Battery management system control method and device, server and storage medium

Abstract

The application relates to the technical field of battery management systems, in particular to a battery management system control method and device, a server and a storage medium, wherein the method comprises the following steps: obtaining historical data of a battery management system (BMS) uploaded by a vehicle; predicting cloud parameters of the BMS according to the historical data, and obtaining vehicle-end parameters uploaded by the vehicle, wherein the vehicle-end parameters of the BMS are predicted by the vehicle according to current data of a battery pack; and adaptively adjusting a control algorithm of the BMS on the vehicle according to the cloud parameters and the vehicle-end parameters, wherein the BMS is controlled by the vehicle according to the adjusted control algorithm. Therefore, the problems that the fusion of the local BMS at the vehicle end and the cloud BMS is not considered in the prior art, and the BMS cannot be individually controlled are solved.

Description

Technical Field

[0001] This invention relates to the field of battery management system technology, and more specifically to battery management system control methods, devices, servers, and storage media. Background Technology

[0002] With the promotion of new energy vehicles and their increasing share in the automotive market, the massive amounts of data on cloud platforms have become a valuable asset for every automaker. Real-time recording of vehicle data and processing of this massive amount of data, combined with the integration of BMS (Battery Management System) and cloud-based big data algorithms, can overcome the limitations of on-board BMS computing resources, endowing the BMS with more functions, providing personalized user strategies, and significantly improving the performance of both the BMS and the entire vehicle.

[0003] Vehicle-side BMS algorithms are primarily determined by testing under average battery usage conditions. However, in actual user situations, differences in driving styles and usage habits mean that vehicle-side BMS cannot provide personalized solutions for every individual user. Currently, most cloud-based BMS systems are designed with algorithms based on massive amounts of data in the cloud, without considering the advantages of a local vehicle-side BMS. Summary of the Invention

[0004] One objective of this invention is to provide a battery management system control method to solve the problem that the prior art does not consider the integration of vehicle-side local BMS and cloud-based BMS to achieve personalized differential control of BMS; a second objective is to provide a battery management system control device; a third objective is to provide a server; and a fourth objective is to provide a computer-readable storage medium.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A battery management system control method is applied to a server, comprising the following steps: acquiring historical data of the battery management system (BMS) uploaded by a vehicle; predicting cloud parameters of the BMS based on the historical data, and acquiring vehicle-side parameters uploaded by the vehicle, wherein the vehicle predicts the vehicle-side parameters of the BMS based on current data of the battery pack; adaptively adjusting the control algorithm of the BMS on the vehicle based on the cloud parameters and the vehicle-side parameters, wherein the vehicle controls the BMS according to the adjusted control algorithm.

[0007] Based on the above technical means, the embodiments of this application can predict the cloud parameters of the BMS based on the historical data of the vehicle's local BMS, and predict the vehicle-side parameters of the BMS using the current data of the battery pack. Taking into full account different user habits, the control algorithm of the vehicle's BMS is adaptively adjusted according to the cloud parameters and vehicle-side parameters. This fully utilizes the advantages of cloud computing resources and the advantages of vehicle-side retention of user habits to achieve control and management of the battery management system, thereby significantly improving the performance of the BMS and the entire vehicle.

[0008] Furthermore, the cloud parameters include cloud SOH, cloud actual driving power, cloud average daily driving mileage, cloud actual charging time, cloud charging SOC range, cloud average battery pack temperature, and cloud maximum temperature of individual cells. The vehicle-side parameters include vehicle-side SOH, vehicle-side average daily driving mileage, vehicle-side actual charging time, and vehicle-side charging SOC range.

[0009] Furthermore, the control algorithm includes one or more of the following: power algorithm, charging algorithm, SOC usage range, and thermal management strategy.

[0010] Furthermore, the control algorithm for adaptively adjusting the BMS on the vehicle based on the cloud parameters and the vehicle-side parameters includes: determining a recharge reference table based on the cloud SOH, the cloud actual driving power, and the cloud average daily driving mileage; adjusting the recharge reference table based on the vehicle-side SOH and the vehicle-side average daily driving mileage; and adjusting the power algorithm based on the adjusted recharge reference table; determining a reference charging table based on the cloud SOH, the cloud average daily driving mileage, the cloud actual charging time, and the cloud charging SOC range; adjusting the reference charging table based on the vehicle-side SOH, the vehicle-side actual charging time, and the vehicle-side charging SOC range; and adjusting the charging algorithm based on the adjusted reference charging table; determining a reference SOC range based on the cloud average daily driving mileage and the cloud driving SOC range; adjusting the reference SOC range based on the vehicle-side actual attenuation, the vehicle-side average daily driving mileage, and the vehicle-side driving SOC range; and adjusting the SOC usage range based on the adjusted reference SOC range; and adjusting the thermal management strategy based on the cloud SOH, the cloud average battery pack temperature, and the cloud maximum temperature of a single battery cell.

