WO2021036132A1 - Charging scheduling method for unmanned vehicle group and cloud management server - Google Patents

Charging scheduling method for unmanned vehicle group and cloud management server Download PDF

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WO2021036132A1
WO2021036132A1 PCT/CN2019/128608 CN2019128608W WO2021036132A1 WO 2021036132 A1 WO2021036132 A1 WO 2021036132A1 CN 2019128608 W CN2019128608 W CN 2019128608W WO 2021036132 A1 WO2021036132 A1 WO 2021036132A1
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charging
vehicle
time
charging station
power consumption
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PCT/CN2019/128608
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French (fr)
Chinese (zh)
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柯志达
林春敏
肖苹苹
李鸿海
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厦门金龙联合汽车工业有限公司
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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
    • 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]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
    • G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
    • G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/20—Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
    • G08G1/202—Dispatching vehicles on the basis of a location, e.g. taxi dispatching
    • 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
    • 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
    • Y02T90/14—Plug-in 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
    • 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 charging scheduling, and in particular to a charging scheduling method of an unmanned vehicle group and a cloud management server.
  • a Chinese invention patent with application number CN201610609436.X discloses a method and device for charging an unmanned vehicle. The method determines whether the battery needs to be charged according to the current power value of the battery in the unmanned vehicle; if the battery needs to be charged, determines the target charging station of the unmanned vehicle; drives to the target charging station , And charge the battery through the target charging station; if it is detected that the power value of the battery in the unmanned vehicle is greater than the target power value, the charging is stopped and the charging fee is paid.
  • the invention is only for a single unmanned vehicle, and judges whether to charge according to the power of the unmanned vehicle, and does not consider the remaining charging points of the charging station, which may cause charging congestion and cannot meet the charging plan of the unmanned vehicle fleet.
  • the Chinese invention patent with application number CN201910017770.X discloses an electric vehicle charging scheduling optimization method based on particle swarm algorithm.
  • the Metropolis acceptance criterion can be effectively used to avoid solving the local optimal point and find the global optimal point, so as to obtain the optimal solution of the electric vehicle driving path.
  • the invention is aimed at a single electric vehicle, and finally obtains the optimal driving path in consideration of factors such as electric power, air-conditioning status, and traffic congestion.
  • the invention cannot monitor the status of electric vehicles in real time, and requires manual charging, which will cause the problem that the remaining power cannot reach the designated charging station.
  • the charging station factor is not considered, and it cannot meet the charging plan of the unmanned vehicle fleet.
  • the purpose of the present invention is to provide a charging scheduling method and a cloud management server for an unmanned vehicle group, which can manage the unmanned vehicle group in a timely manner through the scheduling management of the cloud management server
  • the charging plan of the vehicle ensures that the unmanned vehicle can reach the charging station, reduces the waiting time for charging, and improves the charging efficiency.
  • the present invention provides a charging scheduling method for an unmanned vehicle group, including:
  • the calculation model of the shortest path can be predicted and calculated based on the running route information and the real-time status information of the vehicle; obtain the shortest path from the current vehicle to a certain charging station and the travel time and power consumption of the shortest path;
  • the calculation model of the preferred charging station is established.
  • the calculation model of the preferred charging station can be calculated according to the real-time status information of the vehicle and the charging waiting time of the charging station to obtain the minimum time consumption of the current vehicle to the charging station and complete the charging.
  • the charging station is a charging station that meets the minimum time consumption;
  • the vehicle status of the unmanned vehicle group is marked as operating status and charging status;
  • Carry out the real-time monitoring process for vehicles marked as operating status obtain real-time status information of the vehicle, and predict and calculate the shortest path calculation model based on the real-time status information of the vehicle to obtain the shortest path for the vehicle to reach each charging station and predict the remaining power; When all predicted remaining power levels are less than the set remaining power threshold, mark the vehicle as a charging state;
  • For vehicles marked as charging state perform the charging process: determine the preferred charging station through the calculation model of the preferred charging station, and guide the vehicle to go to the preferred charging station through the shortest path for charging; when the vehicle is put into operation again, mark the vehicle as operating state .
  • the operating route information includes charging station information and road section information;
  • the real-time status information includes real-time remaining power, real-time position information, and weighting factors of the vehicle;
  • the length, driving time and power consumption of each road section are obtained;
  • the shortest path calculation model is used to obtain the shortest path for the vehicle to a certain charging station, and the shortest path is the shortest length path , The shortest time path or the minimum power consumption.
  • calculation model of the shortest length path includes:
  • LtSi is the shortest length path from the current vehicle to the charging station Si
  • ⁇ EtSi is the power consumption of the current vehicle to complete the shortest length path LtSi
  • TtSi is the travel time of the current vehicle to complete the shortest length path LtSi
  • ⁇ 3 is the current vehicle in
  • the position ratio parameter of the road section Rj, ⁇ 3 and road section Rj identify the current position information of the vehicle
  • Lj, T Rj and E Rj are the road section length, average travel time and average power consumption of road section Rj, respectively. Respectively, they are the sum of the lengths of the complete road sections passed by the shortest length path LtS i , the sum of the average travel time and the sum of the average power consumption.
  • calculation model of the shortest time path includes:
  • TtSi is the shortest travel time from the current vehicle to the charging station Si
  • LtSi is the shortest travel time path from the current vehicle to the charging station Si, that is, the shortest time path
  • ⁇ EtSi is the power consumption of the current vehicle to complete the shortest time path LtSi
  • ⁇ 3 is the position ratio parameter of the current vehicle on the road section Rj
  • ⁇ 3 and road section Rj identify the current position information of the vehicle
  • Lj, T Rj and E Rj are the road section length, average travel time and average power consumption of road section Rj, respectively, Respectively, they are the sum of the lengths of the complete road sections passed by the shortest time path LtS i , the sum of the average travel time and the sum of the average power consumption.
  • calculation model of the minimum power consumption path includes:
  • ⁇ EtSi is the minimum power consumption of the current vehicle to the charging station Si
  • LtSi is the minimum power consumption path of the current vehicle to the charging station Si according to the minimum power consumption
  • TtSi is the driving of the current vehicle to complete the minimum power consumption path LtSi Time
  • ⁇ 3 is the position ratio parameter of the current vehicle on the road section Rj
  • ⁇ 3 and road section Rj identify the current position information of the vehicle
  • Lj, T Rj and E Rj are the road section length, average travel time and average power consumption of road section Rj, respectively, Respectively, they are the sum of the lengths of the complete road sections passed by the minimum power consumption path LtSi, the sum of the average travel time and the sum of the average power consumption.
  • weighting factors include vehicle model battery model weight parameters ⁇ 1, ⁇ 2; battery age weight parameters ⁇ 1, ⁇ 2; passenger load weight parameters m1, m2; air conditioning state weight parameters k1, k2; environmental weight parameters e1, e2;
  • the average power consumption and average driving time of the road section are obtained by the following formula:
  • T Ri ⁇ 2* ⁇ 2*m2*e2*(Tui+k2*Tpi)/Li
  • E Ri and T Ri are the average power consumption and average travel time of the road section Ri; Eui is the rated average power consumption when the air conditioner is not turned on on the route Ri, Epi is the rated average additional power consumption when the air conditioner is turned on on the route Ri, and Tui is The rated average travel time of the route Ri when the air conditioner is not turned on, and Tpi is the rated average additional travel time of the route Ri when the air conditioner is turned on.
  • the charging station includes a plurality of charging points, and the charging waiting time of the charging station is a minimum value of the charging waiting time of each charging point of the charging station;
  • VjCos min(VjTtSi+ ⁇ VjSiCmkTwait+ ⁇ VjSiCmkTc)
  • ⁇ VjSiCmkTwait is the predicted waiting time of the vehicle Vj to the charging point SiCmk of the charging station Si;
  • VjTtSi is the predicted travel time required for the shortest path from the vehicle Vj to the charging station Si;
  • ⁇ SiCmkTc is the remaining charge point SiCmk of the charging station Si Charging time;
  • VjCos is the estimated minimum time consumed by the vehicle Vj to the charging point and completing the charging;
  • ⁇ VjSiCmkTc is the expected charging time of the vehicle Vj at the charging point Si;
  • the charging station that meets the VjCos conditions is the preferred charging station.
  • the preferred charging station is selected among alternative charging stations, and the alternative charging stations meet the conditions:
  • ⁇ EtSi is the power consumption of the current vehicle arriving at the charging station Si
  • Et is the real-time remaining power of the current vehicle.
  • ESi is the predicted remaining power of the current vehicle driving to the charging station Si
  • Et is the real-time remaining power of the current vehicle.
  • the charging scheduling method further includes a charging queue.
  • the vehicle When the vehicle is marked as a charging state, the vehicle enters the charging queue, and the charging process is executed according to the sequence of the queue.
  • the present invention also provides a cloud management server, including an application server, a database server, a Web server, and a communication server,
  • the application server is used to execute a charging scheduling program, and the charging scheduling program implements the charging scheduling method of the unmanned vehicle group as described above;
  • the database server is used to provide access services, and the accessed information includes: operating route information and scheduling information of the unmanned vehicle group, the scheduling information is obtained according to the charging scheduling method;
  • the communication server is used to establish a communication connection between the cloud management server and the unmanned vehicle and charging station.
