Whole vehicle energy consumption prediction management method, device and equipment and storage medium

Abstract

The application provides a whole vehicle energy consumption prediction management method, device and equipment and a storage medium. The method comprises the following steps: identifying whether a target vehicle has started a prediction energy management function; if it is determined that the prediction energy management function has been started, the whole vehicle control is not performed on the target vehicle in a preset time period after the start of operation by using the prediction energy management function, and actual driving energy consumption data of the target vehicle in the preset time period after the start of operation is obtained; historical driving energy consumption data pre-stored in the cloud of the target vehicle is obtained; it is judged whether the actual driving energy consumption data is within a first preset range of the historical driving energy consumption data; if the actual driving energy consumption data is within the first preset range, the battery capacity of the target vehicle is controlled by the prediction energy management according to the historical driving energy consumption data; and if the actual driving energy consumption data is not within the first preset range, the battery capacity of the target vehicle is controlled by the prediction energy management according to pre-calibrated energy consumption data, so that the battery capacity of the target vehicle is accurately controlled.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automobile manufacturing, and in particular to a whole vehicle energy consumption prediction management method and device, equipment and a storage medium. BACKGROUND

[0002] Plug-in energy vehicles can collect route information and use prediction performance management technology to optimize battery power consumption curves, thereby achieving the effect of energy saving and emission reduction. In the prediction performance management technology, the accuracy of whole vehicle energy prediction directly affects the energy saving effect.

[0003] In the prior art, whole vehicle energy prediction generally uses a lookup table method, that is, referring to pre-experimentally calibrated energy consumption curves to calculate vehicle energy consumption on a predicted route.

[0004] However, the inventors have found that the prior art at least has the following technical problems: the actual energy consumption of a vehicle during driving is greatly affected by various factors, resulting in inaccurate energy consumption prediction and thus inaccurate control of the battery power of the vehicle. SUMMARY

[0005] The present application provides a whole vehicle energy consumption prediction management method, device, equipment and storage medium to overcome the problem of inaccurate control of vehicle energy consumption.

[0006] In a first aspect, the present application provides a whole vehicle energy consumption prediction management method, comprising:

[0007] identifying whether a target vehicle has started a prediction energy management function;

[0008] if it is determined that the prediction energy management function has been started, not using the prediction energy management function to control the whole vehicle in a preset time period after the start of the operation of the target vehicle, and obtaining actual driving energy consumption data of the target vehicle in the preset time period after the start of the operation;

[0009] obtaining historical driving energy consumption data of the target vehicle pre-stored in the cloud;

[0010] determining whether the actual driving energy consumption data is within a first preset range of the historical driving energy consumption data;

[0011] if it is within the first preset range, performing prediction energy management control on the battery power of the target vehicle according to the historical driving energy consumption data;

[0012] if it is not within the first preset range, performing prediction energy management control on the battery power of the target vehicle according to pre-calibrated energy consumption data.

[0013] In a possible design, the battery power of the target vehicle is predicted and controlled according to historical driving energy consumption data, including: obtaining target route information of the target vehicle; and controlling the battery power of the target vehicle according to the target route information and the historical driving energy consumption data based on a preset algorithm.

[0014] In a possible design, after identifying whether the target vehicle has started the prediction energy management function, the method further includes: if it is determined that the prediction energy management function has not been started, obtaining historical driving energy consumption data of the target vehicle in a preset historical period; fitting historical average driving energy consumption data at different speeds according to the actual speed and the historical driving energy consumption data; and updating the historical driving energy consumption data pre-stored in the cloud according to the historical average driving energy consumption data.

[0015] In a possible design, it is determined whether the historical average driving energy consumption data at different speeds is within a second preset range of the pre-labeled energy consumption data of the target vehicle; if yes, no operation is performed; and if no, the historical driving energy consumption data pre-stored in the cloud of the target vehicle is updated.

[0016] In a possible design, the historical driving energy consumption data pre-stored in the cloud of the target vehicle is updated, including: updating the historical driving energy consumption data pre-stored in the cloud by using a Kalman filtering method.

