WO2024082227A1 - 车辆续航里程的预测方法、装置、设备及计算机存储介质 - Google Patents

车辆续航里程的预测方法、装置、设备及计算机存储介质 Download PDF

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WO2024082227A1
WO2024082227A1 PCT/CN2022/126510 CN2022126510W WO2024082227A1 WO 2024082227 A1 WO2024082227 A1 WO 2024082227A1 CN 2022126510 W CN2022126510 W CN 2022126510W WO 2024082227 A1 WO2024082227 A1 WO 2024082227A1
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WIPO (PCT)
Prior art keywords
vehicle
model
information
range
predicting
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PCT/CN2022/126510
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English (en)
French (fr)
Inventor
林海波
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宁德时代新能源科技股份有限公司
宁德时代(上海)智能科技有限公司
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Priority to PCT/CN2022/126510 priority Critical patent/WO2024082227A1/zh
Publication of WO2024082227A1 publication Critical patent/WO2024082227A1/zh

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
    • B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
    • B60L58/12—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries responding to state of charge [SoC]
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • B60W40/09—Driving style or behaviour

Definitions

  • the present application relates to the field of new energy vehicle technology, and in particular to a method, device, equipment and computer storage medium for predicting vehicle range.
  • the cruising range of a new energy vehicle refers to the distance traveled by the new energy vehicle from the current power state of the power battery to the end of the test specified in the standard. Since the cruising range of a new energy vehicle is affected by many factors, the estimated cruising range is often very different from the actual driving distance.
  • the present application provides a method, device, equipment and computer storage medium for predicting vehicle cruising range, which improves the prediction accuracy of vehicle cruising range.
  • an embodiment of the present application provides a method for predicting a vehicle's cruising range, the method comprising:
  • the vehicle mirror model Controlling the vehicle mirror model to operate under the vehicle operating condition, and predicting the energy consumption of the vehicle under the vehicle operating condition;
  • the vehicle mirror model is constructed according to the digital twin algorithm;
  • the vehicle range of the vehicle is predicted based on the remaining power and the energy consumption.
  • a vehicle mirror model with the same dynamics and energy consumption performance as the vehicle is constructed, and the vehicle operating conditions are closely displayed to predict the vehicle range, thereby improving the prediction accuracy of the vehicle range of new energy vehicles.
  • determining the vehicle operating condition according to the environmental information and the historical driving information includes:
  • the vehicle operating condition is determined according to the driving style and the environmental information.
  • the vehicle operating conditions are determined by taking into account the user's driving style and environmental information.
  • the environmental information includes: first scene information and second scene information detected by a sensor of the vehicle;
  • the first scene information includes at least: weather and road conditions
  • the second scenario information includes at least: navigation information and traffic information.
  • the historical driving information includes: a single stepping depth of the accelerator pedal and a change rate of the single stepping depth of the accelerator pedal;
  • the analyzing the historical driving information to determine the user's driving style includes:
  • the driving style of the user is determined according to the single pedaling depth and the single pedaling depth change rate.
  • the user's driving style is determined according to the single pedaling depth and the single pedaling depth change rate, so as to improve the accuracy of the determined user's driving style.
  • the method before controlling the vehicle mirror model to operate under the vehicle operating condition, the method further includes:
  • the vehicle mirror model is constructed based on the digital twin algorithm and the hardware structure and control logic of the vehicle.
  • a vehicle mirror model corresponding to the vehicle is constructed based on the digital twin algorithm and the vehicle's hardware structure and control logic to simulate the actual vehicle's dynamics and energy consumption performance.
  • the vehicle mirror model includes at least the following models mirrored with the vehicle:
  • Power battery model drive motor model, high and low voltage accessory models, transmission model, body model, tire model, brake model, steering model, and suspension model.
  • the method further includes:
  • the vehicle mirror model is corrected according to the error.
  • the vehicle mirror model is corrected by comparing the actual vehicle operating state with the virtual operating state of the vehicle mirror model, thereby improving the accuracy of the vehicle mirror model during virtual operation.
  • the method further includes:
  • the driving information of the vehicle during driving is saved.
  • the user's driving information is saved so that the user's driving style can be analyzed later based on the stored multiple driving information.
  • an embodiment of the present application provides a device for predicting a vehicle cruising range, the device comprising:
  • An acquisition module is used to acquire the vehicle's environmental information, remaining power, and historical driving information during the vehicle's driving;
  • a determination module configured to determine a vehicle operating condition based on the environmental information and the historical driving information
  • a calculation module used to control the vehicle mirror model to operate under the vehicle operating condition and calculate the energy consumption of the vehicle under the vehicle operating condition;
  • the vehicle mirror model is constructed according to the digital twin algorithm;
  • a prediction module is used to obtain the vehicle range of the vehicle according to the remaining power and the energy consumption.
  • an embodiment of the present application provides a vehicle range prediction device, the device comprising:
  • an embodiment of the present application provides a computer-readable storage medium having computer program instructions stored thereon, and when the computer program instructions are executed by a processor, the method for predicting the vehicle range as described in any one of the first aspects above is implemented.
  • an embodiment of the present application provides a computer program product.
  • the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the vehicle range prediction method as described in any one of the above-mentioned first aspects.
  • the present application provides a method, device, equipment and computer storage medium for predicting vehicle cruising range.
  • the vehicle's environmental information, remaining power and historical driving information are obtained; the vehicle's operating conditions are determined based on the environmental information and historical driving information; the vehicle mirror model is controlled to operate under the vehicle operating conditions, and the vehicle's energy consumption under the vehicle operating conditions is calculated; the vehicle's cruising range is predicted based on the remaining power and energy consumption.
  • FIG1 is a schematic flow chart of a method for predicting vehicle cruising range provided in an embodiment of the present application
  • FIG2 is a schematic flow chart of another method for predicting vehicle cruising range provided in an embodiment of the present application.
  • FIG3 is a schematic flow chart of another method for predicting vehicle cruising range provided in an embodiment of the present application.
  • FIG4 is a schematic flow chart of another method for predicting vehicle cruising range provided in an embodiment of the present application.
  • FIG5 is a schematic flow chart of another method for predicting vehicle cruising range provided in an embodiment of the present application.
  • FIG6 is a schematic flow chart of another method for predicting vehicle cruising range provided in an embodiment of the present application.
  • FIG7 is a schematic structural diagram of a device for measuring vehicle cruising range provided in an embodiment of the present application.
  • FIG8 is a schematic structural diagram of another device for measuring vehicle cruising range provided in an embodiment of the present application.
