WO2017028333A1 - 高速公路电动汽车快速充电站的规划方法 - Google Patents

高速公路电动汽车快速充电站的规划方法 Download PDF

Info

Publication number
WO2017028333A1
WO2017028333A1 PCT/CN2015/088388 CN2015088388W WO2017028333A1 WO 2017028333 A1 WO2017028333 A1 WO 2017028333A1 CN 2015088388 W CN2015088388 W CN 2015088388W WO 2017028333 A1 WO2017028333 A1 WO 2017028333A1
Authority
WO
WIPO (PCT)
Prior art keywords
electric vehicle
charging station
fast charging
charged
charging
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2015/088388
Other languages
English (en)
French (fr)
Inventor
穆云飞
董晓红
贾宏杰
余晓丹
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tianjin University
Original Assignee
Tianjin University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tianjin University filed Critical Tianjin University
Priority to US15/747,549 priority Critical patent/US10360519B2/en
Publication of WO2017028333A1 publication Critical patent/WO2017028333A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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"
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/10—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 characterised by the energy transfer between the charging station and the vehicle
    • B60L53/11—DC charging controlled by the charging station, e.g. mode 4
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/10—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 characterised by the energy transfer between the charging station and the vehicle
    • B60L53/14—Conductive energy transfer
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/66—Data transfer between charging stations and vehicles
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/67—Controlling two or more charging stations
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/70—Interactions with external data bases, e.g. traffic centres
    • B60L2240/72—Charging station selection relying on external data
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/80—Time limits
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/72—Electric energy management in electromobility
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/12—Electric charging stations
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/14—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

