WO2021159659A1 - 一种电动汽车高速公路智能充电方法及系统 - Google Patents

一种电动汽车高速公路智能充电方法及系统 Download PDF

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Publication number
WO2021159659A1
WO2021159659A1 PCT/CN2020/100565 CN2020100565W WO2021159659A1 WO 2021159659 A1 WO2021159659 A1 WO 2021159659A1 CN 2020100565 W CN2020100565 W CN 2020100565W WO 2021159659 A1 WO2021159659 A1 WO 2021159659A1
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electric vehicle
charging
information
charging station
highway
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PCT/CN2020/100565
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English (en)
French (fr)
Inventor
李卫民
刘国庆
李邦超
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山东中科先进技术研究院有限公司
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Priority to EP20891462.2A priority Critical patent/EP3919318A4/en
Publication of WO2021159659A1 publication Critical patent/WO2021159659A1/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
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/66—Data transfer between charging stations and vehicles
    • 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
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/67—Controlling two or more charging stations
    • 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
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/68—Off-site monitoring or control, e.g. remote control
    • 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
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/60—Navigation input
    • B60L2240/68—Traffic data
    • 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
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/70—Interactions with external data bases, e.g. traffic centres
    • B60L2240/72—Charging station selection relying on external data
    • 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
    • B60L2260/00—Operating Modes
    • B60L2260/40—Control modes
    • B60L2260/50—Control modes by future state prediction
    • B60L2260/52—Control modes by future state prediction drive range estimation, e.g. of estimation of available travel distance
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/72—Electric energy management in electromobility
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/12—Electric charging stations
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/16—Information or communication technologies improving the operation of electric vehicles
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/16—Information or communication technologies improving the operation of electric vehicles
    • Y02T90/167—Systems integrating technologies related to power network operation and communication or information technologies for supporting the interoperability of electric or hybrid vehicles, i.e. smartgrids as interface for battery charging of electric vehicles [EV] or hybrid vehicles [HEV]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S30/00—Systems supporting specific end-user applications in the sector of transportation
    • Y04S30/10—Systems supporting the interoperability of electric or hybrid vehicles
    • Y04S30/12—Remote or cooperative charging

Definitions

  • the invention relates to the field of electric vehicle highway charging, in particular to an electric vehicle highway intelligent charging method and system.
  • the purpose of the present invention is to provide an electric vehicle highway intelligent charging method and system, which can make the electric vehicle reach the final destination with the shortest total driving time (including driving time and waiting time and charging time).
  • the present invention provides the following solutions:
  • An intelligent charging method for electric vehicles on highways including:
  • the electric vehicle is charged according to the reservation queue.
  • the determining the shortest path according to the electric vehicle information, highway information, and charging station information specifically includes:
  • an improved A* shortest path search algorithm is adopted to determine the shortest path from the electric vehicle to the charging station.
  • the update event includes a congestion event on an expressway, a congestion event at a charging station, or an event that an electric vehicle driver artificially changes the route.
  • the sending the reservation update request to the charging station and determining the reservation queue of the charging station specifically includes:
  • a reservation queue of the charging station is generated.
  • a highway intelligent charging system for electric vehicles including:
  • An obtaining module used to obtain the remaining driving range of the electric vehicle and the remaining driving range of the electric vehicle;
  • the first judgment module is used to judge whether the remaining mileage of the electric vehicle is greater than the remaining mileage of the electric vehicle
  • the collection module is used to collect electric vehicle information, highway information, and charging station information when the total mileage is greater than the mileage of the remaining power;
  • the end module is used to end when the total mileage is less than or equal to the mileage of the remaining power without charging the electric vehicle;
  • the shortest path determination module is used to determine the shortest path according to the electric vehicle information, highway information and charging station information;
  • An update reservation request generating module configured to generate an update reservation request according to the shortest path
  • the second judgment module is used to judge whether an update event occurs
  • the return module is used to return to "get the remaining mileage of the electric vehicle and the remaining power of the electric vehicle" when an update event occurs;
  • the reservation queue determination module is configured to send the reservation update request to the charging station when no update event occurs, and determine the reservation queue of the charging station;
  • the third judgment module is used to judge whether there is an update event for the second time
  • the reservation queue adjustment module is used to adjust the reservation queue when an update event occurs
  • the first charging module is configured to charge the electric vehicle according to the adjusted reservation queue
  • the second charging module is used to charge the electric vehicle according to the reservation queue when there is no update event.