  • FIG9 is a schematic structural diagram of another device for measuring vehicle cruising range provided in an embodiment of the present application.
  • FIG10 is a schematic structural diagram of another device for measuring vehicle cruising range provided in an embodiment of the present application.
  • FIG11 is a schematic structural diagram of a device for measuring vehicle range provided in an embodiment of the present application.
  • the inventors have improved the prediction accuracy of vehicle cruising range by constructing a vehicle mirror model with the same dynamics and energy consumption performance as the vehicle, and predicting the vehicle cruising range based on the vehicle operating conditions that are closely displayed.
  • the embodiments of the present application provide a method, device, equipment and computer storage medium for predicting the cruising range of a vehicle.
  • the following first introduces the method for predicting the cruising range of a vehicle provided in the embodiments of the present application.
  • Fig. 1 shows a schematic flow chart of a method for predicting a vehicle cruising range provided in an embodiment of the present application. As shown in Fig. 1, the method for predicting a vehicle cruising range provided in an embodiment of the present application includes the following steps: S101 to S104.
  • the vehicle's environmental information, remaining power, and historical driving information are obtained through a wired method, such as a vehicle control bus.
  • the vehicle's environmental information, remaining power, and historical driving information are obtained wirelessly.
  • the vehicle operating conditions are information used to characterize the external environment during the vehicle's driving process.
  • the vehicle operating condition is a data set that combines environmental information and historical driving information.
  • the vehicle operating condition is a classification result determined based on environmental information and historical driving information.
  • the vehicle operating conditions are processed in the cloud.
  • the vehicle operating condition is obtained by processing a vehicle controller of the vehicle.
  • the vehicle mirror model is constructed according to the digital twin algorithm.
  • the vehicle mirror model is controlled to run under the vehicle operating condition, and the energy consumption of the vehicle under the vehicle operating condition is predicted according to the real-time energy consumption of the vehicle mirror model under the vehicle operating condition.
  • the vehicle mirror model is controlled to operate under the vehicle operating condition, and the energy consumption of the vehicle under the vehicle operating condition is predicted according to the average energy consumption of the vehicle mirror model to complete the remaining mileage under the vehicle operating condition.
  • the vehicle range of the vehicle is predicted based on the quotient of the remaining power and the energy consumption.
  • a vehicle mirror model with the same dynamics and energy consumption performance as the vehicle is constructed, and the vehicle operating conditions are closely displayed to predict the vehicle range, thereby improving the prediction accuracy of the vehicle range of new energy vehicles.
  • FIG2 is a schematic flowchart of another method for predicting vehicle cruising range provided in an embodiment of the present application.
  • S103 may include: S1031 - S1032.
  • the historical driving information is the historical record of the same driving user of the vehicle.
  • the historical driving information is averaged and the calculation result is compared with the threshold range of each driving style to determine the user's driving style.
  • the user's user identity is obtained, the historical driving information is classified according to the user's user identity, and the user's historical driving information corresponding to the user identity is extracted, and the user's driving style is determined based on the user's historical driving information.
  • the user's user identity is determined by the face image collected by the image acquisition device in the car, the user's voice collected by the voice acquisition device in the car, or the driving card recognized by the radio frequency identification device in the car.
  • the vehicle operating condition is a data set that combines driving style and historical driving information.
  • the vehicle operating condition is a classification result determined based on the driving style and historical driving information.
  • the vehicle operating conditions are determined by taking into account the user's driving style and environmental information.
  • the environmental information includes: first scene information and second scene information detected by a sensor of the vehicle;
  • the first scene information includes at least: weather and road conditions
  • the second scenario information includes at least: navigation information and traffic information.
  • FIG3 is a schematic flow chart of another method for predicting vehicle cruising range provided in an embodiment of the present application.
  • S1031 may include: S10311.
  • the historical driving information includes: a single stepping depth of the accelerator pedal and a change rate of a single stepping depth of the accelerator pedal;
  • an average calculation is performed based on the single pedaling depth and the single pedaling depth change rate, and the user's driving style is determined based on the calculation result and the driving style threshold range.
  • the user's driving style is determined based on the single pedaling depth and the single pedaling depth change rate to improve the accuracy of the determined user's driving style.
  • FIG4 is a schematic flow chart of another method for predicting vehicle cruising range provided in an embodiment of the present application. As shown in FIG4, the method also includes: S1-S2.
  • the vehicle's hardware structure and control logic are pre-stored inside the vehicle.
  • S2 builds a vehicle mirror model based on the digital twin algorithm and the vehicle's hardware structure and control logic.
  • the constructed vehicle mirror model is similar to the actual vehicle's dynamics and energy consumption performance.
  • a vehicle mirror model corresponding to the vehicle is constructed based on the digital twin algorithm and the vehicle's hardware structure and control logic, thereby simulating the actual vehicle's dynamics and energy consumption performance.
  • the vehicle mirror model includes at least the following models mirrored with the vehicle:
  • Power battery model, drive motor model, high and low voltage accessory models, transmission system model, body model, tire model, brake model, steering model, suspension model, etc. are used to simulate the physical structure of the vehicle.
  • the vehicle mirror model may further include at least the following models mirrored with the vehicle:
  • Domain controller control models power component control models, chassis component control models, etc. can be used to simulate vehicle control of actual vehicles.
  • FIG5 is a schematic flow chart of another method for predicting vehicle cruising range provided in an embodiment of the present application. As shown in FIG5 , the method further includes: S3 - S5.
  • the vehicle operating status of the vehicle and the virtual operating status of the vehicle mirror model are obtained in real time.
  • the vehicle operating state of the vehicle and the virtual operating state of the vehicle mirror model are obtained according to a preset time interval.
  • the error of the vehicle mirror model relative to the vehicle is a difference or a proportional coefficient.
  • the vehicle mirror model is corrected in real time or at a preset time interval according to the error.
  • the vehicle mirror model is corrected by comparing the actual vehicle operating state with the virtual operating state of the vehicle mirror model, thereby improving the accuracy of the vehicle mirror model during virtual operation.
  • FIG6 is a schematic flow chart of another method for predicting vehicle cruising range provided in an embodiment of the present application. As shown in FIG6 , the method further includes: S6.
  • the user's driving information is saved so that the user's driving style can be analyzed later based on the stored multiple driving information.
  • the driving information may include: a single depression depth of the accelerator pedal and a change rate of a single depression depth of the accelerator pedal.
  • the driving information may also include at least: the number of times the user steps on the accelerator pedal per unit mileage, the energy consumption per unit mileage, and the interval between braking and starting.
