Energy management system and method for autonomous vehicle

Abstract

The invention provides an energy management system and method for an autonomous vehicle, and relates to the technical field of vehicles. The system comprises an information collection module used for collecting vehicle driving information and sending the collected vehicle driving information to a cost calculation module; the energy complementing module is used for acquiring energy complementing information and sending the energy complementing information to the cost calculation module; the order module is used for acquiring order information and transmitting the order information to the calculation module; the cost calculation module is used for performing minimum cost calculation according to the vehicle driving information, the energy supplement information and the order information to obtain a minimum cost calculation result, generating a route plan and a vehicle speed plan according to the minimum cost calculation result, and sending the route plan and the vehicle speed plan to the vehicle energy management module; and the vehicle energy management module is used for controlling energy consumption and energy supplementation in the vehicle running process according to the route planning and the vehicle speed planning. And performing energy management on the autonomous vehicle to improve the operation benefit of the autonomous vehicle.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to an energy management system and method for an autonomous driving vehicle. Background Art

[0002] The existing hybrid electric vehicle energy management control strategy is mainly based on rules or optimization algorithms. The most widely used one is rule-based energy management, which is limited by the experience of the calibration personnel, the calibration environment and the calibration conditions. It can only achieve the normal driving of the vehicle, but cannot consider the cost of vehicle energy consumption and the optimal operation income.

[0003] In summary, how to manage the energy of autonomous vehicles to improve the operating benefits of autonomous vehicles is an issue that technical personnel in this field urgently need to solve. Summary of the invention

[0004] In view of this, the present application provides an energy management system and method for an autonomous driving vehicle, aiming to manage the energy of the autonomous driving vehicle to improve the operating income of the autonomous driving vehicle.

[0005] In a first aspect, the present application provides an energy management system for an autonomous driving vehicle.

[0006] The information collection module is used to collect vehicle driving information; and send the collected vehicle driving information to the cost calculation module;

[0007] An energy replenishment module, used for acquiring energy replenishment information; and sending the energy replenishment information to the cost calculation module;

[0008] An order module is used to obtain order information; and pass the order information to a cost calculation module;

[0009] The cost calculation module is used to perform minimum cost calculation according to the vehicle driving information, the energy replenishment information and the order information to obtain a minimum cost calculation result; generate a route plan and a vehicle speed plan according to the minimum cost calculation result; and send the route plan and the vehicle speed plan to the vehicle energy management module;

[0010] The vehicle energy management module is used to control the energy consumption and energy replenishment during the operation of the vehicle according to the route planning and the vehicle speed planning.

[0011] Optionally, the vehicle driving information includes vehicle position information, current vehicle speed information and driving demand, vehicle temperature information and vehicle basic information; the vehicle position information includes the longitude, latitude and altitude of the vehicle position; the vehicle temperature information includes battery temperature and the temperature inside and outside the vehicle; the vehicle basic information includes power mode, battery SOC and fuel level.

[0012] Optionally, the energy replenishment information includes location information of charging piles and gas stations and energy cost information.

[0013] Optionally, the order information includes current order information and predicted order information; the order information includes order time period, order duration, order location, order examples and order fees.

[0014] Optionally, the cost calculation module is further used for:

[0015] The vehicle's energy consumption and energy replenishment strategy are dynamically adjusted based on the real-time vehicle operating status and surrounding environment information.

[0016] In a second aspect, the present application provides an energy management method for an autonomous driving vehicle.

[0017] Collect vehicle driving information;

[0018] Get energy replenishment information;

[0019] Get order information;

[0020] Performing a minimum cost calculation based on the vehicle driving information, the energy replenishment information, and the order information to obtain a minimum cost calculation result; and generating a route plan and a vehicle speed plan based on the minimum cost calculation result;

[0021] The energy consumption and energy replenishment during the operation of the vehicle are controlled according to the route planning and the vehicle speed planning.