Definitions

  • the invention belongs to the technical field of electric vehicle rapid charging station planning, and relates to a transportation network and an electric vehicle related characteristic, a user travel behavior and the like to select a fixed volume planning method.
  • the charging station can provide charging services for more electric vehicle users, and the total cost of charging station infrastructure and the waiting cost of users With the lowest cost as the goal, it provides a planning method for the rapid charging station of expressway electric vehicles, and solves the problem of site selection and constant volume of electric vehicle fast charging stations on expressways.
  • the present invention provides a method for planning a rapid charging station for a highway electric vehicle, including predicting the space-time distribution of the charging load of the electric vehicle, determining the address of the fast charging station, and determining the number of charging devices in the fast charging station. ; Specific steps are as follows:
  • Step 1 Prediction of the space-time distribution of the charging load of the electric vehicle, that is, determining the location and time for each electric vehicle in the expressway to be charged;
  • Step 2 According to the prediction result of the space-time distribution of the charging load of the electric vehicle, the shared nearest neighbor clustering algorithm is used to determine the address of the fast charging station;
  • Step 1 Count the longest distance Ran sc that all electric vehicles need to be able to travel after charging. Using the central limit theorem, the distance from which the 99% electric vehicle can travel in the remaining power is obtained from the fitted normal distribution. Defined as the service radius SR of all fast charging stations;
  • Step 2 Determine a distance matrix D, where d ij represents the distance between the position P i to be charged and the position P j to be charged, i, j belongs to 1 to n, and n is the total number of electric vehicles that need to be charged;
  • Step 3 Determine a similarity matrix S; determine, according to the distance matrix D, the number of locations in the service radius SR where each location to be charged is required to be charged; the location P i to be charged and the location to be charged P j Similarity s ij :
  • NN(i) and NN(j) are respectively a set of locations within the service radius SR where the locations P i and P j to be charged are to be charged; size is the set NN(i) and NN ( j) the number of elements in the intersection;
  • Step 4 The similarity matrix S is summed by rows to obtain the charging demand l 1,j of each position that needs to be charged.
  • Step 5 Determine the address of the fast charging station, sort the charging demand from large to small to form a charging demand sequence, and use the position requiring charging corresponding to the maximum charging demand as the first clustering center, that is, the candidate fast charging station 1;
  • the candidate fast charging station service radius SR is a set of locations C(1) to be charged, and the address of the candidate fast charging station and the set of locations C(1) requiring charging in the candidate fast charging station service radius SR are stored to the fast.
  • the location set C(2) of the candidate fast charging station service radius SR that needs to be charged is obtained at the same time, and the address of the candidate fast charging station and the candidate fast charging station service radius SR need to be charged.
  • the set of locations C(2) is stored in the fast charging station set CS; and so on, the addresses of all candidate fast charging stations are determined;
  • the address of the candidate fast charging station in the candidate fast charging station service radius SR that needs to be charged is less than the preset threshold d and the corresponding service radius SR needs to be charged.
  • the set of locations is deleted from the fast charging station set CS, and the remaining candidate addresses in the fast charging station set CS are the determined fast charging station addresses;
  • Step 3 According to the prediction result of the space-time distribution of the charging load of the electric vehicle and the determined address of the fast charging station, the queuing theory is used to determine the number c of the charging devices in the fast charging station.
  • step 1 specifically includes the following steps:
  • Step 1-1 Obtain the following basic data:
  • Electric vehicle data including the electric vehicle type, the probability density function of the battery capacity of each type of electric vehicle, the upper and lower boundaries of the battery capacity of each type of electric vehicle, the mathematical relationship between the battery capacity of each type of electric vehicle and its cruising range;
  • Traffic data including the probability distribution matrix of traffic start and end points P m ⁇ m and the probability of the car entering the expressway time t s ;
  • Traffic Traffic survey data start points of starting and ending point matrix A m ⁇ m, using the start and end points of the high speed traffic entrance matrix A m ⁇ m (1) to give the start points of traffic probability P m ⁇ m matrix according to the formula;
  • m is the number of highway entrances and exits
  • a ij is the average number of cars from high-speed entrances and exits i to high-speed entrances and exits j
  • p ij is the probability that a car will go from high-speed entrances and exits i to high-speed entrances and exits j every day
  • B i The average number of cars entering the highway from the high-speed entrance and exit i every day
  • Basic information of expressway including the number of entrances and exits, the number of entrances and exits, the coordinates of entrances and exits, and the distance between entrances and exits;
  • Step 1-2 determine the following data:
  • the electric cars entering the expressway are numbered according to the order of entrance and exit;
  • Step 1-3-1 determining the parameters of the i-th electric vehicle, including: electric vehicle type, maximum capacity Cap, initial battery state SOC i , battery state SOC c when charging is required, and the i-th electric vehicle can be driven before charging From Ran ac , the longest distance Ran sc that an electric car needs to be charged after charging; the steps are as follows:
  • the probability density function of the battery capacity of such types of electric vehicles and electric vehicles determining the type of vehicle to determine the maximum capacity of the i-th vehicle electric power Cap Monte Carlo method; and set the initial state of the battery into the freeway and the SOC i Battery state SOC c when charging is required;
  • the Monte Carlo method is used to determine the maximum endurance of the i-th electric vehicle.
  • the mileage Ran mc is further determined according to the initial battery state SOC i , the battery state SOC c when charging, and the maximum cruising range Ran mc , using the formula (3) to determine the distance Ran ac that the i-th electric vehicle can travel before charging; 4) Determine the farthest distance Ran sc that the i-th electric car needs to be charged after charging;
  • ⁇ is the energy efficiency coefficient of the battery
  • Step 1-3-2 Determine the parameters of the traffic behavior of the i-th electric vehicle, including: departure point, destination, driving route, entering high-speed time Et s , travel distance D od ; determining the starting point according to the number of the i-th electric vehicle According to the starting point of the i-th electric vehicle and the traffic start and end point probability matrix P m ⁇ m , the destination is determined by the Monte Carlo method; the travel path is determined according to the shortest path algorithm; the travel distance D od is obtained according to the basic information of the expressway; Enter the probability distribution of the highway time t s , use the Monte Carlo method to determine the electric car entering the high speed time Et s ; and set the travel speed v;
  • Step 1-3-3 Determine whether the travel distance D od of the i-th electric vehicle is greater than the distance Ran ac that the i-th electric vehicle can travel before charging.
  • step two If i ⁇ EB t , return to step 1-3-1, and vice versa, perform step two;
  • Step 1-3-4 Determine the time and position of the i-th electric car to be charged, according to the starting point, driving route, travel speed v of the i-th electric car, and the distance that the i-th electric car can travel before charging.
  • Step 3 specifically includes the following steps:
  • Step 3-1 using the time required for each electric vehicle in the expressway determined in step one to be charged and the address of the fast charging station determined in step two, determining the peak hour of each electric vehicle reaching each fast charging station, and The peak value is set to the number ⁇ of the electric vehicle reaching the fast charging station per unit time;
  • Step 3-2 Determine the number of chargers in the fast charging station c:
  • ⁇ 2 represents the number of electric vehicles served by the charger per unit time
  • c represents the number of chargers in the quick charging station
  • Equation (12) is optimized for the constraint condition, and the number c of the chargers can be obtained.
  • C s is the cost per unit hour converted to a single charger, in units of yuan, calculated by the following equation (13);
  • C w is the cost per user unit time. Unit: yuan / vehicle;
  • t w is the maximum waiting time, the unit: minutes;
  • V 0 is the cost of the charger over the life cycle
  • i r is the interest rate
  • p is the life of the charger.
  • the gas stations of traditional cars and fast charging stations are not completely consistent.
  • the location and time of the electric vehicle to be charged are considered to carry out the site selection and constant volume, and the position of the conventional gas station can greatly satisfy the charging requirement of the electric vehicle, thereby promoting the development of the electric vehicle.
  • FIG. 1 is a flow chart of a planning method of the present invention
  • Figure 2 shows the battery capacity and cruising range of the M1 model in the simulation by polynomial fitting of the original data
  • Figure 3 is a probability distribution of the car entering the highway time t s ;
  • FIG. 4 is a flow chart of predicting a spatiotemporal distribution of an electric vehicle charging load in the present invention
  • 5 is a schematic diagram of a shared-type nearest neighbor clustering algorithm
  • Figure 6 is a diagram showing the furthest distance Ran sc distribution of an electric vehicle that can be driven after being charged in the present invention
  • Figure 7 is a fast charging station address determined in the first embodiment of the present invention.
  • Figure 8 is a quick charging station address determined in Scheme 2 of the present invention.
  • the method for planning a rapid charging station for a highway electric vehicle is as shown in FIG. 1 , which mainly includes time and space distribution prediction of charging load of an electric vehicle, determining an address of a fast charging station, and determining charging in a fast charging station.
  • the number of machines; the specific steps are as follows:
  • Step 1 Prediction of the space-time distribution of the charging load of the electric vehicle (EV), that is, determining the location and time for each EV in the expressway to be charged;
  • Step 1-1 Obtain the following basic data:
  • Electric vehicle data including the EV type, the probability density function of the battery capacity of each type of EV type, the upper and lower boundaries of the battery capacity of each type of EV type, the mathematical relationship between the battery capacity of each type of EV type and its cruising range; used in simulation examples
  • the EVs are divided into four categories: four-wheeled trucks (L7e) with a load between 400kg and 550kg, four-wheeled buses (M1) with an 8-seater, and trucks with a maximum load of 3,500kg (N1).
  • a truck (N2) with a load of 3500-12000kg.
  • the function of each type of EV battery capacity and cruising range is obtained from the database.
  • the battery capacity and cruising range of the M1 model are as shown in Figure 2 by polynomial fitting of the original data.
  • Traffic data including the probability distribution matrix of traffic start and end points P m ⁇ m and the probability of the car entering the expressway time t s ;
  • Traffic Traffic survey data start points of starting and ending point matrix A m ⁇ m, using the start and end points of the high speed traffic entrance A m ⁇ m matrix obtained according to the equation P m ⁇ m (1);
  • m is the number of highway entrances and exits
  • a ij is the average number of cars from high-speed entrances and exits i to high-speed entrances and exits j
  • p ij is the probability that a car will go from high-speed entrances and exits i to high-speed entrances and exits j every day
  • B i The average number of cars entering the highway from the high-speed entrance and exit i every day
  • Basic information of expressway including the number of entrances and exits, the number of entrances and exits, the coordinates of entrances and exits, and the distance between entrances and exits; the simulation example uses the round-the-island expressway, and the expressway information is shown in Table 1 below.
  • Step 1-2 determine the following data:
  • the proportions of the models we set are: 10%, 84%, 3%, 3%; the average number of EVs entering the highway every day is EB t is 17,297.
  • the average number EB i of EVs entering the highway from the high-speed entrance i every day can be obtained by EB t , B t and A m ⁇ m .
  • the EVs entering the expressway are numbered according to the order of entry and exit;
  • Step 1-3-1 determining the parameters of the i-th EV (as shown in Figure 1, considering the battery characteristics), including: electric vehicle type, maximum capacity Cap, initial battery state SOC i , battery state SOC when charging is required c .
  • the distance that the i-th EV can travel before charging is Ran ac .
  • the Monte Carlo method is used to determine the Ran mc of the i-th EV; and then according to the SOC i and the SOC c , Ran mc , use the formula (3) to determine the Ran ac of the i-th EV; use the formula (4) to determine the Ran sc of the i-th EV;
  • ⁇ is the energy efficiency coefficient of the battery, and the value of ⁇ in the example is 1;
  • Step 1-3-2 Determine the parameters of the i-th EV traffic behavior (as shown in Figure 1, considering the traffic behavior), including: destination, driving route, entering high-speed time Et s , travel distance D od ;
  • the destination is determined by the Monte Carlo method;
  • the driving path is determined according to the shortest path algorithm;
  • the travel distance D od is obtained according to the basic information of the expressway;
  • the probability distribution of the road time t s is determined by the Monte Carlo method to determine the EV entering the high speed time Et s ; and the travel speed v is set, and the value of v in the example is 90 km/h;
  • Step 1-3-3 Determine whether the travel distance D od of the i-th EV is greater than the distance Ran ac that the i-th EV can travel before charging.
  • step two If i ⁇ EB t , return to step 1-3-1, and vice versa, perform step two;
  • Step 1-3-4 Determine the time and location at which the i-th EV needs to be charged, and determine the i-th EV based on the starting point of the i-th EV, the driving route, v, the Ran ac of the i-th EV, and the Et s of the EV.
  • the position P i to be charged and the time t c , i i+1; if i ⁇ EB t , return to step 1-3-1, otherwise, perform step two.
  • Step 2 According to the prediction result of the space-time distribution of the EV charging load, the shared nearest neighbor clustering algorithm is used to determine the address of the fast charging station, as shown in FIG. 5;
  • Step 2-1 Count the Ran sc of all EVs, and use the central limit theorem to obtain the distance that 99% of the EV can travel in the remaining power from the fitted normal distribution. Define the distance as the service of all fast charging stations. Radius SR;
  • Step 2-2 Determine the distance matrix D, where d ij represents the distance between the position P i to be charged and the position P j to be charged, i, j belongs to 1 to n, and n is the total number of EVs to be charged;
  • Step 2-3 Determine the similarity matrix S, and determine, according to the distance matrix D, the number of locations in the service radius SR where each location to be charged is required to be charged; the similarity s ij of P i and P j :
  • NN(i) and NN(j) are respectively a set of locations in the service radius SR where P i and P j need to be charged; size is the intersection of the set NN(i) and NN(j) The number of elements;
  • Step 2-4 summing the similarity matrix S by rows to obtain the charging demand l 1,j of each position that needs to be charged.
  • Step 2-5 determining the address of the fast charging station, sorting the charging demand from large to small to form a charging demand sequence, and the charging demanding position corresponding to the charging demand as the first clustering center, that is, the candidate fast charging station 1; At the same time, the location set C(1) of the candidate fast charging station service radius SR that needs to be charged is obtained, and the address of the candidate fast charging station and the location set C(1) of the candidate fast charging station service radius SR that needs to be charged are stored. To the fast charging station set CS;
  • the location set C(2) of the candidate fast charging station service radius SR that needs to be charged is obtained at the same time, and the address of the candidate fast charging station and the candidate fast charging station service radius SR need to be charged.
  • the set of locations C(2) is stored in the fast charging station set CS; and so on, the addresses of all candidate fast charging stations are determined;
  • the address of the candidate fast charging station in the candidate fast charging station service radius SR that needs to be charged is less than the threshold and the corresponding service radius SR
  • the set of locations that need to be charged is deleted from the fast charging station set CS, and the remaining candidate addresses in the fast charging station set CS are the determined fast charging station addresses.
  • Step 3 According to the space-time distribution prediction result of the EV charging load and the determined fast charging station address, the queuing theory is used to determine the number of charging machines in the fast charging station.
  • Step 3-1 Using step 1 to determine the time required for each EV in the highway to be charged and the address of the fast charging station determined in step 2, determine the peak value of each hour of EV reaching each fast charging station, and set the peak value to EV. The number of ⁇ reaching the fast charging station per unit time;
  • Step 3-2 Determine the number of chargers in the fast charging station c:
  • ⁇ 2 represents the number of electric vehicles served by the charger per unit time.
  • ⁇ 2 is taken as 6;
  • c is the number of chargers, and
  • equation (11) is the objective function.
  • C s is the cost of a single charger converted to each hour, which is calculated by equation (13);
  • C w is the cost per user unit time, C w in the simulation example Take 17 yuan / vehicle, t w is the maximum waiting time, t w takes 20 minutes in the simulation example.
  • V 0 is the cost of the charger in the whole life cycle. In the simulation example, V 0 is 240,000 yuan; i r is the interest rate, i r is 0.1 in the simulation example; p is the life of the charger In the simulation example, p is 10.
  • the invention is applied to the traffic network of the roundabout expressway to prove the feasibility and effectiveness of the planning method.
  • Solution 1 The address of the fast charging station is obtained by using the shared nearest neighbor clustering algorithm as shown in Fig. 7.
  • Option 2 The information of the existing service area on the high speed is shown in Table 3. If priority is given to building a fast charging station in an existing service area, then the address of the remaining fast charging station is determined using the present invention. The planned fast charging station address distribution is shown in Figure 8.
  • the hourly EV of the scheme 1 and the scheme 2 is determined to reach the peak value of each fast charging station, that is, ⁇ , as shown in Tables 4 and 5.
  • Table 4 The number of EV unit time arrivals in each fast charging station in Option 1
  • Table 8 compares the planning results of Scheme 1 and Scheme 2 from the number of charging stations, the number of charging machines, the charging failure rate and the total cost, which indicates that the planning method satisfies the electric power compared with the maximum interception traffic volume in the service area. There is a comparative advantage in car demand.