  • the vehicle range prediction method obtains the vehicle's environmental information, remaining power, and historical driving information during the vehicle's driving process; determines the vehicle's operating conditions based on the environmental information and historical driving information; controls the vehicle mirror model to operate under the vehicle's operating conditions, calculates the vehicle's energy consumption under the vehicle's operating conditions; and predicts the vehicle's range based on the remaining power and energy consumption.
  • FIG7 is a schematic structural diagram of a vehicle range prediction device provided in an embodiment of the present application.
  • the vehicle range prediction device 200 provided in an embodiment of the present application includes the following modules:
  • the acquisition module 201 is used to acquire the vehicle's environmental information, remaining power, and historical driving information during the vehicle's driving;
  • a determination module 202 for determining a vehicle operating condition based on environmental information and historical driving information
  • the calculation module 203 is used to control the vehicle mirror model to operate under the vehicle operating condition and calculate the energy consumption of the vehicle under the vehicle operating condition; the vehicle mirror model is constructed according to the digital twin algorithm;
  • the prediction module 204 is used to obtain the vehicle range of the vehicle according to the remaining power and energy consumption.
  • the determination module 202 may be used to analyze the historical driving information to determine the user's driving style
  • the vehicle operating conditions are determined by taking into account the user's driving style and environmental information.
  • the environmental information includes: first scene information and second scene information detected by a sensor of the vehicle;
  • the first scene information includes at least: weather and road conditions
  • the second scenario information includes at least: navigation information and traffic information.
  • the historical driving information includes: a single stepping depth of the accelerator pedal and a change rate of the single stepping depth of the accelerator pedal;
  • the determination module 202 may also be used to determine the user's driving style according to the single pedaling depth and the single pedaling depth change rate.
  • the user's driving style is determined according to the single pedaling depth and the single pedaling depth change rate, so as to improve the accuracy of the determined user's driving style.
  • FIG8 is a schematic structural diagram of another device for predicting vehicle cruising range provided in an embodiment of the present application.
  • the device 200 for predicting vehicle cruising range may further include: a model building module 205 for acquiring the hardware structure and control logic of the vehicle;
  • a vehicle mirror model is built based on the digital twin algorithm as well as the vehicle's hardware structure and control logic.
  • a vehicle mirror model corresponding to the vehicle is constructed based on the digital twin algorithm and the vehicle's hardware structure and control logic, thereby simulating the actual vehicle's dynamics and energy consumption performance.
  • the vehicle mirror model includes at least the following models mirrored with the vehicle:
  • Power battery model drive motor model, high and low voltage accessory models, transmission model, body model, tire model, brake model, steering model, and suspension model.
  • FIG9 is a schematic structural diagram of another device for predicting vehicle cruising range provided in an embodiment of the present application.
  • the device 200 for predicting vehicle cruising range may further include: a correction module 206 for acquiring a vehicle operating state of the vehicle and a virtual operating state of a vehicle mirror model;
  • the vehicle mirror model is corrected according to the error.
  • the vehicle mirror model is corrected by comparing the actual vehicle running state with the virtual running state of the vehicle mirror model, thereby improving the accuracy of the vehicle mirror model during virtual running.
  • FIG10 is a schematic structural diagram of another device for predicting vehicle range provided in an embodiment of the present application.
  • the device 200 for predicting vehicle range may further include: a storage module 207 for storing driving information during vehicle driving.
  • the user's driving information is saved so that the user's driving style can be analyzed later based on the stored multiple driving information.
  • the vehicle range prediction device obtains the vehicle's environmental information, remaining power, and historical driving information during the vehicle's driving process; determines the vehicle's operating conditions based on the environmental information and historical driving information; controls the vehicle mirror model to operate under the vehicle's operating conditions, calculates the vehicle's energy consumption under the vehicle's operating conditions; and predicts the vehicle's range based on the remaining power and energy consumption.
  • FIG11 is a schematic structural diagram of a vehicle range prediction device provided in an embodiment of the present application. As shown in FIG11 , the vehicle range prediction device provided in an embodiment of the present application includes:
  • the above-mentioned processor 301 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.
  • CPU central processing unit
  • ASIC application specific integrated circuit
  • the memory 302 may include a large capacity memory for data or instructions.
  • the memory 302 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these.
  • the memory 302 may include removable or non-removable (or fixed) media.
  • the memory 302 may be inside or outside the integrated gateway disaster recovery device.
  • the memory 302 is a non-volatile solid-state memory.
  • the memory 302 may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical/tangible memory storage device.
  • ROM read-only memory
  • RAM random access memory
  • magnetic disk storage medium device e.g., a magnetic disk
  • optical storage medium device e.g., a flash memory device
  • electrical, optical or other physical/tangible memory storage device e.g., a flash memory device
  • the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present application.
  • the processor 301 implements any one of the vehicle range prediction methods in the above embodiments by reading and executing computer program instructions stored in the memory 302 .
  • the vehicle range prediction device may further include a communication interface 303 and a bus 310. As shown in FIG3, the processor 301, the memory 302, and the communication interface 303 are connected via the bus 310 and communicate with each other.
  • the communication interface 303 is mainly used to implement communication between various modules, devices, units and/or equipment in the embodiments of the present application.
  • Bus 310 includes hardware, software or both, and the components of the prediction device of the vehicle cruising range are coupled to each other.
  • the bus may include an accelerated graphics port (AGP) or other graphics bus, an enhanced industrial standard architecture (EISA) bus, a front-end bus (FSB), a hypertransport (HT) interconnect, an industrial standard architecture (ISA) bus, an infinite bandwidth interconnect, a low pin count (LPC) bus, a memory bus, a micro channel architecture (MCA) bus, a peripheral component interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a serial advanced technology attachment (SATA) bus, a video electronics standard association local (VLB) bus or other suitable bus or a combination of two or more of these.
  • bus 310 may include one or more buses.
  • the vehicle range prediction device can execute the vehicle range prediction method in the embodiment of the present application, thereby realizing the vehicle range prediction method and device described in combination with Figures 1 and 2.
  • the present application also provides a computer-readable storage medium, on which computer program instructions are stored.
  • the computer program instructions are executed by the processor, the vehicle range prediction method as described above is implemented.
  • the present application also provides a computer program product accordingly.
  • the instructions in the computer program product are executed by the processor of an electronic device, the electronic device executes the vehicle range prediction method as described above.
  • the functional blocks shown in the above structural block diagram can be implemented as hardware, software, firmware or a combination thereof.
  • it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc.
  • ASIC application-specific integrated circuit
  • the elements of the present application are programs or code segments used to perform the required tasks.
  • the program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier.