[0011] Based on the above technical means, the embodiments of this application can adaptively adjust the BMS algorithm on the vehicle according to different situations, and adaptively control the vehicle-side power algorithm, charging algorithm, SOC usage range, and thermal management strategy.

[0012] Furthermore, the historical data includes battery pack temperature, state of charge (SOC), battery pack current, battery pack voltage, and current cumulative total time (t). now Current cumulative throughput Ahnow And one or more of the mileage.

[0013] Furthermore, the formula for calculating SOH is as follows:

[0014] SOH=f1(T,I c ,OC1,Ah)+2(T,SOC2,t),

[0015] Where f1 is cyclic decay; f2 is calendar decay; T is temperature; I c t represents the charging rate; SOC1 represents the SOC range; Ah represents the cumulative charge throughput; SOC2 represents the storage SOC; and t represents the storage time.

[0016] Furthermore, the vehicle predicts the vehicle-side parameters of the BMS based on the current data of the battery pack, including: predicting the vehicle's driving frequency and usage habits based on the SOH, the vehicle's cumulative mileage, the daily average mileage, the driving SOC range, and the actual driving power.

[0017] Based on the above-mentioned technical means, the embodiments of this application can predict the driving frequency and usage habits of the vehicle based on the current data of the battery pack, and fully take into account the usage habits of different users.

[0018] A battery management system control device is applied to a server, comprising: an acquisition module for acquiring historical data of the battery management system (BMS) uploaded by a vehicle; a prediction module for predicting cloud parameters of the BMS based on the historical data and acquiring vehicle-side parameters uploaded by the vehicle, wherein the vehicle predicts the vehicle-side parameters of the BMS based on current data of the battery pack; and a control module for adaptively adjusting the control algorithm of the BMS on the vehicle based on the cloud parameters and the vehicle-side parameters, wherein the vehicle controls the BMS according to the adjusted control algorithm.

[0019] Furthermore, the cloud parameters include cloud SOH, cloud actual driving power, cloud average daily driving mileage, cloud actual charging time, cloud charging SOC range, cloud average battery pack temperature, and cloud maximum temperature of individual cells. The vehicle-side parameters include vehicle-side SOH, vehicle-side average daily driving mileage, vehicle-side actual charging time, and vehicle-side charging SOC range.

[0020] Furthermore, the control algorithm includes one or more of the following: power algorithm, charging algorithm, SOC usage range, and thermal management strategy.

[0021] Furthermore, the control module is further configured to: determine a recharge reference table based on the cloud-based SOH, the cloud-based actual driving power, and the cloud-based average daily driving mileage; adjust the recharge reference table based on the vehicle-side SOH and the vehicle-side average daily driving mileage; and adjust the power algorithm based on the adjusted recharge reference table; determine a reference charging table based on the cloud-based SOH, the cloud-based average daily driving mileage, the cloud-based actual charging time, and the cloud-based charging SOC range; adjust the reference charging table based on the vehicle-side SOH, the vehicle-side actual charging time, and the vehicle-side charging SOC range; and adjust the charging algorithm based on the adjusted reference charging table; determine a reference SOC range based on the cloud-based average daily driving mileage and the cloud-based driving SOC range; adjust the reference SOC range based on the vehicle-side actual attenuation, the vehicle-side average daily driving mileage, and the vehicle-side driving SOC range; and adjust the SOC usage range based on the adjusted reference SOC range; and adjust the thermal management strategy based on the cloud-based SOH, the cloud-based average battery pack temperature, and the cloud-based maximum temperature of a single battery cell.

[0022] Furthermore, the historical data includes battery pack temperature, state of charge (SOC), battery pack current, battery pack voltage, and current cumulative total time (t). now Current cumulative throughput Ah now And one or more of the mileage.

