Heating control method and device for vehicle battery, electronic equipment and medium

Abstract

The invention provides a heating control method and device for a vehicle battery, electronic equipment and a medium. The heating control method comprises the steps of obtaining current driving related characteristic data of a target vehicle; inputting the driving related characteristic data into a pre-trained driving mileage prediction model, and predicting the predicted driving mileage of the target vehicle through the driving mileage prediction model; inputting the predicted driving mileage and the driving related characteristic data into a pre-trained heating strategy matching model, and obtaining a current heating strategy of a target vehicle for a vehicle battery output by the heating strategy matching model; and heating the target vehicle battery based on the heating parameter indicated by the heating strategy. By adopting the technical scheme provided by the invention, the driving demand of the user can be automatically identified, the user does not need to preset the driving demand, the optimal matching of the driving heating strategy is realized, and unnecessary electric energy consumption possibly caused by adopting a fixed heating strategy is avoided on the premise of meeting the driving mileage demand.

Description

Technical Field

[0001] The present application relates to the field of battery heating technology, and in particular to a heating control method, device, electronic device, and medium for a vehicle battery. Background Art

[0002] Power batteries degrade in low-temperature winter conditions. For pure electric vehicles, as the battery is the sole source of power, its performance degradation directly impacts the vehicle's range in low-temperature environments. Current vehicles generally use fixed on-the-go heating strategies, or require users to pre-set mileage and then adjust the heating strategy based on that mileage.

[0003] However, on the one hand, fixed heating strategies are not suitable for all usage scenarios and cannot be flexibly adjusted according to actual conditions; on the other hand, users are required to make presets in advance, which increases the complexity and redundancy of operations. Summary of the Invention

[0004] In view of this, the embodiments of the present application provide a vehicle battery heating control method, device, electronic device and medium, which can automatically identify the user's driving needs without the user having to preset the driving needs, achieve the optimal matching of the driving heating strategy, and avoid the unnecessary energy consumption that may be caused by the use of a fixed heating strategy while meeting the mileage requirements.

[0005] This application mainly includes the following aspects: In a first aspect, an embodiment of the present application provides a heating control method for a vehicle battery, the heating control method comprising: Obtaining the target vehicle's current driving-related characteristic data; Inputting the driving-related characteristic data into a pre-trained mileage prediction model, and predicting the expected mileage of the target vehicle using the mileage prediction model; Inputting the estimated mileage and the driving-related characteristic data into a pre-trained heating strategy matching model, and obtaining a current heating strategy for the vehicle battery of the target vehicle output by the heating strategy matching model; The target vehicle battery is heated based on the heating parameters indicated by the heating strategy.

[0006] Furthermore, the mileage prediction model is established in the following manner: Preprocessing the historical driving related data of the target vehicle within a preset time period to obtain the preprocessed historical driving related data; Extracting historical driving-related feature data related to a single mileage of the vehicle from the pre-processed historical driving-related data; Based on the training data set in the historical driving related feature data, the initial mileage prediction model is trained to obtain a trained initial mileage prediction model; Inputting a validation dataset from the historical driving-related feature data into the trained initial mileage prediction model to obtain an error of the trained initial mileage prediction model; If the error is less than a preset threshold, the trained initial mileage prediction model is determined as the mileage prediction model.

[0007] Furthermore, the heating strategy matching model is established in the following manner: Based on a training data set in historical driving related feature data, an initial heating strategy matching model is trained to obtain a trained initial heating strategy matching model; Inputting a validation data set from the historical driving-related feature data into the trained initial heating strategy matching model to obtain an error of the trained initial heating strategy matching model; If the error is less than a preset threshold, the trained initial heating strategy matching model is determined as the heating strategy matching model.