  • "Machine-readable medium" may include any medium capable of storing or transmitting information.
  • machine-readable media examples include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, and the like.
  • the code segment can be downloaded via a computer network such as the Internet, an intranet, etc.
  • each square block in the flowchart and/or block diagram and the combination of each square block in the flowchart and/or block diagram can be realized by computer program instructions.
  • These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, so that these instructions executed by the processor of a computer or other programmable data processing device enable the realization of the function/action specified in one or more square blocks of the flowchart and/or block diagram.
  • a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit.
  • each square block in the block diagram and/or flowchart and the combination of square blocks in the block diagram and/or flowchart can also be realized by the dedicated

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Abstract

一种车辆续航里程的预测方法，方法包括：在车辆行驶的过程中，获取车辆的环境信息、剩余电量以及历史驾驶信息（S101）；根据环境信息和历史驾驶信息，确定车辆运行工况（S102）；控制车辆镜像模型在车辆运行工况下运行，预测车辆在车辆运行工况下的能耗（S103）；车辆镜像模型根据数字孪生算法构建得到；根据剩余电量和能耗，预测车辆的车辆续航里程（S104）。通过构建与车辆的动力学和能耗表现相同的车辆镜像模型，提高了车辆续航里程的预测准确度。

Description

车辆续航里程的预测方法、装置、设备及计算机存储介质 技术领域
本申请涉及系新能源汽车技术领域，特别是涉及一种车辆续航里程的预测方法、装置、设备及计算机存储介质。
背景技术
由于全球面对环境污染加剧以及能源短缺，新能源汽车已经成为国际上主要国家以及汽车企业的重要发展方向。
新能源汽车的续航里程是指新能源汽车从动力蓄电池当前电量状态开始到标准规定的试验结束时所走的里程。由于新能源汽车的续航里程受很多因素影响，续航里程估计值往往与实际行驶距离相差很大。
相关技术无法预测新能源汽车的续航里程。因此，如何提高车辆续航里程的预测准确度，成为业内的一个难题。
发明内容
本申请提供一种车辆续航里程的预测方法、装置、设备及计算机存储介质，提高了车辆续航里程的预测准确度。
第一方面，本申请实施例提供了一种车辆续航里程的预测方法，所述方法包括：
在车辆行驶的过程中，获取车辆的环境信息、剩余电量以及历史驾驶信息；
根据所述环境信息和所述历史驾驶信息，确定车辆运行工况；
控制车辆镜像模型在所述车辆运行工况下运行，预测所述车辆在所述车辆运行工况下的能耗；所述车辆镜像模型根据数字孪生算法构建得到的；
根据所述剩余电量和所述能耗，预测所述车辆的车辆续航里程。
通过本申请实施例的技术方案，构建了与车辆的动力学和能耗表现相同的车辆镜像模型，以及贴近显示的车辆运行工况预测车辆续航里程，实现提高新能源车辆的车辆续航里程的预测准确度。
在一种可能的实现方式中，所述根据所述环境信息和所述历史驾驶信息，确定车辆运行工况，包括：
根据所述历史驾驶信息进行分析，确定用户的驾驶风格；
根据所述驾驶风格和所述环境信息确定所述车辆运行工况。
通过该实现方式的技术方案，考虑用户的驾驶风格和环境信息，确定了车辆运行工况。
在一种可能的实现方式中，所述环境信息包括：所述车辆的传感器检测到的第一场景信息和第二场景信息；
所述第一场景信息至少包括：天气、路面条件；
所述第二场景信息至少包括：导航信息、交通信息。
通过该实现方式的技术方案，确定了影响车辆运行工况的多个因素，包括天气、路面条件、导航信息、交通信息等等。
在一种可能的实现方式中，所述历史驾驶信息包括：油门踏板的单次踩踏深度和油门踏板的单次踩踏深度变化率；
所述根据所述历史驾驶信息进行分析，确定用户的驾驶风格，包括：
根据所述单次踩踏深度和单次踩踏深度变化率，确定所述用户的驾驶风格。
通过该实现方式的技术方案，根据单次踩踏深度和单次踩踏深度变化率，确定用户的驾驶风格，以提高确定的用户的驾驶风格的准确性。
在一种可能的实现方式中，在所述控制车辆镜像模型在所述车辆运行工况下运行之前，所述方法还包括：
获取车辆的硬件结构和控制逻辑；
基于数字孪生算法以及所述车辆的所述硬件结构和所述控制逻辑，构建所述车辆镜像模型。
通过该实现方式的技术方案，基于数字孪生算法以及车辆的硬件结 构和控制逻辑，构建了与车辆相对应的车辆镜像模型，实现模拟实际的车辆的动力学和能耗表现。
在一种可能的实现方式中，所述车辆镜像模型至少包括与所述车辆镜像的以下模型：
动力电池模型、驱动电机模型、高低压附件模型、传动系模型、车身模型、轮胎模型、制动模型、转向模型、悬架模型。
通过该实现方式的技术方案，确定模拟实际的车辆的动力学和能耗表现的具体模型。
在一种可能的实现方式中，所述方法还包括：
获取所述车辆的车辆运行状态和所述车辆镜像模型的虚拟运行状态；