  • the APP server is used to provide smart terminal APP invocation service, and is used to push scheduling information.
  • the charging scheduling method of the unmanned vehicle group of the present invention uses big data technology to analyze the average power consumption and driving time of each section of the road in the park, monitors the current status information of the unmanned vehicles in operation in real time, and combines the road sections of the park For power consumption, driving time and charging conditions at charging points of charging stations, make a charging plan in advance to ensure that unmanned vehicles can reach the charging station, reduce waiting time for charging, and improve charging efficiency.
  • FIG. 1 is a flowchart of charging scheduling of an unmanned vehicle group according to a preferred embodiment of the present invention
  • Figure 2 is a flow chart of operation monitoring
  • FIG. 3 is a flowchart of charging arrangements
  • Fig. 4 is a block diagram of a charging scheduling system for a group of unmanned vehicles of the present invention.
  • the present invention discloses a charging scheduling method for a group of unmanned vehicles.
  • the group of unmanned vehicles (or a fleet of unmanned vehicles) operates A prescribed route in a park, and there are multiple charging stations in the route, and each charging station has several charging points.
  • the unmanned vehicle group needs to be charged scheduling management to reasonably arrange the timing of unmanned vehicle charging, reduce waiting for charging, and avoid problems such as power shortage and breakdown.
  • the charging scheduling method runs in the cloud management server and includes the following steps:
  • Step S101 Road condition information preparation
  • the road section numbers are R1, R2,..., Rn, and the corresponding road section length is L1, L2,..., Ln;
  • the charging station numbers are S1, S2,..., Sm, each charging station includes several charging points, the charging point numbers of the charging station Si are SiCm1, SiCm2,..., SiCmk.
  • Step S102 vehicle information preparation
  • the average power consumption can be expressed as:
  • the average travel time can be expressed as:
  • the above basic data is stored in the cloud management server and is continuously updated based on the operational data of the vehicles.
  • the vehicles of the unmanned vehicle group are provided with a logo, which is used to mark the vehicle in the operating state, charging state, or stopped state.
  • the cloud management server performs operation monitoring on the vehicles marked as operating state, and monitors the charging state
  • the charging schedule of the vehicle is carried out, and the state transition diagram of the vehicle is shown in Figure 1.
  • the unmanned vehicle in operation is marked as an operating state and is subject to real-time monitoring.
  • the cloud management server obtains the real-time remaining power and current location information of the current vehicle, and then determines the time for the current vehicle to charge.
  • Step S202 The cloud management server traversing vehicle V calculated shortest path to the charging station S i LtS i, obtain the shortest path links LtS i elapsed, then calculate the consumption ⁇ EtS i obtained by LtS i and with the desired amount of the shortest path Time TtS i :
  • ⁇ 3 is the position ratio parameter of the current vehicle in the current road section Rj (the vehicle is driving at a certain point of the road section Rj)
  • Lj, T Rj and E Rj are the road section length, average travel time and average power consumption of road section Rj, respectively, They are the sum of the lengths of the complete road sections passed by the shortest path LtS i , the sum of the average travel time, and the sum of the average power consumption.
  • the vehicle V is calculated priority to the shortest path to the charging station S i LtS i, i.e., the length of the shortest path, the shortest path is obtained by further LtS i travel time and power consumption.
  • the vehicle V may be preferentially calculate the vehicle V to the minimum time charging station S i, which is the minimum time stamp is TTSI, then calculate the shortest time at the shortest time path LtS i and power consumption ⁇ EtSi; or preferentially calculate the vehicle V minimum charge to the charging station S i consumption, the power consumption the minimum mark ⁇ EtSi, then calculate the minimal power consumption at the minimum power consumption and the travel time path LtS i TtSi.
  • Step S203 Calculate the remaining power ESi of the current vehicle V traveling to the charging station Si:
  • Step S204 Set the remaining power threshold when the vehicle V arrives at the charging station to be charged as Ec; traverse all ESi. If all ESi is less than or equal to Ec, then the vehicle V needs to be charged.
  • Step S205 Mark the vehicle V as being charged. Enter the charging schedule flow; otherwise, return to step S201 for real-time monitoring.
  • the cloud management server determines the preferred charging station and guides the vehicle to the preferred charging station for charging.
  • Step S301 The cloud management server selects the reachable charging station as a candidate charging station, and updates the shortest path and travel time of the vehicle to the candidate charging station:
  • the value of ⁇ means that the current vehicle cannot reach the charging station.
  • Step S302 The cloud management server obtains the remaining charging time of the charging point SiCmk of the current charging station Si as ⁇ SiCmkTc.
  • the remaining charging time ⁇ SiCmkTc 0.
  • Step S303 Traverse the charging point SiCmk, and calculate the waiting time ⁇ VjSiCmkTwait from the vehicle Vi to the charging point SiCmk:
  • VjTtSi is the travel time required for the shortest path from the vehicle Vj to the charging station Si.
  • Step S304 Calculate the minimum time consumption VjCos from the vehicle Vj to the charging point:
  • VjCos min(VjTtSi+ ⁇ VjSiCmkTc+ ⁇ VjSiCmkTwait)
  • ⁇ VjSiCmkTc is the estimated charging time of the vehicle Vj at the charging point Si.
  • Step S305 Select the charging point corresponding to the minimum time consumption VjCos, which is the preferred charging point.
  • the cloud management server pushes information to the vehicle Vj and guides the vehicle Vj to the preferred charging station for charging.
  • the cloud management server will perform the charging process of the vehicle. real time monitoring.
  • step S201 is re-entered to accept operation monitoring.
  • the invention uses big data technology to analyze the average power consumption and average driving time of each road section in the park, monitors the current status information of the unmanned vehicles in operation in real time, and combines the power consumption of the road section in the park, the driving time and the busy charging point of the charging station. In the case of idle charging, make a charging plan in advance and determine the preferred charging station to ensure that the unmanned vehicle can safely reach the charging station, reduce the waiting time for charging, and improve the charging efficiency.
  • the present invention also discloses a cloud management server 10.
  • the cloud management server 10, unmanned vehicles 31, and charging stations 32 form a charging scheduling system for unmanned vehicle groups.
  • the cloud management server 10 includes The business server 102 (or application server), the database server 103, the Web server 105 and the communication server 101; among them, the business server 102 exposes the business logic to the client program through various protocols. It provides access to business logic for use by client applications.
  • One or more computers running in the local area network and database management system software together constitute the database server 103.
  • the database server 103 provides services for client applications.
  • the Web server 105 specializes in processing HTTP requests, allowing the administrator to access by web browsing on the PC terminal 42.
  • the server also provides an APP server 104, which can push information to the APP 41 of the administrator's smart terminal, and provide the administrator with convenient management services anytime and anywhere.
  • the charging scheduler runs in the business server 102, and communicates with the unmanned vehicle 31 and the charging station 32 through the communication server 101, the wireless network 20 (such as 3G, 4G or 5G and other mobile communication networks) in real time to realize the cloud management server 10 and
  • the charging scheduler caches the real-time data in the database server 103, and then stores the operating data in the archive database and the archive database according to business needs.
  • Historical database such as storing the running route information of the group of unmanned vehicles and the execution status of the daily charging schedule in the archive database.

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Abstract

A charging scheduling method for an unmanned vehicle (31) group, and a cloud management server (10). Said method comprises: performing a data preparation process, to set operation map information and vehicle information of an unmanned vehicle (31) group; performing a real-time monitoring process on a vehicle marked as an operation state, to acquire current state information of the vehicle, and determine a battery replacement time; and performing a charging arrangement process on a vehicle marked as a charging state, to determine a preferred charging station (32), and guide the vehicle to travel through the shortest path to the preferred charging station (32) for charging. The present invention analyzes the average power consumption and travel time of each road section within a park by means a big data technology, monitors, in real time, the state of the unmanned vehicle (31), and makes a charging plan in advance, ensuring that the unmanned vehicle (31) can reach the charging station (32) safely, thereby reducing the charging waiting time and improving the charging efficiency.

Description

一种无人驾驶车辆群组的充电调度方法和云管理服务器Charging scheduling method for unmanned vehicle group and cloud management server 技术领域Technical field
本发明涉及电动车辆充电调度领域，尤其涉及一种无人驾驶车辆群组的充电调度方法和云管理服务器。The invention relates to the field of electric vehicle charging scheduling, and in particular to a charging scheduling method of an unmanned vehicle group and a cloud management server.
背景技术Background technique
随着智能驾驶技术、电池技术的发展以及电池生产成本的降低、充电站的普及建设，纯电动无人驾驶车辆已投入到园区内的运营。如何规划无人驾驶车辆车队的充电计划成为实际上必须解决的问题。With the development of intelligent driving technology and battery technology, the reduction of battery production costs, and the popularization of charging stations, pure electric unmanned vehicles have been put into operation in the park. How to plan the charging plan of the unmanned vehicle fleet has become a problem that must be solved in fact.