[0008] Furthermore, the driving-related characteristic data includes at least one of the following items: vehicle driving date, vehicle driving date type, vehicle driving time period, vehicle driving times, vehicle model, vehicle external ambient temperature, battery initial temperature, battery initial state of charge, vehicle single driving mileage, battery heating power, battery heating start-up temperature, battery heating exit temperature, battery heating state of charge when battery heating is turned on, battery heating state of charge when battery heating is exited, and battery heating power consumption.

[0009] In a second aspect, an embodiment of the present application further provides a heating control device for a vehicle battery, the control device comprising: An acquisition module obtains the current driving-related characteristic data of the target vehicle; a mileage prediction module, which inputs the driving-related characteristic data into a pre-trained mileage prediction model and predicts the expected mileage of the target vehicle using the mileage prediction model; a heating strategy matching module, inputting the estimated mileage and the driving-related characteristic data into a pre-trained heating strategy matching model, and obtaining a current heating strategy for the vehicle battery of the target vehicle output by the heating strategy matching model; The heating module heats the target vehicle battery based on the heating device indicated by the heating strategy.

[0010] Furthermore, the mileage prediction model is established in the following manner: Preprocessing the historical driving related data of the target vehicle within a preset time period to obtain the preprocessed historical driving related data; Extracting historical driving-related feature data related to a single mileage of the vehicle from the pre-processed historical driving-related data; Based on the training data set in the historical driving related feature data, the initial mileage prediction model is trained to obtain a trained initial mileage prediction model; Inputting a validation dataset from the historical driving-related feature data into the trained initial mileage prediction model to obtain an error of the trained initial mileage prediction model; If the error is less than a preset threshold, the trained initial mileage prediction model is determined as the mileage prediction model.

[0011] Furthermore, the heating strategy matching model is established in the following manner: Based on a training data set in historical driving related feature data, an initial heating strategy matching model is trained to obtain a trained initial heating strategy matching model; Inputting a validation data set from the historical driving-related feature data into the trained initial heating strategy matching model to obtain an error of the trained initial heating strategy matching model; If the error is less than a preset threshold, the trained initial heating strategy matching model is determined as the heating strategy matching model.

[0012] Furthermore, the driving-related characteristic data includes at least one of the following items: vehicle driving date, vehicle driving date type, vehicle driving time period, vehicle driving times, vehicle model, vehicle external ambient temperature, battery initial temperature, battery initial state of charge, vehicle single driving mileage, battery heating power, battery heating start-up temperature, battery heating exit temperature, battery heating state of charge when battery heating is turned on, battery heating state of charge when battery heating is exited, and battery heating power consumption.

[0013] In a third aspect, an embodiment of the present application further provides an electronic device comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and the machine-readable instructions are executed by the processor to execute the steps of the vehicle battery heating control method described in the first aspect or any possible implementation of the first aspect.

[0014] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the vehicle battery heating control method described in the first aspect or any possible embodiment of the first aspect are executed.

[0015] The embodiments of the present application provide a vehicle battery heating control method, device, electronic device, and medium, which obtain current driving-related characteristic data of a target vehicle; input the driving-related characteristic data into a pre-trained driving mileage prediction model, and predict the expected driving mileage of the target vehicle using the driving mileage prediction model; input the expected driving mileage and the driving-related characteristic data into a pre-trained heating strategy matching model, and obtain the target vehicle's current heating strategy for the vehicle battery output by the heating strategy matching model; and heat the target vehicle battery based on heating parameters indicated by the heating strategy.

[0016] In this way, there is no need for users to preset driving requirements, and the optimal matching of driving heating strategies can be achieved. On the premise of meeting mileage requirements, unnecessary energy consumption that may be caused by adopting a fixed heating strategy can be avoided.