对比所述车辆运行状态和所述虚拟运行状态，得到所述车辆镜像模型相对于所述车辆的误差；
根据所述误差对所述车辆镜像模型进行修正。
通过该实现方式的技术方案，实现通过将实际的车辆的车辆运行状态和车辆镜像模型的虚拟运行状态的对比，对车辆镜像模型进行修正，提高车辆镜像模型在虚拟运行时的准确性。
在一种可能的实现方式中，所述方法还包括：
保存所述车辆行驶的过程中的驾驶信息。
通过该实现方式的技术方案，保存用户的驾驶信息，以便后续基于存储的多个驾驶信息分析用户的驾驶风格。
第二方面，本申请实施例提供了一种车辆续航里程的预测装置，所述装置包括：
获取模块，用于在车辆行驶的过程中，获取车辆的环境信息、剩余电量以及历史驾驶信息；
确定模块，用于根据所述环境信息和所述历史驾驶信息，确定车辆运行工况；
计算模块，用于控制车辆镜像模型在所述车辆运行工况下运行，计算所述车辆在所述车辆运行工况下的能耗；所述车辆镜像模型根据数字孪生算法构建得到的；
预测模块，用于根据所述剩余电量和所述能耗，得到所述车辆的车辆续航里程。
第三方面，本申请实施例提供了一种车辆续航里程的预测设备，所述设备包括：
处理器以及存储有计算机程序指令的存储器；
所述处理器执行所述计算机程序指令时实现如上述第一方面任意一项所述的车辆续航里程的预测方法。
第四方面，本申请实施例提供了一种计算机可读存储介质，所述计算机可读存储介质上存储有计算机程序指令，所述计算机程序指令被处理器执行时实现如上述第一方面任意一项所述的车辆续航里程的预测方法。
第五方面，本申请实施例提供了一种计算机程序产品，所述计算机程序产品中的指令由电子设备的处理器执行时，使得所述电子设备执行如上述第一方面任意一项所述的车辆续航里程的预测方法。
本申请提供一种车辆续航里程的预测方法、装置、设备及计算机存储介质，在车辆行驶的过程中，获取车辆的环境信息、剩余电量以及历史驾驶信息；根据环境信息和历史驾驶信息，确定车辆运行工况；控制车辆镜像模型在车辆运行工况下运行，计算车辆在车辆运行工况下的能耗；根据剩余电量和能耗，预测车辆的车辆续航里程。通过构建与车辆的动力学和能耗表现相同的车辆镜像模型，以及贴近显示的车辆运行工况预测车辆续航里程，实现提高车辆续航里程的预测准确度。
附图说明
为了更清楚地说明本申请实施例的技术方案，下面将对本申请实施例中所需要使用的附图作简单地介绍，显而易见地，下面所描述的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据附图获得其他的附图。
图1为本申请实施例提供的一种车辆续航里程的预测方法的示意性流程示意图；
图2为本申请实施例提供的另一种车辆续航里程的预测方法的示意 性流程示意图；
图3为本申请实施例提供的另一种车辆续航里程的预测方法的示意性流程示意图；
图4为本申请实施例提供的另一种车辆续航里程的预测方法的示意性流程示意图；
图5为本申请实施例提供的另一种车辆续航里程的预测方法的示意性流程示意图；
图6为本申请实施例提供的另一种车辆续航里程的预测方法的示意性流程示意图；
图7为本申请实施例提供的一种车辆续航里程的装置的示意性结构示意图；
图8为本申请实施例提供的另一种车辆续航里程的装置的示意性结构示意图；
图9为本申请实施例提供的另一种车辆续航里程的装置的示意性结构示意图；
图10为本申请实施例提供的另一种车辆续航里程的装置的示意性结构示意图；
图11为本申请实施例提供的车辆续航里程的设备的示意性结构示意图。
在附图中，附图并未按照实际的比例绘制。
具体实施方式
为使本申请实施例的目的、技术方案和优点更加清楚，下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚地描述，显然，所描述的实施例是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
除非另有定义，本申请所使用的所有的技术和科学术语与属于本申请的技术领域的技术人员通常理解的含义相同；本申请中在申请的说明书 中所使用的术语只是为了描述具体的实施例的目的，不是旨在于限制本申请；本申请的说明书和权利要求书及上述附图说明中的术语“包括”和“具有”以及它们的任何变形，意图在于覆盖不排他的包含。
在本申请中提及“实施例”意味着，结合实施例描述的特定特征、结构或特性可以包含在本申请的至少一个实施例中。在说明书中的各个位置出现该短语并不一定均是指相同的实施例，也不是与其它实施例互斥的独立的或备选的实施例。
本申请中出现的“多个”指的是两个以上(包括两个)。
由于新能源汽车的续航里程受很多因素影响，续航里程估计值往往与实际行驶距离相差很大。相关技术无法预测新能源汽车的续航里程，基于此发明人通过构建与车辆的动力学和能耗表现相同的车辆镜像模型，以及贴近显示的车辆运行工况预测车辆续航里程，实现提高车辆续航里程的预测准确度。
为了解决现有技术问题，本申请实施例提供了一种车辆续航里程的预测方法、装置、设备及计算机存储介质。下面首先对本申请实施例所提供的车辆续航里程的预测方法进行介绍。
图1示出了本申请一个实施例提供的一种车辆续航里程的预测方法的示意性流程示意图。如图1所示，本申请实施例提供的车辆续航里程的预测方法包括以下步骤：S101至S104。
S101，在车辆行驶的过程中，获取车辆的环境信息、剩余电量以及历史驾驶信息；
作为S101的一种实现方式，通过有线方式，例如车辆控制总线，获取车辆的环境信息、剩余电量以及历史驾驶信息。
作为S101的另一种实现方式，通过无线方式获取车辆的环境信息、剩余电量以及历史驾驶信息。
S102，根据环境信息和历史驾驶信息，确定车辆运行工况；
车辆运行工况是用于表征车辆行驶过程中的外界环境的信息。
作为S102的一种实现方式，车辆运行工况是结合环境信息和历史驾驶信息的数据集合。
作为S102的另一种实现方式，车辆运行工况是根据环境信息和历史驾驶信息确定的一种分类结果。
作为S102的另一种实现方式，车辆运行工况是在云端处理得到的。
作为S102的另一种实现方式，车辆运行工况是在车辆的车辆控制器处理得到的。
S103，控制车辆镜像模型在车辆运行工况下运行，预测车辆在车辆运行工况下的能耗；
作为S103的一种实现方式，车辆镜像模型根据数字孪生算法构建得到的。
作为S103的另一种实现方式，控制车辆镜像模型在车辆运行工况下运行，根据车辆镜像模型在车辆运行工况下的实时能耗，预测车辆在车辆运行工况下的能耗。