  • the shortest path determining module specifically includes:
  • the shortest path determination unit is used to determine the shortest path from the electric vehicle to the charging station by using an improved A* shortest path search algorithm according to the electric vehicle information, highway information and charging station information.
  • the update event includes a congestion event on an expressway, a congestion event on a charging station, or an event that an electric vehicle driver artificially changes the route.
  • the reservation queue determination module specifically includes:
  • a request sending unit configured to send the reservation update request to the charging station
  • the charging time/charging pile determining unit is configured to determine the charging time and the charging pile according to the update reservation request;
  • the reservation queue generating unit is configured to generate the reservation queue of the charging station according to the charging time and the charging pile.
  • the present invention discloses the following technical effects:
  • the intelligent charging method and system for electric vehicles on highway provided by the present invention can make the electric vehicles reach their final destination with the shortest total driving time (including driving time and waiting time and charging time) under consideration of various dynamic constraints.
  • the present invention adopts an improved A* shortest path search algorithm to solve the shortest path problem of dynamic constraints, overcomes the shortcoming of the traditional A* algorithm that does not consider constraints, and makes the shortest path determination process more accurate.
  • Figure 1 is a flow chart of the intelligent charging method for an electric vehicle on a highway according to the present invention
  • FIG. 2 is a schematic diagram of determining the shortest path from the electric vehicle to the charging station of the present invention
  • Fig. 3 is a structural diagram of the intelligent charging system for an electric vehicle on an expressway of the present invention.
  • the purpose of the present invention is to provide an electric vehicle highway intelligent charging method and system, which can make the electric vehicle reach the final destination with the shortest total driving time (including driving time and waiting time and charging time).
  • the method of the present invention relies on a charging scheduling model, which is composed of three parts: electric vehicles, highways and electric vehicle charging stations.
  • a charging scheduling model which is composed of three parts: electric vehicles, highways and electric vehicle charging stations.
  • electric vehicles When an electric vehicle enters the highway, if its remaining mileage exceeds its remaining battery mileage, it will stop at the charging station at least once.
  • the choice of charging station depends on the charging strategy followed by the electric vehicle. For example, the vehicle can choose to charge at the last charging station that arrives.
  • the personal characteristics of an electric car are its itinerary, status, type and schedule.
  • the car's journey is composed of three parts: starting position, starting time and destination position.
  • the state of the electric vehicle at time k is characterized by its position on the highway, driving speed, preferred speed (the optimal speed selected according to the schedule), driving, waiting and charging time, and the current state of the battery.
  • the car type consists of the car's maximum driving speed, maximum battery capacity, minimum allowable battery capacity, fast charging power, and power consumption per kilometer.
  • the schedule includes the charging station to be docked and the target state of charge for battery charging.
  • the characteristics of the expressway are expressed as the location of the exit and entrance, the location of the charging station, and the speed limit of the expressway.
  • the characteristics of the charging station are the types of chargers supported and the corresponding number of charging piles.
  • the charging station has a queue Q(k) representing electric vehicles waiting for an idle charging pile in the charging station.
  • Each charging station has its own reservation system, including the estimated time of arrival and required Charge time. Based on this reservation system, the charging station can estimate the queue length Qc(k) at a given time (k) in the future.
  • the intelligent charging method of the present invention aims to intelligently select charging stations to reduce the total travel time. This strategy requires real-time information. This method assumes that there is a communication infrastructure that allows charging stations and vehicles to communicate with each other, and allows vehicles to receive highway-related information. All these assumptions do not exceed the capabilities of existing technologies. Vehicles can connect vehicles and people through mobile communication technology.
  • Fig. 1 is a flow chart of the intelligent charging method for an electric vehicle on a highway according to the present invention.