通常采用人工监控每辆无人驾驶车辆的电量，根据经验判断是否需要充电和安排整个车队的充电计划。随着无人驾驶车辆车队的数量增多，采用人工方式会消耗大量人力资源。会产生、监控效率低、车辆充电拥挤、排队时间长、充电站使用效率低的问题。Usually, manually monitor the power of each unmanned vehicle, judge whether it needs to be charged based on experience, and arrange a charging plan for the entire fleet. As the number of unmanned vehicle fleets increases, the use of manual methods will consume a lot of human resources. Problems such as low monitoring efficiency, crowded vehicle charging, long queuing time, and low use efficiency of charging stations will occur.
申请号CN201610609436.X的中国发明专利公开了一种无人驾驶车辆的充电方法和装置。该方法根据无人驾驶车辆中电池的当前电量值确定所述电池是否需要进行充电；如果所述电池需要进行充电，则确定所述无人驾驶车辆的目标充电站；行驶到所述目标充电站，并通过所述目标充电站对所述电池进行充电；若检测到所述无人驾驶车辆中电池的电量值大于目标电量值，则停止充电并支付充电费用。该发明只针对单个无人驾驶车辆，根据无人驾驶车辆的电量来判断是否充电，没有考虑充电站的剩余充电点，可能造成充电拥挤，无法满足无人驾驶车辆车队的充电规划。A Chinese invention patent with application number CN201610609436.X discloses a method and device for charging an unmanned vehicle. The method determines whether the battery needs to be charged according to the current power value of the battery in the unmanned vehicle; if the battery needs to be charged, determines the target charging station of the unmanned vehicle; drives to the target charging station , And charge the battery through the target charging station; if it is detected that the power value of the battery in the unmanned vehicle is greater than the target power value, the charging is stopped and the charging fee is paid. The invention is only for a single unmanned vehicle, and judges whether to charge according to the power of the unmanned vehicle, and does not consider the remaining charging points of the charging station, which may cause charging congestion and cannot meet the charging plan of the unmanned vehicle fleet.
申请号CN201910017770.X的中国发明专利公开了一种基于粒子群算法的电动汽车充电调度优化方法。首先,当电动汽车的电量值较低时，用户需要先向服 务器发送充电请求，服务器收到请求后会根据电动汽车电池的剩余能量以及空调状态，估算出可行驶的剩余里程数；接着，根据电动汽车的当前位置及周边充电站分布情况，同时要参考道路拥堵情况，选择出可到达的最优充电站，并为电动汽车的用户规划最优行驶路径。算法得到最优路径的过程中，可以有效的利用Metropolis接受准则避免解出局部最优点，找出全局最优点，从而得到电动汽车行驶路径的最优解。该发明针对单辆电动车，考虑电量、空调状态、交通拥堵等因素最后得出最优行驶路径。该发明无法实时监控电动车的状态，需要人工请求充电，会造成剩余电量无法到达指定充电站的问题，同时没有考虑充电站因素，无法满足无人驾驶车辆车队的充电规划。The Chinese invention patent with application number CN201910017770.X discloses an electric vehicle charging scheduling optimization method based on particle swarm algorithm. First, when the battery level of the electric vehicle is low, the user needs to send a charging request to the server first. After receiving the request, the server will estimate the remaining mileage that can be driven based on the remaining energy of the electric vehicle battery and the state of the air conditioner; The current location of electric vehicles and the distribution of surrounding charging stations, while referring to road congestion, select the optimal charging station that can be reached, and plan the optimal driving path for users of electric vehicles. In the process of obtaining the optimal path by the algorithm, the Metropolis acceptance criterion can be effectively used to avoid solving the local optimal point and find the global optimal point, so as to obtain the optimal solution of the electric vehicle driving path. The invention is aimed at a single electric vehicle, and finally obtains the optimal driving path in consideration of factors such as electric power, air-conditioning status, and traffic congestion. The invention cannot monitor the status of electric vehicles in real time, and requires manual charging, which will cause the problem that the remaining power cannot reach the designated charging station. At the same time, the charging station factor is not considered, and it cannot meet the charging plan of the unmanned vehicle fleet.
发明内容Summary of the invention
有鉴于现有技术的上述缺陷，本发明的目的是提供一种无人驾驶车辆群组的充电调度方法和云管理服务器，能通过云管理服务器的调度管理，及时做好无人驾驶车辆群组的车辆的充电计划，保证无人驾驶车辆能够到达充电站，减少充电等待时间，提高充电效率。In view of the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a charging scheduling method and a cloud management server for an unmanned vehicle group, which can manage the unmanned vehicle group in a timely manner through the scheduling management of the cloud management server The charging plan of the vehicle ensures that the unmanned vehicle can reach the charging station, reduces the waiting time for charging, and improves the charging efficiency.
为实现上述目的，本发明提供了一种无人驾驶车辆群组的充电调度方法，包括：In order to achieve the above objective, the present invention provides a charging scheduling method for an unmanned vehicle group, including:
获取无人驾驶车辆群组的运行路线信息；Obtain the operating route information of the unmanned vehicle group;
获取无人驾驶车辆群组中车辆的实时状态信息；Obtain real-time status information of vehicles in the unmanned vehicle group;
以及获取充电站的充电等待时间；And get the charging waiting time of the charging station;
建立最短路径的计算模型，所述最短路径的计算模型可根据运行路线信息和车辆的实时状态信息预测计算；得到当前车辆到某一充电站的最短路径和通过最短路径的行驶时间和电量消耗；Establish a calculation model of the shortest path, the calculation model of the shortest path can be predicted and calculated based on the running route information and the real-time status information of the vehicle; obtain the shortest path from the current vehicle to a certain charging station and the travel time and power consumption of the shortest path;
建立优选充电站的计算模型，所述优选充电站的计算模型可根据车辆的实 时状态信息和充电站的充电等待时间计算，得到当前车辆的到充电站并完成充电的最小时间消耗，所述优选充电站为满足最小时间消耗的充电站；The calculation model of the preferred charging station is established. The calculation model of the preferred charging station can be calculated according to the real-time status information of the vehicle and the charging waiting time of the charging station to obtain the minimum time consumption of the current vehicle to the charging station and complete the charging. The charging station is a charging station that meets the minimum time consumption;
无人驾驶车辆群组的车辆状态被标记为运营状态和充电状态；The vehicle status of the unmanned vehicle group is marked as operating status and charging status;
对标记为运营状态的车辆，执行实时监控流程：获取车辆的实时状态信息，根据车辆的实时状态信息，通过最短路径的计算模型预测计算，得到车辆到达各充电站的最短路径和预测剩余电量；当所有预测剩余电量均小于设定的剩余电量阈值时，将车辆标记为充电状态；Carry out the real-time monitoring process for vehicles marked as operating status: obtain real-time status information of the vehicle, and predict and calculate the shortest path calculation model based on the real-time status information of the vehicle to obtain the shortest path for the vehicle to reach each charging station and predict the remaining power; When all predicted remaining power levels are less than the set remaining power threshold, mark the vehicle as a charging state;
对标记为充电状态的车辆，执行充电流程：通过优选充电站的计算模型确定优选充电站，并引导车辆通过所述最短路径前往优选充电站充电；当车辆重新投入运营，将车辆标记为运营状态。For vehicles marked as charging state, perform the charging process: determine the preferred charging station through the calculation model of the preferred charging station, and guide the vehicle to go to the preferred charging station through the shortest path for charging; when the vehicle is put into operation again, mark the vehicle as operating state .
进一步的，所述运行路线信息包括充电站信息和路段信息；所述实时状态信息包括车辆的实时剩余电量、实时位置信息和加权因子；Further, the operating route information includes charging station information and road section information; the real-time status information includes real-time remaining power, real-time position information, and weighting factors of the vehicle;
根据路段信息和加权因子，得到各路段的长度、行驶时间和电量消耗；According to the road section information and weighting factors, the length, driving time and power consumption of each road section are obtained;
根据所述运行路线信息中各充电站及各路段的拓扑关系、车辆的实时位置信息，通过最短路径的计算模型计算，得到车辆前往某一充电站的最短路径，所述最短路径是最短长度路径、最短时间路径或最小电量消耗。According to the topological relationship of each charging station and each road section in the operating route information, and the real-time position information of the vehicle, the shortest path calculation model is used to obtain the shortest path for the vehicle to a certain charging station, and the shortest path is the shortest length path , The shortest time path or the minimum power consumption.
进一步的，所述最短长度路径的计算模型包括：Further, the calculation model of the shortest length path includes:
Figure PCTCN2019128608-appb-000001
Figure PCTCN2019128608-appb-000001
Figure PCTCN2019128608-appb-000002
Figure PCTCN2019128608-appb-000002
Figure PCTCN2019128608-appb-000003
Figure PCTCN2019128608-appb-000003
其中，LtSi为当前车辆到充电站Si的最短长度路径，ΔEtSi为当前车辆行 驶完成该最短长度路径LtSi的电量消耗，TtSi为当前车辆行驶完成该最短长度路径LtSi的行驶时间；α3为当前车辆在路段Rj的位置比例参数，α3和路段Rj标识车辆的当前位置信息；Lj、T Rj、E Rj分别为路段Rj的路段长度、平均行驶时间和平均电量消耗，
Figure PCTCN2019128608-appb-000004
分别为最短长度路径LtS i所经过的完整路段的路段长度之和，平均行驶时间之和和平均电量消耗之和。 Among them, LtSi is the shortest length path from the current vehicle to the charging station Si, ΔEtSi is the power consumption of the current vehicle to complete the shortest length path LtSi, TtSi is the travel time of the current vehicle to complete the shortest length path LtSi; α3 is the current vehicle in The position ratio parameter of the road section Rj, α3 and road section Rj identify the current position information of the vehicle; Lj, T Rj and E Rj are the road section length, average travel time and average power consumption of road section Rj, respectively.