Landscapes

  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Transportation (AREA)
  • Power Engineering (AREA)
  • Business, Economics & Management (AREA)
  • Economics (AREA)
  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
  • Quality & Reliability (AREA)
  • Theoretical Computer Science (AREA)
  • Marketing (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Operations Research (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Game Theory and Decision Science (AREA)
  • Development Economics (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)
  • Navigation (AREA)

Abstract

一种高速公路电动汽车快速充电站的规划方法，该规划方法包括：步骤一、电动汽车充电负荷的时空分布预测，即确定高速公路中每辆电动汽车需要充电的位置和时间；步骤二、根据电动汽车充电负荷的时空分布预测结果，利用共享型最近邻居聚类算法确定快速充电站的地址；步骤三、根据电动汽车充电负荷的时空分布预测结果和确定的快速充电站地址，利用排队论理论确定快速充电站中充电机的个数c。由于电池特性，传统汽车的加油站和快速充电站并不是完全吻合的。本规划方法中考虑电动汽车需要充电的位置和时间来进行选址定容，比传统加油站的位置能够较大的满足电动汽车的充电需要，从而促进电动汽车的发展。

Description

高速公路电动汽车快速充电站的规划方法 技术领域
本发明属于电动汽车快速充电站规划技术领域，涉及交通网络和电动汽车相关特性，用户出行行为等信息来选址定容的规划方法。
背景技术
目前为止，充电站规划方面的研究还比较初步，尚没有形成完整而系统的充电站规划模型与方法。现有的有关选址定容的文献，多数根据电动汽车续航里程和交通网络粗略的估算充电站个数，或者根据交通网络流量进行的选址定容。但是由于电动汽车相关特性，电动汽车快速充电站和加油站也许并不是完全吻合的。
发明内容
针对现有技术中存在的不足，考虑电动汽车相关特性和交通行为的不确定性，以充电站能够为更多的电动汽车用户提供充电服务，且充电站基础设施成本和用户的等待成本的总成本最小为目标，提供了一种高速公路电动汽车快速充电站的规划方法，解决高速公路上电动汽车快速充电站的选址和定容的问题。
为了解决上述技术问题，本发明提出的一种高速公路电动汽车快速充电站的规划方法，包括电动汽车充电负荷的时空分布预测、确定快速充电站的地址和确定快速充电站中充电机的个数；具体步骤如下：
步骤一、电动汽车充电负荷的时空分布预测，即确定高速公路中每辆电动汽车需要充电的位置和时间；
步骤二、根据电动汽车充电负荷的时空分布预测结果，利用共享型最近邻居聚类算法确定快速充电站的地址；包括：
步骤1、统计所有电动汽车需要充电后能行驶的最远距离Ransc，利用中心极限定理，由拟合的正态分布得到99％的电动汽车在余下电量时都可以行驶的距离，将该距离定义为所有快速充电站的服务半径SR；
步骤2、确定距离矩阵D，其中，dij代表需要充电的位置Pi到需要充电的位置Pj之间的距离，i，j属于1～n，n为需要充电的电动车总数；
步骤3、确定相似度矩阵S；根据距离矩阵D，确定每个需要充电的位置所在服务半径SR内包含的需要充电的位置的个数；需要充电的位置Pi和需要充电的位置Pj的相似度sij：
sij＝size(NN(i)∩NN(j))             (5)
式(5)中，NN(i)和NN(j)分别是需要充电的位置Pi和Pj所在服务半径SR内的需要充电的位置的集合；size是求集合NN(i)和NN(j)交集中元素的个数；
步骤4：将相似度矩阵S按行求和得到每个需要充电的位置的充电需求l1,j，
Figure PCTCN2015088388-appb-000001
步骤5：确定快速充电站的地址，将充电需求从大到小排序形成充电需求数列，将充电需求最大对应的需要充电的位置作为第一个聚类中心，即候选快速充电站1；同时得到该候选快速充电站服务半径SR内的需要充电的位置集合C(1)，将候选快速充电站的地址和该候选快速充电站服务半径SR内的需要充电的位置集合C(1)储存到快速充电站集合CS中；
依次在充电需求数列中选择下一个充电需求对应的需要充电的位置，如果该需要充电的位置与快速充电站集合CS中候选快速充电站之间的相似度都为0时，则该需要充电的位置作 为候选快速充电站2，同时得到该候选快速充电站服务半径SR内的需要充电的位置集合C(2)，将候选快速充电站的地址和该候选快速充电站服务半径SR内的需要充电的位置集合C(2)储存到快速充电站集合CS中；以此类推，确定所有候选快速充电站的地址；
根据预设阀值d，将候选快速充电站服务半径SR内的需要充电的位置集合中的个数小于预设阀值d的候选快速充电站的地址和相应的服务半径SR内的需要充电的位置集合从快速充电站集合CS中删除，快速充电站集合CS中余下的候选地址即为确定的快速充电站地址；
步骤三、根据电动汽车充电负荷的时空分布预测结果和确定的快速充电站地址，利用排队论理论确定快速充电站中充电机的个数c。
进一步讲，上述步骤一中具体包括以下步骤：
步骤1-1、获取如下基础数据：
电动汽车数据：包括电动汽车类型、每类电动汽车类型的电池容量的概率密度函数、每类电动汽车类型的电池容量的上下边界、每类电动汽车类型的电池容量与其续航里程的数学关系；
交通数据：包括交通起止点概率矩阵Pm×m和汽车进入高速公路时间ts的概率分布；
通过交通起止点调查数据得到高速出入口交通起止点矩阵Am×m，利用该高速出入口交通起止点矩阵Am×m根据公式(1)得到交通起止点概率矩阵Pm×m；
pij＝aij/Bi(1≤i≤m,1≤j≤m)             (1)
公式(1)中，m是高速公路出入口个数，aij是每天从高速出入口i到高速出入口j汽车的平均数量，pij是指某一汽车每天从高速出入口i到高速出入口j的概率；Bi每天从高速出入口i进入高速公路的汽车的平均数量，
Figure PCTCN2015088388-appb-000002
高速公路基本信息：包括出入口个数，出入口编号，出入口坐标，出入口之间的距离；
步骤1-2、确定如下数据：
确定不同电动汽车类型的比例、每天进入高速公路的电动汽车的平均数量EBt、每天从高速出入口i进入高速公路的电动汽车的平均数量EBi；
根据每天进入高速公路的电动汽车的平均数量EBt和每天从高速出入口i进入高速公路的电动汽车的平均数量EBi，按照出入口顺序对进入高速公路的电动汽车进行编号；
步骤1-3、确定进入高速公路的所有电动汽车充电负荷的时空分布，设：i＝1；
步骤1-3-1、确定第i辆电动汽车的参数，包括：电动汽车类型、最大容量Cap、初始电池状态SOCi、需要充电时电池状态SOCc、第i辆电动汽车充电前可以行驶的距离Ranac，电动汽车需要充电后可以行驶的最远距离Ransc；步骤如下：
利用蒙特卡洛方法确定第i辆电动汽车的电动汽车类型；
根据确定的电动汽车类型和该类电动汽车类型的电池容量的概率密度函数，利用蒙特卡洛方法确定第i辆电动电汽车的最大容量Cap；并设置进入高速公路时的初始电池状态SOCi以及需要充电时电池状态SOCc；
根据上述确定的第i辆电动电汽车的电动汽车类型、最大容量Cap和对应的该类电动汽车类型的电池容量与其续航里程的数学关系，利用蒙特卡洛方法确定第i辆电动汽车的最大续航里程Ranmc；再根据初始电池状态SOCi、需要充电时电池状态SOCc、最大续航里程Ranmc，利用公式(3)确定第i辆电动汽车需要充电前可以行驶的距离Ranac；利用公式(4)确定第i辆电动汽车需要充电后行驶的最远距离Ransc；
Ranac＝η×(SOCi-SOCc)×Ranmc         (3)
Ransc＝η×SOCc×Ranmc          (4)
式(3)和式(4)中：η是电池的能量效率系数；
步骤1-3-2：确定第i辆电动汽车交通行为的参数，包括：出发点，目的地，行驶路径，进入高速时间Ets，出行距离Dod；根据第i辆电动汽车的编号确定其出发点，根据第i辆电动汽车的出发点和交通起止点概率矩阵Pm×m，利用蒙特卡洛方法确定目的地；根据最短路径算法确定行驶路径；根据高速公路基本信息得到出行距离Dod；根据汽车进入高速公路时间ts的概率分布，利用蒙特卡洛方法确定电动汽车进入高速时间Ets；并设置出行速度v；
步骤1-3-3：判断第i辆电动汽车的出行距离Dod是否大于第i辆电动汽车需要充电前可以行驶的距离Ranac，
如果Dod＞Ranac，则第i辆电动汽车需要充电，执行步骤3-4；反之，第i辆电动汽车在此次出行中不需要充电，i＝i+1；
若i＜EBt，则返回步骤1-3-1，反之，执行步骤二；
步骤1-3-4：确定第i辆电动汽车需要充电的时间和位置，根据第i辆电动汽车的出发点、行驶路径、出行速度v、第i辆电动汽车需要充电前可以行驶的距离Ranac和电动汽车进入高速时间Ets，确定第i辆电动汽车需要充电的位置Pi和时间tc，i＝i+1；若i＜EBt，则返回步骤1-3-1，反之，执行步骤二。
步骤三具体包括以下步骤：
步骤3-1、利用步骤一所确定的高速公路中每辆电动汽车需要充电的时间和步骤二所确定的快速充电站的地址，确定每小时电动汽车到达每个快速充电站的峰值，并将该峰值设为电动汽车单位时间内到达快速充电站的个数λ；
步骤3-2：确定快速充电站中充电机的个数c：
计算平均等待队长Ls和等待时间Wq：
Figure PCTCN2015088388-appb-000003
Figure PCTCN2015088388-appb-000004
ρ＝λ/(c×μ2)               (9)
Figure PCTCN2015088388-appb-000005
式(7)至式(10)中，μ2表示充电机单位时间内服务电动汽车的个数；c表示快速充电站中充电机的个数，以下述式(11)为目标函数，以下述式(12)为约束条件进行优化求解，即可得到充电机个数c，
min z＝Csc+CwLs         (11)
s.t. Wq＜tw        (12)
式(11)和式(12)中，Cs是单个充电机折算到每个小时的成本，单位：元，由下述式(13)计算可得；Cw是用户出行单位时间的成本，单位：元/辆；tw是最大等待时间，单位：分钟；
Figure PCTCN2015088388-appb-000006
式(13)中V0是全寿命周期内的充电机成本；ir是利率；p是充电机的寿命。
与现有技术相比，本发明的有益效果是：
由于电池特性，传统汽车的加油站和快速充电站并不是完全吻合的。本发明规划方法中考虑电动汽车需要充电的位置和时间来进行选址定容，比传统加油站的位置能够较大的满足电动汽车的充电需要，从而促进电动汽车的发展。
附图说明
图1是本发明规划方法的流程框图；
图2是仿真中M1车型的电池容量与续航里程通过对原始数据进行多项式拟合；
图3是汽车进入高速公路时间ts的概率分布；
图4是本发明中电动汽车充电负荷的时空分布预测流程图；
图5是共享型最近邻居聚类算法的示意图；
图6是本发明中电动汽车需要充电后可以行驶的最远距离Ransc分布图；
图7是本发明中方案1中确定的快速充电站地址；
图8是本发明中方案2中确定的快速充电站地址。
具体实施方式
下面结合具体实施方式对本发明作进一步说明如下：
本发明所提供的一种高速公路电动汽车快速充电站的规划方法，其流程如图1所示，主要包括电动汽车充电负荷的时空分布预测、确定快速充电站的地址和确定快速充电站中充电机的个数；具体步骤如下：
步骤一、电动汽车(EV)充电负荷的时空分布预测，即确定高速公路中每辆EV需要充电的位置和时间；
步骤1-1、获取如下基础数据：