[0022] Optionally, the vehicle driving information includes vehicle position information, current vehicle speed information and driving demand, vehicle temperature information and vehicle basic information; the vehicle position information includes the longitude, latitude and altitude of the vehicle position; the vehicle temperature information includes battery temperature and the temperature inside and outside the vehicle; the vehicle basic information includes power mode, battery SOC and fuel level.

[0023] Optionally, the energy replenishment information includes location information of charging piles and gas stations and energy cost information.

[0024] Optionally, the order information includes current order information and predicted order information; the order information includes order time period, order duration, order location, order examples and order fees.

[0025] Optionally, the method further includes:

[0026] The vehicle's energy consumption and energy replenishment strategy are dynamically adjusted based on the real-time vehicle operating status and surrounding environment information.

[0027] The present application provides an energy management system for an autonomous driving vehicle. The system includes: an information collection module, which is used to collect vehicle driving information and send the collected vehicle driving information to a cost calculation module; an energy replenishment module, which is used to obtain energy replenishment information and send the energy replenishment information to a cost calculation module; an order module, which is used to obtain order information and send the order information to a cost calculation module; a cost calculation module, which is used to perform a minimum cost calculation based on vehicle driving information, energy replenishment information and order information, obtain a minimum cost calculation result, generate a route planning and a vehicle speed planning based on the minimum cost calculation result, and send the route planning and the vehicle speed planning to a vehicle energy management module; a vehicle energy management module, which is used to control the energy consumption and energy replenishment during the operation of the vehicle according to the route planning and the vehicle speed planning. In this way, according to the vehicle driving information, energy replenishment information and order information, a minimum cost calculation is performed to obtain a minimum cost calculation result, and then a route planning and a vehicle speed planning are generated, and the energy consumption and energy replenishment during the operation of the vehicle are controlled according to the route planning and the vehicle speed planning. In this way, energy management of autonomous driving vehicles can improve the operating income of autonomous driving vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in this embodiment or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0029] Figure 1 A flowchart of an energy management method for an autonomous driving vehicle provided in an embodiment of the present application;

[0030] Figure 2 A schematic diagram of the structure of an energy management system for an autonomous driving vehicle provided in an embodiment of the present application;

[0031] Figure 3 A schematic diagram of calculating mileage and travel time provided in an embodiment of the present application;

[0032] Figure 4 The table provided for the embodiment of the present application is a schematic diagram of the initial values ​​defined in the vehicle speed and average power comparison table;

[0033] Figure 5 A schematic diagram of calculating the remaining fuel and power consumption for each trip provided in an embodiment of the present application;

[0034] Figure 6 A schematic diagram of an embodiment of the present application for calculating energy consumption and cost and planning the journey and vehicle speed. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. The present application provides an energy management system and method for an autonomous driving vehicle, which is used in the field of vehicle technology. The above is only an example and does not limit the application field of the method and system name provided in the present application.

[0036] The existing hybrid electric vehicle energy management control strategy is mainly based on rules or optimization algorithms. The most widely used one is rule-based energy management, which is limited by the experience of the calibration personnel, the calibration environment and the calibration conditions. It can only achieve the normal driving of the vehicle, but cannot consider the cost of vehicle energy consumption and the optimal operation income.

[0037] After research, the inventor proposed the technical solution of this application. The energy management system of the autonomous driving vehicle includes: an information collection module, which is used to collect vehicle driving information and send the collected vehicle driving information to the cost calculation module; an energy replenishment module, which is used to obtain energy replenishment information and send the energy replenishment information to the cost calculation module; an order module, which is used to obtain order information and send the order information to the cost calculation module; the cost calculation module is used to perform the minimum cost calculation according to the vehicle driving information, energy replenishment information and order information, obtain the minimum cost calculation result, generate route planning and vehicle speed planning according to the minimum cost calculation result, and send the route planning and vehicle speed planning to the vehicle energy management module; the vehicle energy management module is used to control the energy consumption and energy replenishment during the operation of the vehicle according to the route planning and vehicle speed planning. In this way, according to the vehicle driving information, energy replenishment information and order information, the minimum cost calculation result is obtained, and then the route planning and vehicle speed planning are generated, and the energy consumption and energy replenishment during the operation of the vehicle are controlled according to the route planning and vehicle speed planning. In this way, the energy management of the autonomous driving vehicle can improve the operating income of the autonomous driving vehicle.

