An energy management method, device and electronic equipment
Patent Information
- Application Number
- CN202611140091.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-29
- Publication Date
- 2026-09-25
Smart Images
Figure CN122808686A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of vehicles, and more particularly to an energy management method, apparatus, and electronic device. Background Technology
[0002] With the development of new energy technologies, new energy vehicles (such as plug-in hybrid electric vehicles, PHEVs) are being used more and more widely in urban and intercity transportation.
[0003] New energy vehicles can be managed in detail to achieve intelligent and energy-saving vehicle use. Therefore, an energy management method for new energy vehicles is needed to achieve whole-vehicle energy management. Summary of the Invention
[0004] To address the issue of how to achieve vehicle energy management for new energy vehicles, this application provides an energy management method, device, and electronic device. This application also provides a computer program product and a computer-readable storage medium.
[0005] The embodiments of this application adopt the following technical solutions:
[0006] In a first aspect, this application provides an energy management method applied to an electronic device, the method comprising:
[0007] Obtain the vehicle's navigation route;
[0008] Based on the navigation route, obtain the weather forecast information corresponding to the navigation route;
[0009] Generate a first energy management strategy based on the navigation route and weather forecast information;
[0010] Identify the vehicle's current driving environment and obtain identification results. The identification results are used to describe the weather conditions and / or road conditions at the vehicle's location.
[0011] Based on the first energy management strategy and the identification results, a second energy management strategy is obtained for the current vehicle energy management.
[0012] According to the method in the first aspect, the first energy management strategy is predicted in advance based on the navigation path and weather forecast information, and the second energy management strategy is obtained based on the identification results of the current driving environment and the predicted first energy management strategy. This can reduce the data processing pressure of generating the second energy management strategy, improve the adaptability of the second energy management strategy to the current driving environment, and enhance the intelligence and energy-saving level of the vehicle's energy management.
[0013] In one implementation of the first aspect, the method further includes obtaining traffic information corresponding to the navigation path;
[0014] Based on the navigation route and weather forecast information, a first energy management strategy is generated, including:
[0015] The navigation path is segmented to obtain multiple road segments;
[0016] Predict future road conditions based on weather forecasts and road condition information, and obtain road condition prediction results;
[0017] The first energy management strategy is generated based on the road condition prediction results.
[0018] In one implementation of the first aspect, a second energy management strategy for current vehicle energy management is obtained based on the first energy management strategy and the identification result, including:
[0019] If the confidence level of the identification result is less than the preset first confidence level threshold, the first energy management strategy is adopted as the second energy management strategy.
[0020] In one implementation of the first aspect, a second energy management strategy for current vehicle energy management is obtained based on the first energy management strategy and the identification result, and further includes:
[0021] If the confidence level of the identification result is greater than the preset second confidence level threshold, and the identification result is consistent with the weather forecast information and the road condition prediction result, the first energy management strategy is adopted as the second energy management strategy.
[0022] In one implementation of the first aspect, a second energy management strategy for current vehicle energy management is obtained based on the first energy management strategy and the identification result, and further includes:
[0023] If the confidence level of the identification result is greater than the second confidence threshold, and the identification result is inconsistent with the weather forecast information and / or road condition prediction results, if the risk level of the identification result is greater than the preset first risk value, a second energy management strategy is generated based on conservative control.
[0024] In one implementation of the first aspect, a second energy management strategy for current vehicle energy management is obtained based on the first energy management strategy and the identification result, and further includes:
[0025] If the confidence level of the identification result is less than or equal to the second confidence threshold and greater than or equal to the first confidence threshold, when the identification result is inconsistent with the weather forecast information and / or road condition prediction result, the fusion risk level of the weather forecast information and / or road condition prediction result and the identification result shall be calculated.
[0026] When the risk level of fusion is greater than the preset first risk value, a second energy management strategy is generated based on conservative control.
[0027] In one implementation of the first aspect, a second energy management strategy for current vehicle energy management is obtained based on the first energy management strategy and the identification result, and further includes:
[0028] When the fusion risk level is less than or equal to the first risk value, the first energy management strategy is adopted as the second energy management strategy, or the first energy management strategy is adjusted according to the identification results to generate the second energy management strategy.
[0029] Secondly, one embodiment of this application provides an energy management device, which is applied to an electronic device, and the device includes:
[0030] The navigation information acquisition module is used to acquire the vehicle's navigation route;
[0031] The weather information acquisition module is used to obtain the weather forecast information corresponding to the navigation path based on the navigation path.
[0032] The recognition module is used to identify the current driving environment of the vehicle and obtain the recognition results. The recognition results are used to describe the weather conditions and / or road conditions at the vehicle's location.
[0033] An energy management strategy generation module is used to: generate a first energy management strategy based on the navigation path and weather forecast information; and obtain a second energy management strategy for the current vehicle energy management based on the first energy management strategy and the identification result.
[0034] Thirdly, this application provides an electronic device, which includes a memory and a processor;
[0035] The processor executes instructions stored in memory to cause the electronic device to perform the method as described in the first aspect.
[0036] Fourthly, this application provides a computer program product containing instructions that, when executed by a computing device system, cause a cluster of computing devices to perform the method as described in the first aspect.
[0037] Fifthly, this application provides a computer-readable storage medium including computer program instructions, which, when executed by a computer system, cause the computer system to perform the method as described in the first aspect. Attached Figure Description
[0038] Figure 1 The diagram shown is a schematic diagram of an electronic device structure according to an embodiment of this application;
[0039] Figure 2 The diagram shown is a schematic diagram of an energy management device according to an embodiment of this application;
[0040] Figure 3 The diagram shown is a schematic flowchart of an energy management method according to an embodiment of this application;
[0041] Figure 4 The diagram shown is a schematic representation of a system structure according to an embodiment of this application;
[0042] Figure 5 The diagram shown is a schematic diagram of the second energy management strategy generation logic according to an embodiment of this application;
[0043] Figure 6 This is a schematic diagram of an electronic device structure according to an embodiment of this application. Detailed Implementation
[0044] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0045] The terminology used in the implementation section of this application is for the purpose of explaining specific embodiments of this application only, and is not intended to limit this application.
