected to the various battery monitoring units of the battery pack. The battery monitoring units act as actuators, performing cell current, temperature, voltage, high voltage, and insulation detection within the battery pack, and also directly driving the high-voltage relays of the battery pack. Battery thermal management, equalization control, charging time, and SOC estimation are all implemented by battery control algorithms deployed in the regional controllers. The central computing unit, in conjunction with the regional controllers, performs corresponding calculations based on the different values ​​of the state parameters contained in the received battery monitoring information to obtain the control parameters that best match the current battery state, thereby achieving precise battery control.

[0177] A third aspect of this application provides a vehicle, such as Figure 10 As shown, it includes: a memory 1001, a processor 1002, and a vehicle battery control program stored in the memory 1001 and executable on the processor 1002. When the processor 1002 executes the vehicle battery control program, it implements the steps of the vehicle battery control method.

[0178] The vehicle may include:

[0179] The memory 1001, the processor 1002, and the computer program stored on the memory 1001 and capable of running on the processor 1002.

[0180] When the processor 1002 executes the program, it implements the steps of the vehicle battery control method provided in the above embodiments.

[0181] Furthermore, the vehicle also includes:

[0182] Communication interface 1003 is used for communication between memory 1001 and processor 1002.

[0183] The memory 1001 is used to store computer programs that can run on the processor 1002.

[0184] The memory 1001 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0185] If the memory 1001, processor 1002, and communication interface 1003 are implemented independently, then the communication interface 1003, memory 1001, and processor 1002 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized into address buses, data buses, control buses, etc. For ease of representation, Figure 10 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0186] Optionally, in a specific implementation, if the memory 1001, processor 1002, and communication interface 1003 are integrated on a single chip, then the memory 1001, processor 1002, and communication interface 1003 can communicate with each other through an internal interface.

[0187] The processor 1002 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0188] This embodiment also provides a computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs, which can be executed by one or more processors to implement the steps of the vehicle battery control method.

[0189] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0190] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0191] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0192] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can read and execute instructions from or in conjunction with such an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). In addition, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically by optically scanning paper or other media, then editing, interpreting or otherwise processing them as necessary, and then storing them in computer memory.

[0193] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0194] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium. When executed, the program includes one or a combination of the steps of the method embodiments.

[0195] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0196] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A vehicle battery control system, characterized in that, include: Several battery packs and at least one area controller; the battery packs are equipped with a battery monitoring unit and multiple battery cells; The battery monitoring unit is used to collect the cell status parameters of each cell and monitor the operation of the battery pack, and send the cell status parameters and the monitored battery status information to the area controller as battery monitoring information. The area controller is used to receive the battery monitoring information sent by the battery monitoring unit, and generate control instructions for each battery pack based on the battery monitoring information. The specific steps for generating control instructions for each battery pack based on the battery monitoring information are as follows: the battery monitoring information is processed using a battery control algorithm to obtain a first control parameter, and control instructions for the battery pack are generated using the first control parameter. The vehicle battery control system also includes a central computing unit, and the area controller is connected to the central computing unit; The central computing unit receives the battery monitoring information and generates a second control parameter based on the battery monitoring information. The area controller is also configured to generate control commands for each battery pack based on the second control parameters; The process of generating the second control parameter based on battery monitoring information specifically includes: The battery monitoring information is input into a preset parameter prediction model to obtain the second control parameter output by the parameter prediction model; The area controller is also used to generate control instructions for each battery pack based on the second control parameter when the battery monitoring information contains state parameters within a preset critical range; When the battery monitoring information does not contain any state parameters within a preset critical range, control commands for each battery pack are generated based on the battery monitoring information.

2. The vehicle battery control system according to claim 1, characterized in that, The at least one area controller includes: a first area controller, which is connected to each battery pack.

3. The vehicle battery control system according to claim 1, characterized in that, The at least one area controller includes a first area controller and a second area controller; the first area controller is communicatively connected to the second area controller, and the second area controller is connected to each battery pack.