[0038] In order to enable those skilled in the art to better understand the present application, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application. It should be noted that, for ease of description, only the parts related to the relevant invention are shown in the accompanying drawings. In the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0039] See also Figure 1 , Figure 1 A flowchart of an energy management method for an autonomous driving vehicle provided in an embodiment of the present application includes:

[0040] S101: Collect vehicle driving information.

[0041] The information collection module 110 collects vehicle driving information, wherein the vehicle driving information includes vehicle position information, current vehicle speed information and driving demand, vehicle temperature information and vehicle basic information; the vehicle position information includes the longitude, latitude and altitude of the vehicle position; the vehicle temperature information includes the battery temperature and the temperature inside and outside the vehicle; the vehicle basic information includes the power mode, battery SOC and fuel level.

[0042] S102: Send the collected vehicle driving information to the cost calculation module 140.

[0043] The information collection module 110 sends the collected vehicle driving information to the cost calculation module 140 .

[0044] S103: Obtain energy replenishment information.

[0045] The energy replenishment module 120 obtains energy replenishment information, which includes the location information of charging piles and gas stations and energy cost information. Considering the location and distance of charging piles and gas stations, the energy replenishment cost is reduced and the energy utilization efficiency is improved by optimizing the selection of energy replenishment time and location. Specifically, the location and distance of charging piles and gas stations can be obtained through map information, and the selection of energy replenishment time and location can be determined by an optimization algorithm.

[0046] S104: Send the energy replenishment information to the cost calculation module 140.

[0047] The energy replenishment module 120 sends the energy replenishment information to the cost calculation module 140 .

[0048] S105: Obtain order information.

[0049] The order module 130 obtains order information. The order information includes current order information and predicted order information; the order information includes order time period, order duration, order location, order examples, and order fees. Considering the customer's order time period, duration, location, and distance, the subsequent order information is predicted to calculate the feasible recharging time. Specifically, the customer's order time period can be predicted through historical data analysis, the order duration can be determined by the customer's needs, and the location and distance can be calculated through GPS data. Predicting subsequent order information can be predicted by a machine learning algorithm.

[0050] In some embodiments, subsequent operation plans can be predicted and optimized. Based on the current operation status and the predicted information, the subsequent operation plan is optimized, including vehicle scheduling, energy management, etc., so as to further reduce the operation cost and improve the operation efficiency. Specifically, the predicted information can be predicted by a machine learning algorithm, and the optimization of the subsequent operation plan can be determined by an optimization algorithm.

[0051] S106: Send the order information to the cost calculation module 140.

[0052] The order module 130 sends the order information to the cost calculation module 140 .

[0053] S107: Perform minimum cost calculation based on vehicle driving information, energy replenishment information and order information to obtain a minimum cost calculation result.

[0054] The cost calculation module 140 performs the minimum cost calculation based on the vehicle driving information, the charging information and the order information to obtain the minimum cost calculation result. For the items that need to be controlled, the algorithm is summarized as follows:

[0055] u opt ∈={u|J(u)is minimized}

[0056] J=[F 1 (u),F 2 (u),…,F n (u)]

[0057] u=[u 1 ,u 2 ,…,u m ]

[0058] h(u)≤0

[0059] In the formula, u=[u 1 ,u 2 ,…,u m ] refers to multiple relevant control parameters corresponding to this optimization problem, such as the working power (speed, torque) of the engine and motor and the energy replenishment cost, time cost, cargo revenue and passenger revenue, etc.