电动汽车数据：包括EV类型、每类EV类型的电池容量的概率密度函数、每类EV类型的电池容量的上下边界、每类EV类型的电池容量与其续航里程的数学关系；仿真算例中使用的是根据欧盟电动汽车数据库统计分析将EV分为四类：载重在400kg到550kg之间的四轮货车(L7e)，8座位的四轮客车(M1)，载重最大为3500kg货车(N1)，载重在3500-12000kg的货车(N2)。并由该数据库统计得到每类EV电池容量，续航里程等函数关系。其中M1车型的电池容量与续航里程通过对原始数据进行多项式拟合如图2所示。
交通数据：包括交通起止点概率矩阵Pm×m和汽车进入高速公路时间ts的概率分布；
通过交通起止点调查数据得到高速出入口交通起止点矩阵Am×m，利用该高速出入口交通起止点矩阵Am×m根据公式(1)得到Pm×m；
pij＝aij/Bi(1≤i≤m,1≤j≤m)             (1)
公式(1)中，m是高速公路出入口个数，aij是每天从高速出入口i到高速出入口j汽车的平均数量，pij是指某一汽车每天从高速出入口i到高速出入口j的概率；Bi每天从高速出入口i进入高速公路的汽车的平均数量，
Figure PCTCN2015088388-appb-000007
仿真中ts的概率分布如图3所示。
高速公路基本信息：包括出入口个数，出入口编号，出入口坐标，出入口之间的距离；仿真算例中使用的是环岛高速公路，其高速公路信息如下表1所示。
表1环岛高速公路出入口信息
编号 距1出口的 坐标 编号 距1出口的 坐标
  距离(km) (x,y)   距离(km) (x,y)
1 0 (213,184) 25 269 (137,26)
2 9 (214,177) 26 279 (119,26)
3 18 (214,169) 27 287 (111,27)
4 28 (214,160) 28 302 (105,27)
5 41 (217,149) 29 317 (93,33)
6 56 (222,138) 30 328 (83,41)
7 78 (215,112) 31 339 (67,52)
8 84 (215,109) 32 351 (63,61)
9 94 (225,106) 33 365 (62,72)
10 105 (226,98) 34 384 (60,89)
11 115 (224,89) 35 402 (63,103)
12 132 (217,76) 36 427 (79,116)
13 137 (214,73) 37 439 (87,124)
14 148 (208,66) 38 449 (91,131)
15 155 (205,61) 39 460 (95,141)
16 162 (200,56) 40 492 (110,156)
17 169 (198,53) 41 508 (129,164)
18 186 (194,51) 42 523 (140,169)
19 195 (184,47) 43 538 (151,175)
20 208 (168,38) 44 551 (161,178)
21 215 (161,36) 45 563 (173,179)
22 221 (156,33) 46 577 (180,180)
23 229 (146,27) 47 587 (184,181)
24 237 (213,184) 总共 612 (193,184)
步骤1-2、确定如下数据：
确定不同EV类型的比例、每天进入高速公路的EV的平均数量EBt、每天从高速出入口i进入高速公路的EV的平均数量EBi；
仿真算例中我们设置车型(L7e，M1，N1，N2)的比例分别是：10％，84％，3％，3％；每天进入高速公路的EV的平均数量EBt是17297辆。每天从高速出入口i进入高速公路的EV的平均数量EBi可由EBt、Bt和Am×m得到。
根据EBt和EBi，按照出入口顺序对进入高速公路的EV进行编号；
步骤1-3、确定进入高速公路的所有电动汽车充电负荷的时空分布，设：i＝1；如图4所示；
步骤1-3-1、确定第i辆EV的参数(如图1所示，考虑到的电池特性)，包括：电动汽车类型、最大容量Cap、初始电池状态SOCi、需要充电时电池状态SOCc、第i辆EV充电前可以行驶的距离Ranac，EV需要充电后可以行驶的最远距离Ransc；步骤如下：
根据第i辆EV的编号确定其出发点，利用蒙特卡洛方法确定第i辆EV的EV类型；
根据确定的EV类型和该类EV类型的电池容量的概率密度函数，利用蒙特卡洛方法确定第i辆EV的Cap；并设置进入高速公路时的SOCi以及SOCc，仿真算例中，SOCi取0.8，SOCc取0.15～0.3；
根据上述确定的第i辆EV的EV类型、Cap和对应的该类EV类型的电池容量与其续航里程的数学关系，利用蒙特卡洛方法确定第i辆EV的Ranmc；再根据SOCi、SOCc、Ranmc，利用公式(3)确定第i辆EV的Ranac；利用公式(4)确定第i辆EV的Ransc；
Ranac＝η×(SOCi-SOCc)×Ranmc         (3)
Ransc＝η×SOCc×Ranmc         (4)
式(3)和式(4)中：η是电池的能量效率系数，算例中η的取值为1；
步骤1-3-2：确定第i辆EV交通行为的参数(如图1所示，考虑到的交通行为)，包括：目的地，行驶路径，进入高速时间时间Ets，出行距离Dod；根据第i辆EV的出发点和交通起止点概率矩阵Pm×m，利用蒙特卡洛方法确定目的地；根据最短路径算法确定行驶路径；根据高速公路基本信息得到出行距离Dod；根据汽车进入高速公路时间ts的概率分布，利用蒙特卡洛方法确定EV进入高速时间Ets；并设置出行速度v，算例中v的取值为90km/h；
步骤1-3-3：判断第i辆EV的出行距离Dod是否大于第i辆EV需要充电前可以行驶的距离Ranac，
如果Dod＞Ranac，则第i辆EV需要充电，执行步骤3-4；反之，第i辆EV在此次出行中不需要充电，i＝i+1；
若i＜EBt，则返回步骤1-3-1，反之，执行步骤二；
步骤1-3-4：确定第i辆EV需要充电的时间和位置，根据第i辆EV的出发点、行驶路径、v、第i辆EV的Ranac和EV的Ets确定，第i辆EV需要充电的位置Pi和时间tc，i＝i+1；若i＜EBt，则返回步骤1-3-1，反之，执行步骤二。
步骤二、根据EV充电负荷的时空分布预测结果，利用共享型最近邻居聚类算法确定快速充电站的地址，如图5所示；包括：
步骤2-1、统计所有EV的Ransc，利用中心极限定理，由拟合的正态分布得到99％的EV在余下电量时都可以行驶的距离，将该距离定义为所有快速充电站的服务半径SR；
步骤2-2、确定距离矩阵D，其中，dij代表需要充电的位置Pi到需要充电的位置Pj之间的距离，i，j属于1～n，n为需要充电的EV总数；
步骤2-3、确定相似度矩阵S，根据距离矩阵D，确定每个需要充电的位置所在服务半径SR内包含的需要充电的位置的个数；Pi和Pj的相似度sij：
sij＝size(NN(i)∩NN(j))           (5)
式(5)中，NN(i)和NN(j)分别是Pi和Pj所在服务半径SR内的需要充电的位置的集合；size是求集合NN(i)和NN(j)交集中元素的个数；
步骤2-4：将相似度矩阵S按行求和得到每个需要充电的位置的充电需求l1,j，
Figure PCTCN2015088388-appb-000008
步骤2-5：确定快速充电站的地址，将充电需求从大到小排序形成充电需求数列，将充电需求最大对应的需要充电的位置作为第一个聚类中心，即候选快速充电站1；同时得到该候选快速充电站服务半径SR内的需要充电的位置集合C(1)，将候选快速充电站的地址和该候选快速充电站服务半径SR内的需要充电的位置集合C(1)储存到快速充电站集合CS中；
依次在充电需求数列选择下一个充电需求对应的需要充电的位置，如果该需要充电的位置与快速充电站集合CS中候选快速充电站之间的相似度都为0时，则该需要充电的位置作为候选快速充电站2，同时得到该候选快速充电站服务半径SR内的需要充电的位置集合C(2)，将候选快速充电站的地址和该候选快速充电站服务半径SR内的需要充电的位置集合C(2)储存到快速充电站集合CS中；以此类推，确定所有候选快速充电站的地址；
根据预设阀值d(仿真算例中取90)将候选快速充电站服务半径SR内的需要充电的位置集合中的个数小于阀值的候选快速充电站的地址和相应的服务半径SR内的需要充电的位置集合从快速充电站集合CS中删除，快速充电站集合CS中余下的候选地址即为确定的快速充电站地址。
步骤三、根据EV充电负荷的时空分布预测结果和确定的快速充电站地址，利用排队论理论确定快速充电站中充电机的个数。
步骤3-1、利用步骤一确定高速公路中每辆EV需要充电的时间和步骤二确定的快速充电站的地址，确定每小时EV到达每个快速充电站的峰值，并将该峰值设为EV单位时间内到达快速充电站的个数λ；
步骤3-2：确定快速充电站中充电机的个数c：
计算平均等待队长Ls和等待时间Wq
Figure PCTCN2015088388-appb-000009
Figure PCTCN2015088388-appb-000010
ρ＝λ/(c×μ2)        (9)
Figure PCTCN2015088388-appb-000011
式(7)至式(10)中，μ2表示充电机单位时间内服务电动汽车的个数，仿真算例中μ2取6；c表示充电机个数，以式(11)为目标函数，以式(12)为约束条件进行优化求解，即可得到充电机个数c，
min z＝Csc+CwLs           (11)
s.t. Wq＜tw            (12)
式(11)和式(12)中，Cs是单个充电机折算到每个小时的成本，由式(13)计算可得；Cw是用户出行单位时间的成本，仿真算例中Cw取17元/辆，tw是最大等待时间，仿真算例中tw取20分钟。
Figure PCTCN2015088388-appb-000012
式(13)中V0是全寿命周期内的充电机成本，仿真算例中取V0为24万元；ir是利率，仿真算例中取ir为0.1；p是充电机的寿命，仿真算例中取p为10。
仿真算例：
将本发明应用到环岛高速公路的交通网络上，来证明规划方法的可行性与有效性。
根据EV充电负荷的时空分布预测结果，统计Ransc，如图6所示。从而得到最终的服务半径SR，SR＝17.02km。
方案1：利用共享型最近邻居聚类算法得到快速充电站的地址如图7所示。
方案2：该高速上现有服务区的信息如表3所示。如果优先考虑现有服务区中建设快速充电站，然后在利用本发明确定余下快速充电站的地址。规划的快速充电站地址分布如图8所示。
表3高速中服务区分布情况
Figure PCTCN2015088388-appb-000013
Figure PCTCN2015088388-appb-000014
根据EV充电负荷的时空分布预测结果和确定的快速充电站地址，分别确定方案1和方案2的每小时EV到达每个快速充电站的峰值即λ，如表4和5所示。
然后求解以最小成本为目标的函数得到方案1和方案2每个快速充电站的充电机个数分别如表6和7所示。
表4方案1中每个快速充电站中EV单位时间到达的个数
Figure PCTCN2015088388-appb-000015
表5方案2中每个快速充电站中EV单位时间到达的个数
Figure PCTCN2015088388-appb-000016
表6方案1中EV快速充电站的定容结果
Figure PCTCN2015088388-appb-000017
表7方案2中EV快速充电站的定容结果
Figure PCTCN2015088388-appb-000018
通过表8将方案1和方案2规划结果从充电站个数，充电机个数，充电失败率以及总成本等方面的比较，表明与服务区截取交通流量最大相比，该规划方法在满足电动汽车需求方面有着相对优势。
表8方案1和方案2规划结果的对比
  方案1 方案2 差别
充电站个数 15 18 3
充电机个数 316 346 30
充电失败率 2.46％ 5.56％ 3.1％
总成本(百万/年) 43.36 47.16 3.80
基础设施成本(百万/年) 8.03 8.79 0.76
等待成本((百万/年)) 35.33 38.37 3.04
以上所述仅是本发明的一个应用场景，凡依本发明申请专利范围所做的均等变化，或者应用到其他高速公路上，应属本发明的覆盖范围。