[0046] In vehicle application scenarios, factors affecting the operation of new energy vehicles include internal and external factors. Internal factors refer to the condition of the new energy vehicle itself, such as the vehicle's hardware configuration and the current condition of its components (battery state of charge, aging of the battery and other components, and operating parameter configurations of the battery and other components, etc.). External factors refer to the external environmental conditions when the vehicle is driving, such as the road conditions of the road the vehicle is currently traveling on.
[0047] Therefore, in a feasible energy management scheme, an energy management strategy for new energy vehicles is formulated based on information such as driving conditions, battery state of charge (SOC), navigation path, and vehicle load.
[0048] Furthermore, in vehicle application scenarios, weather conditions also affect the operation of new energy vehicles. For example, rain can affect road conditions, and wind direction and force can affect vehicle drag. Therefore, a feasible energy management solution should formulate energy management strategies for new energy vehicles based on weather forecast data, driving behavior, road conditions, and other information.
[0049] However, in real-world scenarios, weather forecast data does not perfectly match actual weather conditions. Furthermore, severe weather (such as rain, snow, icing, dense fog, and strong winds) significantly impacts vehicle traction, braking safety, battery thermal efficiency, and air conditioning energy consumption. Therefore, continuing to control the vehicle according to an energy management strategy based on weather forecast data when there is a mismatch between forecast and actual weather conditions may lead to the following problems:
[0050] 1. Mismatch between strategy and actual road conditions: The system continues to operate according to the sunny weather strategy in complex weather conditions such as rain and snow, which may easily cause safety hazards such as braking slippage;
[0051] 2. Low energy efficiency: Failure to adjust battery thermal control and air conditioning strategies in advance during periods of drastic temperature changes leads to increased energy consumption and delayed thermal management.
[0052] 3. Accelerated battery life degradation: Abnormal temperature control scenarios such as high-temperature fast charging and high-altitude deep discharging occur frequently, and are not linked to environmental prediction, resulting in a rapid decrease in the cell's state of harmonics (SOH).
[0053] 4. Limitations in forecasting capabilities: It is difficult to predict sudden weather changes based solely on map and navigation data, and the lack of visual perception of environmental risks and real-time verification mechanisms leads to forecasting bias.
[0054] To address the aforementioned problems, one embodiment of this application provides an energy management method. In this method, a first energy management strategy is formulated based on navigation path information and weather forecast information; current weather data is acquired during vehicle operation, and the initial energy management strategy is adjusted based on the weather data to generate a second energy management strategy; the vehicle is controlled according to the second energy management strategy to achieve an intelligent and energy-saving vehicle energy management strategy.
[0055] The methods described in this application are applied to electronic devices. This application does not specifically limit the electronic devices to which the methods of this application are applied. For example, the electronic device may be a vehicle (the vehicle's infotainment system), a local device installed in the vehicle to provide energy management strategies for the vehicle, or a remote device (e.g., a cloud server) remotely connected to the vehicle to provide energy management strategies for the vehicle.
[0056] Specifically, the method provided in any embodiment of this application can be applied to electronic devices such as in-vehicle systems, mobile phones, tablet computers, personal digital assistants (PDAs), desktop computers, laptop computers, ultra-mobile personal computers (UMPCs), handheld computers, and netbooks. This application does not impose any special restrictions on the specific form of the aforementioned electronic devices.
[0057] Figure 1 The diagram shown is a schematic diagram of an electronic device structure according to an embodiment of this application.
[0058] The method provided in any embodiment of this application can be applied to... Figure 1 In the electronic device 100 shown.
[0059] Electronic device 100 may include processor 110, external memory interface 120, internal memory 121, interface 130, power management module 141, antenna 1, communication module 150, audio module 170, sensor module 180, camera 193, display screen 194, etc.
[0060] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the electronic device 100. In other embodiments of this application, the electronic device 100 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.
[0061] The processor 110 may be an on-chip device (SOC) or other architecture. The processor 110 may include a central processing unit (CPU) and may further include other types of processors.
[0062] Processor 110 may include one or more processing units. For example, the processing units of processor 110 may include any combination of one or more of the following: Central Processing Unit (CPU), Digital Signal Processor (DSP), Microcontroller Unit (MCU), Digital Signal Processor (DSP), Application Processor (AP), Graphics Processing Unit (GPU), Neural-network Processing Units (NPU), Image Signal Processing (ISP), Modem Processor, Controller, Video Codec, and Baseband Processor. Processing units of processor 110 may also include other processing units besides those described above.
[0063] In processor 110, different processing units can be independent devices or integrated into one or more processors. The controller can generate operation control signals based on the instruction opcode and timing signals to control instruction fetching and execution.
[0064] The processor may also include necessary hardware accelerators or logic processing hardware circuitry, such as an ASIC, or one or more integrated circuits for controlling the execution of the program in this application. Furthermore, the processor may have the capability to operate one or more software programs, which may be stored in a storage medium.
[0065] The processor 110 may also include a memory for storing instructions and data. In some embodiments, the memory in the processor 110 is a cache memory. This memory can store instructions or data that the processor 110 has just used or that are used repeatedly. If the processor 110 needs to use the instruction or data again, it can retrieve it directly from the memory. This avoids repeated accesses, reduces the waiting time of the processor 110, and thus improves the efficiency of the system.
[0066] In some embodiments, processor 110 may include one or more interfaces.
[0067] The interfaces of processor 110 may include an inter-integrated circuit (I2C) interface, an inter-integrated circuit sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a subscriber identity module (SIM) interface, and / or a universal serial bus (USB) interface, etc.