Figure PCTCN2019128608-appb-000004
Respectively, they are the sum of the lengths of the complete road sections passed by the shortest length path LtS i , the sum of the average travel time and the sum of the average power consumption.
进一步的，所述最短时间路径的计算模型包括：Further, the calculation model of the shortest time path includes:
Figure PCTCN2019128608-appb-000005
Figure PCTCN2019128608-appb-000005
Figure PCTCN2019128608-appb-000006
Figure PCTCN2019128608-appb-000006
Figure PCTCN2019128608-appb-000007
Figure PCTCN2019128608-appb-000007
其中，TtSi为当前车辆到充电站Si的最短行驶时间；LtSi为当前车辆到充电站Si的最短行驶时间的路径，即最短时间路径，ΔEtSi为当前车辆行驶完成该最短时间路径LtSi的电量消耗，α3为当前车辆在路段Rj的位置比例参数，α3和路段Rj标识车辆的当前位置信息；Lj、T Rj、E Rj分别为路段Rj的路段长度、平均行驶时间和平均电量消耗，
Figure PCTCN2019128608-appb-000008
分别为最短时间路径LtS i所经过的完整路段的路段长度之和，平均行驶时间之和和平均电量消耗之和。 Among them, TtSi is the shortest travel time from the current vehicle to the charging station Si; LtSi is the shortest travel time path from the current vehicle to the charging station Si, that is, the shortest time path, ΔEtSi is the power consumption of the current vehicle to complete the shortest time path LtSi, α3 is the position ratio parameter of the current vehicle on the road section Rj, α3 and road section Rj identify the current position information of the vehicle; Lj, T Rj and E Rj are the road section length, average travel time and average power consumption of road section Rj, respectively,
Figure PCTCN2019128608-appb-000008
Respectively, they are the sum of the lengths of the complete road sections passed by the shortest time path LtS i , the sum of the average travel time and the sum of the average power consumption.
进一步的，所述最小电量消耗路径的计算模型包括：Further, the calculation model of the minimum power consumption path includes:
Figure PCTCN2019128608-appb-000009
Figure PCTCN2019128608-appb-000009
Figure PCTCN2019128608-appb-000010
Figure PCTCN2019128608-appb-000010
Figure PCTCN2019128608-appb-000011
Figure PCTCN2019128608-appb-000011
其中，ΔEtSi为当前车辆到达充电站Si的最小电量消耗，LtSi为当前车辆根据该最小电量消耗得到的到充电站Si的最小电量消耗路径，TtSi为当前车辆行驶完成该最小电量消耗路径LtSi的行驶时间；α3为当前车辆在路段Rj的位置比例参数，α3和路段Rj标识车辆的当前位置信息；Lj、T Rj、E Rj分别为路段Rj的路段长度、平均行驶时间和平均电量消耗，
Figure PCTCN2019128608-appb-000012
分别为最小电量消耗路径LtSi所经过的完整路段的路段长度之和，平均行驶时间之和和平均电量消耗之和。 Among them, ΔEtSi is the minimum power consumption of the current vehicle to the charging station Si, LtSi is the minimum power consumption path of the current vehicle to the charging station Si according to the minimum power consumption, and TtSi is the driving of the current vehicle to complete the minimum power consumption path LtSi Time; α3 is the position ratio parameter of the current vehicle on the road section Rj, α3 and road section Rj identify the current position information of the vehicle; Lj, T Rj and E Rj are the road section length, average travel time and average power consumption of road section Rj, respectively,
Figure PCTCN2019128608-appb-000012
Respectively, they are the sum of the lengths of the complete road sections passed by the minimum power consumption path LtSi, the sum of the average travel time and the sum of the average power consumption.
进一步的，所述加权因子包括车型电池型号权重参数α1、α2；电池使用年限权重参数β1、β2；载客量权重参数m1、m2；空调状态权重参数k1、k2；环境权重参数e1、e2；Further, the weighting factors include vehicle model battery model weight parameters α1, α2; battery age weight parameters β1, β2; passenger load weight parameters m1, m2; air conditioning state weight parameters k1, k2; environmental weight parameters e1, e2;
所述路段的平均电量消耗和平均行驶时间通过如下公式获得：The average power consumption and average driving time of the road section are obtained by the following formula:
E Ri＝α1*β1*m1*e1*(Eui+k1*Epi)/Li E Ri =α1*β1*m1*e1*(Eui+k1*Epi)/Li
T Ri＝α2*β2*m2*e2*(Tui+k2*Tpi)/Li T Ri =α2*β2*m2*e2*(Tui+k2*Tpi)/Li
其中，E Ri和T Ri为路段Ri的平均电量消耗和平均行驶时间；Eui为路径Ri不开空调时的额定平均电量消耗，Epi为路径Ri的开空调时的额定平均附加电量消耗，Tui为路径Ri的不开空调时的额定平均行驶时间，Tpi为路径Ri的开空调时的额定平均附加行驶时间。 Among them, E Ri and T Ri are the average power consumption and average travel time of the road section Ri; Eui is the rated average power consumption when the air conditioner is not turned on on the route Ri, Epi is the rated average additional power consumption when the air conditioner is turned on on the route Ri, and Tui is The rated average travel time of the route Ri when the air conditioner is not turned on, and Tpi is the rated average additional travel time of the route Ri when the air conditioner is turned on.
进一步的，所述充电站包括多个充电点,所述充电站的充电等待时间为充电站的各充电点的充电等待时间的最小值；Further, the charging station includes a plurality of charging points, and the charging waiting time of the charging station is a minimum value of the charging waiting time of each charging point of the charging station;
所述优选充电站的计算模型：The calculation model of the preferred charging station:
Figure PCTCN2019128608-appb-000013
Figure PCTCN2019128608-appb-000013
VjCos＝min(VjTtSi+ΔVjSiCmkTwait+ΔVjSiCmkTc)VjCos=min(VjTtSi+ΔVjSiCmkTwait+ΔVjSiCmkTc)
其中，ΔVjSiCmkTwait为预测的车辆Vj到充电站Si的充电点SiCmk的等待时间；VjTtSi为预测的车辆Vj到充电站Si的最短路径所需的行驶时间； ΔSiCmkTc为充电站Si的充电点SiCmk的剩余充电时间；VjCos为预测的车辆Vj到充电点并完成充电的最小时间消耗；ΔVjSiCmkTc为车辆Vj在充电点Si的预计充电时间；Among them, ΔVjSiCmkTwait is the predicted waiting time of the vehicle Vj to the charging point SiCmk of the charging station Si; VjTtSi is the predicted travel time required for the shortest path from the vehicle Vj to the charging station Si; ΔSiCmkTc is the remaining charge point SiCmk of the charging station Si Charging time; VjCos is the estimated minimum time consumed by the vehicle Vj to the charging point and completing the charging; ΔVjSiCmkTc is the expected charging time of the vehicle Vj at the charging point Si;
达成VjCos条件的充电站为优选充电站。The charging station that meets the VjCos conditions is the preferred charging station.
进一步的，所述优选充电站是在备选充电站中选择，所述备选充电站满足条件：Further, the preferred charging station is selected among alternative charging stations, and the alternative charging stations meet the conditions:
ΔEtSi≤EtΔEtSi≤Et
其中，ΔEtSi为当前车辆到达充电站Si的电量消耗；Et为当前车辆的实时剩余电量。Among them, ΔEtSi is the power consumption of the current vehicle arriving at the charging station Si; Et is the real-time remaining power of the current vehicle.
进一步的，所述预测剩余电量通过以下公式获得：Further, the predicted remaining power is obtained by the following formula:
ESi＝Et-ΔEtSiESi=Et-ΔEtSi
其中，ESi为当前车辆行驶到充电站Si的预测剩余电量，Et为当前车辆的实时剩余电量。Among them, ESi is the predicted remaining power of the current vehicle driving to the charging station Si, and Et is the real-time remaining power of the current vehicle.
进一步的，所述充电调度方法还包括充电队列，当车辆被标记为充电状态时，所述车辆进入充电队列，并依照队列的先后顺序执行充电流程。Further, the charging scheduling method further includes a charging queue. When the vehicle is marked as a charging state, the vehicle enters the charging queue, and the charging process is executed according to the sequence of the queue.