Claims (3)

  1. 一种高速公路电动汽车快速充电站的规划方法，其特征在于，该规划方法包括电动汽车充电负荷的时空分布预测、确定快速充电站的地址和确定快速充电站中充电机的个数；步骤如下：
    步骤一、电动汽车充电负荷的时空分布预测，即确定高速公路中每辆电动汽车需要充电的位置和时间；
    步骤二、根据电动汽车充电负荷的时空分布预测结果，利用共享型最近邻居聚类算法确定快速充电站的地址；包括：
    步骤1、统计所有电动汽车需要充电后能行驶的最远距离Ransc，利用中心极限定理，由拟合的正态分布得到99％的电动汽车在余下电量时都可以行驶的距离，将该距离定义为所有快速充电站的服务半径SR；
    步骤2、确定距离矩阵D，其中，dij代表需要充电的位置Pi到需要充电的位置Pj之间的距离，i，j属于1～n，n为需要充电的电动车总数；
    步骤3、确定相似度矩阵S；根据距离矩阵D，确定每个需要充电的位置所在服务半径SR内包含的需要充电的位置的个数；需要充电的位置Pi和需要充电的位置Pj的相似度sij：
    sij＝size(NN(i)∩NN(j))         (5)
    式(5)中，NN(i)和NN(j)分别是需要充电的位置Pi和Pj所在服务半径SR内的需要充电的位置的集合；size是求集合NN(i)和NN(j)交集中元素的个数；
    步骤4：将相似度矩阵S按行求和得到每个需要充电的位置的充电需求l1,j，
    Figure PCTCN2015088388-appb-100001
    步骤5：确定快速充电站的地址；将充电需求从大到小排序形成充电需求数列，将充电需求最大对应的需要充电的位置作为第一个聚类中心，即候选快速充电站1；同时得到该候选快速充电站服务半径SR内的需要充电的位置集合C(1)，将候选快速充电站的地址和该候选快速充电站服务半径SR内的需要充电的位置集合C(1)储存到快速充电站集合CS中；
    依次在充电需求数列选择下一个充电需求对应的需要充电的位置，如果该需要充电的位置与快速充电站集合CS中候选快速充电站之间的相似度都为0时，则该需要充电的位置作为候选快速充电站2，同时得到该候选快速充电站服务半径SR内的需要充电的位置集合C(2)，将候选快速充电站的地址和该候选快速充电站服务半径SR内的需要充电的位置集合C(2)储存到快速充电站集合CS中；以此类推，确定所有候选快速充电站的地址；
    根据预设阀值d，将候选快速充电站服务半径SR内的需要充电的位置集合中的个数小于阀值的候选快速充电站的地址和相应的服务半径SR内的需要充电的位置集合从快速充电站集合CS中删除，快速充电站集合CS中余下的候选快速充电站的地址即为确定的快速充 电站地址；
    步骤三、根据电动汽车充电负荷的时空分布预测结果和确定的快速充电站地址，利用排队论理论确定快速充电站中充电机的个数c。
  2. 根据权利要求1所述高速公路电动汽车快速充电站的规划方法，其特征在于，步骤一具体包括以下步骤：
    步骤1、获取如下基础数据：
    电动汽车数据：包括电动汽车类型、每类电动汽车类型的电池容量的概率密度函数、每类电动汽车类型的电池容量的上下边界、每类电动汽车类型的电池容量与其续航里程的数学关系；
    交通数据：包括交通起止点概率矩阵Pm×m和汽车进入高速公路时间ts的概率分布；
    通过交通起止点调查数据得到高速出入口交通起止点矩阵Am×m，利用该高速出入口交通起止点矩阵Am×m根据公式(1)得到交通起止点概率矩阵Pm×m；
    pij＝aij/Bi(1≤i≤m,1≤j≤m)        (1)
    公式(1)中，m是高速公路出入口个数，aij是每天从高速出入口i到高速出入口j汽车的平均数量，pij是指某一汽车每天从高速出入口i到高速出入口j的概率；Bi每天从高速出入口i进入高速公路的汽车的平均数量，
    Figure PCTCN2015088388-appb-100002
    高速公路基本信息：包括出入口个数，出入口编号，出入口坐标，出入口之间的距离；
    步骤2、确定如下数据：
    确定不同电动汽车类型的比例、每天进入高速公路的电动汽车的平均数量EBt、每天从高速出入口i进入高速公路的电动汽车的平均数量EBi；
    根据每天进入高速公路的电动汽车的平均数量EBt和每天从高速出入口i进入高速公路的电动汽车的平均数量EBi，按照出入口顺序对进入高速公路的电动汽车进行编号；
    步骤3、确定进入高速公路的所有电动汽车充电负荷的时空分布，设：i＝1；
    步骤3-1、确定第i辆电动汽车的参数，包括：电动汽车类型、最大容量Cap、初始电池状态SOCi、需要充电时电池状态SOCc、第i辆电动汽车充电前可以行驶的距离Ranac，电动汽车需要充电后可以行驶的最远距离Ransc；步骤如下：
    利用蒙特卡洛方法确定第i辆电动汽车的电动汽车类型；
    根据确定的电动汽车类型和该类电动汽车类型的电池容量的概率密度函数，利用蒙特卡洛方法确定第i辆电动电汽车的最大容量Cap；并设置进入高速公路时的初始电池状态SOCi以及需要充电时电池状态SOCc；
    根据上述确定的第i辆电动电汽车的电动汽车类型、最大容量Cap和对应的该类电动汽车类型的电池容量与其续航里程的数学关系，利用蒙特卡洛方法确定第i辆电动汽车的最大续航里程Ranmc；再根据初始电池状态SOCi、需要充电时电池状态SOCc、最大续航里程Ranmc，利用公式(3)确定第i辆电动汽车需要充电前可以行驶的距离Ranac；利用公式(4)确定第i辆电动汽车需要充电后行驶的最远距离Ransc；
    Ranac＝η×(SOCi-SOCc)×Ranmc       (3)
    Ransc＝η×SOCc×Ranmc          (4)
    式(3)和式(4)中：η是电池的能量效率系数；
    步骤3-2：确定第i辆电动汽车交通行为的参数，包括：出发点，目的地，行驶路径，进入高速时间Ets，出行距离Dod；根据第i辆电动汽车的编号确定其出发点，根据第i辆电动汽车的出发点和交通起止点概率矩阵Pm×m，利用蒙特卡洛方法确定目的地；根据最短路径算法确定行驶路径；根据高速公路基本信息得到出行距离Dod；根据汽车进入高速公路时间ts的概率分布，利用蒙特卡洛方法确定电动汽车进入高速时间Ets；并设置出行速度v；
    步骤3-3：判断第i辆电动汽车的出行距离Dod是否大于第i辆电动汽车需要充电前可以行驶的距离Ranac，
    如果Dod＞Ranac，则第i辆电动汽车需要充电，执行步骤3-4；反之，第i辆电动汽车在此次出行中不需要充电，i＝i+1；
    若i＜EBt则返回步骤3-1，反之，执行步骤二；
    步骤3-4：确定第i辆电动汽车需要充电的时间和位置，根据第i辆电动汽车的出发点、行驶路径、出行速度v、第i辆电动汽车需要充电前可以行驶的距离Ranac和电动汽车进入高速时间Ets确定第i辆电动汽车需要充电的位置Pi和时间tc，i＝i+1；若i＜EBt，则返回步骤3-1，反之，执行步骤二。
  3. 根据权利要求1或2所述高速公路电动汽车快速充电站的规划方法，其特征在于，步骤三具体包括以下步骤：
    步骤1、利用步骤一所确定的高速公路中每辆电动汽车需要充电的时间和步骤二所确定的快速充电站的地址，确定每小时电动汽车到达每个快速充电站的峰值，并将该峰值设为电动汽车单位时间内到达快速充电站的个数λ；
    步骤2：确定快速充电站中充电机的个数c：
    计算平均等待队长Ls和等待时间Wq：
    Figure PCTCN2015088388-appb-100003
    Figure PCTCN2015088388-appb-100004
    ρ＝λ/(c×μ2)           (9)
    Figure PCTCN2015088388-appb-100005
    式(7)至式(10)中，μ2表示充电机单位时间内服务电动汽车的个数；c表示快速充电站中充电机的个数，以下述式(11)为目标函数，以下述式(12)为约束条件进行优化求解，即可得到充电机个数c，
    min  z＝Csc+CwLs           (11)
    s.t.  Wq＜tw         (12)
    式(11)和式(12)中，Cs是单个充电机折算到每个小时的成本，单位：元，由下述式(13)计算可得；Cw是用户出行单位时间的成本，单位：元/辆；tw是最大等待时间，单位：分钟；
    Figure PCTCN2015088388-appb-100006
    式(13)中V0是全寿命周期内的充电机成本；ir是利率；p是充电机的寿命。
PCT/CN2015/088388 2015-08-19 2015-08-28 高速公路电动汽车快速充电站的规划方法 Ceased WO2017028333A1 (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US15/747,549 US10360519B2 (en) 2015-08-19 2015-08-28 Planning method of electric vehicle fast charging stations on the expressway