[0068] Interface 130 is used to provide an interface for external access of electronic device 100 to processor 110.
[0069] In one embodiment, interface 130 is an interface of processor 110. In another embodiment, interface 130 includes an interface conversion module for converting one type of interface to another. One end of the interface conversion module provides external access for electronic device 100, and the other end of the interface conversion module is connected to the interface of processor 110.
[0070] It is understood that the interface connection relationships between the modules illustrated in the embodiments of this application are merely illustrative and do not constitute a structural limitation on the electronic device 100. In other embodiments of this application, the electronic device 100 may also employ different interface connection methods or combinations of multiple interface connection methods as described in the above embodiments.
[0071] Internal memory 121 can be used to store computer executable program code, which includes instructions.
[0072] The internal memory 121 may include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a given function (such as sound playback, image playback, etc.). The data storage area may store data generated during the use of the electronic device 100 (such as vehicle driving data, navigation data, etc.).
[0073] Internal memory 121 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc. Processor 110 executes various functional applications and data processing of electronic device 100 by running instructions stored in internal memory 121 and / or instructions stored in memory disposed in the processor.
[0074] The internal memory 121 may be a read-only memory (ROM), other types of static storage devices that can store static information and instructions, random access memory (RAM), or other types of dynamic storage devices that can store information and instructions. It may also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), a magnetic disk storage medium, or other magnetic storage devices. Alternatively, it may be any computer-readable medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer.
[0075] Processor 110 and internal memory 121 can be combined into a single processing device, or more commonly they are separate components.
[0076] The external storage interface 120 can be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the electronic device 100. The external memory card communicates with the processor 110 through the external storage interface 120 to perform data storage functions. For example, music, video, and other files can be saved on the external memory card.
[0077] The power management module 141 manages the power supply to various components of the electronic device 100. In one embodiment, the power management module 141 includes a charging management module and a battery. The charging management module receives charging input from a charger (e.g., a charging station).
[0078] The power management module 141 can also be used to monitor parameters such as battery capacity, battery cycle count, and battery health status (leakage current, impedance). In some other embodiments, the power management module 141 may also be located within the processor 110. In other embodiments, the power management module 141 and the charging management module 140 may also be located in the same device.
[0079] The wireless communication function of electronic device 100 can be realized through antenna 1, communication module 150, etc.
[0080] Antenna 1 is used to transmit and receive electromagnetic wave signals. Antenna 1 may include one or more physical antennas. Each antenna in electronic device 100 can be used to cover one or more communication frequency bands. Different antennas can also be reused to improve antenna utilization.
[0081] The communication module 150 may be one or more devices integrating at least one communication processing module. The communication module 150 can provide wireless communication solutions, including 2G / 3G / 4G / 5G, for use on the electronic device 100. The communication module 150 can also provide wireless communication solutions, including wireless local area networks (WLANs) (such as Wireless Fidelity (Wi-Fi) networks), Bluetooth (BT), Global Navigation Satellite System (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies, for use on the electronic device 100.
[0082] The display screen 194 is used to display images, videos, etc. In some embodiments, the electronic device 100 may include one or N display screens 194, where N is a positive integer greater than 1.
[0083] Camera 193 is used to capture still images or videos. In some embodiments, electronic device 100 may include one or N cameras 193, where N is a positive integer greater than 1.
[0084] The audio module 170 is used to convert digital audio information into analog audio signals for output, and also to convert analog audio input into digital audio signals. The audio module 170 can also be used for encoding and decoding audio signals. In some embodiments, the audio module 170 may be located in the processor 110, or some functional modules of the audio module 170 may be located in the processor 110.
[0085] In some embodiments, the electronic device 100 further includes a speaker, a microphone, etc. The electronic device 100 can implement audio functions through the audio module 170, the speaker, the microphone, and an application processor, such as music playback and voice control.
[0086] The sensor module 180 may include pressure sensors, gyroscope sensors, barometric pressure sensors, magnetic sensors, accelerometers, distance sensors, proximity sensors, fingerprint sensors, temperature sensors, touch sensors, ambient light sensors, bone conduction sensors, etc.
[0087] In some embodiments, the electronic device 100 also includes buttons, indicators, etc.
[0088] For example, buttons include vehicle start button, volume buttons, air conditioning control buttons, etc. Buttons can be mechanical buttons or touch-sensitive buttons. Electronic device 100 can receive button input and generate key signal inputs related to user settings and function control of electronic device 100.
[0089] Indicators can be indicator lights, used to indicate vehicle status such as speed, charging status, and battery level; they can also be used to indicate messages such as fault alarms and low battery alarms.
[0090] In addition to the aforementioned components, the electronic device runs an operating system. For example, iOS. ® Operating system, Android ® Operating system, Windows ® Operating systems, such as vehicle operating systems, can be used to install and run applications. The software system of electronic device 100 can adopt a layered architecture, event-driven architecture, microkernel architecture, microservice architecture, or cloud architecture.
[0091] Furthermore, in one embodiment, the method of this application embodiment can be applied to an electronic device. This electronic device may refer in whole or in part to electronic device 100.
[0092] In another embodiment, the method of this application can be applied to a system composed of multiple electronic devices, where each electronic device in the system executes a portion of the method steps. Any electronic device in the system can refer to electronic device 100 in whole or in part.
[0093] In order to implement the energy management method proposed in the embodiments of this application, an embodiment of this application also proposes an energy management device.
[0094] The energy management device of this application embodiment is applied to an electronic device, which can refer to electronic device 100.
[0095] This application does not limit the specific way of implementing the energy management device. Those skilled in the art can design the implementation method of the device according to the actual situation.
[0096] For example, in one embodiment, the device is installed on the electronic device in hardware (e.g., a functional chip). In another embodiment, the device is installed in the operating system of the electronic device as software code. Yet another embodiment, the device is installed on the electronic device as a combination of hardware and software.
