Planning method of electric vehicle fast charging stations on the expressway
First Claim
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, comprisingStep 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 Ransc 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 dij, where dij represents the distance between charging location Pi where an EV needs charging and a next charging location Pj 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 sij between charging location Pi and charging location Pj is defined by equation (5);
sij=size(NN(i)∩
NN(j))
(5)wherein, NN(i) and NN(j) are sets of charging locations within service radius SR of charging locations Pi and Pj, 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 l1,j of each charging location according to the sum of rows of the similarity matrix S by equation (6);
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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.
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Citations
4 Claims
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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 Ransc 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 dij, where dij represents the distance between charging location Pi where an EV needs charging and a next charging location Pj 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 sij between charging location Pi and charging location Pj is defined by equation (5);
sij=size(NN(i)∩
NN(j))
(5)wherein, NN(i) and NN(j) are sets of charging locations within service radius SR of charging locations Pi and Pj, 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 l1,j of each charging location according to the sum of rows of the similarity matrix S by equation (6); - View Dependent Claims (2, 3, 4)
- 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;
Specification