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201510515806.9 2015-08-19
CN201510515806.9A CN105160428B (zh) 2015-08-19 2015-08-19 高速公路电动汽车快速充电站的规划方法

Publications (1)

Publication Number Publication Date
WO2017028333A1 true WO2017028333A1 (zh) 2017-02-23

Family

ID=54801279

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2015/088388 Ceased WO2017028333A1 (zh) 2015-08-19 2015-08-28 高速公路电动汽车快速充电站的规划方法

Country Status (3)

Country Link
US (1) US10360519B2 (zh)
CN (1) CN105160428B (zh)
WO (1) WO2017028333A1 (zh)

Cited By (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108932558A (zh) * 2018-05-18 2018-12-04 国网江苏省电力有限公司徐州供电分公司 一种对外开放型电动公交充电站负荷预测方法
CN109204017A (zh) * 2018-09-13 2019-01-15 国网福建省电力有限公司 一种电动汽车充电网络的监控方法
CN109636070A (zh) * 2019-01-30 2019-04-16 国网安徽省电力有限公司 一种电动汽车充电站布局优化的决策支持系统
CN110648013A (zh) * 2019-08-26 2020-01-03 广东工业大学 一种基于对偶型最大熵的电动汽车充电负荷预测方法
CN110705746A (zh) * 2019-08-27 2020-01-17 北京交通大学 电动出租车快速充电站优化配置方法
CN110826813A (zh) * 2019-11-13 2020-02-21 上海恒能泰企业管理有限公司璞能电力科技工程分公司 基于家用电动汽车用户充电差异性需求的电网优化方法
CN111400662A (zh) * 2020-03-17 2020-07-10 国网上海市电力公司 一种考虑电动汽车充电需求的空间负荷预测方法
CN111680930A (zh) * 2020-06-17 2020-09-18 云南省设计院集团有限公司 一种基于特征可达圈的电动汽车充电站选址评估方法
CN111861145A (zh) * 2020-06-29 2020-10-30 东南大学 考虑高速公路路网的服务区电动汽车充电站配置方法
CN112993980A (zh) * 2021-02-23 2021-06-18 国网浙江省电力有限公司电力科学研究院 一种电动汽车充电负荷时空概率分布模型计算方法
CN114548612A (zh) * 2022-04-27 2022-05-27 惠州市丝鹭新能源科技有限公司 一种新能源充电桩停车调度系统
CN116562425A (zh) * 2023-03-30 2023-08-08 浙江安吉智电控股有限公司 多充电场站用户负荷预测方法、装置、售电代理系统
CN112507506B (zh) * 2020-09-18 2024-02-02 长安大学 基于遗传算法的共享汽车定价规划模型的多目标优化方法
CN118446444A (zh) * 2024-04-07 2024-08-06 浙江数智交院科技股份有限公司 一种服务区电动汽车充电桩布局规划方法
CN118608199A (zh) * 2024-08-08 2024-09-06 国网浙江省电力有限公司诸暨市供电公司 一种用于智慧社区的新能源充电桩规划方法
CN119831648A (zh) * 2024-12-19 2025-04-15 常州市规划设计院 一种多周期电动汽车充电设施的布局方法