作为S103的另一种实现方式，控制车辆镜像模型在车辆运行工况下运行，根据车辆镜像模型在车辆运行工况下的完成剩余里程的平均能耗，预测车辆在车辆运行工况下的能耗。
S104，根据剩余电量和能耗，预测车辆的车辆续航里程。
作为S104的一种实现方式，根据剩余电量和能耗的商，预测车辆的车辆续航里程。
通过本申请实施例的技术方案，构建了与车辆的动力学和能耗表现相同的车辆镜像模型，以及贴近显示的车辆运行工况预测车辆续航里程，实现提高新能源车辆的车辆续航里程的预测准确度。
在一种可能的实现方式中，图2为本申请实施例提供的另一种车辆续航里程的预测方法的示意性流程示意图，如图2所示，S103可以包括：S1031-S1032。
S1031，根据历史驾驶信息进行分析，确定用户的驾驶风格；
作为S1031的一种实现方式，默认历史驾驶信息均是该车辆的同一驾驶用户的历史记录，通过对历史驾驶信息进行平均计算，将计算结果与各个驾驶风格的阈值范围比较，确定用户的驾驶风格。
作为S1031的另一种实现方式，获取用户的用户身份标识，根据用 户的用户身份标识对历史驾驶信息进行分类，并提取出对应用户身份标识的用户历史驾驶信息，根据用户历史驾驶信息确定用户的驾驶风格。
其中，用户的用户身份标识通过车内的图像采集装置采集得到的人脸图像、车内的语音采集装置采集得到的用户语音、或者车内的射频识别装置识别的驾驶卡等等确定的。
S1032，根据驾驶风格和环境信息确定车辆运行工况。
作为S1032的一种实现方式，车辆运行工况是结合驾驶风格和历史驾驶信息的数据集合。
作为S1032的另一种实现方式，车辆运行工况是根据驾驶风格和历史驾驶信息确定的一种分类结果。
通过该实现方式的技术方案，考虑用户的驾驶风格和环境信息，确定了车辆运行工况。
在一种可能的实现方式中，环境信息包括：车辆的传感器检测到的第一场景信息和第二场景信息；
第一场景信息至少包括：天气、路面条件；
第二场景信息至少包括：导航信息、交通信息。
通过该实现方式的技术方案，确定了影响车辆运行工况的多个因素，包括天气、路面条件、导航信息、交通信息等等。
在一种可能的实现方式中，图3为本申请实施例提供的另一种车辆续航里程的预测方法的示意性流程示意图，如图3所示，S1031，可以包括：S10311。
历史驾驶信息包括：油门踏板的单次踩踏深度和油门踏板的单次踩踏深度变化率；
S10311，根据单次踩踏深度和单次踩踏深度变化率，确定用户的驾驶风格。
作为S10311的一种实现方式，根据单次踩踏深度和单次踩踏深度变化率进行平均计算，根据计算结果和驾驶风格阈值范围，确定用户的驾驶风格
通过该实现方式的技术方案，根据单次踩踏深度和单次踩踏深度 变化率，确定用户的驾驶风格，以提高确定的用户的驾驶风格的准确性。
在一种可能的实现方式中，在S103之前，图4为本申请实施例提供的另一种车辆续航里程的预测方法的示意性流程示意图，如图4所示，方法还包括：S1-S2。
S1，获取车辆的硬件结构和控制逻辑；
作为S1的一种实现方式，车辆的硬件结构和控制逻辑是预先存储在车辆内部的。
S2，基于数字孪生算法以及车辆的硬件结构和控制逻辑，构建车辆镜像模型。
作为S2的一种实现方式，构建的车辆镜像模型与实际的车辆的动学和能耗表现相似。
通过该实现方式的技术方案，基于数字孪生算法以及车辆的硬件结构和控制逻辑，构建了与车辆相对应的车辆镜像模型，实现模拟实际的车辆的动力学和能耗表现。
在一种可能的实现方式中，车辆镜像模型至少包括与车辆镜像的以下模型：
动力电池模型、驱动电机模型、高低压附件模型、传动系模型、车身模型、轮胎模型、制动模型、转向模型、悬架模型等等，用于模拟车辆的物理结构。
在另一种可能的实现方式中，车辆镜像模型还可以至少包括与车辆镜像的以下模型：
域控制器控制模型、动力部件控制模型、底盘部件控制模型等等，可以用于模拟实际的车辆的车辆控制。
通过该实现方式的技术方案，确定模拟实际的车辆的动力学和能耗表现的具体模型。
在一种可能的实现方式中，图5为本申请实施例提供的另一种车辆续航里程的预测方法的示意性流程示意图，如图5所示，方法还包括：S3-S5。
S3，获取车辆的车辆运行状态和车辆镜像模型的虚拟运行状态；
作为S3的一种实现方式，实时获取获取车辆的车辆运行状态和车辆镜像模型的虚拟运行状态。
作为S3的另一种实现方式，根据预设时间间隔获取获取车辆的车辆运行状态和车辆镜像模型的虚拟运行状态。
S4，对比车辆运行状态和虚拟运行状态，得到车辆镜像模型相对于车辆的误差；
作为S4的一种实现方式，车辆镜像模型相对于车辆的误差是差值或者比例系数。
S5，根据误差对车辆镜像模型进行修正。
作为S5的一种实现方式，实时或者按照预设时间间隔，根据误差对车辆镜像模型进行修正。
作为S5的另一种实现方式，在误差大于预设误差阈值的情况下，根据误差对车辆镜像模型进行修正。
通过该实现方式的技术方案，实现通过将实际的车辆的车辆运行状态和车辆镜像模型的虚拟运行状态的对比，对车辆镜像模型进行修正，提高车辆镜像模型在虚拟运行时的准确性。
在一种可能的实现方式中，图6为本申请实施例提供的另一种车辆续航里程的预测方法的示意性流程示意图，如图6所示，方法还包括：S6。
S6，保存车辆行驶的过程中的驾驶信息。
通过该实现方式的技术方案，保存用户的驾驶信息，以便后续基于存储的多个驾驶信息分析用户的驾驶风格。
在一个实例中，驾驶信息可以包括：油门踏板的单次踩踏深度、油门踏板的单次踩踏深度变化率。
在另一个实例中，驾驶信息还可以至少包括：用户单位里程油门踏板的踩踏次数、单位里程耗能、刹车起步间隔时间。
本申请提供的车辆续航里程的预测方法，在车辆行驶的过程中，获取车辆的环境信息、剩余电量以及历史驾驶信息；根据环境信息和历史驾驶信息，确定车辆运行工况；控制车辆镜像模型在车辆运行工况下运行， 计算车辆在车辆运行工况下的能耗；根据剩余电量和能耗，预测车辆的车辆续航里程。通过构建与车辆的动力学和能耗表现相同的车辆镜像模型，以及贴近显示的车辆运行工况预测车辆续航里程，实现提高车辆续航里程的预测准确度。
基于上述实施例提供的车辆续航里程的预测方法，相应地，本申请还提供了车辆续航里程的预测装置的具体实现方式。图7为本申请实施例提供的一种车辆续航里程的预测装置的示意性结构示意图，如图7所示，本申请实施例提供的车辆续航里程的预测装置200包括以下模块：
获取模块201，用于在车辆行驶的过程中，获取车辆的环境信息、剩余电量以及历史驾驶信息；
确定模块202，用于根据环境信息和历史驾驶信息，确定车辆运行工况；