[0097] Furthermore, in one embodiment, the device is mounted on an electronic device.
[0098] In another embodiment, the device is mounted on multiple electronic devices, which together form a complete device structure. Different electronic devices may install the same functional modules, or different electronic devices may install different functional modules.
[0099] In the description of the embodiments of this application, for the sake of convenience, the device is described by dividing it into various modules according to its functions. The division of each module is only a logical functional division. When implementing the embodiments of this application, the functions of each module can be implemented in one or more software and / or hardware.
[0100] Specifically, the apparatus proposed in this application can be fully or partially integrated onto a single physical entity (e.g., a GPU or other type of processor), or it can be physically separated. These modules can be implemented entirely in software via processing element calls; entirely in hardware; or some modules can be implemented in software via processing element calls, while others are implemented in hardware. For example, the detection module can be a separate processing element or integrated into a chip in an electronic device. The implementation of other modules is similar. Furthermore, these modules can be fully or partially integrated together or implemented independently. During implementation, each step of the above method or each of the above modules can be completed through integrated logic circuits in the hardware of the processor element or through software instructions.
[0101] For example, these modules can be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs). Alternatively, these modules can be integrated together as a system-on-a-chip (SOC).
[0102] Figure 2The diagram shown is a schematic diagram of an energy management device according to an embodiment of this application.
[0103] like Figure 2 As shown, the energy management device 200 includes a navigation information acquisition module 201, a weather information acquisition module 202, an energy management strategy generation module 203, an identification module 204, and an energy management strategy generation module 203.
[0104] The navigation information acquisition module 201 is used to acquire the vehicle's navigation route.
[0105] The weather information acquisition module 202 is used to acquire weather forecast information along the vehicle's driving route based on the vehicle's navigation path.
[0106] The energy management strategy generation module 203 is used to generate a first energy management strategy based on the vehicle's navigation path and the weather forecast information obtained by the weather information acquisition module 202.
[0107] The recognition module 204 is used to recognize the vehicle's current driving environment and obtain recognition results. The recognition results are used to describe the weather conditions and / or road conditions at the vehicle's location.
[0108] The energy management strategy generation module 203 is also used to obtain a second energy management strategy for the current vehicle energy management based on the first energy management strategy and the identification result identified by the identification module 204 during the vehicle's journey along the path of the navigation information.
[0109] Figure 3 The diagram shown is a schematic flowchart of an energy management method according to an embodiment of this application.
[0110] In one embodiment, the electronic device performs as follows Figure 3 The following process is shown to generate the current energy management strategy used for vehicle energy management.
[0111] S300, obtain the vehicle's navigation route.
[0112] In one embodiment, the road condition information includes any one or a combination of gradient, speed limit, road grade, traffic lights, curve curvature, charging station location, and altitude.
[0113] In one embodiment, the electronic device is equipped with Figure 2 The energy management device 200 shown is executed by the navigation information acquisition module 201 in step S300.
[0114] Furthermore, in one embodiment, in S300, traffic information corresponding to the navigation path is also obtained.
[0115] Specifically, in one embodiment, the traffic information in the navigation information is traffic information recorded in the navigation system or other service systems.
[0116] This application does not limit the specific implementation of S300. Those skilled in the art can design the implementation of S300 according to actual needs.
[0117] For example, in one embodiment, the energy management device 200 is installed in the vehicle's infotainment system. The vehicle's infotainment system has navigation functionality. The driver sets a navigation destination in the infotainment system, which generates a navigation route based on the driver's navigation destination and obtains the corresponding traffic information. The navigation route and the corresponding traffic information are sent to the navigation information acquisition module 201.
[0118] For example, in another embodiment, the energy management device 200 is installed in the vehicle's infotainment system. The driver's mobile phone is connected to the infotainment system (e.g., via Bluetooth). The driver sets a navigation destination on the mobile phone, which generates a navigation route based on the driver's navigation destination and obtains the corresponding traffic information. The navigation route and the corresponding traffic information are sent by the mobile phone to the infotainment system, and the navigation information acquisition module 201 acquires the navigation route and the corresponding traffic information received by the infotainment system.
[0119] S301: Obtain the weather forecast information corresponding to the navigation path.
[0120] In one embodiment, the weather forecast information includes any one or a combination of temperature, sunny weather, foggy weather, rain, snow, rainfall, visibility, sunshine intensity, snowfall, and duration of weather events.
[0121] In one embodiment, the electronic device is equipped with Figure 2 The energy management device 200 shown is executed by the weather information acquisition module 202 in step S301.
[0122] The embodiments of this application do not limit the specific implementation of S301. Those skilled in the art can design the implementation of S301 according to actual needs.
[0123] For example, in one embodiment, the weather information acquisition module 202 determines the areas traversed by the navigation path based on the navigation path. The weather information acquisition module 202 connects to a weather forecast server in the cloud and acquires weather forecast information for the areas traversed by the navigation path.
[0124] Specifically, in one embodiment, the weather information acquisition module 202 divides the navigation path into multiple road segments, marking the location and mileage of each segment. The weather information acquisition module 202 obtains the corresponding weather forecast information based on the location of the road segment, and finally obtains the future time window temperature, rainfall, snowfall, relative humidity, wind speed, and weather level label (e.g., sunny, foggy, rainy, snowy) for each road segment.
[0125] S302, based on the navigation path and the corresponding weather forecast information, predicts the energy management strategy for the vehicle while traveling along the navigation path, and generates a first energy management strategy.
[0126] In one embodiment, the electronic device is equipped with Figure 2 The energy management device 200 shown is executed by the energy management strategy generation module 203 in step S302.
