Development of an assessment model for predicting public electric vehicle charging stations

Abstract

Abstract: Deploying an adequate electric vehicle (EV) charging infrastructure to support the increasing EV market is one of the major strategic goals of the U.S. government. This requires a well-designed EV charging network. The distribution and capability of the existing charging networks in terms of EV population, location, charging rate, and time of charging in San Diego is examined. A mathematical model to calculate the demand number of public Level 2 chargers universally applicable is developed. The study showed that although San Diego has sufficient chargers to accommodate the existing EV’s charging demand, the current public charging distribution network is neither well designed nor effectively used. To eliminate the waste resulting from the inefficiently designed charging infrastructure and maximize the usage rate of each charger, it is recommended that the designed optimal model to be utilized and the charging location priority be implemented to improve the availability and accessibility of charging network in the City of San Diego. ᅟ: Introduction: The purpose of this study is to identify current problems with the existing electric vehicle public charging stations and come up with solutions to improve the availability and accessibility of public charging stations in the City of San Diego. The objective of this research project is also to develop a mathematical model to predict the demand of EV chargers in any city including in the City of San Diego. Methods: A mix of quantitative and qualitative research methods are used to analyze the problem. The first phase of this project is to determine the study area by identifying the existing problems and issues from existing sources, and formulating hypothesis. Results: The distribution and capability of the existing charging networks in terms of EV population, location, charging rate, and time of charging in San Diego was examined. A mathematical model to calculate the demand number of public Level 2 chargers for the City of San Diego and for each zip code was developed. Among 361 tested public Level 2 chargers distributed in 34 communities, 66 chargers located at 37 charging stations distributed in 22 communities were found to be nonoperational or damaged but still operational. They accounted for 18% of the total number of tested EV charging stations and 12.7% of the total public Level 2 in San Diego. The model tested using data from San Francisco Bay Area, and Los Angeles County matched well to the predictions. Conclusions: The conclusion is that although San Diego has sufficient chargers to accommodate the existing EV’s charging demand, the current public charging distribution network is neither well designed nor effectively used. To eliminate the waste resulting from the inefficiently designed charging infrastructure and maximize the usage rate of each charger, it is recommended that the designed optimal model to be utilized and the charging location priority be implemented to improve the availability and accessibility of charging network in the City of San Diego. This model is easily applicable in the European environment since all the five significant independent variables (B/E - Battery capacity to EV Range Ratio, D-Driver Traveling Distance, β - Ratio of EV driver charges away from home, PrefL2- percentage that EV driver prefers to charge on Level 2 stations, and TL2- duration of public Level 2 chargers’ work per day) are easy to obtain. Hence this proposed model has universal applicability.