Families Citing this family (83)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105957252B (zh) * 2016-02-04 2018-08-03 中惠创智无线供电技术有限公司 基于射频识别的多功能充电桩
CN107819343A (zh) * 2016-02-04 2018-03-20 吴红平 电动汽车用基于自适应控制的直流充电站及其工作方法
CN105608543B (zh) * 2016-03-16 2019-03-26 福建工程学院 充电站配电量配置方法
US20170308948A1 (en) * 2016-04-25 2017-10-26 Ford Global Technologies, Llc Electrified vehicle activity center ranking
CN106130110B (zh) * 2016-07-15 2018-12-25 华北电力大学 基于分层概率选择出行地的电动出租车充电站定容方法
CN106295898A (zh) * 2016-08-15 2017-01-04 万马联合新能源投资有限公司 一种设置充电桩的方法
CN106197459B (zh) * 2016-08-15 2019-05-21 浙江爱充网络科技有限公司 考虑航程及充电站位置的电动汽车路径寻优方法
CN107054111B (zh) * 2016-11-21 2020-04-24 蔚来汽车有限公司 基于车辆参数确定车辆补能信息来完善加电网络的方法
CN107067130B (zh) * 2016-12-12 2021-05-07 浙江大学 一种基于电动汽车马尔可夫充电需求分析模型的快速充电站容量规划方法
CN106828140B (zh) * 2016-12-14 2019-05-24 国网北京市电力公司 电动公交车参数配置方法和装置
CN106845727A (zh) * 2017-02-15 2017-06-13 天津大学 考虑配网潮流约束的高速公路快速充电站启发式规划方法
CN106951978B (zh) * 2017-02-20 2021-03-05 国网天津市电力公司 一种基于改进K-means算法的城市集中型充电站规划方法
CN106960279B (zh) * 2017-03-16 2020-10-23 天津大学 考虑用户参与度的电动汽车能效电厂特征参数评估方法
CN107067110B (zh) * 2017-04-14 2020-07-10 天津大学 车-路-网模式下电动汽车充电负荷时空预测方法
US10853720B1 (en) * 2017-04-26 2020-12-01 EMC IP Holding Company LLC Traffic condition forecasting using matrix compression and deep neural networks
CN107180274B (zh) * 2017-05-09 2020-04-24 东南大学 一种电动汽车充电设施规划典型场景选取和优化方法
CN107609670B (zh) * 2017-08-10 2020-12-01 浙江工业大学 一种基于copula算法电动汽车充电站负荷预测方法
CN107464028B (zh) * 2017-09-13 2020-11-06 杭州骑迹科技有限公司 一种电池柜的位置部署方法和网络服务器
JP6597752B2 (ja) * 2017-11-01 2019-10-30 マツダ株式会社 車両用表示装置
CN108133329B (zh) * 2017-12-29 2021-06-08 天津大学 考虑充电反馈效应的电动汽车出行与充电需求分析方法
CN108182537B (zh) * 2017-12-30 2022-03-18 国网天津市电力公司电力科学研究院 基于排队论的电动出租车快速充电站服务特性分析方法
CN108334991B (zh) * 2018-02-12 2020-06-09 清华大学 一种电动汽车充电站规划方法及系统
CN108656989B (zh) * 2018-05-03 2020-04-21 南京大学 一种针对周期性运动的电动小车的无线交互充电方法
CN109190935B (zh) * 2018-08-15 2021-08-17 天津大学 一种考虑服务区和车辆事故的高速公路充电站规划方法
CN109409939B (zh) * 2018-10-08 2022-02-22 国网天津市电力公司电力科学研究院 用于配网电压控制的双层电动汽车快速充电电价修正方法
CN109583708B (zh) * 2018-11-08 2021-06-04 国网浙江省电力有限公司经济技术研究院 多智能体微观交通配流模型的建立方法
CN109508830B (zh) * 2018-11-15 2022-09-02 云南电网有限责任公司 一种电动汽车时空动态负荷预测的方法
CN109501630B (zh) * 2018-12-04 2022-06-10 国网电动汽车服务有限公司 一种电动汽车充电方案实时推荐方法及系统
CN111291948B (zh) * 2018-12-06 2024-03-01 北京嘀嘀无限科技发展有限公司 一种服务设备部署方法、装置、电子设备及存储介质
CN109711630A (zh) * 2018-12-28 2019-05-03 郑州大学 一种基于出行概率矩阵的电动汽车快充站选址定容方法
CN109840635B (zh) * 2019-01-29 2023-06-02 三峡大学 基于电压稳定性和充电服务质量的电动汽车充电站规划方法
CN110111001B (zh) * 2019-05-06 2023-07-28 广东工业大学 一种电动汽车充电站的选址规划方法、装置以及设备
CN110189025B (zh) * 2019-05-30 2023-10-10 国网上海市电力公司 考虑不同负荷增长的电动汽车充电站规划方案获取方法
CN110516935B (zh) * 2019-08-13 2022-01-07 北京航空航天大学 一种基于端边云架构的矿车无人驾驶运输系统路权云智能分配方法
CN110728421B (zh) * 2019-08-30 2024-04-19 山东理工大学 一种基于充电需求大数据的路网充电优化方法
CN110662175B (zh) * 2019-09-11 2020-11-06 哈尔滨工程大学 一种基于无线可充电传感器网络的移动车速度控制方法
CN110689200B (zh) * 2019-09-30 2022-07-12 杭州电子科技大学 一种电动汽车长途运输中的充电路径导航方法
US11515587B2 (en) * 2019-10-10 2022-11-29 Robert Bosch Gmbh Physics-based control of battery temperature
CN110895638B (zh) * 2019-11-22 2022-12-06 国网福建省电力有限公司 考虑电动汽车充电站选址定容的主动配电网模型建立方法
CN110968837B (zh) * 2019-11-25 2023-04-18 南京邮电大学 电动汽车充电站选址定容的方法
CN110880054B (zh) * 2019-11-27 2022-05-20 国网四川省电力公司天府新区供电公司 一种电动网约车充换电路径的规划方法
CN112288122B (zh) * 2019-11-28 2024-02-27 南京行者易智能交通科技有限公司 一种基于客流od大数据的公交快速通勤线路设计方法
CN110826821B (zh) * 2019-12-04 2022-11-11 海南电网有限责任公司 一种基于分布式光储充的配电网规划方法
CN110949149B (zh) * 2019-12-12 2022-12-06 海南电网有限责任公司 一种电动汽车充电定位方法及系统
CN111461395B (zh) * 2020-02-24 2022-08-02 合肥工业大学 临时配送中心的选址方法和系统
CN111401696B (zh) * 2020-02-28 2023-09-22 国网浙江省电力有限公司台州供电公司 一种计及可再生资源不确定性的配电系统协调规划方法
CN111461441B (zh) * 2020-04-03 2023-09-12 国网辽宁省电力有限公司 基于电动汽车泊停态势划分的多类充电设施优化配置方法
CN111582585B (zh) * 2020-05-11 2022-03-22 燕山大学 一种充电站与无线充电站的联合规划方法及系统
CN111628496B (zh) * 2020-05-19 2022-01-25 南京工程学院 一种电动汽车充电站选址和最大充电负荷确定方法
CN111651899A (zh) * 2020-06-28 2020-09-11 北京理工大学 考虑用户选择行为的换电站鲁棒选址定容方法和系统
CN111861192A (zh) * 2020-07-16 2020-10-30 云南电网有限责任公司 一种电动汽车充电站的选址方法及装置
CN111667202A (zh) * 2020-07-16 2020-09-15 云南电网有限责任公司 一种电动汽车充电站的选址方法及装置
CN112070341B (zh) * 2020-07-24 2024-06-14 杭州电子科技大学 一种面向多机器人充电策略的分布式求解方法
CN111861022B (zh) * 2020-07-28 2022-05-13 国网天津市电力公司滨海供电分公司 一种基于大数据分析优化电动汽车充电站选址的方法
EP3960530A1 (en) * 2020-08-31 2022-03-02 Bayerische Motoren Werke Aktiengesellschaft Apparatus, method and computer program for determining a plurality of information of a location of a charging station
CN112215415A (zh) * 2020-09-29 2021-01-12 长安大学 基于概率模型最优分位点的汽车充电负荷场景预测方法
CN112381325B (zh) * 2020-11-27 2023-11-21 云南电网有限责任公司电力科学研究院 一种加氢站规划方法
US20220332209A1 (en) * 2021-04-20 2022-10-20 Volta Charging, Llc System and method for estimating the optimal number and mixture of types of electric vehicle charging stations at one or more points of interest
CN113362460B (zh) * 2021-04-28 2022-08-30 北京理工大学 全域新能源汽车充电地图构建与推荐方法
CN113283623A (zh) * 2021-05-15 2021-08-20 韦涛 兼容储能充电桩的电动运载工具电量路径规划方法
CN113326883B (zh) * 2021-06-03 2022-08-30 中创三优（北京）科技有限公司 充电站功率利用率预测模型的训练方法、装置及介质
CN113240216A (zh) * 2021-07-12 2021-08-10 长沙理工大学 充电站服务范围计算方法、系统、设备及计算机存储介质
CN113435790B (zh) * 2021-07-29 2022-11-01 广东工业大学 一种电动公交车充换电站的设计方法
CN114021795B (zh) * 2021-10-27 2024-07-19 北京交通大学 一种考虑电动汽车充电需求的充电站规划方法及系统
CN114169609B (zh) * 2021-12-08 2024-11-01 国网宁夏电力有限公司经济技术研究院 一种考虑光伏耦合的电动汽车充电站规划方法
CN114418300B (zh) * 2021-12-16 2024-07-16 国网上海市电力公司 一种基于城市功能分区和居民出行大数据的多类型电动汽车充电设施规划方法
CN114491882B (zh) * 2021-12-30 2024-06-07 南通沃太新能源有限公司 一种考虑电池续航能力的ev储能充电网络规划方法
CN114626571A (zh) * 2021-12-31 2022-06-14 深圳市麦谷科技有限公司 车辆的行驶里程预测方法及系统
CN114943362B (zh) * 2022-03-22 2025-03-11 上海电力大学 一种基于可调分级充电服务费的快充负荷充电引导方法
CN114662984B (zh) * 2022-04-19 2023-04-18 国网浙江电动汽车服务有限公司 一种车辆区域充电需求的分析方法、装置及介质
CN114819370A (zh) * 2022-05-09 2022-07-29 国网浙江省电力有限公司 基于改进混合算法的电动汽车充电站选址定容方法及装置
CN115099474A (zh) * 2022-06-13 2022-09-23 合肥工业大学 电动汽车与燃油汽车联合送货的路径规划方法和系统
DE102022115574A1 (de) 2022-06-22 2023-12-28 Bayerische Motoren Werke Aktiengesellschaft Verfahren und Vorrichtung zur Prädiktion der Wartezeit an einer Ladestation
US12377881B2 (en) 2022-06-27 2025-08-05 State Farm Mutual Automobile Insurance Company Delivery hand off procedure when electric vehicle (EV) is about to lose power
US12196566B2 (en) * 2022-10-11 2025-01-14 Signal 4D Predicting vehicle travel range
CN115395521B (zh) * 2022-10-25 2023-03-24 国网天津市电力公司营销服务中心 一种可再生能源、储能和充电桩协同规划方法及系统
CN116448137B (zh) * 2023-05-30 2024-01-16 山东博恒新能源有限公司 智能化高速公路能源管理系统
CN117078046B (zh) * 2023-10-12 2024-01-09 国网湖北省电力有限公司经济技术研究院 一种电动公交车有线无线联合充电优化方法、系统及设备
CN117408498B (zh) * 2023-12-15 2024-02-23 陕西德创数字工业智能科技有限公司 一种基于新能源大数据的公共充电站选址定容定桩方法
CN118095736B (zh) * 2024-03-04 2025-03-25 南京大学 一种续航不确定性下考虑充电站容量的充电站规划方法与系统
CN118061816B (zh) * 2024-04-24 2024-08-02 中科军源(南京)智能技术有限公司 预装式移动超级充电站
CN118129789B (zh) * 2024-05-10 2024-07-05 成都大学 一种新能源汽车的路径规划方法
CN119090094B (zh) * 2024-11-06 2025-02-11 山东精工电子科技股份有限公司 一种基于大数据的新能源汽车充电站充电量需求预测系统