[0127] Specifically, in one embodiment, the energy management strategy generation module 203 segments the navigation path (e.g., every 1km or divided by key turning points); matches weather forecast information with the path segments to obtain future weather forecast information for each segment; combines the road condition information of each segment to predict the future road surface conditions of that segment (e.g., icing, road surface slipperiness, water accumulation, visibility, etc.); and formulates a corresponding first energy management strategy based on the predicted road surface conditions of the segment and the vehicle's current status information (which includes any one or more of the following: current SOC, battery temperature, engine status, driving mode, and air conditioning load).
[0128] In one embodiment, the first energy management strategy includes:
[0129] Speed planning: Based on state information, recommend speeds for segmented routes (e.g., reduce speed when encountering icy or flooded sections to help prevent drive wheel slippage or lateral loss of control).
[0130] SOC planning: Based on status information, the system sets the target SOC for each sub-path in advance according to the weather forecast, thermal management load and traffic conditions of the entire route (e.g., in mountainous areas and low-temperature weather, a higher SOC is reserved for thermal management to avoid insufficient power caused by too low SOC when the battery temperature is low and the load is high).
[0131] Power mode switching: Based on status information, dynamically adjust the power output ratio of the engine and motor, and the specific mode (e.g., Electric vehicle (EV) mode, Hybrid electric vehicle (HEV) mode).
[0132] Kinetic energy recovery intensity planning: Plan the braking energy recovery intensity of each road section (e.g., low, medium, high or disabled);
[0133] Thermal management planning: Combining path temperature changes, air conditioning demand forecasts, and battery status, dynamically plan the timing, intensity, and priority of starting cooling fans, battery heating, and compressors (e.g., proactively addressing high and low temperature road sections to prevent batteries from becoming too cold or too hot).
[0134] Battery health: Based on weather conditions, temperature forecasts, and charging and discharging frequencies, dynamic measures are taken to slow down battery aging, limit peak current, reduce frequent deep charging and discharging, and adjust operating temperature (e.g., by optimizing SOC fluctuations through path prediction to extend cell life).
[0135] S303 identifies the vehicle's current driving environment while the vehicle is traveling along the navigation route, obtains the identification results, and uses the identification results to describe the weather conditions and / or road conditions at the vehicle's location.
[0136] In one embodiment, the electronic device is equipped with Figure 2 The energy management device 200 shown is executed by the identification module 204 in step S303.
[0137] This application does not limit the specific implementation of the identification module 204. Those skilled in the art can design the implementation of the identification module 204 according to actual needs.
[0138] In one embodiment, the recognition module 204 is implemented based on visual recognition. Specifically, in one embodiment, the recognition module 204 includes depth-based recognition...
[0139] Lightweight neural networks built from learning models (such as YOLOv5, MobileNetV3, ResNet50, etc.).
[0140] In another embodiment, the identification results are also obtained based on other methods. For example, the outdoor temperature is obtained based on a temperature sensor outside the vehicle. As another example, the outdoor wind direction and / or wind speed is obtained based on a wind speed sensor outside the vehicle.
[0141] S304, during the process of the vehicle traveling along the path of the navigation information, a second energy management strategy for the current vehicle energy management is obtained based on the first energy management strategy and the identification result identified by the identification module 204.
[0142] In one embodiment, the electronic device is equipped with Figure 2 The energy management device 200 shown is executed by the energy management strategy generation module 203 in step S303.
[0143] In one embodiment, in S304, it is determined whether the identification result identified by the identification module 204 matches the previously generated first energy management strategy, thereby further determining whether to adopt the previously generated first energy management strategy as the second energy management strategy, or to generate the second energy management strategy based on the identification result identified by the identification module 204.
[0144] For example, if the identification result matches the first energy management strategy, it means that the first energy management strategy can be adapted to the real driving environment, and the first energy management strategy is adopted as the second energy management strategy.
[0145] If the identification result does not match the first energy management strategy, it means that the first energy management strategy cannot adapt to the real driving environment. In this case, the identification result generates a second energy management strategy (the generation process of the second energy management strategy can refer to the generation process of the first energy management strategy). Alternatively, the first energy management strategy can be adjusted (fine-tuned) according to the identification result to generate the second energy management strategy.
[0146] According to the method of this application embodiment, a first energy management strategy is predicted in advance based on the navigation path and weather forecast information, and a second energy management strategy is obtained based on the identification result of the current driving environment and the predicted first energy management strategy. This can reduce the data processing pressure of generating the second energy management strategy, improve the adaptability of the second energy management strategy to the current driving environment, and enhance the intelligence and energy-saving level of the vehicle's energy management.
[0147] Specifically, in one embodiment, the identification result is compared with weather forecast information and / or road condition prediction results based on the weather forecast information. If the weather conditions corresponding to the identification result are inconsistent with the weather forecast information, and / or the road conditions corresponding to the identification result are inconsistent with the road condition prediction results, then the identification result does not match the first energy management strategy.
[0148] Specifically, in one embodiment, the identification result being consistent with the weather forecast information means that the weather condition description in the identification result is the same as the weather forecast information or the deviation between the two is less than a preset deviation threshold.
[0149] For example, if the weather forecast indicates sunny weather, but the recognition module 204 identifies rain, then the recognition result is inconsistent with the weather forecast information.
[0150] For example, if the weather forecast indicates a daytime temperature of 18 degrees Celsius, and the comparison and recognition module 204 identifies an outdoor temperature of 19 degrees Celsius, then the recognition result is consistent with the weather forecast information (the deviation value is 1 degree Celsius, which is less than the preset deviation value threshold).
[0151] For example, if the weather forecast indicates a daytime temperature of 12 degrees Celsius, but the recognition module 204 identifies an outdoor temperature of 26 degrees Celsius, then the recognition result is inconsistent with the weather forecast information (the deviation value is 14 degrees Celsius, which is greater than the preset deviation value threshold). The recognition result is inconsistent with the weather forecast information and does not match the first energy management strategy.