[0017] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 One of the flow charts of a vehicle battery heating control method provided in an embodiment of the present application is shown; Figure 2 A second flowchart of a vehicle battery heating control method provided in an embodiment of the present application is shown; Figure 3 An example diagram showing historical driving related feature data; Figure 4 A third flowchart of a vehicle battery heating control method provided in an embodiment of the present application is shown; Figure 5 A schematic structural diagram of a vehicle battery heating control device provided in an embodiment of the present application is shown; Figure 6A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of illustration and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowcharts can be implemented out of sequence, and steps without logical context can be reversed or implemented simultaneously. In addition, those skilled in the art, under the guidance of the contents of this application, can add one or more other operations to the flowchart, or remove one or more operations from the flowchart.

[0021] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present application.

[0022] The following methods, devices, electronic devices or computer-readable storage media of the embodiments of the present application can be applied to any scenario requiring vehicle battery heating control. The embodiments of the present application are not limited to specific application scenarios. Any scheme using the vehicle battery heating control method and device provided by the embodiments of the present application is within the scope of protection of this application.

[0023] It's worth noting that power batteries degrade in low-temperature winter conditions. For pure electric vehicles, since the power battery is the sole source of power, its performance degradation directly impacts the vehicle's range in low-temperature environments. Current vehicles generally use fixed heating strategies while driving, or require users to pre-set mileage and then adjust the heating strategy based on that mileage. However, fixed heating strategies aren't suitable for all scenarios and can't be flexibly adjusted to suit actual conditions. Furthermore, requiring users to pre-set these strategies increases operational complexity and redundancy.

[0024] In response to the above problems, the embodiments of the present application propose a vehicle battery heating control method, device, electronic device and medium, which can automatically identify the user's driving needs without the user having to preset the driving needs, achieve the optimal matching of the driving heating strategy, and avoid the unnecessary energy consumption that may be caused by the use of a fixed heating strategy while meeting the mileage requirements.

[0025] To facilitate understanding of the present application, the technical solutions provided in the present application are described in detail below in conjunction with specific embodiments.

[0026] See also Figure 1 , Figure 1 This is one of the flow charts of a vehicle battery heating control method provided in an embodiment of the present application.

[0027] In the embodiment of the present application, the vehicle's range in low-temperature environments is currently improved by selecting a specific heating method (such as motor waste heat, PTC heating, etc.) and setting a corresponding heating strategy. Since the execution of the driving heating strategy relies on the power battery to power the heating component (usually PTC), the vehicle's range is directly related to the net discharge energy after deducting the heating energy consumption. For example, the vehicle defaults to formulating a heating strategy based on a scenario of running from a full charge to the lowest SOC (state of charge). When the starting SOC is at a medium or low level, if the user has not set the mileage, the vehicle will still execute according to the full-charge strategy, which may result in low range benefits from heating, or even the battery being exhausted due to excessive heating before reaching the destination, thereby affecting the vehicle's safety and user experience.

[0028] like Figure 1 As shown in , the vehicle battery heating control method provided by the embodiment of the present application includes the following steps: Step S101: Acquire driving-related characteristic data of a target vehicle.

[0029] Here, the vehicle side collects driving-related characteristic data in real time. The driving-related characteristic data includes at least one of the following items: vehicle travel date, vehicle travel date type, vehicle travel time period, vehicle travel times, vehicle model, vehicle external ambient temperature, battery initial temperature, battery initial state of charge, vehicle single trip mileage, battery heating power, battery heating start temperature, battery heating exit temperature, battery heating start state of charge, battery heating exit state of charge, and battery heating power consumption.

[0030] Step S102: input the driving-related characteristic data into a pre-trained driving mileage prediction model, and predict the expected driving mileage of the target vehicle through the driving mileage prediction model.

[0031] The following combination Figure 2To illustrate how to build a mileage prediction model.

[0032] See also Figure 2 , Figure 2 This is the second flow chart of a vehicle battery heating control method provided in an embodiment of the present application.

[0033] like Figure 2 As shown in , as an example, a mileage prediction model is established in the following way: Step S201 : pre-processing the historical driving related data of the target vehicle within a preset time period to obtain the pre-processed historical driving related data.