[0023] Furthermore, the formula for calculating SOH is as follows:

[0024] SOH=f1(T,I c ,OC1,Ah)+2(T,SOC2,t),

[0025] Where f1 is cyclic decay; f2 is calendar decay; T is temperature; I c t represents the charging rate; SOC1 represents the SOC range; Ah represents the cumulative charge throughput; SOC2 represents the storage SOC; and t represents the storage time.

[0026] Furthermore, the prediction module is further used to predict the vehicle's driving frequency and usage habits based on the SOH, the vehicle's cumulative mileage, the daily average mileage, the driving SOC range, and the actual driving power.

[0027] A server includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the battery management system control method as described in the above embodiments.

[0028] A computer-readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the battery management system control method as described in the above embodiments.

[0029] The beneficial effects of this invention are:

[0030] (1) The embodiments of this application can predict the cloud parameters of the BMS based on the historical data of the local BMS of the vehicle, and predict the vehicle-side parameters of the BMS based on the current data of the battery pack. It fully considers different user habits, and adaptively adjusts the control algorithm of the BMS on the vehicle based on the cloud parameters and vehicle-side parameters. It makes full use of the computing resource advantages of the cloud and the advantage of retaining user habits on the vehicle side to realize the control and management of the battery management system, and fully improve the performance of the BMS and the whole vehicle.

[0031] (2) The embodiments of this application can adaptively adjust the BMS algorithm on the vehicle according to different situations, and adaptively control the vehicle-side power algorithm, charging algorithm, SOC usage range, and thermal management strategy.

[0032] (3) The embodiments of this application can predict the driving frequency and usage habits of the vehicle based on the current data of the battery pack, and fully take into account the usage habits of different users.

[0033] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0034] Figure 1 A flowchart of a battery management system control method provided in an embodiment of this application;

[0035] Figure 2 This is a schematic diagram of the power limiting process provided in an embodiment of this application;

[0036] Figure 3 This is a schematic diagram of the charging rate limiting process provided in the embodiments of this application;

[0037] Figure 4 This is a schematic diagram of the SOC window adjustment process provided in the embodiments of this application;

[0038] Figure 5 This is a schematic diagram of the cooling start-up temperature adjustment provided in an embodiment of this application;

[0039] Figure 6 A flowchart of a battery management system control method provided in one embodiment of this application;

[0040] Figure 7 A schematic diagram of the battery management system control device provided in the embodiments of this application;

[0041] Figure 8 This is a schematic diagram of the server structure provided in an embodiment of this application. Detailed Implementation

[0042] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0043] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0044] Specifically, Figure 1 This is a flowchart of a battery management system control method provided in an embodiment of this application.

[0045] like Figure 1 As shown, the battery management system control method includes the following steps:

[0046] In step S101, historical data uploaded by the vehicle to the battery management system (BMS) is obtained.

[0047] Historical data includes battery pack temperature, state of charge (SOC), battery pack current, battery pack voltage, and current cumulative total time (t). now Current cumulative throughput Ah now And one or more of the mileage.

[0048] It should be noted that the embodiments of this application perform data cleaning on the data uploaded by the battery management system to obtain historical data, including: processing the battery pack temperature by the big data platform to obtain the historical average temperature; processing the SOC signal to obtain the historical average SOC usage under driving conditions and the historical average SOC usage under stationary conditions; processing the current signal to obtain the historical average charging rate under driving conditions; and processing the battery pack current and total battery pack voltage signals to obtain the historical actual driving power. The big data processing includes functions such as data acquisition, data cleaning and preprocessing, data storage, data encryption, data download, data backup, data analysis and modeling, and data visualization. It should also possess the analytical engines and machine learning algorithm databases required for data mining to realize the modeling and analysis of big data analysis scenarios.

[0049] In step S102, the cloud parameters of the BMS are predicted based on historical data, and the vehicle-side parameters uploaded by the vehicle are obtained. The vehicle predicts the vehicle-side parameters of the BMS based on the current data of the battery pack.

[0050] Among them, cloud parameters include cloud SOH (State of Health, power battery health status), cloud actual driving power, cloud average daily driving range, cloud actual charging time, cloud charging SOC range, cloud average battery pack temperature, and cloud maximum temperature of individual cells. Vehicle-side parameters include vehicle-side SOH, vehicle-side average daily driving range, vehicle-side actual charging time, and vehicle-side charging SOC range.

[0051] It is understood that the embodiments of this application can predict the cloud parameters of the BMS based on the historical data of the BMS, and obtain the vehicle-side parameters uploaded by the vehicle, for subsequent adjustment of the control algorithm of the BMS on the vehicle.

