WO2021036132A1 - Procédé de planification de charge pour groupe de véhicules sans pilote et serveur de gestion en nuage - Google Patents

Procédé de planification de charge pour groupe de véhicules sans pilote et serveur de gestion en nuage 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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English (en)
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

La présente invention concerne un procédé de planification de charge pour un groupe de véhicules sans pilote (31), et un serveur de gestion en nuage (10). Ledit procédé comprend : la réalisation d'un processus de préparation de données, pour établir des informations de carte d'opération et des informations de véhicule d'un groupe de véhicules sans pilote (31) ; la réalisation d'un processus de surveillance en temps réel sur un véhicule marqué comme en état de fonctionnement, pour acquérir des informations d'état actuel du véhicule, et déterminer un temps de remplacement de la batterie ; et la réalisation d'un processus d'agencement de charge sur un véhicule marqué comme en état de charge, pour déterminer une station de charge préférée (32), et guider le véhicule en l'amenant à se déplacer par le trajet le plus court vers la station de charge préférée (32) en vue de la charge. La présente invention analyse la consommation d'énergie moyenne et le temps de déplacement de chaque section de route à l'intérieur d'un parc au moyen d'une technologie de mégadonnées, surveille, en temps réel, l'état du véhicule sans pilote (31), et effectue un plan de charge à l'avance, garantissant que le véhicule sans pilote (31) peut atteindre la station de charge (32) en toute sécurité, ce qui permet de réduire le temps d'attente de charge et d'améliorer l'efficacité de charge.
PCT/CN2019/128608 2019-08-29 2019-12-26 Procédé de planification de charge pour groupe de véhicules sans pilote et serveur de gestion en nuage WO2021036132A1 (fr)

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Application Number Priority Date Filing Date Title
CN201910805055.2A CN110533901B (zh) 2019-08-29 2019-08-29 一种无人驾驶车辆群组的充电调度方法和云管理服务器
CN201910805055.2 2019-08-29

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WO2021036132A1 true WO2021036132A1 (fr) 2021-03-04

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