Planning method of electric vehicle fast charging stations on the expressway

• US 10,360,519 B2
• Filed: 08/28/2015
• Issued: 07/23/2019
• Est. Priority Date: 08/19/2015
• Status: Active Grant

• Current Assignee: Tianjin University
• Original Assignee: Tianjin University
• Inventors: Mu, Yunfei, Dong, Xiaohong, Jia, Hongjie, Yu, Xiaodan
• Primary Examiner(s): Fantu, Yalkew
• Application Number:
  US15/747,549
• Publication Number:
  US 20180240047A1
• Time in Patent Office: 1,425 Days
• Field of Search: 320109
• CPC Class Codes: B60L 2240/72 Charging station selection relying on external data
• CPC Class Codes: B60L 2240/80 Time limits
• CPC Class Codes: B60L 53/11 DC charging controlled by the charging station, e.g. mode 4
• CPC Class Codes: B60L 53/14 Conductive energy transfer
• CPC Class Codes: B60L 53/66 Data transfer between charging stations and vehicles
• CPC Class Codes: B60L 53/67 Controlling two or more charging stations
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: Y02T 10/70 Energy storage systems for electromobility, e.g. batteries
• CPC Class Codes: Y02T 10/7072 Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
• CPC Class Codes: Y02T 10/72 Electric energy management in electromobility
• CPC Class Codes: Y02T 90/12 Electric charging stations
• CPC Class Codes: Y02T 90/14 Plug-in electric vehicles
• CPC Class Codes: Y02T 90/16 Information or communication technologies improving the operation of electric vehicles

Abstract

A planning method of EV fast charging stations on the expressway, comprising the following steps: Step 1: forecasting the spatial and temporal distribution of EV charging load, that is to determine the time and location of each EV needing charging on the expressway; Step 2: based on the forecast result achieved by Step 1, determining the locations of the fast charging stations on the expressway by the nearest neighbor clustering algorithm; Step 3: according to the spatial and temporal forecast result of the EV charging load and the locations of the fast charging stations, determining the number c of the chargers in each fast charging station by queuing theory. Due to battery characteristics, traditional gas stations do not completely match the fast charging stations. In the planning method, the locations and times for charging of the EVs are considered to determine the capacities and locations of the fast charging stations, which can meet the charging needs of the EVs more than the traditional gas station, and thus promote the development of EVs.

Claims

1. A method for planning of a plurality of electric vehicle (EV) fast charging stations along an expressway on which a plurality of EVs are travelling, comprising

Step 1:

forecasting a spatial and temporal distribution of EV charging load, which is an estimation of times and locations at which one of the EVs that needs a charging;

Step 2;

based on the estimation of Step 1, determining locations of the EV fast charging stations along the expressway by using a nearest neighbor clustering algorithm, which further comprises;

Step 2.1;

calculating maximum travel range Ran_sc per one charging for each EV; and

determining, by a normal distribution fitting method, a service radius(SR) of fast charging stations, which is defined as a travel distance which 99% of all the EVs traveling on the expressway can travel with their remaining battery charges;

Step 2.2;

determining distance matrix D with a property d_ij, where d_ij represents the distance between charging location P_i where an EV needs charging and a next charging location P_j where the EV also needs charging, i and j are each independently a number of 1˜

n, and n represents the total number of EVs that need charging;

Step 2.3;

based on the distance matrix D, determining similarity matrix S, wherein for each charging location within a service radius SR, a total number of charging locations within that service radius SR is determined; and

similarity s_ij between charging location P_i and charging location P_j is defined by equation (5);

s_ij=size(NN(i)∩

NN(j)) 

 

(5)

wherein, NN(i) and NN(j) are sets of charging locations within service radius SR of charging locations P_i and P_j, respectively;

“

size”

means a number of shared charging locations in an intersection set of NN(i) and NN(j);

Step 2.4;

calculating charging demand l_1,j of each charging location according to the sum of rows of the similarity matrix S by equation (6);

2. The method according to claim 1, wherein Step 1 further comprising:

Step 1.1;

obtaining following basic data;

EV data, which comprise types of the EVs, probability density functions of battery capacity of each of the types, upper and lower boundaries of battery capacity of each of the types, and a mathematical relationship between battery capacity and maximum travel range of each of the types;

Traffic data, which comprise probability matrix P<sub>m×

m</sub> of traffic origin point and destination point and probability distribution of the time t_s when vehicles entering the expressway;

which further comprises;

obtaining traffic origin-destination matrix A<sub>m×

m</sub> of the expressway'"'"'s entrance-exits from investigation data of traffic origin and destination points, and obtaining the matrix P<sub>m×

m</sub> according to equation (1) by using the matrix A<sub>m×

m</sub>;

p_ij=a_ij/B_i(1≤

i≤

m,1≤

j≤

m) 

 

(1)

wherein m is the number of expressway entrance-exits;

a_ij is a daily average number of EVs passing from an expressway entrance-exit i to another expressway entrance-exit j;

p_ij is daily probability of a vehicle passing from the expressway entrance-exit i to the expressway entrance-exit j, B_i is an daily average number of vehicle entering the expressway via the expressway entrance-exit i;

3. The method according to claim 1, wherein Step 3 further comprises:

Step 3.1;

determining peak value λ

of the EVs, which represents the largest number of EVs arrived at each fast charging station per hour by using the time of each EV needing charging as determined in Step 1 and the locations of fast charging stations as determined in Step 2;

Step 3.2;

determining number c, which represents the number of chargers at each fast charging station;

which further comprises;

calculating average length L_s of a waiting queue and waiting time W_q by equations (7)-(10);

4. The method according to claim 2, wherein Step 3 further comprises:

Step 3.1;

determining peak value λ

of EVs, which represents the largest number of EVs arrived at each fast charging station per hour by using the time of each EV needing charging as determined in Step 1 and the locations of fast charging stations as determined in Step 2;

Step 3.2;

determining number c, which represents the number of chargers at each fast charging station;

which further comprises;

calculating average length L_s of a waiting queue and waiting time W_q by equations (7)-(10);