[0034] Here, the historical driving related data include: vehicle driving date, vehicle driving time period, vehicle model, frame number, vehicle model code, vehicle external ambient temperature, battery initial temperature, battery initial state of charge, vehicle single mileage, battery heating power, battery heating start temperature, battery heating exit temperature, battery heating power consumption, battery cell maximum temperature, battery cell minimum temperature, battery SOC, battery total voltage and battery total current, etc.

[0035] In step S201, the cloud is used to obtain historical driving data due to its high computing power, massive storage capacity, and historical traceability. After obtaining the historical driving data from the cloud, it is preprocessed. This includes deduplication and removal of data outside the theoretical range.

[0036] Step S202 : extracting historical travel-related feature data related to a single travel mileage of the vehicle from the pre-processed historical travel-related data.

[0037] In driving-related feature data, the correspondence between vehicle travel dates and vehicle travel date attributes, as well as the vehicle travel time period, are key influencing variables of user driving patterns. For these two key influencing variables, separate data tables are constructed in the cloud. Based on historical driving data, mileage under each influencing variable, as well as related data influencing mileage, is extracted. Vehicle travel date attributes include weekdays, weekends, and holidays.

[0038] Here, historical driving-related characteristic data is normalized and used as the independent variable x, with single-trip mileage as the dependent variable y. Machine learning is used to train the model, identifying the impact of driving-related characteristic data on single-trip mileage. Based on this, the relationship y = f(x) between single-trip mileage and driving-related characteristic data is established.

[0039] like Figure 3As shown in , as an example, all the driving events of a vehicle in one year are taken as an example, and the historical driving related feature data of each day is extracted. Specifically, (1) the corresponding relationship between the vehicle driving date and the vehicle driving date attribute is recorded day i (i is an integer from 1 to 366); (2) Record the time period t corresponding to the nth trip in the i-th day i-n (n is an integer not less than 1); (3) Calculate the mileage S corresponding to the nth trip in the i-th day i-n , S i-n =S total-i-n -S total-i-n-initial , where S total-i-n is the total mileage of the vehicle at the end of the nth trip on the i-th day, S total-i-n-initial is the total mileage of the vehicle at the start of the nth trip on the i-th day; (4) record the vehicle model v _num ; (5) Record the ambient temperature T corresponding to each driving date amb-i ; (6) Record the initial battery temperature T corresponding to the nth driving in the i-th day bat-i-n ; (7) Record the battery initial state of charge (SOC) corresponding to the nth driving time in the i-th day i-n ; (8) Record the heating power P during the nth driving process in the i-th day heat-i-n ; (9) Record the heating start temperature T during the nth driving process in the i-th day heat-on-i-n , Heating exit temperature T heat-off-i-n , State of charge SOC when heating is turned on heat-on-i-n and state of charge SOC when heating exits heat-off-i-n ; (10) Calculate the heating power consumption during the nth driving process in the i-th day.

[0040] In an embodiment of the present application, after extracting the historical driving related feature data, the historical driving related feature data is cleaned. In order to ensure the accuracy of the feature calculation, it is necessary to ensure that the cleaned data has continuity and there should be no long-term data loss.

[0041] Step S203 : inputting the verification data set in the historical driving-related feature data into the trained initial mileage prediction model to obtain the error of the trained initial mileage prediction model.

[0042] After model training reaches the end condition, the validation dataset is fed into the trained initial mileage prediction model for evaluation. If the calculated error is less than the preset threshold, the model training is successful. Otherwise, continue adjusting model parameters and iterating training until the model accuracy reaches the target requirement.

[0043] See again Figure 1In step S103 , the estimated mileage and the driving-related characteristic data are input into a pre-trained heating strategy matching model to obtain the current heating strategy for the vehicle battery of the target vehicle output by the heating strategy matching model.

[0044] The following combination Figure 4 To illustrate how to establish a heating strategy matching model.

[0045] See also Figure 4 , Figure 4 This is the third flow chart of a vehicle battery heating control method provided in an embodiment of the present application.