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100079004A1 (en) * 2008-10-01 2010-04-01 Keefe Robert A System and Method for Managing the Distributed Generation of Power by a Plurality of Electric Vehicles
CN102722767A (zh) * 2012-07-02 2012-10-10 山东鲁能智能技术有限公司 电动汽车充换电站布点规划系统及方法
CN102880921A (zh) * 2012-10-16 2013-01-16 山东电力集团公司电力科学研究院 一种电动汽车充电站选址优化方法
CN104318357A (zh) * 2014-10-15 2015-01-28 东南大学 电动汽车换电网络协调规划方法

Family Cites Families (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5548200A (en) * 1994-07-06 1996-08-20 Norvik Traction Inc. Universal charging station and method for charging electric vehicle batteries
US5986430A (en) * 1998-07-06 1999-11-16 Motorola, Inc. Method for ultra-rapidly charging a rechargeable battery using multi-mode regulation in a vehicular recharging system
US6614204B2 (en) * 2001-12-21 2003-09-02 Nicholas J. Pellegrino Charging station for hybrid powered vehicles
TW201230598A (en) * 2010-04-26 2012-07-16 Proterra Inc Fast charge stations for electric vehicles in areas with limited power availability
US8350526B2 (en) * 2011-07-25 2013-01-08 Lightening Energy Station for rapidly charging an electric vehicle battery
CN102521488A (zh) * 2011-11-28 2012-06-27 山东电力集团公司济南供电公司 一种电动汽车换电站选址方法
US9132742B2 (en) * 2012-02-23 2015-09-15 International Business Machines Corporation Electric vehicle (EV) charging infrastructure with charging stations optimumally sited
US8963494B2 (en) * 2012-05-18 2015-02-24 Tesla Motors, Inc. Charge rate optimization
US9728990B2 (en) * 2012-10-31 2017-08-08 Tesla, Inc. Fast charge mode for extended trip
CN104077635B (zh) * 2014-07-09 2017-12-26 北京交通大学 一种基于光伏发电系统的电动汽车充电站充电优化方法
CN104182595B (zh) * 2014-09-15 2018-01-23 国家电网公司 一种充电站群的负荷模拟方法及系统

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100079004A1 (en) * 2008-10-01 2010-04-01 Keefe Robert A System and Method for Managing the Distributed Generation of Power by a Plurality of Electric Vehicles
CN102722767A (zh) * 2012-07-02 2012-10-10 山东鲁能智能技术有限公司 电动汽车充换电站布点规划系统及方法
CN102880921A (zh) * 2012-10-16 2013-01-16 山东电力集团公司电力科学研究院 一种电动汽车充电站选址优化方法
CN104318357A (zh) * 2014-10-15 2015-01-28 东南大学 电动汽车换电网络协调规划方法

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
GE, SHAOYUN ET AL.: "An Optimization Approach for the Layout and Location of Electric Vehicle Charging Stations", ELECTRIC POWER, vol. 45, no. 11, 5 November 2012 (2012-11-05), pages 96 - 101, ISSN: 1004-9649 *
POURAZARM, S. ET AL.: "Optimal Routing of Electric Vehicles in Networks with Charging Nodes: A Dynamic Programming, Approach", ELECTRIC VEHICLE CONFERENCE, 2014 IEEE INTERNATIONAL, 19 December 2014 (2014-12-19), XP032744177, ISSN: 1498-2795 *
QIAN, BIN ET AL.: "Optimal Planning of Battery Charging and Exchange Stations for Electric Vehicles", AUTOMATION OF ELECTRIC POWER SYSTEMS, vol. 38, no. 2, 25 January 2014 (2014-01-25), pages 64 - 69, ISSN: 1000-1026 *

Cited By (20)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108932558B (zh) * 2018-05-18 2021-09-28 国网江苏省电力有限公司徐州供电分公司 一种对外开放型电动公交充电站负荷预测方法
CN108932558A (zh) * 2018-05-18 2018-12-04 国网江苏省电力有限公司徐州供电分公司 一种对外开放型电动公交充电站负荷预测方法
CN109204017A (zh) * 2018-09-13 2019-01-15 国网福建省电力有限公司 一种电动汽车充电网络的监控方法
CN109636070A (zh) * 2019-01-30 2019-04-16 国网安徽省电力有限公司 一种电动汽车充电站布局优化的决策支持系统
CN109636070B (zh) * 2019-01-30 2023-01-31 国网安徽省电力有限公司 一种电动汽车充电站布局优化的决策支持系统
CN110648013A (zh) * 2019-08-26 2020-01-03 广东工业大学 一种基于对偶型最大熵的电动汽车充电负荷预测方法
CN110705746A (zh) * 2019-08-27 2020-01-17 北京交通大学 电动出租车快速充电站优化配置方法
CN110705746B (zh) * 2019-08-27 2022-08-05 北京交通大学 电动出租车快速充电站优化配置方法
CN110826813A (zh) * 2019-11-13 2020-02-21 上海恒能泰企业管理有限公司璞能电力科技工程分公司 基于家用电动汽车用户充电差异性需求的电网优化方法
CN111400662A (zh) * 2020-03-17 2020-07-10 国网上海市电力公司 一种考虑电动汽车充电需求的空间负荷预测方法
CN111400662B (zh) * 2020-03-17 2024-02-06 国网上海市电力公司 一种考虑电动汽车充电需求的空间负荷预测方法
CN111680930A (zh) * 2020-06-17 2020-09-18 云南省设计院集团有限公司 一种基于特征可达圈的电动汽车充电站选址评估方法
CN111861145A (zh) * 2020-06-29 2020-10-30 东南大学 考虑高速公路路网的服务区电动汽车充电站配置方法
CN112507506B (zh) * 2020-09-18 2024-02-02 长安大学 基于遗传算法的共享汽车定价规划模型的多目标优化方法
CN112993980A (zh) * 2021-02-23 2021-06-18 国网浙江省电力有限公司电力科学研究院 一种电动汽车充电负荷时空概率分布模型计算方法
CN114548612A (zh) * 2022-04-27 2022-05-27 惠州市丝鹭新能源科技有限公司 一种新能源充电桩停车调度系统
CN116562425A (zh) * 2023-03-30 2023-08-08 浙江安吉智电控股有限公司 多充电场站用户负荷预测方法、装置、售电代理系统
CN118446444A (zh) * 2024-04-07 2024-08-06 浙江数智交院科技股份有限公司 一种服务区电动汽车充电桩布局规划方法
CN118608199A (zh) * 2024-08-08 2024-09-06 国网浙江省电力有限公司诸暨市供电公司 一种用于智慧社区的新能源充电桩规划方法
CN119831648A (zh) * 2024-12-19 2025-04-15 常州市规划设计院 一种多周期电动汽车充电设施的布局方法

Also Published As

Publication number Publication date
CN105160428A (zh) 2015-12-16
CN105160428B (zh) 2018-04-06
US20180240047A1 (en) 2018-08-23
US10360519B2 (en) 2019-07-23

Similar Documents

Publication Publication Date Title
WO2017028333A1 (zh) 高速公路电动汽车快速充电站的规划方法
Li et al. Public charging station location determination for electric ride-hailing vehicles based on an improved genetic algorithm
Vosooghi et al. Shared autonomous electric vehicle service performance: Assessing the impact of charging infrastructure
CN110816549B (zh) 能源管理装置、模型管理方法以及计算机程序
Liang et al. Plug-in electric vehicle charging demand estimation based on queueing network analysis
Wang et al. A two-stage charging facilities planning method for electric vehicle sharing systems
CN102842109B (zh) 停车场服务水平量化分级的评价方法
CN110288212A (zh) 基于改进的mopso的电动出租车新建充电站选址方法
CN106951999A (zh) 一种交通出行方式与出发时刻联合选择的建模与分析方法
CN115344653A (zh) 一种基于用户行为的电动汽车充电站选址方法
Su et al. Forecast of electric vehicle charging demand based on traffic flow model and optimal path planning
CN114418300A (zh) 一种基于城市功能分区和居民出行大数据的多类型电动汽车充电设施规划方法
CN107180274A (zh) 一种电动汽车充电设施规划典型场景选取和优化方法
CN112036624B (zh) 一种基于区域内电动汽车充电负荷预测的电网调度方法
CN107067130B (zh) 一种基于电动汽车马尔可夫充电需求分析模型的快速充电站容量规划方法
JP2023175992A (ja) エネルギー供給システムおよび情報処理装置
CN105262167A (zh) 区域内电动汽车有序充电控制方法
Ali et al. Optimal battery sizing and stops’ allocation for electrified fleets using data-driven driving cycles: A case study for the city of Cairo
CN111861527A (zh) 一种电动汽车充电站的确定方法、装置及存储介质
CN106130110B (zh) 基于分层概率选择出行地的电动出租车充电站定容方法
CN108133329A (zh) 考虑充电反馈效应的电动汽车出行与充电需求分析方法
Wang et al. Optimization of charging-station location and capacity determination based on optical storage, charging integration, and multi-strategy fusion
Sadhukhan et al. Optimal placement of electric vehicle charging stations in a distribution network
CN106845727A (zh) 考虑配网潮流约束的高速公路快速充电站启发式规划方法
Guo et al. Selective multi-grade charging scheduling and rebalancing for one-way car-sharing systems

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 15901537

Country of ref document: EP

Kind code of ref document: A1

WWE Wipo information: entry into national phase

Ref document number: 15747549

Country of ref document: US

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 15901537

Country of ref document: EP

Kind code of ref document: A1

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 24/09/2018)

122 Ep: pct application non-entry in european phase

Ref document number: 15901537

Country of ref document: EP

Kind code of ref document: A1