[0152] For example, if the weather forecast indicates sunny weather, and S302 predicts that the road surface condition will not be icy based on the weather forecast, but the identification module 204 identifies that the road surface is icy, then the identification result is inconsistent with the road surface condition prediction result, and the identification result does not match the first energy management strategy.
[0153] Furthermore, considering that the recognition results identified by the recognition module 204 may be inaccurate, in one embodiment, a confidence level for the recognition results is introduced in S304.
[0154] For example, in one embodiment, when the confidence level of the identification result identified by the identification module 204 is less than the preset first confidence level threshold (e.g., 60%), the identification result identified by the identification module 204 is determined to be unreliable, and the previously generated first energy management strategy is directly adopted as the second energy management strategy.
[0155] Furthermore, to ensure driving safety, in one embodiment, in S304, it is determined whether to trigger a "conservative control" mechanism based on weather conditions and / or road conditions. The "conservative control" mechanism is a preset control scheme designed to maximize driving safety.
[0156] Specifically, in one embodiment, the "conservative control" mechanism includes:
[0157] (1) When entering a section of severe weather (e.g., rain, icing):
[0158] Power distribution is prioritized in HEV mode;
[0159] Start the engine in advance to maintain battery charge;
[0160] Limit regenerative braking intensity to prevent wheel slippage (Note: In low-traction road conditions such as rain, snow, and ice, regenerative braking may cause drive wheel slippage and vehicle instability, affecting braking safety. Therefore, in severe weather conditions, regenerative braking should be gradually reduced or even disabled to prioritize driving stability and safety).
[0161] The vehicle speed planning adopts a conservative strategy;
[0162] (2) In areas with favorable weather (e.g., sunny weather):
[0163] Increase the usage rate of EV mode and optimize economic efficiency;
[0164] Increased energy recovery intensity is permitted;
[0165] (3) Synchronously adjust the cooling / thermal management power output strategy.
[0166] In one embodiment, when the risk level corresponding to the weather forecast information is greater than a preset first risk threshold, a first energy management strategy is generated based on conservative control.
[0167] In S304, when the confidence level of the identification result identified by the identification module 204 is less than the preset first confidence level threshold (e.g., 60%), the previously generated first energy management strategy is adopted as the second energy management strategy.
[0168] If the confidence level of the identification result is greater than a preset second confidence threshold (e.g., 80%), and if the identification result is inconsistent with the weather forecast information and / or the predicted road conditions, a second energy management strategy is generated based on the identification result; or, the first energy management strategy is adjusted based on the identification result to generate the second energy management strategy. Specifically, during the generation of the second energy management strategy, if the risk level of the identification result is greater than a preset first risk threshold, a second energy management strategy is generated based on conservative control.
[0169] If the confidence level of the identification result is less than or equal to a preset second confidence threshold (e.g., 80%) and greater than or equal to a preset first confidence threshold (e.g., 60%), and if the identification result is inconsistent with the weather forecast information and / or the predicted road conditions, then the fusion risk level of the identification result and the predicted weather forecast information and / or road conditions is calculated. If the fusion risk level is greater than the preset first risk threshold, a second energy management strategy is generated based on conservative control. If the fusion risk level is less than or equal to the preset first risk threshold, the previously generated first energy management strategy is adopted as the second energy management strategy, or the first energy management strategy is adjusted (fine-tuned) according to the identification result to generate the second energy management strategy.
[0170] In one embodiment, the fusion risk level is calculated based on the following formula ( ).
[0171] = × + × . (Formula 1)
[0172] In formula 1:
[0173] The risk level is the prediction result of weather forecast information and / or road conditions, which is output by the empirical model of forecast accuracy;
[0174] The risk level of the identification result is output by the image recognition model of the identification module 204;
[0175] Weighting of weather forecast information The weights for the identification results. Specifically, it refers to determining based on historical experience data, satisfying + = 1.
[0176] Following S304, the vehicle uses the second energy management strategy obtained from S304 for overall vehicle control. Control commands are coordinated and executed by the vehicle controller in conjunction with subsystems such as the drive system, battery system, motor controller, and engine controller.
[0177] According to the method in this application embodiment, a first energy management strategy is generated based on the navigation path and weather forecast information, which can identify severe weather sections in advance, preset a safer and more reasonable driving strategy, and improve the overall vehicle operation safety.
[0178] According to the method of this application embodiment, a first energy management strategy is obtained based on the recognition result for the actual driving environment, realizing a closed-loop control process of "prediction (generating the first energy management strategy) + perception (recognition result) + arbitration (obtaining the second energy management strategy)", which enhances the intelligence and adaptability of energy management.
[0179] The method according to the embodiments of this application supports path segmentation strategy, dynamic switching between EV and HEV modes, and real-time adjustment of power and SOC allocation, thereby improving the environmental adaptability of the control system.
[0180] According to the method in the embodiments of this application, the vehicle speed planning, power distribution and air conditioning load estimation based on path prediction improve the energy efficiency of the whole vehicle, prioritize increasing the proportion of EVs under good operating conditions, improve the stability of HEVs in high load and risk sections, achieve a balance between economy and practicality, and realize path-driven dynamic energy efficiency optimization.
[0181] According to the method in the embodiments of this application, the (cooling, heating) feedforward control of the thermal management system is realized by predicting the weather temperature, avoiding lag response, reducing thermal control energy consumption, improving thermal management response efficiency, and reducing system energy consumption.
[0182] According to the method of this application embodiment, frequent charging at high or low temperatures is avoided. The cell capacity is reduced by power limiting, thermal management regulation and SOC control, which effectively delays capacity decay, supports battery health index (SOH) protection strategy and improves battery life.
[0183] The method according to the embodiments of this application, combined with camera recognition verification, improves control accuracy and can dynamically correct the predicted values of weather forecasts.
[0184] The method according to the embodiments of this application can realize real-time optimization of SOC, motor output power, and power mode, and coordinate the operation of the engine and motor in order to maximize fuel economy while meeting power demand.