[0052] In this embodiment, the formula for calculating SOH is:

[0053] SOH=f1(T,I c ,OC1,Ah)+2(T,SOC2,t),

[0054] Where f1 is cyclic decay; f2 is calendar decay; T is temperature; I c t represents the charging rate; SOC1 represents the SOC range; Ah represents the cumulative charge throughput; SOC2 represents the storage SOC; and t represents the storage time.

[0055] In this embodiment of the application, the vehicle predicts the vehicle-side parameters of the BMS based on the current data of the battery pack, including: predicting the vehicle's driving frequency and usage habits based on SOH, cumulative mileage, average daily mileage, driving SOC range and actual driving power.

[0056] It is understood that the embodiments of this application can predict the current driving frequency and user's vehicle usage habits based on SOH, vehicle cumulative mileage, daily average mileage, driving SOC range, and actual driving power.

[0057] In step S103, the control algorithm of the BMS on the vehicle is adaptively adjusted according to the cloud parameters and vehicle parameters, wherein the vehicle controls the BMS according to the adjusted control algorithm.

[0058] It is understood that the embodiments of this application can adaptively adjust the BMS control algorithm on the vehicle based on cloud parameters and vehicle-side parameters, and the vehicle controls the BMS according to the adjusted control algorithm. The control algorithm includes one or more of the following: power algorithm, charging algorithm, SOC usage range, and thermal management strategy.

[0059] In this embodiment, the control algorithm of the BMS on the vehicle is adaptively adjusted based on cloud parameters and vehicle parameters, including: determining a recharge reference table based on cloud SOH, cloud actual driving power, and cloud average daily driving mileage; adjusting the recharge reference table based on vehicle SOH and vehicle average daily driving mileage; and adjusting the power algorithm based on the adjusted recharge reference table; determining a reference charging table based on cloud SOH, cloud average daily driving mileage, cloud actual charging time, and cloud charging SOC range; adjusting the reference charging table based on vehicle SOH, vehicle actual charging time, and vehicle charging SOC range; and adjusting the charging algorithm based on the adjusted reference charging table; determining a reference SOC range based on cloud average daily driving mileage and cloud driving SOC range; adjusting the reference SOC range based on vehicle actual attenuation, vehicle average daily driving mileage, and vehicle driving SOC range; and adjusting the SOC usage range based on the adjusted reference SOC range; and adjusting the thermal management strategy based on cloud SOH, cloud average battery pack temperature, and cloud maximum temperature of individual cells.

[0060] It is understood that the embodiments of this application can adaptively adjust the BMS control algorithm on the vehicle, including but not limited to adaptive control of the vehicle-side power algorithm, charging algorithm, SOC usage range, and thermal management strategy. The specific algorithm is as follows:

[0061] Power Algorithm: Based on cloud-based big data predictions of battery pack degradation, actual user power consumption, and average daily mileage, the vehicle-side power algorithm adaptively controls the power output. A baseline power level (MAP) for battery pack recharging is established. Based on this MAP, adjustments are made to the recharging baseline MAP according to the user's actual degradation and average daily mileage. User usage frequency is segmented based on daily mileage. Restrictions are implemented for high-frequency use, harsh operating conditions, and scenarios with significant battery degradation. The adjustment strategy is illustrated in the diagram below. Figure 2 As shown.

[0062] Charging Algorithm: Based on cloud-based big data predictions of battery pack degradation, average daily mileage, actual charging time, and charging SOC range, a baseline charging MAP is established for the battery pack. This MAP is then adjusted upwards or downwards based on the user's actual degradation, charging time, and charging SOC range. The charging rate is limited to account for battery degradation. For scenarios with good battery health, where the user primarily uses fast charging and the initial charging SOC is low, the charging rate is appropriately increased. A diagram illustrating the rate factor is shown below. Figure 3 As shown.

[0063] SOC Usage Range: Based on user habits such as average daily mileage and driving SOC range, a baseline SOC range for the battery pack is established. This baseline SOC range is then shifted downwards based on the user's actual battery degradation and their average daily mileage and driving SOC range. Regarding SOH degradation, if the user's driving SOC range is narrow, SOCmax is adjusted downwards; if the user's driving SOC range is wide, both SOCmax and SOCmin need to be adjusted downwards. The adjustment range should comprehensively consider the impact on the user's actual driving habits without affecting the user's instrument panel perception. A schematic diagram of the SOC usage window adjustment is shown below. Figure 4 As shown.