[0060] J=[F 1 (u),F 2 (u),…,F n (u)] refers to the corresponding multiple optimization indicators, including fuel consumption, power, NVH speed and torque optimization targets. h(u) refers to the constraints in the optimization process such as torque, speed, SOC and battery power limit, as follows:

[0061] Torque limit: T x,min ≤Tx(t) ≤T x,max , x=eng,mot,gen

[0062] Speed ​​limit: N x,min ≤N x(t) ≤N x,max , x=eng,mot,gen

[0063] Battery SOC limit: SOC min ≤soc(t)≤SOC max

[0064] Battery power limit: P bat,min ≤P bat(t) ≤P bat,max

[0065] Battery SOC change: x(t f )-x target =Δx

[0066] Among them, eng is the engine, mot is the drive motor, and gen is the generator.

[0067] In addition, x target is the target SOC.

[0068] By considering factors such as vehicle energy consumption, energy replenishment cost, time consumption, time cost, and passenger and cargo revenue, and optimizing the balance of these factors, the cost-optimal operation strategy is achieved. Specifically, vehicle energy consumption can be calculated by factors such as vehicle range and driving speed, energy replenishment cost can be calculated by factors such as charging piles and charging prices at gas stations, and refueling prices, time consumption can be calculated by factors such as vehicle driving time and waiting time, time cost can be calculated by factors such as labor cost and opportunity cost, and passenger and cargo revenue can be calculated by factors such as the fare paid by passengers and the transportation cost of goods.

[0069] S108: Generate route planning and vehicle speed planning according to the minimum cost calculation result.

[0070] The cost calculation module 140 generates a route plan and a vehicle speed plan according to the minimum cost calculation result.

[0071] In some implementations, for the cost calculation module 140, the calculation logic is as follows:

[0072] (1) Determine driving demand, that is, divide and combine the trip based on the input order driving distance, the connection point of the next trip, the location of the charging station and the gas station, and calculate the driving mileage and travel time. Figure 3 As shown, Figure 3A schematic diagram of calculating mileage and travel time provided in an embodiment of the present application.

[0073] (2) Calculate energy consumption based on target travel:

[0074] Calculate the power demand P for each duration based on the navigation vehicle speed information:

[0075]

[0076] Where f is the rolling resistance coefficient, α is the slope, CD and A are the vehicle drag coefficient and frontal area respectively, and δ is the rotational mass conversion factor.

[0077] The corresponding power consumption is calculated by the average power consumption Ea1 = P*t, where t is the duration of the trip.

[0078] Fuel consumption is calculated by looking up the table based on vehicle speed and average power, such as Figure 4 As shown, Figure 4 The table provided for the embodiment of the present application is a schematic diagram of the initial values ​​defined in the vehicle speed and average power comparison table, which will be continuously updated as the journey progresses.

[0079] Calculate the remaining fuel and power consumption for each trip. The specific calculation contents are as follows: Figure 5 As shown, Figure 5 A schematic diagram of calculating the remaining fuel and power consumption for each trip provided in an embodiment of the present application.

[0080] The cost of the trip is calculated, based on the fuel consumption, the fuel cost is calculated as Ca1 = Fa1 * 7.5; based on the power consumption, the power consumption cost is calculated as CCa1 = Fa1 * 0.66; and the energy allocation strategy is defined according to the current remaining energy situation, the fuel consumption and power consumption are comprehensively allocated, and the energy consumption is calculated through the energy allocation definition CCCA1 = fuel consumption * 7.5 + power consumption * 0.66. Figure 6 As shown, Figure 6 A schematic diagram of calculating energy consumption and cost and planning the itinerary and vehicle speed is provided in an embodiment of the present application. Energy consumption and cost are calculated, k1 is comprehensively optimized, and itinerary and vehicle speed planning are performed for k1.