计算模块203，用于控制车辆镜像模型在车辆运行工况下运行，计算车辆在车辆运行工况下的能耗；车辆镜像模型根据数字孪生算法构建得到的；
预测模块204，用于根据剩余电量和能耗，得到车辆的车辆续航里程。
在一种可能的实现方式中，确定模块202，可以用于根据历史驾驶信息进行分析，确定用户的驾驶风格；
根据驾驶风格和环境信息确定车辆运行工况。
通过该实现方式的技术方案，考虑用户的驾驶风格和环境信息，确定了车辆运行工况。
在一种可能的实现方式中，环境信息包括：车辆的传感器检测到的第一场景信息和第二场景信息；
第一场景信息至少包括：天气、路面条件；
第二场景信息至少包括：导航信息、交通信息。
通过该实现方式的技术方案，确定了影响车辆运行工况的多个因素，包括天气、路面条件、导航信息、交通信息等等。
在一种可能的实现方式中，历史驾驶信息包括：油门踏板的单次 踩踏深度和油门踏板的单次踩踏深度变化率；
确定模块202，还可以用于根据单次踩踏深度和单次踩踏深度变化率，确定用户的驾驶风格。
通过该实现方式的技术方案，根据单次踩踏深度和单次踩踏深度变化率，确定用户的驾驶风格，以提高确定的用户的驾驶风格的准确性。
在一种可能的实现方式中，图8为本申请实施例提供的另一种车辆续航里程的装置的示意性结构示意图，如图8所示，车辆续航里程的预测装置200还可以包括：构建模型模块205，用于获取车辆的硬件结构和控制逻辑；
基于数字孪生算法以及车辆的硬件结构和控制逻辑，构建车辆镜像模型。
通过该实现方式的技术方案，基于数字孪生算法以及车辆的硬件结构和控制逻辑，构建了与车辆相对应的车辆镜像模型，实现模拟实际的车辆的动力学和能耗表现。
在一种可能的实现方式中，车辆镜像模型至少包括与车辆镜像的以下模型：
动力电池模型、驱动电机模型、高低压附件模型、传动系模型、车身模型、轮胎模型、制动模型、转向模型、悬架模型。
通过该实现方式的技术方案，确定模拟实际的车辆的动力学和能耗表现的具体模型。
在一种可能的实现方式中，图9为本申请实施例提供的另一种车辆续航里程的装置的示意性结构示意图，如图9所示，车辆续航里程的预测装置200还可以包括：修正模块206，用于获取车辆的车辆运行状态和车辆镜像模型的虚拟运行状态；
对比车辆运行状态和虚拟运行状态，得到车辆镜像模型相对于车辆的误差；
根据误差对车辆镜像模型进行修正。
通过该实现方式的技术方案，实现通过将实际的车辆的车辆运行状态和车辆镜像模型的虚拟运行状态的对比，对车辆镜像模型进行修正， 提高车辆镜像模型在虚拟运行时的准确性。
在一种可能的实现方式中，图10为本申请实施例提供的另一种车辆续航里程的装置的示意性结构示意图，如图10所示，车辆续航里程的预测装置200还可以包括：存储模块207，用于保存车辆行驶的过程中的驾驶信息。
通过该实现方式的技术方案，保存用户的驾驶信息，以便后续基于存储的多个驾驶信息分析用户的驾驶风格。
本申请提供的车辆续航里程的预测装置，在车辆行驶的过程中，获取车辆的环境信息、剩余电量以及历史驾驶信息；根据环境信息和历史驾驶信息，确定车辆运行工况；控制车辆镜像模型在车辆运行工况下运行，计算车辆在车辆运行工况下的能耗；根据剩余电量和能耗，预测车辆的车辆续航里程。通过构建与车辆的动力学和能耗表现相同的车辆镜像模型，以及贴近显示的车辆运行工况预测车辆续航里程，实现提高车辆续航里程的预测准确度。
基于上述实施例提供的车辆续航里程的预测方法，相应地，本申请还提供了车辆续航里程的预测设备的具体实现方式。图11为本申请实施例提供的车辆续航里程的预测设备的示意性结构示意图，如图11所示，本申请实施例提供的车辆续航里程的预测设备包括：
处理器301以及存储有计算机程序指令的存储器302。
具体地，上述处理器301可以包括中央处理器(CPU)，或者特定集成电路(Application Specific Integrated Circuit，ASIC)，或者可以被配置成实施本申请实施例的一个或多个集成电路。
存储器302可以包括用于数据或指令的大容量存储器。举例来说而非限制，存储器302可包括硬盘驱动器(Hard Disk Drive，HDD)、软盘驱动器、闪存、光盘、磁光盘、磁带或通用串行总线(Universal Serial Bus，USB)驱动器或者两个或更多个以上这些的组合。在合适的情况下，存储器302可包括可移除或不可移除(或固定)的介质。在合适的情况下，存储器302可在综合网关容灾设备的内部或外部。在特定实施例中，存储器302是非易失性固态存储器。
存储器302可包括只读存储器(ROM)，随机存取存储器(RAM)，磁盘存储介质设备，光存储介质设备，闪存设备，电气、光学或其他物理/有形的存储器存储设备。因此，通常，存储器包括一个或多个编码有包括计算机可执行指令的软件的有形(非暂态)计算机可读存储介质(例如，存储器设备)，并且当该软件被执行(例如，由一个或多个处理器)时，其可操作来执行参考根据本申请的一方面的方法所描述的操作。
处理器301通过读取并执行存储器302中存储的计算机程序指令，以实现上述实施例中的任意一种车辆续航里程的预测方法。
在一个示例中，车辆续航里程的预测设备还可包括通信接口303和总线310。其中，如图3所示，处理器301、存储器302、通信接口303通过总线310连接并完成相互间的通信。
通信接口303，主要用于实现本申请实施例中各模块、装置、单元和/或设备之间的通信。
总线310包括硬件、软件或两者，将车辆续航里程的预测设备的部件彼此耦接在一起。举例来说而非限制，总线可包括加速图形端口(AGP)或其他图形总线、增强工业标准架构(EISA)总线、前端总线(FSB)、超传输(HT)互连、工业标准架构(ISA)总线、无限带宽互连、低引脚数(LPC)总线、存储器总线、微信道架构(MCA)总线、***组件互连(PCI)总线、PCI-Express(PCI-X)总线、串行高级技术附件(SATA)总线、视频电子标准协会局部(VLB)总线或其他合适的总线或者两个或更多个以上这些的组合。在合适的情况下，总线310可包括一个或多个总线。尽管本申请实施例描述和示出了特定的总线，但本申请考虑任何合适的总线或互连。
该车辆续航里程的预测设备可以执行本申请实施例中的车辆续航里程的预测方法，从而实现结合图1和图2描述的车辆续航里程的预测方法和装置。
此外，基于上述实施例提供的车辆续航里程的预测方法，相应地，本申请还提供了一种计算机可读存储介质，计算机可读存储介质上存储有计算机程序指令，计算机程序指令被处理器执行时实现如上述的车辆续航 里程的预测方法。