本发明还提供了一种云管理服务器，包括应用程序服务器、数据库服务器、Web服务器和通信服务器，The present invention also provides a cloud management server, including an application server, a database server, a Web server, and a communication server,
所述应用程序服务器用于执行充电调度程序，所述充电调度程序实现如上所述的无人驾驶车辆群组的充电调度方法；The application server is used to execute a charging scheduling program, and the charging scheduling program implements the charging scheduling method of the unmanned vehicle group as described above;
所述数据库服务器用于提供存取服务，存取的信息包括：无人驾驶车辆群组的运行路线信息和调度信息，所述调度信息是根据所述充电调度方法得到的；The database server is used to provide access services, and the accessed information includes: operating route information and scheduling information of the unmanned vehicle group, the scheduling information is obtained according to the charging scheduling method;
所述通信服务器用于建立所述云管理服务器和无人驾驶车辆、充电站的通信连接。The communication server is used to establish a communication connection between the cloud management server and the unmanned vehicle and charging station.
进一步的，还包括APP服务器，所述APP服务器用于提供智能终端APP的 调用服务，用于推送调度信息。Further, it also includes an APP server, the APP server is used to provide smart terminal APP invocation service, and is used to push scheduling information.
本发明的无人驾驶车辆群组的充电调度方法，利用大数据技术分析园区内每段路的平均电量消耗、行驶时间，实时监控处于运营状态的无人驾驶车辆的当前状态信息，结合园区路段电量消耗、行驶时间及充电站的充电点的充电情况，提前做好充电计划，保证无人驾驶车辆能够到达充电站，减少充电等待时间，提高充电效率。The charging scheduling method of the unmanned vehicle group of the present invention uses big data technology to analyze the average power consumption and driving time of each section of the road in the park, monitors the current status information of the unmanned vehicles in operation in real time, and combines the road sections of the park For power consumption, driving time and charging conditions at charging points of charging stations, make a charging plan in advance to ensure that unmanned vehicles can reach the charging station, reduce waiting time for charging, and improve charging efficiency.
附图说明Description of the drawings
图1是本发明的一个较佳实施例的无人驾驶车辆群组的充电调度流程图；FIG. 1 is a flowchart of charging scheduling of an unmanned vehicle group according to a preferred embodiment of the present invention;
图2是运营监控流程图；Figure 2 is a flow chart of operation monitoring;
图3是充电安排流程图；Figure 3 is a flowchart of charging arrangements;
图4是本发明的无人驾驶车辆群组的充电调度系统的框图。Fig. 4 is a block diagram of a charging scheduling system for a group of unmanned vehicles of the present invention.
具体实施方式detailed description
为进一步说明各实施例，本发明提供有附图。这些附图为本发明揭露内容的一部分，其主要用以说明实施例，并可配合说明书的相关描述来解释实施例的运作原理。配合参考这些内容，本领域普通技术人员应能理解其他可能的实施方式以及本发明的优点。图中的组件并未按比例绘制，而类似的组件符号通常用来表示类似的组件。To further illustrate the various embodiments, the present invention is provided with drawings. These drawings are a part of the disclosure of the present invention, which are mainly used to illustrate the embodiments, and can cooperate with the relevant description in the specification to explain the operation principle of the embodiments. With reference to these contents, those of ordinary skill in the art should be able to understand other possible implementation manners and advantages of the present invention. The components in the figure are not drawn to scale, and similar component symbols are usually used to indicate similar components.
现结合附图和具体实施方式对本发明进一步说明。The present invention will now be further described with reference to the drawings and specific embodiments.
实施例一Example one
如图1-图3所示，本发明公开了一种无人驾驶车辆群组的充电调度方法，在本实施例中，该无人驾驶车辆群组(或称无人驾驶车辆的车队)运营于一园区的规定路线，并在路线中设置有多个充电站，每个充电站设有若干个充电点，当无人驾驶车辆的剩余电量降到一定值时，就需要前往充电站进行充电。为方 便管理，需要对该无人驾驶车辆群组进行充电调度管理，以合理安排无人驾驶车辆的充电时机，减少充电等待、避免缺电抛锚等问题。As shown in Figures 1 to 3, the present invention discloses a charging scheduling method for a group of unmanned vehicles. In this embodiment, the group of unmanned vehicles (or a fleet of unmanned vehicles) operates A prescribed route in a park, and there are multiple charging stations in the route, and each charging station has several charging points. When the remaining power of the unmanned vehicle drops to a certain value, it needs to go to the charging station for charging . In order to facilitate management, the unmanned vehicle group needs to be charged scheduling management to reasonably arrange the timing of unmanned vehicle charging, reduce waiting for charging, and avoid problems such as power shortage and breakdown.
该充电调度方法运行于云管理服务器中，包括以下步骤：The charging scheduling method runs in the cloud management server and includes the following steps:
一、数据准备1. Data preparation
步骤S101：路况信息准备Step S101: Road condition information preparation
对无人驾驶车辆群组的运行地图进行识别分析，对运营车辆运行路线和前往充电站所需经过的路段进行相应编号，其中，路段编号为R1，R2，…，Rn，对应的路段长度为L1，L2，…，Ln；充电站编号为S1，S2，…，Sm，每个充电站包括若干个充电点，充电站Si的充电点编号为SiCm1，SiCm2，…，SiCmk。Identify and analyze the operating map of the unmanned vehicle group, and number the operating route of the vehicle and the road sections that need to pass to the charging station. Among them, the road section numbers are R1, R2,..., Rn, and the corresponding road section length is L1, L2,..., Ln; the charging station numbers are S1, S2,..., Sm, each charging station includes several charging points, the charging point numbers of the charging station Si are SiCm1, SiCm2,..., SiCmk.
步骤S102，车辆信息准备Step S102, vehicle information preparation
通过物联网技术采集各路段在不同运营环境、载客量、空调状态、电池型号、电池使用年限下的电量消耗和行驶时间，并通过大数据统计计算获得各路段的平均电量消耗和平均行驶时间的计算模型。其中，Collect the power consumption and driving time of each road section under different operating environment, passenger capacity, air conditioning status, battery model, and battery life through the Internet of Things technology, and obtain the average power consumption and average driving time of each road section through big data statistical calculations Calculation model. among them,
平均电量消耗可表示为：The average power consumption can be expressed as:
ERi＝α1*β1*m1*e1*(Eui+k1*Epi)/LiERi=α1*β1*m1*e1*(Eui+k1*Epi)/Li
平均行驶时间可表示为：The average travel time can be expressed as:
TRi＝α2*β2*m2*e2*(Tui+k2*Tpi)/LiTRi=α2*β2*m2*e2*(Tui+k2*Tpi)/Li
其中α1、α2为车型电池型号权重参数；β1、β2为电池使用年限权重参数；m1、m2为载客量权重参数；k1、k2为空调状态权重参数(空调关闭时k1、k2值为0，开启时k1、k2值为1)；e1、e2为环境(天气、气温、交通)权重参数,Eui为路径Ri不开空调时的额定平均电量消耗，Epi为路径Ri的开空调时的额定平均附加电量消耗，Tui为路径Ri的不开空调时的额定平均行驶时间，Tpi为路径Ri的开空调时的额定平均附加行驶时间。Among them, α1, α2 are the weight parameters of the vehicle battery model; β1, β2 are the weight parameters of the battery life; m1, m2 are the weight parameters of the passenger capacity; k1, k2 are the weight parameters of the air-conditioning state (k1 and k2 are 0 when the air-conditioning is off, K1 and k2 are 1) when it is turned on; e1 and e2 are the weight parameters of the environment (weather, temperature, traffic), Eui is the rated average power consumption when the air conditioner is not turned on in the route Ri, and Epi is the rated average when the air conditioner is turned on in the route Ri For additional power consumption, Tui is the rated average travel time when the air conditioner is not turned on for the route Ri, and Tpi is the rated average additional travel time when the air conditioner is turned on for the route Ri.
通过数据准备过程，我们完成了无人驾驶车辆群组运营和充电过程的基础 数据准备和量化，以上基础数据存储于云管理服务器中，并根据车辆的运营数据进行不断更新。Through the data preparation process, we have completed the basic data preparation and quantification of the group operation and charging process of unmanned vehicles. The above basic data is stored in the cloud management server and is continuously updated based on the operational data of the vehicles.
在云管理服务器中，无人驾驶车辆群组的车辆设有一标识，用于标记车辆处于运营状态、充电状态或停驶状态，云管理服务器对标记为运营状态的车辆进行运营监控，对充电状态的车辆进行充电安排，车辆的状态转移图如图1所示。In the cloud management server, the vehicles of the unmanned vehicle group are provided with a logo, which is used to mark the vehicle in the operating state, charging state, or stopped state. The cloud management server performs operation monitoring on the vehicles marked as operating state, and monitors the charging state The charging schedule of the vehicle is carried out, and the state transition diagram of the vehicle is shown in Figure 1.
二、运营监控2. Operational monitoring
在本实施例的控制流程中，将运营中的无人驾驶车辆标记为运营状态，接受实时监控。In the control process of this embodiment, the unmanned vehicle in operation is marked as an operating state and is subject to real-time monitoring.
以当前车辆为例，云管理服务器获取当前车辆的实时剩余电量和当前位置信息，进而确定当前车辆进行充电的时机。Taking the current vehicle as an example, the cloud management server obtains the real-time remaining power and current location information of the current vehicle, and then determines the time for the current vehicle to charge.