[0185] Figure 4 The diagram shown is a schematic diagram of a system structure according to an embodiment of this application.
[0186] like Figure 4 As shown, in one embodiment, a navigation information acquisition module 421 and a weather information acquisition module 422 are implemented based on the vehicle infotainment system 1420.
[0187] The driver inputs the navigation destination on the vehicle's infotainment system 420, and the navigation system installed on the vehicle's infotainment system 420 generates a navigation route based on the navigation destination. The navigation information acquisition module 421 accesses a remote server to obtain the traffic information corresponding to the navigation route (refer to S300).
[0188] The weather information acquisition module 422 accesses the remote server according to the navigation path to obtain the weather forecast information corresponding to the navigation path (refer to S301).
[0189] The vehicle infotainment system 420 sends navigation information (navigation route and traffic information) and weather forecast information to the vehicle's overall controller 400.
[0190] The vehicle controller 400 contains an energy management strategy generation module 410.
[0191] The energy management strategy generation module 410 includes a road condition prediction unit 411 and a strategy generation unit 412.
[0192] The road condition prediction unit 411 predicts the road condition of a segment when the vehicle travels on the corresponding segment of the navigation path based on the navigation path, road condition information, and weather forecast information, thus obtaining a prediction result of the road condition. For example, in one embodiment, the road condition prediction unit 411 classifies the weather conditions in the weather forecast information and segments the navigation path, predicting the road condition of each segment based on the weather classification result corresponding to that segment.
[0193] The strategy generation unit 412 generates a first energy management strategy (refer to S302) based on the prediction results of the road condition prediction unit 411 (the first energy management strategy includes vehicle speed planning, battery health, SOC planning, power distribution, power mode, thermal management and kinetic energy recovery planning).
[0194] In one embodiment, a recognition module 431 is deployed in the Advanced Driving Assistance System (ADAS) 430. When the vehicle enters the navigation path, the visual image camera of the vehicle's ADAS 430 collects actual image data of the surroundings (e.g., ahead), and the image algorithm is processed by the internal processor of ADAS 430 to identify weather information and / or road condition information. The recognition results are transmitted to the vehicle controller 400 in real time via CAN bus or Ethernet in the form of data packets.
[0195] Specifically, in one embodiment, the process of identifying weather information and / or road condition information includes:
[0196] Image acquisition and preprocessing: The front-facing camera periodically acquires image frames and performs preprocessing steps such as image grayscale conversion, contrast enhancement, and edge denoising.
[0197] Feature extraction and classification: The preprocessed image is input into the neural network model for recognition, which automatically extracts features related to weather and road conditions in the image, such as watermarks, ice crystals, degree of fogging, density of rain and snow particles, etc.
[0198] Output structured recognition results: The final output is the structured data shown in Table 1 below.
[0199] Table 1
[0200]
[0201] The energy management strategy generation module 410 also includes an identification arbitration decision module 413. The identification arbitration decision module 413 is used to compare the identification result of the identification module 431 with the weather forecast information previously input by the vehicle system 420, so that the energy management strategy generation module 410 can generate a second energy management strategy based on the comparison result.
[0202] Figure 5 The diagram shown is a schematic diagram of the second energy management strategy generation logic according to an embodiment of this application.
[0203] like Figure 5 As shown, in one embodiment, the identification result of the identification module 431 is compared with the weather forecast information previously input by the vehicle system 420 and the road condition prediction result output by the road condition prediction unit 411.
[0204] When the confidence level of the identification result is less than the preset first confidence threshold (e.g., 60%), the previously generated first energy management strategy is used as the second energy management strategy.
[0205] If the confidence level of the identification result is greater than the preset second confidence level threshold (e.g., 80%), and if the identification result is consistent with the weather forecast information and road condition prediction results, the previously generated first energy management strategy is adopted as the second energy management strategy.
[0206] If the confidence level of the identification result is greater than the preset second confidence level threshold (e.g., 80%), and if the identification result is inconsistent with the weather forecast information and / or road condition prediction results, and the risk level of the identification result is greater than the risk level of the weather forecast information and the road condition prediction results, then a second energy management strategy is generated based on conservative control.
[0207] If the confidence level of the identification result is less than or equal to a preset second confidence threshold (e.g., 80%) and greater than or equal to a preset first confidence threshold (e.g., 60%), and the identification result is inconsistent with weather forecast information and / or road condition prediction results, a fusion risk level is calculated. If the fusion risk level is greater than the preset first risk threshold, a second energy management strategy is generated based on conservative control. If the fusion risk level is less than or equal to the preset first risk threshold, the previously generated first energy management strategy is adopted as the second energy management strategy, or the first energy management strategy is adjusted according to the identification result to obtain the second energy management strategy.
[0208] Furthermore, to improve energy management effectiveness, a feedback and self-learning mechanism is introduced in one embodiment. Specifically, in one embodiment, the energy management strategy generation module 410 further includes a feedback and self-learning unit 414.
[0209] Feedback and Self-Learning Unit 414 continuously records the following information:
[0210] The discrepancy between the identification results and road condition information and / or weather forecast information;
[0211] The vehicle's energy consumption performance and stability after the implementation of the second energy management strategy;
[0212] Driver intervention (e.g., whether to forcibly switch power modes).
[0213] Feedback and self-learning unit 414 constructs a feedback sample library based on the above recorded data for subsequent self-learning optimization. The optimization objectives include:
[0214] Establish a regression model to optimize and correct predictions;
[0215] Update the strategy parameter table;
[0216] Remotely update the strategy weight table and the fusion model algorithm version.
[0217] Furthermore, in one embodiment, the external environment and vehicle operating status are monitored during vehicle operation, control parameters are dynamically adjusted, and the strategy is continuously optimized through a feedback mechanism.