基于上述实施例提供的车辆续航里程的预测方法，相应地，本申请还提供了一种计算机程序产品，计算机程序产品中的指令由电子设备的处理器执行时，使得电子设备执行如上述的车辆续航里程的预测方法。
需要明确的是，本申请并不局限于上文所描述并在图中示出的特定配置和处理。为了简明起见，这里省略了对已知方法的详细描述。在上述实施例中，描述和示出了若干具体的步骤作为示例。但是，本申请的方法过程并不限于所描述和示出的具体步骤，本领域的技术人员可以在领会本申请的精神后，作出各种改变、修改和添加，或者改变步骤之间的顺序。
以上的结构框图中所示的功能块可以实现为硬件、软件、固件或者它们的组合。当以硬件方式实现时，其可以例如是电子电路、专用集成电路(ASI C)、适当的固件、插件、功能卡等等。当以软件方式实现时，本申请的元素是被用于执行所需任务的程序或者代码段。程序或者代码段可以存储在机器可读介质中，或者通过载波中携带的数据信号在传输介质或者通信链路上传送。“机器可读介质”可以包括能够存储或传输信息的任何介质。机器可读介质的例子包括电子电路、半导体存储器设备、ROM、闪存、可擦除ROM(EROM)、软盘、CD-ROM、光盘、硬盘、光纤介质、射频(RF)链路，等等。代码段可以经由诸如因特网、内联网等的计算机网络被下载。
还需要说明的是，本申请中提及的示例性实施例，基于一系列的步骤或者装置描述一些方法或***。但是，本申请不局限于上述步骤的顺序，也就是说，可以按照实施例中提及的顺序执行步骤，也可以不同于实施例中的顺序，或者若干步骤同时执行。
上面参考根据本申请的实施例的方法、装置和计算机程序产品的流程图和/或框图描述了本申请的各方面。应当理解，流程图和/或框图中的每个方框以及流程图和/或框图中各方框的组合可以由计算机程序指令实现。这些计算机程序指令可被提供给通用计算机、专用计算机、或其它可编程数据处理装置的处理器，以产生一种机器，使得经由计算机或其它可编程数据处理装置的处理器执行的这些指令使能对流程图和/或框图的 一个或多个方框中指定的功能/动作的实现。这种处理器可以是但不限于是通用处理器、专用处理器、特殊应用处理器或者现场可编程逻辑电路。还可理解，框图和/或流程图中的每个方框以及框图和/或流程图中的方框的组合，也可以由执行指定的功能或动作的专用硬件来实现，或可由专用硬件和计算机指令的组合来实现。
需要说明的是，在不冲突的情况下，本申请中的实施例及实施例中的特征可以相互组合。
最后应说明的是：以上实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换，但这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的精神和范围。

Claims (11)

  1. 一种车辆续航里程的预测方法，其特征在于，所述方法包括：
    在车辆行驶的过程中，获取车辆的环境信息、剩余电量以及历史驾驶信息；
    根据所述环境信息和所述历史驾驶信息，确定车辆运行工况；
    控制车辆镜像模型在所述车辆运行工况下运行，预测所述车辆在所述车辆运行工况下的能耗；所述车辆镜像模型根据数字孪生算法构建得到的；
    根据所述剩余电量和所述能耗，预测所述车辆的车辆续航里程。
  2. 根据权利要求1所述的车辆续航里程的预测方法，其特征在于，所述根据所述环境信息和所述历史驾驶信息，确定车辆运行工况，包括：
    根据所述历史驾驶信息进行分析，确定用户的驾驶风格；
    根据所述驾驶风格和所述环境信息确定所述车辆运行工况。
  3. 根据权利要求1或2所述的车辆续航里程的预测方法，其特征在于，所述环境信息包括：所述车辆的传感器检测到的第一场景信息和第二场景信息；
    所述第一场景信息至少包括：天气、路面条件；
    所述第二场景信息至少包括：导航信息、交通信息。
  4. 根据权利要求2所述的车辆续航里程的预测方法，其特征在于，所述历史驾驶信息包括：油门踏板的单次踩踏深度和油门踏板的单次踩踏深度变化率；
    所述根据所述历史驾驶信息进行分析，确定用户的驾驶风格，包括：
    根据所述单次踩踏深度和单次踩踏深度变化率，确定所述用户的驾驶风格。
  5. 根据权利要求1所述的车辆续航里程的预测方法，其特征在于，在 所述控制车辆镜像模型在所述车辆运行工况下运行之前，所述方法还包括：
    获取车辆的硬件结构和控制逻辑；
    基于数字孪生算法以及所述车辆的所述硬件结构和所述控制逻辑，构建所述车辆镜像模型。
  6. 根据权利要求1所述的车辆续航里程的预测方法，其特征在于，所述车辆镜像模型至少包括与所述车辆镜像的以下模型：
    动力电池模型、驱动电机模型、高低压附件模型、传动系模型、车身模型、轮胎模型、制动模型、转向模型、悬架模型。
  7. 根据权利要求1所述的车辆续航里程的预测方法，其特征在于，所述方法还包括：
    获取所述车辆的车辆运行状态和所述车辆镜像模型的虚拟运行状态；
    对比所述车辆运行状态和所述虚拟运行状态，得到所述车辆镜像模型相对于所述车辆的误差；
    根据所述误差对所述车辆镜像模型进行修正。
  8. 根据权利要求1所述的车辆续航里程的预测方法，其特征在于，所述方法还包括：
    保存所述车辆行驶的过程中的驾驶信息。
  9. 一种车辆续航里程的预测装置，其特征在于，所述装置包括：
    获取模块，用于在车辆行驶的过程中，获取车辆的环境信息、剩余电量以及历史驾驶信息；
    确定模块，用于根据所述环境信息和所述历史驾驶信息，确定车辆运行工况；
    计算模块，用于控制车辆镜像模型在所述车辆运行工况下运行，计算所述车辆在所述车辆运行工况下的能耗；所述车辆镜像模型根据数字孪生算法构建得到的；
    预测模块，用于根据所述剩余电量和所述能耗，得到所述车辆的车辆续航里程。
  10. 一种车辆续航里程的预测设备，其特征在于，所述设备包括：
    处理器以及存储有计算机程序指令的存储器；
    所述处理器执行所述计算机程序指令时实现如权利要求1-8任意一项所述的车辆续航里程的预测方法。
  11. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质上存储有计算机程序指令，所述计算机程序指令被处理器执行时实现如权利要求1-8任意一项所述的车辆续航里程的预测方法。
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