步骤S201：通过物联网技术实时获取当前车辆的实时剩余电量Et、空调状态kt、载客量mt、位置、电池使用年限、环境等,实时剩余电量Et＝SOCt*E，其中E为电池总体可用能量，SOCt为实时剩余电量占电池总体可用能量的百分比。Step S201: Obtain real-time remaining power Et, air conditioning status kt, passenger capacity mt, location, battery life, environment, etc. of the current vehicle in real time through Internet of Things technology, real-time remaining power Et=SOCt*E, where E is the total available battery Energy, SOCt is the percentage of real-time remaining power to the total available energy of the battery.
步骤S202：云管理服务器遍历计算车辆V到充电站S i的最短路径LtS i，得出最短路径LtS i所经过的路段，进而计算获得通过该最短路径LtS i所需的电量消耗ΔEtS i和行驶时间TtS i： Step S202: The cloud management server traversing vehicle V calculated shortest path to the charging station S i LtS i, obtain the shortest path links LtS i elapsed, then calculate the consumption ΔEtS i obtained by LtS i and with the desired amount of the shortest path Time TtS i :
Figure PCTCN2019128608-appb-000014
Figure PCTCN2019128608-appb-000014
Figure PCTCN2019128608-appb-000015
Figure PCTCN2019128608-appb-000015
Figure PCTCN2019128608-appb-000016
Figure PCTCN2019128608-appb-000016
其中，α3为当前车辆在当前路段Rj的位置比例参数(车辆行驶在路段Rj的某个点)，Lj、T Rj、E Rj分别为路段Rj的路段长度、平均行驶时间和平均电量消耗，
Figure PCTCN2019128608-appb-000017
分别为最短路径LtS i所经过的完整路段的路段长度之和，平均行驶时间之和和平均电量消耗之和。 Among them, α3 is the position ratio parameter of the current vehicle in the current road section Rj (the vehicle is driving at a certain point of the road section Rj), Lj, T Rj and E Rj are the road section length, average travel time and average power consumption of road section Rj, respectively,
Figure PCTCN2019128608-appb-000017
They are the sum of the lengths of the complete road sections passed by the shortest path LtS i , the sum of the average travel time, and the sum of the average power consumption.
在本实施例中，优先计算车辆V到充电站S i的最短路径LtS i，即最短长度路径，进而获得通过最短路径LtS i的行驶时间和电量消耗。在具体应用中，也可以优先计算车辆V到充电站S i的最短时间，该最短时间标记为TtSi，进而计算实现该最短时间下的最短时间路径LtS i和电量消耗ΔEtSi；或优先计算车辆V到充电站S i的最小电量消耗，该最小电量消耗标记为ΔEtSi，进而计算实现该最小电量消耗下的最小电量消耗路径LtS i和行驶时间TtSi。 In the present embodiment, the vehicle V is calculated priority to the shortest path to the charging station S i LtS i, i.e., the length of the shortest path, the shortest path is obtained by further LtS i travel time and power consumption. In a particular application, may be preferentially calculate the vehicle V to the minimum time charging station S i, which is the minimum time stamp is TTSI, then calculate the shortest time at the shortest time path LtS i and power consumption ΔEtSi; or preferentially calculate the vehicle V minimum charge to the charging station S i consumption, the power consumption the minimum mark ΔEtSi, then calculate the minimal power consumption at the minimum power consumption and the travel time path LtS i TtSi.
步骤S203：计算当前车辆V行驶到充电站Si的剩余电量ESi：Step S203: Calculate the remaining power ESi of the current vehicle V traveling to the charging station Si:
ESi＝Et-ΔEtSiESi=Et-ΔEtSi
步骤S204：设车辆V到达充电站进行充电时的剩余电量阈值为Ec；遍历所有ESi，如果所有ESi都小于或等于Ec，则车辆V需要充电，进入步骤S205：将车辆V标记为充电状态，进入充电安排流程；否则返回步骤S201，进行实时监控。Step S204: Set the remaining power threshold when the vehicle V arrives at the charging station to be charged as Ec; traverse all ESi. If all ESi is less than or equal to Ec, then the vehicle V needs to be charged. Step S205: Mark the vehicle V as being charged. Enter the charging schedule flow; otherwise, return to step S201 for real-time monitoring.
三、执行充电安排Third, perform charging arrangements
当车辆处于充电状态时，云管理服务器确定优选充电站，并引导车辆前往优选充电站充电。When the vehicle is in a charging state, the cloud management server determines the preferred charging station and guides the vehicle to the preferred charging station for charging.
步骤S301：云管理服务器选取可到达的充电站为备选充电站，更新车辆到达备选充电站的最短路径、行驶时间：Step S301: The cloud management server selects the reachable charging station as a candidate charging station, and updates the shortest path and travel time of the vehicle to the candidate charging station:
Figure PCTCN2019128608-appb-000018
Figure PCTCN2019128608-appb-000018
Figure PCTCN2019128608-appb-000019
Figure PCTCN2019128608-appb-000019
其中值为∞表示当前车辆无法到达该充电站。The value of ∞ means that the current vehicle cannot reach the charging station.
步骤S302：云管理服务器获取当前充电站Si的充电点SiCmk的剩余充电时间为ΔSiCmkTc，当充电点SiCmk为空闲状态没有安排车辆充电时，剩余充电时间ΔSiCmkTc＝0。Step S302: The cloud management server obtains the remaining charging time of the charging point SiCmk of the current charging station Si as ΔSiCmkTc. When the charging point SiCmk is idle and no vehicle charging is scheduled, the remaining charging time ΔSiCmkTc=0.
步骤S303：遍历充电点SiCmk，计算车辆Vi到充电点SiCmk的等待时间ΔVjSiCmkTwait：Step S303: Traverse the charging point SiCmk, and calculate the waiting time ΔVjSiCmkTwait from the vehicle Vi to the charging point SiCmk:
Figure PCTCN2019128608-appb-000020
Figure PCTCN2019128608-appb-000020
其中，VjTtSi为车辆Vj到充电站Si最短路径所需的行驶时间。Among them, VjTtSi is the travel time required for the shortest path from the vehicle Vj to the charging station Si.
步骤S304：计算车辆Vj到充电点的最小时间消耗VjCos：Step S304: Calculate the minimum time consumption VjCos from the vehicle Vj to the charging point:
VjCos＝min(VjTtSi+ΔVjSiCmkTc+ΔVjSiCmkTwait)VjCos=min(VjTtSi+ΔVjSiCmkTc+ΔVjSiCmkTwait)
其中，ΔVjSiCmkTc为车辆Vj在充电点Si的预计充电时间。Among them, ΔVjSiCmkTc is the estimated charging time of the vehicle Vj at the charging point Si.
步骤S305，选取该最小时间消耗VjCos所对应的充电点，即为优选充电点，云管理服务器推送信息给车辆Vj，引导车辆Vj前往优选充电站进行充电，云管理服务器会对车辆的充电过程进行实时监控。Step S305: Select the charging point corresponding to the minimum time consumption VjCos, which is the preferred charging point. The cloud management server pushes information to the vehicle Vj and guides the vehicle Vj to the preferred charging station for charging. The cloud management server will perform the charging process of the vehicle. real time monitoring.
车辆充电完成后，若车辆需要重新返回运营，则将车辆状态置于运营状态，重新进入步骤S201，接受运营监控。After the vehicle is fully charged, if the vehicle needs to return to operation, the vehicle state is placed in the operation state, and step S201 is re-entered to accept operation monitoring.
本发明利用大数据技术分析园区内每个路段的平均电量消耗、平均行驶时间，实时监控运营中的无人驾驶车辆的当前状态信息，结合园区路段电量消耗、行驶时间及充电站的充电点忙闲充电情况，提前做好充电计划，确定优选充电站，保证无人驾驶车辆能够安全到达充电站，减少充电等待时间，提高充电效率。The invention uses big data technology to analyze the average power consumption and average driving time of each road section in the park, monitors the current status information of the unmanned vehicles in operation in real time, and combines the power consumption of the road section in the park, the driving time and the busy charging point of the charging station. In the case of idle charging, make a charging plan in advance and determine the preferred charging station to ensure that the unmanned vehicle can safely reach the charging station, reduce the waiting time for charging, and improve the charging efficiency.
实施例二Example two
如图4所示，本发明还公开了一种云管理服务器10，云管理服务器10和无人驾驶车辆31、充电站32构成无人驾驶车辆群组的充电调度系统，该云管理服务器10包括业务服务器102(或称应用程序服务器)、数据库服务器103、Web服务器105和通信服务器101；其中，业务服务器102通过各种协议把商业逻辑曝露给客户端程序。它提供了访问商业逻辑的途径以供客户端应用程序使用。运行在局域网中的一台或多台计算机和数据库管理系统软件共同构成了数据库服务器103，数据库服务器103为客户应用提供服务，这些服务包括查询、更新、事务管理、索引、高速缓存、查询优化、安全及多用户存取控制等，Web服务器105专门处理HTTP请求，允许管理员通过在PC终端42上以web浏览的方式访问。为实现多元化的管理手段，该服务器还提供APP服务器104，可将信息推送到管理员的智能终端的APP41中，为管理员提供随时随地的便捷管理服务。As shown in Figure 4, the present invention also discloses a cloud management server 10. The cloud management server 10, unmanned vehicles 31, and charging stations 32 form a charging scheduling system for unmanned vehicle groups. The cloud management server 10 includes The business server 102 (or application server), the database server 103, the Web server 105 and the communication server 101; among them, the business server 102 exposes the business logic to the client program through various protocols. It provides access to business logic for use by client applications. One or more computers running in the local area network and database management system software together constitute the database server 103. The database server 103 provides services for client applications. These services include query, update, transaction management, index, cache, query optimization, For security and multi-user access control, the Web server 105 specializes in processing HTTP requests, allowing the administrator to access by web browsing on the PC terminal 42. In order to achieve diversified management methods, the server also provides an APP server 104, which can push information to the APP 41 of the administrator's smart terminal, and provide the administrator with convenient management services anytime and anywhere.