[0017] In a second aspect, the present application provides a whole vehicle energy consumption prediction management device, including:

[0018] A recognition module is configured to identify whether a target vehicle has started a prediction energy management function.

[0019] A first obtaining module is configured to, if it is determined that the prediction energy management function has been started, control the whole vehicle without using the prediction energy management function in a preset time period after starting of the target vehicle, and obtain actual driving energy consumption data of the target vehicle in the preset time period after starting.

[0020] A second obtaining module is configured to obtain historical driving energy consumption data of the target vehicle pre-stored in the cloud.

[0021] A judgment module is configured to determine whether the actual driving energy consumption data is within a first preset range of the historical driving energy consumption data.

[0022] A first control module is configured to, if yes, predict and control the battery power of the target vehicle according to the historical driving energy consumption data.

[0023] A second control module is configured to, if no, predict and control the battery power of the target vehicle according to pre-labeled energy consumption data.

[0024] In a possible design, the first control module is specifically configured to acquire target route information of the target vehicle; and control the battery power of the target vehicle according to the target route information and the historical driving energy consumption data based on a preset algorithm.

[0025] In a third aspect, the present application provides a vehicle electronic control device, comprising: at least one processor and a memory;

[0026] The memory stores computer execution instructions.

[0027] The at least one processor executes the computer execution instructions stored in the memory, so that the at least one processor executes the whole vehicle energy consumption prediction management method as described in the first aspect and various possible designs of the first aspect.

[0028] In a fourth aspect, the present application provides a computer storage medium, and the computer storage medium stores computer execution instructions, when the processor executes the computer execution instructions, the whole vehicle energy consumption prediction management method as described in the first aspect and various possible designs of the first aspect is realized.

[0029] In a fifth aspect, the present application provides a computer program product, comprising a computer program, when the computer program is executed by the processor, the whole vehicle energy consumption prediction management method as described in the first aspect and various possible designs of the first aspect is realized.

[0030] The whole vehicle energy consumption prediction management method, device, equipment and storage medium provided by the present application can obtain the actual driving energy consumption data of the target vehicle in a preset time period when the prediction energy management function is started and the prediction energy management function does not intervene in the whole vehicle control, compare the actual driving energy consumption data with the historical driving energy consumption data pre-stored in the cloud, determine the energy consumption data used in the prediction energy management, obtain a more accurate prediction result, and more accurately control the battery power of the target vehicle according to the prediction result. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0032] Figure 1 The application scenario diagram of the whole vehicle energy consumption prediction management method provided by the present application is shown.

[0033] Figure 2 The flowchart of the whole vehicle energy consumption prediction management method provided by an embodiment of the present application is shown.

[0034] Figure 3 A whole vehicle energy consumption prediction management method flowchart is provided for another embodiment of the present application.

[0035] Figure 4 A whole vehicle energy consumption prediction management device structure diagram is provided for an embodiment of the present application.

[0036] Figure 5 A vehicle electronic control device hardware structure diagram is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0037] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below in connection with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0038] To solve the problem that the prediction result may be inaccurate in the whole vehicle energy consumption prediction management process, the technical solution provided in the embodiments of the present application is as follows: the actual driving energy consumption data of the target vehicle is compared with the historical driving energy consumption data to determine the energy consumption data required in the whole vehicle energy consumption prediction management process, and then the vehicle is controlled according to the optimization result obtained from the energy consumption data. The embodiments will be described in detail below.

[0039] Figure 1 An application scenario diagram of the whole vehicle energy consumption prediction management method provided for an embodiment of the present application is shown in FIG. 1. As shown in FIG. 1, it includes a cloud 101 and a vehicle electronic control device 102. The vehicle electronic control device 102 can be a whole vehicle controller or a controller of an electronic control unit (ECU). Figure 1 The cloud 101 is configured to store the driving energy consumption data sent by the vehicle electronic control device 102 and store the driving energy consumption data. The vehicle electronic control device 102 is configured to obtain the historical driving energy consumption data required for the whole vehicle energy consumption prediction management from the cloud 101, obtain the control parameters by calculating the obtained historical driving energy consumption data, and predict and manage the driving state of the vehicle according to the control parameters.