4. The vehicle battery control system according to claim 3, characterized in that, When the first area controller is in a failed state, the second area controller is used to receive the battery monitoring information sent by the battery monitoring unit, and generate control instructions for each battery pack based on the battery monitoring information. When the second area controller is in a failed state, the first area controller is used to receive the battery monitoring information sent by the battery monitoring unit and generate control commands for each battery pack based on the battery monitoring information.

5. The vehicle battery control system according to claim 1, characterized in that, The battery pack contains multiple cell monitoring units; The cell monitoring unit is connected to each cell in the battery pack in a one-to-one correspondence, and is used to collect the cell status parameters of the connected cells and send the cell status parameters to the battery monitoring unit; the battery monitoring unit interacts with the cell monitoring unit through communication inside the battery pack.

6. The vehicle battery control system according to claim 1, characterized in that, The specific steps of generating control commands for each battery pack based on the second control parameters include: The second control parameter is weighted and calculated with a preset correction value to obtain a third control parameter corresponding to each battery pack, and control commands for each battery pack are generated based on the third control parameter.

7. A vehicle battery control method, characterized in that, Applied to the vehicle battery control system as described in claim 1, the method includes: The area controller receives battery monitoring information from the battery packs and generates control commands for each battery pack based on the battery monitoring information. The steps for generating control commands for each battery pack based on the battery monitoring information include: The area controller uses a battery control algorithm to calculate and process battery monitoring information to obtain first control parameters, and uses the first control parameters to generate control commands for the battery pack. The vehicle battery control system also includes a central computing unit, and the steps for generating control commands for each battery pack from the battery monitoring information include: The central computing unit receives the battery monitoring information and generates a second control parameter based on the battery monitoring information. The area controller generates control commands for each battery pack based on the second control parameters. The step of generating the second control parameter based on battery monitoring information includes: The central computing unit inputs the battery monitoring information into a preset parameter prediction model to obtain the second control parameter output by the parameter prediction model; The step of generating control commands for each battery pack based on the battery monitoring information includes: When the area controller determines whether the battery monitoring information contains state parameters within a preset critical range, if the battery monitoring information contains state parameters within the preset critical range, it generates control instructions for each battery pack according to the second control parameter; if the battery monitoring information does not contain state parameters within the preset critical range, it generates control instructions for each battery pack according to the first control parameter.

8. The vehicle battery control method according to claim 7, characterized in that, The at least one area controller includes a first area controller and a second area controller; the first area controller is communicatively connected to the second area controller, and the second area controller is connected to each battery pack; The method further includes: When the first area controller is in a failed state, the second area controller receives the battery monitoring information sent by the battery monitoring unit and generates control instructions for each battery pack based on the battery monitoring information. Alternatively, when the second area controller is in a failed state, the first area controller is used to receive the battery monitoring information sent by the battery monitoring unit and generate control instructions for each battery pack based on the battery monitoring information.

9. The vehicle battery control method according to claim 8, characterized in that, The steps for generating control commands for each battery pack based on the second control parameter include: The area controller calculates the second control parameter by weighting it with a preset correction value to obtain a third control parameter corresponding to each battery pack, and generates control commands for each battery pack based on the third control parameter.

10. The vehicle battery control method according to claim 9, characterized in that, There are multiple standard values ​​corresponding to the preset correction value, and each standard value corresponds to a different battery type. Before the step of weighting the second control parameter with the preset correction value, the following is also included: The area controller determines the standard value of the preset correction value based on the battery type corresponding to the battery pack.

11. The vehicle battery control method according to claim 7, characterized in that, The method for generating the parameter prediction model includes: Collect cell state parameters during battery operation and establish a battery parameter sample set based on the collected cell state parameters; The preset network model is trained using battery parameter samples from the battery parameter sample set to obtain the trained parameter prediction model.

12. A vehicle, characterized in that, include: The system includes a memory, a processor, and a vehicle battery control program stored in the memory and executable on the processor, wherein the processor, when executing the vehicle battery control program, implements the steps of the vehicle battery control method as described in any one of claims 7-11.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more programs that can be executed by one or more processors to implement the steps of the vehicle battery control method as described in any one of claims 7-11.

Images