[0064] Thermal Management Strategy: Based on cloud-based big data predictions of battery pack degradation and average and maximum battery pack temperatures, the vehicle-side thermal management strategy is adaptively controlled. Regarding State of Harshness (SOH) degradation, if the average temperature experienced by the user exceeds a certain value, the battery pack cooling activation temperature is dynamically increased. The adjustment range should comprehensively consider the combined impact on user charging efficiency and driving energy consumption. A diagram illustrating the cooling activation temperature adjustment is shown below. Figure 5 As shown.

[0065] In summary, the specific flow of the battery system control method in this application embodiment is as follows: Figure 6 As shown:

[0066] Step 1: Obtain temperature, SOC, current, voltage, usage time, and cumulative mileage information from the cloud.

[0067] Step 2: The cloud calculates the battery degradation SOH based on the data: SOH = Cyclic degradation f(SOH1) + Calendar degradation f(SOH2).

[0068] The cloud-based lifespan prediction algorithm, based on cloud data, applies cluster analysis and neural network modeling methods to comprehensively determine the battery lifespan model as follows:

[0069] SOH=f1(T,I c ,OC1,Ah)+2(T,SOC2,t),

[0070] Where f1 is the cyclic decay; f2 is the calendar decay; T is the temperature (K); I c t represents the charging rate; SOC1 represents the SOC range; Ah represents the cumulative charge throughput; SOC2 represents the storage SOC; and t represents the storage time (days).

[0071] Step 3: Dynamically adjust the charging power based on the average daily mileage and the average actual charging power.

[0072] Step 4: Dynamically adjust the charging strategy based on the statistical results of SOC usage range, charging method, and charging time.

[0073] Step 5: Dynamically adjust the SOC usage window based on the average daily mileage and SOH decay.

[0074] Step 6: Dynamically adjust the cooling start temperature based on the average temperature and SOH decay.

[0075] According to the battery management system control method proposed in the embodiments of this application, the cloud parameters of the BMS can be predicted based on the historical data of the local BMS of the vehicle, and the vehicle-side parameters of the BMS can be predicted using the current data of the battery pack. It fully considers different user habits, and adaptively adjusts the control algorithm of the BMS on the vehicle based on the cloud parameters and the vehicle-side parameters. It makes full use of the computing resource advantages of the cloud and the advantage of the vehicle-side retaining user habits to achieve control and management of the battery management system, and fully improve the performance of the BMS and the whole vehicle.

[0076] Next, the battery management system control device according to the embodiments of this application is described with reference to the accompanying drawings.

[0077] Figure 7 This is a block diagram of the battery management system control device according to an embodiment of this application.

[0078] like Figure 7 As shown, the battery management system control device 10 is applied to a server and includes: an acquisition module 100, a prediction module 200, and a control module 300.

[0079] The acquisition module 100 is used to acquire historical data uploaded by the vehicle to the battery management system (BMS); the prediction module 200 is used to predict the cloud parameters of the BMS based on the historical data and acquire the vehicle-side parameters uploaded by the vehicle, wherein the vehicle predicts the vehicle-side parameters of the BMS based on the current data of the battery pack; the control module 300 is used to adaptively adjust the control algorithm of the BMS on the vehicle based on the cloud parameters and the vehicle-side parameters, wherein the vehicle controls the BMS according to the adjusted control algorithm.

[0080] In this embodiment of the application, the cloud parameters include cloud SOH, cloud actual driving power, cloud average daily driving mileage, cloud actual charging time, cloud charging SOC range, cloud average battery pack temperature, and cloud maximum temperature of individual cells. The vehicle-side parameters include vehicle-side SOH, vehicle-side average daily driving mileage, vehicle-side actual charging time, and vehicle-side charging SOC range.

[0081] In the embodiments of this application, the control algorithm includes one or more of the following: power algorithm, charging algorithm, SOC usage range, and thermal management strategy.