[0218] An embodiment of this application also proposes an electronic device. This electronic device is used to execute the method flow or part of the method flow described in the embodiments of this application. This electronic device is a terminal device, base station device, or server device as described in the embodiments of this specification.
[0219] Figure 6 This is a schematic diagram of an electronic device structure according to an embodiment of this application.
[0220] like Figure 6 As shown, the electronic device 2500 includes a memory 2502 for storing computer program instructions and a processor 2501 for executing the program instructions. When the computer program instructions are executed by the processor 2501, the electronic device 2500 is triggered to execute the method steps performed by the terminal device, base station device, or server device as described in the embodiments of this application.
[0221] Specifically, in one embodiment of this application, the aforementioned one or more computer programs are stored in the aforementioned memory 2502. The aforementioned one or more computer programs include instructions that, when executed by the aforementioned electronic device 2500, cause the aforementioned electronic device 2500 to perform the method steps described in the embodiments of this application.
[0222] It is understood that the structural description of the electronic device 2500 in this application does not constitute a specific limitation on the electronic device 2500. In other embodiments of this application, the electronic device 2500 may include other components besides the processor 2501 and the memory 2502.
[0223] In one embodiment, electronic device 2500 may refer to electronic device 100, wherein processor 2501 may refer to processor 110, and memory 2502 may refer to internal memory 121.
[0224] Processor 2501 and memory 2502 can be combined into a single processing device, but more commonly they are separate components.
[0225] An embodiment of this application also provides an electronic chip. This electronic chip is used to execute the method flow or part of the method flow described in the embodiments of this application.
[0226] Specifically, the electronic chip includes a processor for executing program instructions. When the computer program instructions are executed by the processor, the electronic chip is triggered to perform the steps described in the embodiments of this application. The processor of the electronic chip can refer to the processor of the above-described electronic device.
[0227] Optionally, the devices, apparatuses, and modules described in the embodiments of this application may be implemented by computer chips or physical entities, or by products with certain functions.
[0228] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media containing computer-usable program code.
[0229] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.
[0230] Specifically, one embodiment of this application also provides a computer-readable storage medium storing a computer program that, when run on a computer, causes the computer to execute the method provided in the embodiment of this application.
[0231] An embodiment of this application also provides a computer program product, which includes a computer program that, when run on a computer, causes the computer to perform the method provided in the embodiment of this application.
[0232] The embodiments described in this application are described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0233] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0234] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0235] It should also be noted that in the embodiments of this application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, or the existence of B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.
[0236] In this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0237] This application can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.
[0238] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.
[0239] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments of this application can be implemented using electronic hardware, computer software, or a combination of electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0240] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0241] The above description is merely a specific embodiment of this application. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application. The protection scope of this application should be determined by the protection scope of the claims.
Claims
1. An energy management method, characterized in that, The method is applied to an electronic device, and the method includes: Obtain the vehicle's navigation route; Based on the navigation path, obtain the weather forecast information corresponding to the navigation path; A first energy management strategy is generated based on the navigation path and the weather forecast information. Identify the current driving environment of the vehicle and obtain identification results, which are used to describe the weather conditions and / or road conditions at the location of the vehicle; Based on the first energy management strategy and the identification result, a second energy management strategy is obtained for the current vehicle energy management.
2. The method according to claim 1, characterized in that, The method also includes obtaining traffic information corresponding to the navigation path; The step of generating a first energy management strategy based on the navigation path and the weather forecast information includes: The navigation path is segmented to obtain multiple road segments; Based on the weather forecast information and the road condition information, predict the future road surface conditions of the road section and obtain the road surface condition prediction results; The first energy management strategy is generated based on the road condition prediction results.
3. The method according to claim 2, characterized in that, The step of obtaining a second energy management strategy for the current vehicle energy management based on the first energy management strategy and the identification result includes: If the confidence level of the identification result is less than a preset first confidence level threshold, the first energy management strategy is adopted as the second energy management strategy.
4. The method according to claim 3, characterized in that, The step of obtaining a second energy management strategy for currently performing vehicle energy management based on the first energy management strategy and the identification result further includes: If the confidence level of the identification result is greater than a preset second confidence level threshold, and the identification result is consistent with the weather forecast information and the road condition prediction result, the first energy management strategy is adopted as the second energy management strategy.
5. The method according to claim 4, characterized in that, The step of obtaining a second energy management strategy for currently performing vehicle energy management based on the first energy management strategy and the identification result further includes: If the confidence level of the identification result is greater than the second confidence threshold, and the identification result is inconsistent with the weather forecast information and / or the road condition prediction result, if the risk level of the identification result is greater than the preset first risk value, the second energy management strategy is generated based on conservative control.
6. The method according to claim 5, characterized in that, The step of obtaining a second energy management strategy for currently performing vehicle energy management based on the first energy management strategy and the identification result further includes: If the confidence level of the identification result is less than or equal to the second confidence threshold and greater than or equal to the first confidence threshold, and the identification result is inconsistent with the weather forecast information and / or the road condition prediction result, the fusion risk level of the weather forecast information and / or the road condition prediction result and the identification result shall be calculated. When the fusion risk level is greater than the preset first risk value, a second energy management strategy is generated based on conservative control.
7. The method according to claim 6, characterized in that, The step of obtaining a second energy management strategy for currently performing vehicle energy management based on the first energy management strategy and the identification result further includes: When the fusion risk level is less than or equal to the first risk value, the first energy management strategy is adopted as the second energy management strategy, or the first energy management strategy is adjusted according to the identification result to generate the second energy management strategy.
8. An electronic device, characterized in that, The electronic device includes a memory and a processor; The processor is configured to execute instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-7.
9. A computer program product containing instructions, characterized in that, When the instruction is executed by the computing device system, it causes the computing device cluster to perform the method as described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that, It includes computer program instructions, which, when executed by a computer system, perform the method as described in any one of claims 1-7.