充电调度程序运行于业务服务器102中，通过通信服务器101、无线网络20(如3G、4G或5G等移动通信网络)和无人驾驶车辆31、充电站32进行实时通信，实现云管理服务器10和无人驾驶车辆31、充电站32的信息交互，及时根据无人驾驶车辆31和充电站32的信息，执行无人驾驶车辆群组的充电调度方法，将需要充电的无人驾驶车辆31合理地安排到相应充电站32中，使无人驾驶车辆31的总的充电时间最短、效率最高，该充电调度程序将实时数据缓存于数据库服务器103中，进而根据业务需要将运行数据存储于档案数据库和历史数据库，如将无人驾驶车辆群组的运行路线信息和每日的充电调度的执行情况存储于档案数据库中。The charging scheduler runs in the business server 102, and communicates with the unmanned vehicle 31 and the charging station 32 through the communication server 101, the wireless network 20 (such as 3G, 4G or 5G and other mobile communication networks) in real time to realize the cloud management server 10 and The information interaction between the unmanned vehicles 31 and the charging station 32, according to the information of the unmanned vehicles 31 and the charging station 32, executes the charging scheduling method of the unmanned vehicle group in a timely manner, and the unmanned vehicles 31 that need to be charged are reasonably Arrange to the corresponding charging station 32 to make the total charging time of the unmanned vehicle 31 the shortest and the highest efficiency. The charging scheduler caches the real-time data in the database server 103, and then stores the operating data in the archive database and the archive database according to business needs. Historical database, such as storing the running route information of the group of unmanned vehicles and the execution status of the daily charging schedule in the archive database.
尽管结合优选实施方案具体展示和介绍了本发明，但所属领域的技术人员应该明白，在不脱离所附权利要求书所限定的本发明的精神和范围内，在形式 上和细节上可以对本发明做出各种变化，均为本发明的保护范围。Although the present invention is specifically shown and described in conjunction with the preferred embodiments, those skilled in the art should understand that the present invention can be modified in form and detail without departing from the spirit and scope of the present invention defined by the appended claims. Various changes are within the protection scope of the present invention.

Claims (12)

  1. 一种无人驾驶车辆群组的充电调度方法，用于云管理服务器的充电调度，其特征在于，包括：A charging scheduling method for an unmanned vehicle group is used for the charging scheduling of a cloud management server, and is characterized in that it includes:
    获取无人驾驶车辆群组的运行路线信息；获取无人驾驶车辆群组中车辆的实时状态信息；以及获取充电站的充电等待时间；Obtain the operating route information of the unmanned vehicle group; obtain the real-time status information of the vehicles in the unmanned vehicle group; and obtain the charging waiting time of the charging station;
    建立最短路径的计算模型，所述最短路径的计算模型可根据运行路线信息和车辆的实时状态信息预测计算；得到当前车辆到某一充电站的最短路径和通过最短路径的行驶时间和电量消耗；Establish a calculation model of the shortest path, the calculation model of the shortest path can be predicted and calculated based on the running route information and the real-time status information of the vehicle; obtain the shortest path from the current vehicle to a certain charging station and the travel time and power consumption of the shortest path;
    建立优选充电站的计算模型，所述优选充电站的计算模型可根据车辆的实时状态信息和充电站的充电等待时间计算，得到当前车辆的到充电站并完成充电的最小时间消耗，所述优选充电站为满足最小时间消耗的充电站；The calculation model of the preferred charging station is established. The calculation model of the preferred charging station can be calculated according to the real-time status information of the vehicle and the charging waiting time of the charging station to obtain the minimum time consumption of the current vehicle to the charging station and complete the charging. The charging station is a charging station that meets the minimum time consumption;
    无人驾驶车辆群组的车辆状态被标记为运营状态和充电状态；The vehicle status of the unmanned vehicle group is marked as operating status and charging status;
    对标记为运营状态的车辆，执行实时监控流程：获取车辆的实时状态信息，根据车辆的实时状态信息，通过最短路径的计算模型预测计算，得到车辆到达各充电站的最短路径和预测剩余电量；当所有预测剩余电量均小于设定的剩余电量阈值时，将车辆标记为充电状态；Carry out the real-time monitoring process for vehicles marked as operating status: obtain real-time status information of the vehicle, and predict and calculate the shortest path calculation model based on the real-time status information of the vehicle to obtain the shortest path for the vehicle to reach each charging station and predict the remaining power; When all predicted remaining power levels are less than the set remaining power threshold, mark the vehicle as a charging state;
    对标记为充电状态的车辆，执行充电流程：通过优选充电站的计算模型确定优选充电站，并引导车辆通过所述最短路径前往优选充电站充电；当车辆重新投入运营，将车辆标记为运营状态。For vehicles marked as charging state, perform the charging process: determine the preferred charging station through the calculation model of the preferred charging station, and guide the vehicle to go to the preferred charging station through the shortest path for charging; when the vehicle is put into operation again, mark the vehicle as operating state .
  2. 如权利要求1所述的充电调度方法，其特征在于：所述运行路线信息包括充电站信息和路段信息；所述实时状态信息包括车辆的实时剩余电量、实时位置信息和加权因子；The charging scheduling method according to claim 1, wherein the operating route information includes charging station information and road section information; the real-time status information includes the real-time remaining power of the vehicle, real-time position information and weighting factors;
    根据路段信息和加权因子，得到各路段的长度、行驶时间和电量消耗；According to the road section information and weighting factors, the length, driving time and power consumption of each road section are obtained;
    根据所述运行路线信息中各充电站及各路段的拓扑关系、车辆的实时位置 信息，通过最短路径的计算模型计算，得到车辆前往某一充电站的最短路径，所述最短路径是最短长度路径、最短时间路径或最小电量消耗路径。According to the topological relationship of each charging station and each road section in the operating route information, and the real-time position information of the vehicle, the shortest path calculation model is used to obtain the shortest path for the vehicle to a certain charging station, and the shortest path is the shortest length path , The shortest time path or the least power consumption path.
  3. 如权利要求2所述的充电调度方法，其特征在于，所述最短长度路径的计算模型包括：The charging scheduling method according to claim 2, wherein the calculation model of the shortest length path comprises:
    Figure PCTCN2019128608-appb-100001
    Figure PCTCN2019128608-appb-100001
    Figure PCTCN2019128608-appb-100002
    Figure PCTCN2019128608-appb-100002
    Figure PCTCN2019128608-appb-100003
    Figure PCTCN2019128608-appb-100003
    其中，LtSi为当前车辆到充电站Si的最短长度路径，ΔEtSi为当前车辆行驶完成该最短长度路径LtSi的电量消耗，TtSi为当前车辆行驶完成该最短长度路径LtSi的行驶时间；α3为当前车辆在路段Rj的位置比例参数，α3和路段Rj标识车辆的当前位置信息；Lj、T Rj、E Rj分别为路段Rj的路段长度、平均行驶时间和平均电量消耗，
    Figure PCTCN2019128608-appb-100004
    分别为最短长度路径LtSi所经过的完整路段的路段长度之和，平均行驶时间之和和平均电量消耗之和。     Among them, LtSi is the shortest length path from the current vehicle to the charging station Si, ΔEtSi is the power consumption of the current vehicle to complete the shortest length path LtSi, TtSi is the travel time of the current vehicle to complete the shortest length path LtSi; α3 is the current vehicle in The position ratio parameter of the road section Rj, α3 and road section Rj identify the current position information of the vehicle; Lj, T Rj and E Rj are the road section length, average travel time and average power consumption of road section Rj, respectively.
    Figure PCTCN2019128608-appb-100004
    They are the sum of the lengths of the complete sections of the shortest length path LtSi, the sum of the average travel time, and the sum of the average power consumption.