[0082] In this embodiment, the control module 300 is further configured to: determine a recharge reference table based on cloud-based SOH, cloud-based actual driving power, and cloud-based average daily driving mileage; adjust the recharge reference table based on vehicle-side SOH and vehicle-side average daily driving mileage; and adjust the power algorithm based on the adjusted recharge reference table; determine a reference charging table based on cloud-based SOH, cloud-based average daily driving mileage, cloud-based actual charging time, and cloud-based charging SOC range; adjust the reference charging table based on vehicle-side SOH, vehicle-side actual charging time, and vehicle-side charging SOC range; and adjust the charging algorithm based on the adjusted reference charging table; determine a reference SOC range based on cloud-based average daily driving mileage and cloud-based driving SOC range; adjust the reference SOC range based on vehicle-side actual attenuation, vehicle-side average daily driving mileage, and vehicle-side driving SOC range; and adjust the SOC usage range based on the adjusted reference SOC range; and adjust the thermal management strategy based on cloud-based SOH, cloud-based average battery pack temperature, and cloud-based maximum temperature of individual cells.

[0083] In this embodiment of the application, historical data includes battery pack temperature, state of charge (SOC), battery pack current, battery pack voltage, and current cumulative total time (t). now Current cumulative throughput Ah now And one or more of the mileage.

[0084] In this embodiment, the formula for calculating SOH is:

[0085] SOH=f1(T,I c ,OC1,Ah)+2(T,SOC2,t),

[0086] Where f1 is cyclic decay; f2 is calendar decay; T is temperature; I c t represents the charging rate; SOC1 represents the SOC range; Ah represents the cumulative charge throughput; SOC2 represents the storage SOC; and t represents the storage time.

[0087] In this embodiment of the application, the prediction module 200 is further used to predict the vehicle's driving frequency and usage habits based on SOH, cumulative vehicle mileage, daily average mileage, driving SOC range, and actual driving power.

[0088] It should be noted that the foregoing explanation of the battery management system control method embodiment also applies to the battery management system control device of this embodiment, and will not be repeated here.

[0089] According to the battery management system control device proposed in the embodiments of this application, the cloud parameters of the BMS can be predicted based on the historical data of the local BMS of the vehicle, and the vehicle-side parameters of the BMS can be predicted using the current data of the battery pack. Taking into full account different user habits, the control algorithm of the BMS on the vehicle is adaptively adjusted according to the cloud parameters and the vehicle-side parameters. By making full use of the computing resource advantages of the cloud and the advantage of retaining user habits on the vehicle, the control and management of the battery management system can be realized, thereby significantly improving the performance of the BMS and the whole vehicle.

[0090] Figure 8 A schematic diagram of the structure of a server provided in an embodiment of this application. The server may include:

[0091] The memory 801, the processor 802, and the computer program stored on the memory 801 and capable of running on the processor 802.

[0092] When the processor 802 executes the program, it implements the battery management system control method provided in the above embodiments.

[0093] Furthermore, the vehicle also includes:

[0094] Communication interface 803 is used for communication between memory 801 and processor 802.

[0095] The memory 801 is used to store computer programs that can run on the processor 802.

[0096] The memory 801 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0097] If the memory 801, processor 802, and communication interface 803 are implemented independently, then the communication interface 803, memory 801, and processor 802 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 8 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.

[0098] Optionally, in a specific implementation, if the memory 801, processor 802, and communication interface 803 are integrated on a single chip, then the memory 801, processor 802, and communication interface 803 can communicate with each other through an internal interface.

[0099] The processor 802 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0100] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described battery management system control method.

[0101] 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.

[0102] 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.

[0103] Any process or method described 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.

[0104] 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. For example, 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 (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0105] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0106] 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 battery management system control method, characterized in that, The method is applied to a server, and the method includes the following steps: Retrieve historical data uploaded by the vehicle to the Battery Management System (BMS); The cloud parameters of the BMS are predicted based on the historical data, and the vehicle-side parameters uploaded by the vehicle are obtained. The vehicle predicts the vehicle-side parameters of the BMS based on the current data of the battery pack. The control algorithm of the BMS on the vehicle is adaptively adjusted according to the cloud parameters and the vehicle parameters, wherein the vehicle controls the BMS according to the adjusted control algorithm; The cloud parameters include cloud SOH, cloud actual driving power, cloud average daily driving mileage, cloud actual charging time, cloud charging SOC range, cloud average battery pack temperature, and cloud maximum temperature of individual cells. The vehicle-side parameters include vehicle-side SOH, vehicle-side average daily driving mileage, vehicle-side actual charging time, and vehicle-side charging SOC range. The control algorithm that adaptively adjusts the BMS on the vehicle based on the cloud parameters and the vehicle-side parameters includes: A charging reference table is determined based on the cloud-based SOH, the cloud-based actual driving power, and the cloud-based average daily mileage. The charging reference table is then adjusted based on the vehicle-side SOH and the vehicle-side average daily mileage. Finally, the power algorithm is adjusted based on the adjusted charging reference table. A benchmark charging meter is determined based on the cloud-based SOH, the cloud-based average daily mileage, the cloud-based actual charging time, and the cloud-based charging SOC range. The benchmark charging meter is then adjusted based on the vehicle-side SOH, the vehicle-side actual charging time, and the vehicle-side charging SOC range. Finally, the charging algorithm is adjusted based on the adjusted benchmark charging meter. A baseline SOC range is determined based on the cloud-based average daily mileage and cloud-based SOC range. The baseline SOC range is then adjusted based on the vehicle-side attenuation, the vehicle-side average daily mileage, and the vehicle-side SOC range. Finally, the SOC usage range is adjusted based on the adjusted baseline SOC range. The thermal management strategy is adjusted based on the cloud-based State of Health (SOH), the average temperature of the cloud-based battery pack, and the maximum temperature of a single cloud-based cell.