[0046] like Figure 4 As shown in , as an example, a heating strategy matching model is established in the following way: Step S301 : training an initial heating strategy matching model based on a training data set in historical driving-related characteristic data to obtain a trained initial heating strategy matching model.

[0047] Here, the initial heating strategy matching model is trained based on the historical driving-related characteristic data obtained above. The historical driving-related characteristic data is normalized and used as the independent variable x', with the vehicle heating power consumption as the dependent variable y'. Using machine learning, the model is trained to identify the impact of the driving-related characteristic data on the vehicle's heating power consumption per trip. Based on this, the relationship y' = f(x') between the vehicle's heating power consumption per trip and the driving-related characteristic data is established.

[0048] Step S302 : inputting the verification data set in the historical driving-related characteristic data into the trained initial heating strategy matching model to obtain the error of the trained initial heating strategy matching model.

[0049] Step S303: If the error is less than a preset threshold, the trained initial heating strategy matching model is determined as the heating strategy matching model.

[0050] Here, the mileage prediction model and heating strategy matching model trained in the cloud are converted into code and then distributed to the target vehicle's onboard controller via an OTA update. Through vehicle-cloud collaboration, such as OTA updates, these models are iteratively optimized to continuously enhance the user experience. Combined with real-time driving data, the onboard controller automatically calculates and matches the optimal heating strategy that meets the user's mileage requirements, heating the target vehicle's battery.

[0051] In the embodiment of the present application, after the mileage prediction model and heating strategy matching model are trained, historical vehicle driving data is continuously collected. This newly collected historical driving data is collated to obtain updated driving sample data. This updated sample data is then used to iteratively train the mileage prediction model and heating strategy matching model based on a preset period.

[0052] See again Figure 1 , step S104, heating the target vehicle battery based on the heating parameters indicated by the heating strategy.

[0053] Here, the heating parameters indicated by the heating strategy may include at least one of the following items: battery heating power, battery heating start-up temperature, battery heating exit temperature, battery heating start-up state of charge, and battery heating exit state of charge.

[0054] Example: The driving-related characteristic data of a vehicle during a certain driving trip are shown in the following table:

[0055] The data in the table above is fed into the cloud-trained mileage prediction model y = f(x). The predicted mileage is 20 kilometers. The predicted mileage and the driving-related characteristic data in the table above are then fed into the heating strategy prediction model to determine the optimal heating strategy for this trip (as shown in the table below). The vehicle battery is then heated using the heating parameters indicated by the heating strategy.

[0056]

[0057] This application is based on the principle of minimizing vehicle heating power consumption to achieve customized optimal strategy matching applicable to all scenarios, avoiding unnecessary energy consumption that may be caused by the adoption of fixed heating strategies. At the same time, it utilizes the high computing power, massive storage, and historical tracing characteristics of the cloud, takes group characteristics as input, and combines high-computing power algorithms to achieve efficient and high-quality training of mileage prediction models and heating strategy matching models, thereby supporting accurate prediction of vehicle-side usage needs and optimal matching of strategies.

[0058] An embodiment of the present application provides a vehicle battery heating control method, through which the user's driving needs can be automatically identified without the user having to preset the driving needs, thereby achieving optimal matching of the driving heating strategy and avoiding unnecessary power consumption that may be caused by adopting a fixed heating strategy while meeting the mileage requirements.

[0059] Based on the same application concept, the embodiments of the present application also provide a vehicle battery heating control device corresponding to the vehicle battery heating control method provided in the above embodiments. Since the principle of solving the problem by the device in the embodiments of the present application is similar to the vehicle battery heating control method in the above embodiments of the present application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0060] See also Figure 5 , Figure 5 A schematic structural diagram of a vehicle battery heating control device provided in an embodiment of the present application.