[0040]

[0041] ​The technical solutions of the present application and how the technical solutions solve the above technical problems will be described in detail below with specific examples. The following specific examples can be combined with each other, and the same or similar concepts or processes can not be described again in some examples. The embodiments of the present application will be described below with reference to the drawings.

[0042] Figure 2 The whole vehicle energy consumption prediction management method provided by an embodiment of the present application is shown in the flowchart. The execution subject of the embodiment can be the vehicle electronic control device 102 in the embodiment shown in the figure, or any other form of controller. The embodiment is not particularly limited here. As shown in the figure, the method comprises the following steps. Figure 1 Figure 2

[0043] S201: Identify whether the target vehicle has started the prediction energy management function.

[0044] In the embodiment, the driver can set the prediction energy management function of the target vehicle to be in an open state or a closed state according to actual needs.

[0045] For example, a switch for opening or closing the prediction energy management function is provided on the display screen or operation platform of the target vehicle. The driver can open or close the prediction energy management function by operating the switch.

[0046] Specifically, when the driver operates the switch on the display screen or operation platform to be open, the switch generates an opening signal and sends the opening signal to the vehicle electronic control device; when the driver operates the switch on the display screen or operation platform to be closed, the switch generates a closing signal and sends the closing signal to the vehicle electronic control device. The vehicle electronic control device identifies whether the prediction energy management function of the target vehicle has been started by judging whether the received signal is an opening signal or a closing signal.

[0047] S202: If it is determined that the prediction energy management function has been started, the whole vehicle control is not performed by using the prediction energy management function in a preset time period after the start of the operation of the target vehicle, and actual driving energy consumption data of the target vehicle in the preset time period after the start of the operation is obtained.

[0048] Specifically, the working state of the engine of the target vehicle is not changed in the preset time period after the start of the operation, and the working state of the engine is freely converted according to the driving operation of the driver.

[0049] The actual driving energy consumption data includes different vehicle speeds and actual driving energy consumptions corresponding to different vehicle speeds, and the actual driving energy consumption is calculated in advance according to state data of the target vehicle in operation, including torque, speed, speed ratio and transmission efficiency of the target vehicle. ​​

[0050] S203: Obtain the historical driving energy consumption data of the target vehicle pre-stored in the cloud.

[0051] Specifically, the electronic control device of the target vehicle sends a request for obtaining the historical driving energy consumption data to the cloud through a wireless network. After receiving the request, the cloud filters the required historical driving energy consumption data according to the historical time period required to obtain the historical driving energy consumption data in the request, and returns the filtered historical driving energy consumption data to the electronic control device of the target vehicle through the wireless network.

[0052] S204: Determine whether the actual driving energy consumption data is within a first preset range of the historical driving energy consumption data. If it is within the first preset range, perform S205; if it is not within the first preset range, perform S206.

[0053] Specifically, the difference between the actual driving energy consumption data and the historical driving energy consumption data is calculated, and it is determined whether the absolute value of the difference is less than or equal to a first preset value. If the absolute value of the difference is less than or equal to the first preset value, the actual driving energy consumption data is within the first preset range of the historical driving energy consumption data; if the absolute value of the difference is greater than the first preset value, the actual driving energy consumption data is not within the first preset range of the historical driving energy consumption data.

[0054] S205: Perform predictive energy management control on the battery power of the target vehicle according to the historical driving energy consumption data.

[0055] Specifically, obtain the target route information of the target vehicle; based on a preset algorithm, control the battery power of the target vehicle according to the target route information and the historical driving energy consumption data.

[0056] The target route information includes but is not limited to traffic information, vehicle speed information, slope information, traffic light information, and information such as distance to the vehicle in front.

[0057] Specifically, based on a preset algorithm, the target battery power consumption curve during the journey to the destination is obtained according to the target route information and the historical driving energy consumption data, and the operating state of the engine is controlled according to the target battery power consumption curve.

[0058] In this embodiment, the preset algorithm can be a mathematical optimization algorithm based on control method, such as a mathematical algorithm based on rule control and a mathematical algorithm based on fuzzy control, or a mathematical optimization method based on optimization range, such as a mathematical algorithm based on global optimization and a real-time optimization algorithm.