  • a smart charging method for electric vehicles on highways includes:
  • Step 101 Obtain the remaining driving range of the electric vehicle and the remaining driving range of the electric vehicle.
  • Step 102 Determine whether the remaining mileage of the electric vehicle is greater than the remaining battery mileage.
  • Step 103 If yes, collect electric vehicle information, highway information, and charging station information.
  • Step 104 If not, there is no need to charge the electric vehicle.
  • Step 105 Determine the shortest path according to the electric vehicle information, highway information, and charging station information, which specifically includes:
  • an improved A* shortest path search algorithm is adopted to determine the shortest path from the electric vehicle to the charging station.
  • the improved A* shortest path search algorithm adds constraints.
  • the specific expression of the A* shortest path algorithm with constraints is as follows:
  • Fig. 2 is a schematic diagram of determining the shortest path from the electric vehicle to the charging station of the present invention.
  • the node updates the current position, the calculated time cost between two nodes, and the energy consumption, i.e. feasibility analysis result is determined whether the value of E i is maintained at [E i min, E i max ] inner If it is not feasible, continue to explore.
  • the process of determining the shortest path is also the process of finding the best scheduling plan to minimize the total travel time (including driving, waiting and charging time).
  • the determination process must meet the constraints of a single electric vehicle and highway.
  • Is the unit travel time, Wait time in units, Charge time per unit; and Cars are the initial time and the end time position; soc i is the state of charge of the battery, and These are the minimum and maximum power limits for electric vehicles. For a given electric vehicle, choose a timetable so that the sum of driving, waiting, and charging time during the entire journey is minimized, while meeting the energy constraints of electric vehicles.
  • Step 106 Generate a reservation update request according to the shortest path.
  • Step 107 Determine whether there is an update event, the update event includes a congestion event on the expressway, a congestion event on a charging station, or an event that the electric vehicle driver artificially changes the route.
  • Step 108 If not, send the reservation update request to the charging station, and determine the reservation queue of the charging station, which specifically includes:
  • the charging time and the charging pile are determined.
  • a reservation queue of the charging station is generated.
  • Step 109 Secondly determine whether an update event occurs.
  • Step 110 If yes, adjust the reservation queue.
  • Step 111 Charge the electric vehicle according to the adjusted reservation queue.
  • Step 112 If not, charge the electric vehicle according to the reservation queue.
  • FIG. 3 is a structural diagram of the intelligent charging system for an electric vehicle on an expressway of the present invention.
  • a highway intelligent charging system for electric vehicles includes:
  • the obtaining module 201 is used to obtain the remaining driving range of the electric vehicle and the remaining driving range of the electric vehicle.
  • the first judgment module 202 is used for judging whether the remaining mileage of the electric vehicle is greater than the remaining power mileage.
  • the collection module 203 is configured to collect electric vehicle information, highway information, and charging station information when the total mileage is greater than the remaining power mileage.
  • the end module 204 is configured to end when the total mileage is less than or equal to the mileage of the remaining power, the electric vehicle does not need to be charged.
  • the shortest path determining module 205 is configured to determine the shortest path according to the electric vehicle information, highway information, and charging station information;
  • the reservation update request generating module 206 is configured to generate a reservation update request according to the shortest path.
  • the second judgment module 207 is used to judge whether an update event occurs.
  • the return module 208 is used for returning to "acquire the remaining mileage of the electric vehicle and the remaining power of the electric vehicle" when an update event occurs.
  • the reservation queue determination module 209 is configured to send the reservation update request to the charging station when no update event occurs, and determine the reservation queue of the charging station.
  • the third judging module 210 is used for secondly judging whether there is an update event; the update event includes a congestion event on an expressway, a congestion event on a charging station, or an event that an electric vehicle driver artificially changes a route.
  • the reservation queue adjustment module 211 is configured to adjust the reservation queue when an update event occurs
  • the first charging module 212 is configured to charge the electric vehicle according to the adjusted reservation queue
  • the second charging module 213 is configured to charge the electric vehicle according to the reservation queue when there is no update event.
