A Method of EV Detour-to-Recharge Behavior Modeling and Charging Station Deployment

TL;DR

This paper introduces a novel methodology for modeling EV detour-to-recharge behavior and employs a genetic algorithm to optimize charging station deployment, demonstrated through a detailed case study on a transportation network.

Contribution

It presents a new approach to simulate EV recharging behavior and an optimization method for station placement using genetic algorithms, based on travel data.

Findings

Effective EV charging station deployment plan generated

Simulation of EV behavior based on demand data

Case study demonstrates method's applicability

Abstract

Electric vehicles (EVs) are increasingly used in transportation. Worldwide use of EVs, for their limited battery capacity, calls for effective planning of EVs charging stations to enhance the efficiency of using EVs. This paper provides a methodology of describing EV detouring behavior for recharging, and based on this, we adopt the extra driving length caused by detouring and the length of uncompleted route as the indicators of evaluating an EV charging station deployment plan. In this way, we can simulate EV behavior based on travel data (demand). Then, a genetic algorithm (GA) based EV charging station sitting optimization method is developed to obtain an effective plan. A detailed case study based on a 100-node 203-branch transportation network within a 30 km * 30 km region is included to test the effectiveness of our method. Insights from our method may be applicable for charging station planning in various transportation networks.