  4. 如权利要求2所述的充电调度方法，其特征在于，所述最短时间路径的计算模型包括：The charging scheduling method according to claim 2, wherein the calculation model of the shortest time path comprises:
    Figure PCTCN2019128608-appb-100005
    Figure PCTCN2019128608-appb-100005
    Figure PCTCN2019128608-appb-100006
    Figure PCTCN2019128608-appb-100006
    Figure PCTCN2019128608-appb-100007
    Figure PCTCN2019128608-appb-100007
    其中，TtSi为当前车辆到充电站Si的最短行驶时间；LtSi为当前车辆到充电站Si的最短行驶时间的路径，即最短时间路径，ΔEtSi为当前车辆行驶完成 该最短时间路径LtSi的电量消耗，α3为当前车辆在路段Rj的位置比例参数，α3和路段Rj标识车辆的当前位置信息；Lj、T Rj、E Rj分别为路段Rj的路段长度、平均行驶时间和平均电量消耗，
    Figure PCTCN2019128608-appb-100008
    分别为最短时间路径LtS i所经过的完整路段的路段长度之和，平均行驶时间之和和平均电量消耗之和。     Among them, TtSi is the shortest travel time from the current vehicle to the charging station Si; LtSi is the shortest travel time path from the current vehicle to the charging station Si, that is, the shortest time path, ΔEtSi is the power consumption of the current vehicle to complete the shortest time path LtSi, α3 is the position ratio parameter of the current vehicle on the road section Rj, α3 and road section Rj identify the current position information of the vehicle; Lj, T Rj and E Rj are the road section length, average travel time and average power consumption of road section Rj, respectively,
    Figure PCTCN2019128608-appb-100008
    Respectively, they are the sum of the lengths of the complete road sections passed by the shortest time path LtS i , the sum of the average travel time and the sum of the average power consumption.
  5. 如权利要求2所述的充电调度方法，其特征在于，所述最小电量消耗路径的计算模型包括：The charging scheduling method according to claim 2, wherein the calculation model of the minimum power consumption path comprises:
    Figure PCTCN2019128608-appb-100009
    Figure PCTCN2019128608-appb-100009
    Figure PCTCN2019128608-appb-100010
    Figure PCTCN2019128608-appb-100010
    Figure PCTCN2019128608-appb-100011
    Figure PCTCN2019128608-appb-100011
    其中，ΔEtSi为当前车辆到达充电站Si的最小电量消耗，LtSi为当前车辆根据该最小电量消耗得到的到充电站Si的最小电量消耗路径，TtSi为当前车辆行驶完成该最小电量消耗路径LtSi的行驶时间；α3为当前车辆在路段Rj的位置比例参数，α3和路段Rj标识车辆的当前位置信息；Lj、T Rj、E Rj分别为路段Rj的路段长度、平均行驶时间和平均电量消耗，
    Figure PCTCN2019128608-appb-100012
    分别为最小电量消耗路径LtSi所经过的完整路段的路段长度之和，平均行驶时间之和和平均电量消耗之和。     Among them, ΔEtSi is the minimum power consumption of the current vehicle to the charging station Si, LtSi is the minimum power consumption path of the current vehicle to the charging station Si according to the minimum power consumption, and TtSi is the driving of the current vehicle to complete the minimum power consumption path LtSi Time; α3 is the position ratio parameter of the current vehicle on the road section Rj, α3 and road section Rj identify the current position information of the vehicle; Lj, T Rj and E Rj are the road section length, average travel time and average power consumption of road section Rj, respectively,
    Figure PCTCN2019128608-appb-100012
    Respectively, they are the sum of the length of the complete road section passed by the minimum power consumption path LtSi, the sum of the average travel time and the sum of the average power consumption.
  6. 如权利要求2所述的充电调度方法，其特征在于，所述加权因子包括车型电池型号权重参数α1、α2；电池使用年限权重参数β1、β2；载客量权重参数m1、m2；空调状态权重参数k1、k2；环境权重参数e1、e2；The charging scheduling method according to claim 2, wherein the weighting factors include vehicle type battery model weight parameters α1, α2; battery life weight parameters β1, β2; passenger load weight parameters m1, m2; air conditioning state weight Parameters k1, k2; environmental weight parameters e1, e2;
    所述路段的平均电量消耗和平均行驶时间通过如下公式获得：The average power consumption and average driving time of the road section are obtained by the following formula:
    E Ri＝α1*β1*m1*e1*(Eui+k1*Epi)/Li E Ri =α1*β1*m1*e1*(Eui+k1*Epi)/Li
    T Ri＝α2*β2*m2*e2*(Tui+k2*Tpi)/Li T Ri =α2*β2*m2*e2*(Tui+k2*Tpi)/Li
    其中，E Ri和T Ri为路段Ri的平均电量消耗和平均行驶时间；Eui为路径Ri不开空调时的额定平均电量消耗，Epi为路径Ri的开空调时的额定平均附加电量消耗，Tui为路径Ri的不开空调时的额定平均行驶时间，Tpi为路径Ri的开空调时的额定平均附加行驶时间。 Among them, E Ri and T Ri are the average power consumption and average travel time of the road section Ri; Eui is the rated average power consumption when the air conditioner is not turned on on the route Ri, Epi is the rated average additional power consumption when the air conditioner is turned on on the route Ri, and Tui is The rated average travel time of the route Ri when the air conditioner is not turned on, and Tpi is the rated average additional travel time of the route Ri when the air conditioner is turned on.
  7. 如权利要求1所述的充电调度方法，其特征在于：所述充电站包括多个充电点,所述充电站的充电等待时间为充电站的各充电点的充电等待时间的最小值；The charging scheduling method according to claim 1, wherein the charging station includes a plurality of charging points, and the charging waiting time of the charging station is the minimum value of the charging waiting time of each charging point of the charging station;
    所述优选充电站的计算模型：The calculation model of the preferred charging station:
    Figure PCTCN2019128608-appb-100013
    Figure PCTCN2019128608-appb-100013
    VjCos＝min(VjTtSi+ΔVjSiCmkTwait+ΔVjSiCmkTc)VjCos=min(VjTtSi+ΔVjSiCmkTwait+ΔVjSiCmkTc)
    其中，ΔVjSiCmkTwait为预测的车辆Vj到充电站Si的充电点SiCmk的等待时间；VjTtSi为预测的车辆Vj到充电站Si的最短路径所需的行驶时间；ΔSiCmkTc为充电站Si的充电点SiCmk的剩余充电时间；VjCos为预测的车辆Vj到充电点并完成充电的最小时间消耗；ΔVjSiCmkTc为车辆Vj在充电点Si的预计充电时间；Among them, ΔVjSiCmkTwait is the predicted waiting time of the vehicle Vj to the charging point SiCmk of the charging station Si; VjTtSi is the predicted travel time required for the shortest path from the vehicle Vj to the charging station Si; ΔSiCmkTc is the remaining charge point SiCmk of the charging station Si Charging time; VjCos is the estimated minimum time consumed by the vehicle Vj to the charging point and completing the charging; ΔVjSiCmkTc is the expected charging time of the vehicle Vj at the charging point Si;
    达成VjCos条件的充电站为优选充电站。The charging station that meets the VjCos conditions is the preferred charging station.
  8. 如权利要求7所述的充电调度方法，其特征在于：所述优选充电站是在备选充电站中选择，所述备选充电站满足条件：8. The charging scheduling method according to claim 7, wherein the preferred charging station is selected among alternative charging stations, and the alternative charging station meets the condition:
    ΔEtSi≤EtΔEtSi≤Et
    其中，ΔEtSi为当前车辆到达充电站Si的电量消耗；Et为当前车辆的实时剩余电量。Among them, ΔEtSi is the power consumption of the current vehicle arriving at the charging station Si; Et is the real-time remaining power of the current vehicle.
  9. 如权利要求1所述的充电调度方法，其特征在于，所述预测剩余电量通过以下公式获得：The charging scheduling method according to claim 1, wherein the predicted remaining power is obtained by the following formula:
    ESi＝Et-ΔEtSiESi=Et-ΔEtSi
    其中，ESi为当前车辆行驶到充电站Si的预测剩余电量，Et为当前车辆的实时剩余电量。Among them, ESi is the predicted remaining power of the current vehicle driving to the charging station Si, and Et is the real-time remaining power of the current vehicle.
  10. 如权利要求1所述的充电调度方法，其特征在于：所述充电调度方法还包括充电队列，当车辆被标记为充电状态时，所述车辆进入充电队列，并依照队列的先后顺序执行充电流程。The charging scheduling method of claim 1, wherein the charging scheduling method further comprises a charging queue, when the vehicle is marked as a charging state, the vehicle enters the charging queue, and the charging process is executed according to the sequence of the queue .
  11. 一种云管理服务器，其特征在于：包括应用程序服务器、数据库服务器、Web服务器和通信服务器，A cloud management server, characterized in that it includes an application server, a database server, a Web server, and a communication server,
    所述应用程序服务器用于执行充电调度程序，所述充电调度程序实现权利要求1-10任一项所述的无人驾驶车辆群组的充电调度方法；The application server is used to execute a charging scheduling program, which implements the charging scheduling method for an unmanned vehicle group according to any one of claims 1-10;
    所述数据库服务器用于提供存取服务，存取的信息包括：无人驾驶车辆群组的运行路线信息和调度信息，所述调度信息是根据所述充电调度方法得到的；The database server is used to provide access services, and the accessed information includes: operating route information and scheduling information of the unmanned vehicle group, the scheduling information is obtained according to the charging scheduling method;
    所述通信服务器用于建立所述云管理服务器和无人驾驶车辆、充电站的通信连接。The communication server is used to establish a communication connection between the cloud management server and the unmanned vehicle and charging station.
  12. 如权利要求11所述的云管理服务器，其特征在于：还包括APP服务器，所述APP服务器用于提供智能终端APP的调用服务，用于推送调度信息。The cloud management server according to claim 11, further comprising an APP server, the APP server is used to provide smart terminal APP invocation service, and is used to push scheduling information.
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