  • the shortest path determining module 205 specifically includes:
  • the shortest path determining unit is used to determine the shortest path from the electric vehicle to the charging station by using an improved A* shortest path search algorithm according to the electric vehicle information, highway information and charging station information.
  • the reservation queue determining module 209 specifically includes:
  • the request sending unit is configured to send the reservation update request to the charging station.
  • the charging time/charging pile determining unit is configured to determine the charging time and the charging pile according to the update reservation request.
  • the reservation queue generating unit is configured to generate the reservation queue of the charging station according to the charging time and the charging pile.

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
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Abstract

一种电动汽车高速公路智能充电方法及系统。该方法包括：判断电动汽车剩余的行驶里程是否大于剩余电量的行驶里程；若是，采集电动汽车信息、高速公路信息和充电站信息；根据电动汽车信息、高速公路信息和充电站信息，确定最短路径；根据最短路径，生成更新预定请求；判断是否出现更新事件；若是，则返回"获取电动汽车在高速公路上剩余的行驶里程和电动汽车的剩余电量的行驶里程"；若否，则将更新预定请求发送至充电站，确定充电站的预约队列；二次判断是否出现更新事件；若是，则调整预约队列；根据调整后的预约队列对电动汽车充电；若否，则根据预约队列对电动汽车充电。该方法能够使得电动汽车以总行驶时间最短到达最终目的地。

Description

一种电动汽车高速公路智能充电方法及系统
本申请要求于2020年2月14日提交中国专利局、申请号为202010092925.9、发明名称为“一种电动汽车高速公路智能充电方法及系统”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本发明涉及电动汽车高速公路充电领域，特别是涉及一种电动汽车高速公路智能充电方法及系统。
背景技术
目前城市中电动汽车数量在逐年增加，考虑环境的污染以及燃油费的上升，许多家庭已经选用新能源汽车替代传统燃油汽车在城市环境中使用。然而，它也面临着许多挑战。首先，电池能量密度的限制及其对成本的影响，限制了电动汽车的续航里程或自主能力低于燃料电池。其次，电动汽车充电所需的时间是相当长的，另外的缺点是，充电功率的增加会对电池的寿命产生负面影响。只有充分利用基础设施，才能提高电动汽车的采用率。
现阶段情况下选用电动汽车面临的最大挑战就是续航问题，这一问题在充电设施密集的城市中心地区不太明显，然而在高速公路环境下，距离、基础设施和充电时间则成为制约电动汽车长途行驶的主要因素。基本的基础设施，如电子充电基础设施所需的技术和服务，在高速公路上不是到处都有。长时间充电时间可能会导致严重的延迟，不仅仅是因为充电的过程本身，也是因为充电站繁忙导致的潜在等待时间，特别是在节假日时期。