[0059] S206: Perform predictive energy management control on the battery power of the target vehicle according to the pre-calibrated energy consumption data.

[0060] Specifically, target route information of the target vehicle is acquired; and based on a preset algorithm, the battery power of the target vehicle is controlled according to the target route information and the pre-calibrated energy consumption data.

[0061] The pre-calibrated energy consumption data is generated according to energy consumption data of the vehicle type at different speeds obtained through a bench test of the target vehicle.

[0062] In summary, the vehicle energy consumption prediction management method provided in the embodiment can obtain actual driving energy consumption data of the target vehicle in a preset time period when the prediction energy management function is not interfering with the vehicle control, compare the actual driving energy consumption data with the historical driving energy consumption data pre-stored in the cloud, determine the energy consumption data used in the prediction energy management, obtain a more accurate prediction result, and more accurately control the battery power of the target vehicle according to the prediction result.

[0063] Figure 3 The vehicle energy consumption prediction management method flowchart provided in another embodiment of the application is shown in FIG. 6. The embodiment of the application is based on the embodiment provided in the application Figure 2 The specific implementation method of updating the historical driving energy consumption data when the energy prediction management function is not started after S201 is described in detail. As shown in FIG. 6, the method comprises the following steps. Figure 3

[0064] S301: If it is determined that the prediction energy management function is not started, historical driving energy consumption data of the target vehicle in a preset historical period is acquired.

[0065] S302: The historical average driving energy consumption data at different speeds is fitted according to the actual speed and the historical driving energy consumption data.

[0066] Specifically, the historical driving energy consumption data at each speed gear is selected from the historical driving energy consumption data, and the historical driving energy consumption data at each speed gear is averaged to obtain the historical average driving energy consumption data at each speed gear.

[0067] For example, the speed gears can be divided as follows: 20-39 km / h, 40-59 km / h, 60-79 km / h, 80-99 km / h, and 100-119 km / h.

[0068] S303: The historical driving energy consumption data pre-stored in the cloud is updated according to the historical average driving energy consumption data.

[0069] ​Specifically, it is judged whether the historical average driving energy consumption data at different vehicle speeds is within a second preset range of the energy consumption data pre-labeled by the target vehicle; if yes, no operation is performed; if no, the cloud-pre-stored historical driving energy consumption data of the target vehicle is updated.

[0070] In the embodiment, the Kalman filtering method is used to update the cloud-pre-stored historical driving energy consumption data.

[0071] Specifically, the historical driving energy consumption data of the last period is obtained as the predicted historical driving energy consumption data of the current period, and the predicted Gaussian noise deviation of the current period is obtained; the historical driving energy consumption measurement data of the current period and the deviation of the historical driving energy consumption measurement data are obtained; the covariance is calculated according to the predicted Gaussian noise deviation and the deviation of the historical driving energy consumption measurement data; the historical driving energy consumption data of the current period is corrected according to the predicted historical driving energy consumption data, the historical driving energy consumption measurement data of the current period and the covariance, and is uploaded to the cloud to update the pre-stored historical driving energy consumption data.

[0072] To sum up, the vehicle energy consumption prediction management method provided in the embodiment can obtain accurate energy consumption prediction by updating the historical driving energy consumption data when the prediction energy management function is not started, and more accurately control the energy consumption of the vehicle.

[0073] Figure 4 The structure diagram of the vehicle energy consumption prediction management device provided in the embodiment of the application is shown in FIG. 4. Figure 4 As shown in FIG. 4, the vehicle energy consumption prediction management device includes an identification module 401, a first acquisition module 402, a second acquisition module 403, a judgment module 404, a first control module 405 and a second control module 406.

[0074] The identification module 401 is configured to identify whether the prediction energy management function of the target vehicle is started.

[0075] The first acquisition module 402 is configured to, if it is determined that the prediction energy management function is started, perform vehicle control without using the prediction energy management function in a preset time period after the start of the operation of the target vehicle, and acquire the actual driving energy consumption data of the target vehicle in the preset time period after the start of the operation.

[0076] The second acquisition module 403 is configured to acquire the cloud-pre-stored historical driving energy consumption data of the target vehicle.

