Date of Award
Doctor of Philosophy (PhD)
The lack of sufficient public charging stations for electric vehicles has long been recognized as a major hinder for massive adoption of electric vehicles (EV). This dissertation aims to develop a framework for designing charging station infrastructure networks that electric vehicle with limited travel range can be recharge en-route to complete trips to destinations and then would facilitate the adoption of electric vehicles. The rest part of this dissertation is concerned with modeling travel range of electric vehicle and users behavior of deviating from their most preferred routes when siting charging stations. The proposed multi-path refueling location model provides the most cost effective deployment strategy of placing charging stations that are needed on the network to satisfy electric vehicle travel demand between all origin-destination (O-D) pairs. In the second part of the dissertation, heuristic based on greedy adding algorithms are developed to address the computational challenges of the multi-path refueling location model. The heuristics are tested on the Sioux Falls network and a real-life case study of South Carolina and compared with the exact solutions. In reality, however, EV market matures gradually, in other words, not all the cities would become electric vehicle adopters at the current state. In third part of this dissertation, a multi-period multi-path refueling location model is developed to expand EV charging network to dynamically satisfy O-D trips with the growth of EV market. The model captures the dynamics in the topological structure of network and determines the cost effective station rollout scheme on both spatial and temporal dimensions. The multi-period location problem is formulated as a mixed integer linear program and solved by a heuristic based on genetic algorithm. The model and heuristic are justified using the benchmark Sioux Falls road network and implemented in a case study of South Carolina. The results indicate that the charging station rollout scheme is subject to a number of major factors, including geographic distributions of cities, vehicle range, and deviation choice, and is sensitive to the types of charging station sites. The last part of this dissertation presents an extension of the multi-path refueling location model to integrate probabilities of cities becoming EV market into optimization of location decisions. This probability-based model differs from the multi-path model in two major aspects. First it maximizes the total weighted coverage of all cities with a given budget while the multi-path model minimizes the cost of covering all the O-D pairs. Second, instead of only consider one way trips as in the multi-path model, this model extends to also satisfy the round trips from destinations back to origins. A genetic algorithm based heuristic is adopted to solve this probability-based model. Numerical experiments are conducted to justify the incorporation of probability information in optimally siting charging station.
Li, Shengyin, "A Framework for Designing of Electric Vehicle Charging Infrastructure Network" (2015). All Dissertations. 1546.