2. The battery management system control method according to claim 1, characterized in that, The control algorithm includes one or more of the following: power algorithm, charging algorithm, SOC usage range, and thermal management strategy.

3. The battery management system control method according to claim 1, characterized in that, The historical data includes battery pack temperature, state of charge (SOC), battery pack current, battery pack voltage, and current total accumulated time. Current cumulative throughput And one or more of the mileage.

4. The battery management system control method according to claim 2, characterized in that, The formula for calculating SOH is: ， in, It is a cyclic decay; Calendar decay; For temperature; This refers to the charging rate; The SOC range for cloud-based charging; This represents the cumulative charge throughput. For storage SOC; For storage time.

5. The battery management system control method according to claim 2, characterized in that, The vehicle predicts the vehicle-side parameters of the BMS based on the current data of the battery pack, including: The vehicle's driving frequency and usage habits are predicted based on the vehicle's SOH, cumulative mileage, average daily mileage, SOC range, and actual power consumption.

6. A battery management system control device, characterized in that, The device is used in a server, wherein the device includes: The acquisition module is used to acquire historical data uploaded by the vehicle to the Battery Management System (BMS). The prediction module is used to predict the cloud parameters of the BMS based on the historical data and to obtain the vehicle-side parameters uploaded by the vehicle, wherein the vehicle predicts the vehicle-side parameters of the BMS based on the current data of the battery pack. The control module is used to adaptively adjust the control algorithm of the BMS on the vehicle according to the cloud parameters and the vehicle parameters, wherein the vehicle controls the BMS according to the adjusted control algorithm; The cloud parameters include cloud SOH, cloud actual driving power, cloud average daily driving mileage, cloud actual charging time, cloud charging SOC range, cloud average battery pack temperature, and cloud maximum temperature of individual cells. The vehicle-side parameters include vehicle-side SOH, vehicle-side average daily driving mileage, vehicle-side actual charging time, and vehicle-side charging SOC range. The control algorithm that adaptively adjusts the BMS on the vehicle based on the cloud parameters and the vehicle-side parameters includes: A charging reference table is determined based on the cloud-based SOH, the cloud-based actual driving power, and the cloud-based average daily mileage. The charging reference table is then adjusted based on the vehicle-side SOH and the vehicle-side average daily mileage. Finally, the power algorithm is adjusted based on the adjusted charging reference table. A benchmark charging meter is determined based on the cloud-based SOH, the cloud-based average daily mileage, the cloud-based actual charging time, and the cloud-based charging SOC range. The benchmark charging meter is then adjusted based on the vehicle-side SOH, the vehicle-side actual charging time, and the vehicle-side charging SOC range. Finally, the charging algorithm is adjusted based on the adjusted benchmark charging meter. A baseline SOC range is determined based on the cloud-based average daily mileage and cloud-based SOC range. The baseline SOC range is then adjusted based on the vehicle-side attenuation, the vehicle-side average daily mileage, and the vehicle-side SOC range. Finally, the SOC usage range is adjusted based on the adjusted baseline SOC range. The thermal management strategy is adjusted based on the cloud-based State of Health (SOH), the average temperature of the cloud-based battery pack, and the maximum temperature of a single cloud-based cell.

7. A server, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the battery management system control method as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the battery management system control method as described in any one of claims 1-5.

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