[0077] The judgment module 404 is configured to judge whether the actual driving energy consumption data is within a first preset range of the historical driving energy consumption data.

[0078] The first control module 405 is configured to perform the energy management control on the battery power of the target vehicle according to the historical driving energy consumption data if the target vehicle is within the first preset range.

[0079] The second control module 406 is configured to perform the energy management control on the battery power of the target vehicle according to the pre-calibrated energy consumption data if the target vehicle is not within the first preset range.

[0080] In a possible implementation, the first control module 405 is specifically configured to acquire target route information of the target vehicle; and perform the control on the battery power of the target vehicle according to the target route information and the historical driving energy consumption data based on a preset algorithm.

[0081] In a possible implementation, the vehicle energy consumption prediction management apparatus further includes a data updating module 407, which is specifically configured to acquire the historical driving energy consumption data of the target vehicle in a preset historical period if it is determined that the prediction energy management function is not started; fit the historical average driving energy consumption data at different speeds according to the actual vehicle speed and the historical driving energy consumption data; and update the historical driving energy consumption data pre-stored in the cloud according to the historical average driving energy consumption data.

[0082] The apparatus provided in the embodiment can be used to execute the technical solutions of the method embodiments, and has similar implementation principles and technical effects, which will not be described here again.

[0083] Figure 5 A hardware structure schematic diagram of the vehicle electronic control device provided in the embodiment is shown in FIG. 5. Figure 5 As shown in FIG. 5, the vehicle electronic control device in the embodiment includes a processor 501 and a memory 502.

[0084] The memory 502 is configured to store computer execution instructions.

[0085] The processor 501 is configured to execute the computer execution instructions stored in the memory to implement each step performed by the vehicle electronic control device in the above embodiments. For details, refer to the related description in the foregoing method embodiments.

[0086] Optionally, the memory 502 can be independent or integrated with the processor 501.

[0087] When the memory 502 is independently arranged, the vehicle electronic control device further includes a bus 503 configured to connect the memory 502 and the processor 501.

[0088] The embodiment of the present application further provides a computer storage medium, and the computer storage medium stores computer execution instructions. When the processor executes the computer execution instructions, the vehicle energy consumption prediction management method is implemented.

[0089] The embodiment of the present application further provides a computer program product comprising a computer program, which, when executed by a processor, implements the whole vehicle energy consumption prediction management method.

[0090] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other manners. For example, the embodiments of the device described above are merely schematic; the division of the modules is merely logical function division; and there can be another division manner in actual implementation; for example, a plurality of modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different modules can be indirect couplings or communication connections through some interfaces, devices or modules, and can be electrical, mechanical or in other forms.

[0091] The modules illustrated as separated components can or can not be physically separated, and the components illustrated as modules can or can not be physical units, i.e., can be located in one place, or can be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to implement the embodiments of the present application.

[0092] In addition, each functional module in each embodiment of the present application can be integrated in one processing unit, or each module can be physically present alone, or two or more modules can be integrated in one unit. The unit formed by the above modules can be realized in the form of hardware, or in the form of hardware plus software function unit.

[0093] The integrated module realized in the form of software function module can be stored in a computer readable storage medium. The software function module stored in the storage medium includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute part of the steps of the method of each embodiment of the present application.

[0094] It should be understood that the above processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), etc. The general-purpose processor can be a microprocessor, or the processor can also be any conventional processor. The steps of the method disclosed in the present application can be directly embodied as a hardware processor to execute, or be executed by a combination of hardware and software modules in the processor.

[0095] The memory can include a high-speed RAM memory, and can also include a non-volatile storage NVM, for example at least one disk memory, and can also be a U disk, a mobile hard disk, a read-only memory, a magnetic disk or an optical disk, etc.

[0096] 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. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, the bus in the drawings of the present application does not limit to only one bus or one type of bus.

[0097] The storage medium described above can be realized by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0098] An exemplary storage medium is coupled to the processor, so that the processor can read information from the storage medium, and can write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and the storage medium can be located in an Application Specific Integrated Circuit (ASIC). Of course, the processor and the storage medium can also exist as discrete components in an electronic device or a host device.