[0061] like Figure 5 As shown in , the heating control device 510 provided in the embodiment of the present application includes: An acquisition module 511 acquires the current driving-related characteristic data of the target vehicle; The mileage prediction module 512 inputs the driving-related characteristic data into a pre-trained mileage prediction model, and predicts the expected mileage of the target vehicle using the mileage prediction model; A heating strategy matching module 513 inputs the estimated mileage and the driving-related characteristic data into a pre-trained heating strategy matching model, and obtains a current heating strategy for the vehicle battery of the target vehicle output by the heating strategy matching model; The heating module 514 heats the target vehicle battery based on the heating device indicated by the heating strategy.

[0062] Furthermore, the mileage prediction model is established in the following manner: Preprocessing the historical driving related data of the target vehicle within a preset time period to obtain the preprocessed historical driving related data; Extracting historical driving-related feature data related to a single mileage of the vehicle from the pre-processed historical driving-related data; Based on the training data set in the historical driving related feature data, the initial mileage prediction model is trained to obtain a trained initial mileage prediction model; Inputting a validation dataset from the historical driving-related feature data into the trained initial mileage prediction model to obtain an error of the trained initial mileage prediction model; If the error is less than a preset threshold, the trained initial mileage prediction model is determined as the mileage prediction model.

[0063] Furthermore, the heating strategy matching model is established in the following manner: Based on a training data set in historical driving related feature data, an initial heating strategy matching model is trained to obtain a trained initial heating strategy matching model; Inputting a validation data set from the historical driving-related feature data into the trained initial heating strategy matching model to obtain an error of the trained initial heating strategy matching model; If the error is less than a preset threshold, the trained initial heating strategy matching model is determined as the heating strategy matching model.

[0064] Furthermore, the driving-related characteristic data includes at least one of the following items: vehicle driving date, vehicle driving date type, vehicle driving time period, vehicle driving times, vehicle model, vehicle external ambient temperature, battery initial temperature, battery initial state of charge, vehicle single driving mileage, battery heating power, battery heating start-up temperature, battery heating exit temperature, battery heating state of charge when battery heating is turned on, battery heating state of charge when battery heating is exited, and battery heating power consumption.

[0065] An embodiment of the present application provides a heating control device for a vehicle battery. The device can automatically identify the user's driving needs without the user having to preset the driving needs, thereby achieving optimal matching of the driving heating strategy and avoiding unnecessary energy consumption that may be caused by adopting a fixed heating strategy while meeting the mileage requirements.

[0066] See also Figure 6 , Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0067] like Figure 6 As shown in FIG, the electronic device 600 includes a processor 610 , a memory 620 and a bus 630 .

[0068] The memory 620 stores machine-readable instructions executable by the processor 610. When the electronic device 600 is running, the processor 610 communicates with the memory 620 via the bus 630. When the machine-readable instructions are executed by the processor 610, the above-mentioned Figure 1 、 Figure 2 and Figure 4 The specific implementation of the steps of the vehicle battery heating control method in the method embodiment shown can be found in the method embodiment and will not be repeated here.

[0069] The embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the computer program can execute the above-mentioned Figure 1 、 Figure 2 and Figure 4The specific implementation of the steps of the vehicle battery heating control method in the method embodiment shown can be found in the method embodiment and will not be repeated here.

[0070] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the system and device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed system, device and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0071] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0072] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0073] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0074] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A vehicle battery heating control method, characterized in that: The heating control method comprises: Obtaining the target vehicle's current driving-related characteristic data; Inputting the driving-related characteristic data into a pre-trained mileage prediction model, and predicting the expected mileage of the target vehicle using the mileage prediction model; Inputting the estimated mileage and the driving-related characteristic data into a pre-trained heating strategy matching model, and obtaining a current heating strategy for the vehicle battery of the target vehicle output by the heating strategy matching model; The target vehicle battery is heated based on the heating parameters indicated by the heating strategy.

