Abstract
•We model Trip Success Ratio technique to measure the quality of charging station network from drivers accessibility perspective.•We model virtual trip distances, PEV remaining electric range, different driving habits, and different battery capacities using MCS to present more realistic estimations.•The correlation between different battery capacities and different charging station layouts is investigated.•We model the charging station allocation problem using the estimations from Trip Success Ratio Model for different battery capacities.
This paper proposes a new model for optimally allocating Plug–in Electric Vehicle (PEV) Charging Stations (CSs) in the network. The model considers Trip Success Ratio (TSR) in order to enhance CS accessibility for PEV drivers. Diversity of usage and different driving habits are considered in the presented model, as well as different trip types (In-city, Highway). The allocation model has two stages: modeling TSR to estimate Charging Station Service Range (CSSR), and the CS allocation stage. In the first stage, the service range of charging stations has been estimated using TSR with consideration of the uncertainty of trip distances (In-city, Highway) and the uncertainty in the Remaining Electric Range (RER) of PEVs. The estimated CSSR is utilized in the CS allocation stage in order to optimize the CS location set that covers the network with a certain guaranteed TSR level. The allocation problem has been formulated as the Maximum Covering Location Problem (MCLP) in order to make the optimal decision for allocating CSs in the network.