[0081] In some embodiments, the energy consumption and energy replenishment strategy of the vehicle can be adjusted in real time. According to the real-time acquired vehicle operation status and surrounding environment information, the energy consumption and energy replenishment strategy of the vehicle are dynamically adjusted to adapt to different operation scenarios and needs. Specifically, the vehicle operation status and surrounding environment information can be obtained through sensors, GPS and other devices, and the dynamic adjustment strategy can be determined by an optimization algorithm.

[0082] S109: Send the route planning and vehicle speed planning to the vehicle energy management module 150 .

[0083] The cost calculation module 140 sends the route planning and the vehicle speed planning to the vehicle energy management module 150 .

[0084] S110: Controlling energy consumption and energy replenishment during vehicle operation according to route planning and vehicle speed planning.

[0085] The vehicle energy management module 150 controls the energy consumption and energy replenishment during the operation of the vehicle according to the route planning and vehicle speed planning. Specifically, the control of vehicle energy consumption can be controlled by adjusting factors such as the vehicle's driving speed and driving route, and the control of energy replenishment can be controlled by selecting a suitable time and place for energy replenishment, while providing planning suggestions for driving routes and vehicle speeds.

[0086] In the embodiment provided by the present application, the vehicle driving information is first collected, the energy replenishment information and the order information are obtained, and then the minimum cost calculation is performed based on the vehicle driving information, the energy replenishment information and the order information to obtain the minimum cost calculation result. Then, the route planning and the vehicle speed planning are generated based on the minimum cost calculation result, and finally the energy consumption and energy replenishment during the operation of the vehicle are controlled based on the route planning and the vehicle speed planning. In this way, the minimum cost calculation is performed based on the vehicle driving information, the energy replenishment information and the order information to obtain the minimum cost calculation result, and then the route planning and the vehicle speed planning are generated, and the energy consumption and energy replenishment during the operation of the vehicle are controlled based on the route planning and the vehicle speed planning. In this way, energy management of autonomous driving vehicles can improve the operating income of autonomous driving vehicles.

[0087] The above are some specific implementations of the energy management method for an autonomous driving vehicle provided by the embodiments of the present application. Based on this, the present application also provides a corresponding system. The system provided by the embodiments of the present application will be introduced below from the perspective of functional modularization.

[0088] See also Figure 2 , Figure 2 A schematic diagram of the structure of an energy management system for an autonomous driving vehicle provided in an embodiment of the present application. The energy management system 200 for the autonomous driving vehicle includes:

[0089] The information collection module 210 is used to collect vehicle driving information; and send the collected vehicle driving information to the cost calculation module 240;

[0090] The energy replenishment module 220 is used to obtain energy replenishment information; and send the energy replenishment information to the cost calculation module 240;

[0091] An order module 230 is used to obtain order information; and to send the order information to a cost calculation module 240;

[0092] The cost calculation module 240 is used to perform minimum cost calculation according to the vehicle driving information, the energy replenishment information and the order information to obtain a minimum cost calculation result; generate a route plan and a vehicle speed plan according to the minimum cost calculation result; and send the route plan and the vehicle speed plan to the vehicle energy management module 250;

[0093] The vehicle energy management module 250 is used to control the energy consumption and energy replenishment during the operation of the vehicle according to the route planning and the vehicle speed planning.

[0094] Optionally, the vehicle driving information includes vehicle position information, current vehicle speed information and driving demand, vehicle temperature information and vehicle basic information; the vehicle position information includes the longitude, latitude and altitude of the vehicle position; the vehicle temperature information includes battery temperature and the temperature inside and outside the vehicle; the vehicle basic information includes power mode, battery SOC and fuel level.

[0095] Optionally, the energy replenishment information includes location information of charging piles and gas stations and energy cost information.

[0096] Optionally, the order information includes current order information and predicted order information; the order information includes order time period, order duration, order location, order examples and order fees.

[0097] Optionally, the cost calculation module 240 is further used for:

[0098] The vehicle's energy consumption and energy replenishment strategy are dynamically adjusted based on the real-time vehicle operating status and surrounding environment information.