发明内容
本发明的目的是提供一种电动汽车高速公路智能充电方法及系统，能够使电动汽车以总行驶时间(包含行驶时间与等待时间与充电时间)最短到达最终目的地。
为实现上述目的，本发明提供了如下方案：
一种电动汽车高速公路智能充电方法，包括：
获取电动汽车剩余的行驶里程和所述电动汽车的剩余电量的行驶里程；
判断所述电动汽车剩余的行驶里程是否大于所述剩余电量的行驶里程；
若是，采集电动汽车信息、高速公路信息和充电站信息；
若否，则无需对所述电动汽车进行充电；
根据所述电动汽车信息、高速公路信息和充电站信息，确定最短路径；
根据所述最短路径，生成更新预定请求；
判断是否出现更新事件；
若是，则返回“获取电动汽车剩余的行驶里程和所述电动汽车的剩余电量的行驶里程”；
若否，则将所述更新预定请求发送至充电站，确定所述充电站的预约队列；
二次判断是否出现更新事件；
若是，则调整所述预约队列；
根据调整后的预约队列对所述电动汽车充电；
若否，则根据所述预约队列对所述电动汽车充电。
可选的，所述根据所述电动汽车信息、高速公路信息和充电站信息，确定最短路径，具体包括：
根据所述电动汽车信息、高速公路信息和充电站信息采用改进后的A*最短路径搜索算法，确定所述电动汽车到充电站的最短路径。
可选的，所述更新事件包括高速公路出现拥堵事件、充电站出现拥堵事件或电动汽车驾驶员人为改变路线事件。
可选的，所述将所述更新预定请求发送至充电站，确定所述充电站的预约队列，具体包括：
将所述更新预定请求发送至充电站；
根据所述更新预定请求，确定充电时间和充电桩；
根据所述充电时间和充电桩，生成所述充电站的预约队列。
一种电动汽车高速公路智能充电系统，包括：
获取模块，用于获取电动汽车剩余的行驶里程和所述电动汽车的剩余电量的行驶里程；
第一判断模块，用于判断所述电动汽车剩余的行驶里程是否大于所述剩余电量的行驶里程；
采集模块，用于当所述总的行驶里程大于所述剩余电量的行驶里程时，采集电动汽车信息、高速公路信息和充电站信息；
结束模块，用于当所述总的行驶里程小于或等于所述剩余电量的行驶里程时，无需对所述电动汽车进行充电，结束；
最短路径确定模块，用于根据所述电动汽车信息、高速公路信息和充电站信息，确定最短路径；
更新预定请求生成模块，用于根据所述最短路径，生成更新预定请求；
第二判断模块，用于判断是否出现更新事件；
返回模块，用于当出现更新事件时，返回“获取电动汽车剩余的行驶里程和所述电动汽车的剩余电量的行驶里程”；
预约队列确定模块，用于当不出现更新事件时，将所述更新预定请求发送至充电站，确定所述充电站的预约队列；
第三判断模块，用于二次判断是否出现更新事件；
预约队列调整模块，用于当出现更新事件时，调整所述预约队列；
第一充电模块，用于根据调整后的预约队列对所述电动汽车充电；
第二充电模块，用于当不出现更新事件时，根据所述预约队列对所述电动汽车充电。
可选的，所述最短路径确定模块，具体包括：
最短路径确定单元，用于根据所述电动汽车信息、高速公路信息和充电站信息采用改进后的A*最短路径搜索算法，确定所述电动汽车到充电站的最短路径。
可选的，所述更新事件包括高速公路出现拥堵事件、充电站出现拥堵 事件或电动汽车驾驶员人为改变路线事件。
可选的，所述预约队列确定模块，具体包括：
请求发送单元，用于将所述更新预定请求发送至充电站；
充电时间/充电桩确定单元，用于根据所述更新预定请求，确定充电时间和充电桩；
预约队列生成单元，用于根据所述充电时间和充电桩，生成所述充电站的预约队列。
根据本发明提供的具体实施例，本发明公开了以下技术效果：
本发明提供的一种电动汽车高速公路智能充电方法及系统，在考虑各种动态约束的情况下，能够使得电动汽车以总行驶时间(包含行驶时间与等待时间与充电时间)最短到达最终目的地。此外，本发明采用一种改进后的A*最短路径搜索算法来解决动态约束的最短路径问题，克服了传统A*算法不考虑约束的缺点，使得最短路径的确定过程更加精确。
说明书附图
下面结合附图对本发明作进一步说明：
图1为本发明电动汽车高速公路智能充电方法流程图；