2. The heating control method according to claim 1, characterized in that: The mileage prediction model is established in the following way: Preprocessing the historical driving related data of the target vehicle within a preset time period to obtain the preprocessed historical driving related data; Extracting historical driving-related feature data related to a single mileage of the vehicle from the pre-processed historical driving-related data; Based on the training data set in the historical driving related feature data, the initial mileage prediction model is trained to obtain a trained initial mileage prediction model; Inputting a validation dataset from the historical driving-related feature data into the trained initial mileage prediction model to obtain an error of the trained initial mileage prediction model; If the error is less than a preset threshold, the trained initial mileage prediction model is determined as the mileage prediction model.

3. The heating control method according to claim 2, characterized in that: The heating strategy matching model is established in the following way: Based on a training data set in historical driving related feature data, an initial heating strategy matching model is trained to obtain a trained initial heating strategy matching model; Inputting a validation data set from the historical driving-related feature data into the trained initial heating strategy matching model to obtain an error of the trained initial heating strategy matching model; If the error is less than a preset threshold, the trained initial heating strategy matching model is determined as the heating strategy matching model.

4. The heating control method according to claim 1, wherein: The driving-related characteristic data includes at least one of the following items: vehicle driving date, vehicle driving date type, vehicle driving time period, vehicle driving times, vehicle model, vehicle external ambient temperature, battery initial temperature, battery initial state of charge, vehicle single driving mileage, battery heating power, battery heating start-up temperature, battery heating exit temperature, battery heating state of charge when battery heating is turned on, battery heating state of charge when battery heating is exited, and battery heating power consumption.

5. A heating control device for a vehicle battery, characterized in that: The control device comprises: An acquisition module obtains the current driving-related characteristic data of the target vehicle; a mileage prediction module, which inputs the driving-related characteristic data into a pre-trained mileage prediction model and predicts the expected mileage of the target vehicle using the mileage prediction model; a heating strategy matching module, inputting the estimated mileage and the driving-related characteristic data into a pre-trained heating strategy matching model, and obtaining a current heating strategy for the vehicle battery of the target vehicle output by the heating strategy matching model; The heating module heats the target vehicle battery based on the heating device indicated by the heating strategy.

6. The heating control device according to claim 5, characterized in that: The mileage prediction model is established in the following way: Preprocessing the historical driving related data of the target vehicle within a preset time period to obtain preprocessed historical driving related data; Extracting historical driving-related feature data related to a single mileage of the vehicle from the pre-processed historical driving-related data; Based on the training data set in the historical driving related feature data, the initial mileage prediction model is trained to obtain a trained initial mileage prediction model; Inputting a validation dataset from the historical driving-related feature data into the trained initial mileage prediction model to obtain an error of the trained initial mileage prediction model; If the error is less than a preset threshold, the trained initial mileage prediction model is determined as the mileage prediction model.

7. The heating control device according to claim 6, characterized in that: The heating strategy matching model is established in the following way: Based on a training data set in historical driving related feature data, an initial heating strategy matching model is trained to obtain a trained initial heating strategy matching model; Inputting a validation data set from the historical driving-related feature data into the trained initial heating strategy matching model to obtain an error of the trained initial heating strategy matching model; If the error is less than a preset threshold, the trained initial heating strategy matching model is determined as the heating strategy matching model.

8. The heating control device according to claim 5, characterized in that: The driving-related characteristic data includes at least one of the following items: vehicle driving date, vehicle driving date type, vehicle driving time period, vehicle driving times, vehicle model, vehicle external ambient temperature, battery initial temperature, battery initial state of charge, vehicle single driving mileage, battery heating power, battery heating start-up temperature, battery heating exit temperature, battery heating state of charge when battery heating is turned on, battery heating state of charge when battery heating is exited, and battery heating power consumption.

9. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the memory communicate via the bus. When the machine-readable instructions are run by the processor, the steps of the vehicle battery heating control method as described in any one of claims 1 to 4 are executed.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the vehicle battery heating control method according to any one of claims 1 to 4 are executed.

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