[0099] The embodiments of the present application also provide corresponding devices and computer storage media for implementing the solutions provided by the embodiments of the present application.

[0100] The device includes a memory and a processor, the memory is used to store instructions or codes, and the processor is used to execute the instructions or codes so that the device executes the method described in any embodiment of the present application.

[0101] The computer storage medium stores codes, and when the codes are executed, a device executing the codes implements the method described in any embodiment of the present application.

[0102] Through the description of the above implementation methods, it can be known that those skilled in the art can clearly understand that all or part of the steps in the above-mentioned embodiment method can be implemented by means of software plus a general hardware platform. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a storage medium, such as a read-only memory (ROM) / RAM, a magnetic disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network communication device such as a router) to execute the methods described in each embodiment of the present application or some parts of the embodiments.

[0103] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0104] It should also be noted that the various embodiments in this specification are described in a progressive manner, and the same and similar parts between the various embodiments can refer to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment. The system embodiments described above are merely schematic, in which the units described as separate components may or may not be physically separated, and the components indicated as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0105] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An energy management system for an autonomous driving vehicle, characterized in that: include: An information collection module, used to collect vehicle driving information; Send the collected vehicle driving information to the cost calculation module; An energy replenishment module, used for acquiring energy replenishment information; and sending the energy replenishment information to the cost calculation module; An order module is used to obtain order information; and pass the order information to a cost calculation module; The cost calculation module is used to perform minimum cost calculation according to the vehicle driving information, the energy replenishment information and the order information to obtain a minimum cost calculation result; Generate a route plan and a vehicle speed plan according to the minimum cost calculation result; send the route plan and the vehicle speed plan to a vehicle energy management module; The vehicle energy management module is used to control the energy consumption and energy replenishment during the operation of the vehicle according to the route planning and the vehicle speed planning.

2. The system according to claim 1, characterized in that The vehicle driving information includes vehicle position information, current vehicle speed information and driving demand, vehicle temperature information and vehicle basic information; the vehicle position information includes the longitude, latitude and altitude of the vehicle position; the vehicle temperature information includes the battery temperature and the temperature inside and outside the vehicle; the vehicle basic information includes the power mode, battery SOC and fuel level.

3. The system according to claim 1, characterized in that The energy replenishment information includes location information of charging piles and gas stations and energy cost information.

4. The system according to claim 1, characterized in that The order information includes current order information and predicted order information; the order information includes order time period, order duration, order location, order examples and order fees.

5. The system according to claim 1, characterized in that The cost calculation module is also used to: The vehicle's energy consumption and energy replenishment strategy are dynamically adjusted based on the real-time vehicle operating status and surrounding environment information.

6. An energy management method for an autonomous driving vehicle, characterized in that: include: Collect vehicle driving information; Get energy replenishment information; Get order information; Performing a minimum cost calculation based on the vehicle driving information, the energy replenishment information, and the order information to obtain a minimum cost calculation result; Generate route planning and vehicle speed planning according to the minimum cost calculation result; The energy consumption and energy replenishment during the operation of the vehicle are controlled according to the route planning and the vehicle speed planning.

7. The method according to claim 6, characterized in that The vehicle driving information includes vehicle position information, current vehicle speed information and driving demand, vehicle temperature information and vehicle basic information; the vehicle position information includes the longitude, latitude and altitude of the vehicle position; the vehicle temperature information includes the battery temperature and the temperature inside and outside the vehicle; the vehicle basic information includes the power mode, battery SOC and fuel level.

8. The method according to claim 6, characterized in that The energy replenishment information includes location information of charging piles and gas stations and energy cost information.

9. The method according to claim 6, characterized in that The order module includes current order information and predicted order information; the order information includes order time period, order duration, order location, order examples and order fees.

10. The method according to claim 6, characterized in that The method further comprises: The vehicle's energy consumption and energy replenishment strategy are dynamically adjusted based on the real-time vehicle operating status and surrounding environment information.

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