图2为本发明电动汽车至充电站的最短路径确定示意图；
图3为本发明电动汽车高速公路智能充电系统结构图。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
本发明的目的是提供一种电动汽车高速公路智能充电方法及系统，能够使电动汽车以总行驶时间(包含行驶时间与等待时间与充电时间)最短到达最终目的地。
为使本发明的上述目的、特征和优点能够更加明显易懂，下面结合附图和具体实施方式对本发明作进一步详细的说明。
本发明的方法依托一个充电调度模型，该模型由三部分组成：电动汽车、高速公路和电动汽车充电站。一辆电动汽车进入高速公路，如果它剩余的行驶里程超过了它剩余电量的行驶里程，它将在充电站处至少停一次。充电站的选择取决于电动汽车所遵循的充电策略，例如车辆可以选择在最后一个到达的充电站充电。电动汽车的个人特征是它的行程、状态、类型和计划表。汽车的行程由起动位置、起动时间和目的地位置三部分组成。电动汽车在k时刻的状态表征为在高速公路上的位置、行驶速度、首选速度(根据计划表可选择的最优速度)，行驶、等待和充电时间，以及电池当前状态。汽车类型由汽车最大行驶速度、电池最大容量、最低允许电池容量、快速充电功率以及每公里消耗电量组成。计划表则包含要停靠的充电站以及电池充电目标荷电状态。高速公路的特征则表示为出口与入口的位置、充电站的位置以及高速公路的速度限制。充电站的特征则是所支持的充电器类型和相应数量的充电桩。对于每一种充电器类型，充电站都有一个队列Q(k)表示在充电站中等待空闲充电桩的电动汽车，每个充电站都有自己的预订系统，包括预计到达时间和所需的收费时间。基于此预约系统，充电站可以估计未来给定时间(k)的队列长度Qc(k)。
本发明的智能充电方法旨在智能地选择充电站，以减少总行程时间。这种策略需要实时信息。该方法假设有一个通信基础设施，允许充电站和车辆彼此通信，并允许车辆接收与高速公路相关的信息。所有这些假设都不超出现有技术的能力。车辆可以通过移动通信技术连接车辆和人员。
图1为本发明电动汽车高速公路智能充电方法流程图。如图1所示，一种电动汽车高速公路智能充电方法包括：
步骤101：获取电动汽车剩余的行驶里程和所述电动汽车的剩余电量的行驶里程。
步骤102：判断所述电动汽车剩余的行驶里程是否大于所述剩余电量的行驶里程。
步骤103：若是，采集电动汽车信息、高速公路信息和充电站信息。
步骤104：若否，则无需对所述电动汽车进行充电。
步骤105：根据所述电动汽车信息、高速公路信息和充电站信息，确定最短路径，具体包括：
根据所述电动汽车信息、高速公路信息和充电站信息采用改进后的A*最短路径搜索算法，确定所述电动汽车到充电站的最短路径。
改进后的A*最短路径搜索算法增加了约束条件带约束的A*最短路径算法的具体表示如下：
1.初始化一个优先队列，其返回值为路径。接收一个描述所有可能路径、源节点和目标节点以及电动汽车(包括状态和类型)的图作为输入。图2为本发明电动汽车至充电站的最短路径确定示意图。
2.取出并更新当前节点、时间、消耗能量。
3.如果当前节点为最优目标节点，则返回路径为当前节点路径。否则，继续探索。
4.探索相邻节点时，更新当前节点位置，计算两个节点之间的时间成本以及能量消耗，判断可行性分析即E i的结果值是否保持在[E i min，E i max]内，如果不可行，继续探索。
5.更新累计消耗能量及时间。
6.与至今为止最优路径作比较，如果相邻节点总花费时间更短，则将相邻节点数据替代最优节点数据，否则将继续探索。
7.返回最优路径，即最短路径。
最短路径的确定过程也就是寻找最佳调度方案的过程，使总旅行时间(包括开车、等待和充电时间)最小化。该确定过程必须满足单个电动汽车和高速公路的约束。
Figure PCTCN2020100565-appb-000001
Figure PCTCN2020100565-appb-000002
Figure PCTCN2020100565-appb-000003
其中,
Figure PCTCN2020100565-appb-000004
为单位行驶时间,
Figure PCTCN2020100565-appb-000005
为单位等待时间,