[0099] Those of ordinary skill in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by program instruction-related hardware. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the foregoing storage medium includes various storage media that can store program codes, such as ROM, RAM, magnetic disk or optical disk.

[0100] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for predicting and managing vehicle energy consumption, characterized in that, include: Identify whether the target vehicle has activated its predictive energy management function; If it is determined that the predictive energy management function has been enabled, then the predictive energy management function will not be used for vehicle control of the target vehicle during a preset time period after the start of operation, and the actual driving energy consumption data of the target vehicle during the preset time period after the start of operation will be obtained. Obtain the historical driving energy consumption data of the target vehicle pre-stored in the cloud; Determine whether the actual drive energy consumption data is within a first preset range of the historical drive energy consumption data; If within the first preset range, predictive energy management control is performed on the battery capacity of the target vehicle based on historical driving energy consumption data; If the battery level is not within the first preset range, predictive energy management control is performed on the battery level of the target vehicle based on the pre-calibrated energy consumption data.

2. The method according to claim 1, characterized in that, The predictive energy management control of the target vehicle's battery capacity based on historical driving energy consumption data includes: Obtain the target route information of the target vehicle; Based on a preset algorithm, the battery level of the target vehicle is controlled according to the target route information and the historical driving energy consumption data.

3. The method according to claim 1 or 2, characterized in that, After identifying whether the target vehicle has enabled the predictive energy management function, the method further includes: If it is determined that the predictive energy management function is not enabled, then the historical driving energy consumption data of the target vehicle within a preset historical period is obtained. Based on the actual vehicle speed and the historical driving energy consumption data, historical average driving energy consumption data at different vehicle speeds are fitted. The historical drive energy consumption data pre-stored in the cloud is updated based on the historical average drive energy consumption data.

4. The method according to claim 3, characterized in that, The step of updating the historical drive energy consumption data pre-stored in the cloud based on the historical average drive energy consumption data includes: Determine whether the historical average driving energy consumption data at different vehicle speeds is within the second preset range of the energy consumption data pre-calibrated for the target vehicle; If it falls within the second preset range, no operation will be performed; If the target vehicle is not within the second preset range, the historical driving energy consumption data pre-stored in the cloud will be updated.

5. The method according to claim 4, characterized in that, The update of the target vehicle's pre-stored historical driving energy consumption data in the cloud includes: Kalman filtering is used to update the historical drive energy consumption data pre-stored in the cloud.

6. A vehicle energy consumption prediction and management device, characterized in that, include: The identification module is used to identify whether the target vehicle has activated the predictive energy management function; The first acquisition module is used to, if it is determined that the predictive energy management function has been enabled, not to use the predictive energy management function for vehicle control of the target vehicle within a preset time period after the start of operation, and to acquire the actual driving energy consumption data of the target vehicle within the preset time period after the start of operation. The second acquisition module is used to acquire the historical driving energy consumption data of the target vehicle pre-stored in the cloud. The judgment module is used to determine whether the actual driving energy consumption data is within a first preset range of the historical driving energy consumption data; The first control module is used to perform predictive energy management control on the battery power of the target vehicle based on historical driving energy consumption data if the target vehicle is within a first preset range. The second control module is used to perform predictive energy management control on the battery power of the target vehicle based on pre-calibrated energy consumption data if the battery power is not within the first preset range.

7. The apparatus according to claim 6, characterized in that, The first control module is specifically used to acquire the target route information of the target vehicle; based on a preset algorithm, it controls the battery power of the target vehicle according to the target route information and the historical driving energy consumption data.

8. A vehicle electronic control device, characterized in that, include: At least one processor and memory; The memory stores computer-executed instructions; The at least one processor executes computer execution instructions stored in the memory, causing the at least one processor to perform the vehicle energy consumption prediction and management method as described in any one of claims 1 to 5.

9. A computer storage medium, characterized in that, The computer storage medium stores computer execution instructions, and when the processor executes the computer execution instructions, it implements the vehicle energy consumption prediction and management method as described in any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the vehicle energy consumption prediction and management method according to any one of claims 1 to 5.

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