Figure PCTCN2020100565-appb-000006
为单位充电 时间；
Figure PCTCN2020100565-appb-000007
与
Figure PCTCN2020100565-appb-000008
分别为汽车初始时刻与终点时刻位置；soc i为电池荷电状态，
Figure PCTCN2020100565-appb-000009
与
Figure PCTCN2020100565-appb-000010
分别为电动汽车的最低与最高电量限制。对于给定的电动汽车，选择一个时间表，以便在整个行程中行驶、等待和充电时间的总和最小化，同时满足电动汽车的能源限制。
引入一种改进后的A*最短路径搜索算法来解决这个问题中的约束，即增加
Figure PCTCN2020100565-appb-000011
约束条件。每次探索相邻节点时，都要测试E i的结果值是否保持在[E i min，E i max]内。在这种情况下，避免了相应的电位路径。如果未在电量限制范围内，则不将邻居节点排入潜在节点的队列。
步骤106：根据所述最短路径，生成更新预定请求。
步骤107：判断是否出现更新事件，所述更新事件包括高速公路出现拥堵事件、充电站出现拥堵事件或电动汽车驾驶员人为改变路线事件。
若是，则返回“获取电动汽车剩余的行驶里程和所述电动汽车的剩余电量的行驶里程”。
步骤108：若否，则将所述更新预定请求发送至充电站，确定所述充电站的预约队列，具体包括：
将所述更新预定请求发送至充电站。
根据所述更新预定请求，确定充电时间和充电桩。
根据所述充电时间和充电桩，生成所述充电站的预约队列。
步骤109：二次判断是否出现更新事件。
步骤110：若是，则调整所述预约队列。
步骤111：根据调整后的预约队列对所述电动汽车充电。
步骤112：若否，则根据所述预约队列对所述电动汽车充电。
对应于上述一种电动汽车高速公路智能充电方法，本发明还提供一种电动汽车高速公路智能充电系统。图3为本发明电动汽车高速公路智能充电系统结构图。一种电动汽车高速公路智能充电系统包括：
获取模块201，用于获取电动汽车剩余的行驶里程和所述电动汽车的 剩余电量的行驶里程。
第一判断模块202，用于判断所述电动汽车剩余的行驶里程是否大于所述剩余电量的行驶里程。
采集模块203，用于当所述总的行驶里程大于所述剩余电量的行驶里程时，采集电动汽车信息、高速公路信息和充电站信息。
结束模块204，用于当所述总的行驶里程小于或等于所述剩余电量的行驶里程时，无需对所述电动汽车进行充电，结束。
最短路径确定模块205，用于根据所述电动汽车信息、高速公路信息和充电站信息，确定最短路径；
更新预定请求生成模块206，用于根据所述最短路径，生成更新预定请求。
第二判断模块207，用于判断是否出现更新事件。
返回模块208，用于当出现更新事件时，返回“获取电动汽车剩余的行驶里程和所述电动汽车的剩余电量的行驶里程”。
预约队列确定模块209，用于当不出现更新事件时，将所述更新预定请求发送至充电站，确定所述充电站的预约队列。
第三判断模块210，用于二次判断是否出现更新事件；所述更新事件包括高速公路出现拥堵事件、充电站出现拥堵事件或电动汽车驾驶员人为改变路线事件。
预约队列调整模块211，用于当出现更新事件时，调整所述预约队列；
第一充电模块212，用于根据调整后的预约队列对所述电动汽车充电；
第二充电模块213，用于当不出现更新事件时，根据所述预约队列对所述电动汽车充电。
所述最短路径确定模块205，具体包括：
最短路径确定单元，用于根据所述电动汽车信息、高速公路信息和充电站信息采用改进后的A*最短路径搜索算法，确定所述电动汽车到充电站的最短路径。
所述预约队列确定模块209，具体包括：
请求发送单元，用于将所述更新预定请求发送至充电站。
充电时间/充电桩确定单元，用于根据所述更新预定请求，确定充电时间和充电桩。
预约队列生成单元，用于根据所述充电时间和充电桩，生成所述充电站的预约队列。
上面结合附图对本发明的实施方式作了详细说明，但是本发明并不限于上述实施方式，在所属技术领域普通技术人员所具备的知识范围内，还可以在不脱离本发明宗旨的前提下做出各种变化。

Claims (8)

  1. 一种电动汽车高速公路智能充电方法，其特征在于，包括：
    获取电动汽车剩余的行驶里程和所述电动汽车的剩余电量的行驶里程；
    判断所述电动汽车剩余的行驶里程是否大于所述剩余电量的行驶里程；
    若是，采集电动汽车信息、高速公路信息和充电站信息；
    若否，则无需对所述电动汽车进行充电；
    根据所述电动汽车信息、高速公路信息和充电站信息，确定最短路径；
    根据所述最短路径，生成更新预定请求；
    判断是否出现更新事件；
    若是，则返回“获取电动汽车剩余的行驶里程和所述电动汽车的剩余电量的行驶里程”；
    若否，则将所述更新预定请求发送至充电站，确定所述充电站的预约队列；
    二次判断是否出现更新事件；
    若是，则调整所述预约队列；
    根据调整后的预约队列对所述电动汽车充电；
    若否，则根据所述预约队列对所述电动汽车充电。
  2. 根据权利要求1所述的电动汽车高速公路智能充电方法，其特征在于，所述根据所述电动汽车信息、高速公路信息和充电站信息，确定最短路径，具体包括：
    根据所述电动汽车信息、高速公路信息和充电站信息采用改进后的A*最短路径搜索算法，确定所述电动汽车到充电站的最短路径。
  3. 根据权利要求1所述的电动汽车高速公路智能充电方法，其特征在于，所述更新事件包括高速公路出现拥堵事件、充电站出现拥堵事件或电动汽车驾驶员人为改变路线事件。
  4. 根据权利要求1所述的电动汽车高速公路智能充电方法，其特征 在于，所述将所述更新预定请求发送至充电站，确定所述充电站的预约队列，具体包括：
    将所述更新预定请求发送至充电站；
    根据所述更新预定请求，确定充电时间和充电桩；
    根据所述充电时间和充电桩，生成所述充电站的预约队列。
  5. 一种电动汽车高速公路智能充电系统，其特征在于，包括：
    获取模块，用于获取电动汽车剩余的行驶里程和所述电动汽车的剩余电量的行驶里程；
    第一判断模块，用于判断所述电动汽车剩余的行驶里程是否大于所述剩余电量的行驶里程；
    采集模块，用于当所述总的行驶里程大于所述剩余电量的行驶里程时，采集电动汽车信息、高速公路信息和充电站信息；
    结束模块，用于当所述总的行驶里程小于或等于所述剩余电量的行驶里程时，无需对所述电动汽车进行充电，结束；
    最短路径确定模块，用于根据所述电动汽车信息、高速公路信息和充电站信息，确定最短路径；
    更新预定请求生成模块，用于根据所述最短路径，生成更新预定请求；
    第二判断模块，用于判断是否出现更新事件；
    返回模块，用于当出现更新事件时，返回“获取电动汽车剩余的行驶里程和所述电动汽车的剩余电量的行驶里程”；
    预约队列确定模块，用于当不出现更新事件时，将所述更新预定请求发送至充电站，确定所述充电站的预约队列；
    第三判断模块，用于二次判断是否出现更新事件；
    预约队列调整模块，用于当出现更新事件时，调整所述预约队列；
    第一充电模块，用于根据调整后的预约队列对所述电动汽车充电；
    第二充电模块，用于当不出现更新事件时，根据所述预约队列对所述电动汽车充电。
  6. 根据权利要求5所述的电动汽车高速公路智能充电系统，其特征在于，所述最短路径确定模块，具体包括：
    最短路径确定单元，用于根据所述电动汽车信息、高速公路信息和充电站信息采用改进后的A*最短路径搜索算法，确定所述电动汽车到充电站的最短路径。
  7. 根据权利要求5所述的电动汽车高速公路智能充电系统，其特征在于，所述更新事件包括高速公路出现拥堵事件、充电站出现拥堵事件或电动汽车驾驶员人为改变路线事件。
  8. 根据权利要求5所述的电动汽车高速公路智能充电系统，其特征在于，所述预约队列确定模块，具体包括：
    请求发送单元，用于将所述更新预定请求发送至充电站；
    充电时间/充电桩确定单元，用于根据所述更新预定请求，确定充电时间和充电桩；
    预约队列生成单元，用于根据所述充电时间和充电桩，生成所述充电站的预约队列。
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