Abstract

The global trend toward a more sustainable future, based on economics and societal behavior, have assisted in making electric vehicles (EV) more attractive to consumers. New technology implemented in EVs continuously improves their range, charging time, and battery capacity. Therefore, the number of EV sales increased significantly within the last few years. In order to handle the demand from the growth in EV sales, the development of a user-orientated distribution of charging stations is needed which requires substantial knowledge about user patterns in charging behavior. Understanding real data from existing charging stations that is analyzed with rigorous statistical methods gives valuable insight for the development of empirical models of charging behavior. In order to initiate this work, a case study approach of public Level-2 charging stations in Rhode Island (RI) were analyzed. Research questions range from how charging stations are being used to which kind of areas influence this behavior and what patterns exist toward calendar dependence. After processing the data, single charging stations were classified into functional areas followed by statistical analysis performed with descriptive statistics, visualizing data, hypothesis testing, clustering, regressive models, and forecasting. Based on the data, there is a strong connection between the total duration of charging events, actual charging time, and the amount of charging events. Not only are chargers utilized differently based on frequency and location, many users use charging stations as parking spots. This pattern exists regardless of charging fees. The charging behavior varies greatly between the different functional areas. Geographical areas seem to have less influence on charging behavior, seemingly more like a mixture of functional areas. Approximately, only about one third of the RI EV drivers are using RI charging stations. There is mainly a decreasing median amount of charges per user, which speaks to either more home charging or larger battery capacity. Areas in which people are working have less charging events on weekends and have a strong peak of charging events in the morning. Areas in which people are spending their free time have the same amount or more charges on weekends and do not have peak times. Timeseries forecasting models found that, both currently and in the near future, there are enough charging stations in RI. However, this does not imply that all the charging stations are in the correct locations, just that the volume of plugs available in RI is sufficient for the current EV charging population.

Highlights

  • 1.2 RESEARCH GOALS The goal of this research is to develop a set of empirical models describing charging behavior by providing a statistical analysis on data collected from existing Electric Vehicle (EV) charging stations throughout Rhode Island

  • Certain station location selection models are based on existing optimization routines or heuristics that can find charging locations based on reducing queuing times via prediction of existing data from non-EV vehicles (Chen et al, 2013; De Weerdt, Gerding, Stein, Robu, & Jennings, 2012; Worley, Klabjan, & Sweda, 2012)

  • The final code can be knitted to a Portable Document Format (PDF) or Word to have a presentable version of the code with all results and explanations

Read more

Summary

BACKROUND

The electric vehicle (EV) market is rapidly growing, in the first quarter of 2018, 312,400 EVs have been sold worldwide reaching 59% more than last year (EV-Volumes, n.d.). This research’s outcomes will provide a better understanding of consumer charging behavior based on real data from public stations in Rhode Island. These outcomes will assist the RI Office of Energy Resources (OER), RI Department of Transportation (RIDOT), and RI Department of Environmental Management (DEM) to understand their customer and to make consumer-centered charging infrastructure decisions. Results will establish knowledge for future analysis of the charging infrastructure use and demand at various location types. This information can assist in determining the need for public charging demand and promote charging infrastructure installation at various parking facilities. The information would be valuable to improve the sustainable transportation infrastructure for all users and the potential to integrate future parties, such as autonomous vehicles

RESEARCH GOALS
CHAPTER 2 – Literature Review
EV-MARKET EVOLUTION
EVS IN RHODE ISLAND Rhode Island has visions about the future development and the
CHARGING STATION LOCATION MODELS
CHAPTER 3 – Methodology
DATA AND DATA PROCESSING Data from 50 Level-2 public stations out of 73
CLASSIFICATION OF FUNCTIONAL AREAS
STATISTICAL ANALYSIS
VISUALIZING DATA
HYPOTHESIS TESTING & CLUSTERING
TIME SERIES - REGRESSIVE MODELS & FORECASTING
CHAPTER 4 – Results & Discussion
GENERAL USAGE OF CHARGING STATIONS This section focuses on answering Research
USAGE WITH LOCATIONS AS A FACTOR
GEOGRAPHICAL AREAS
TIME DEPENDENT TRENDS This chapter answers the Research Questions 3
CHAPTER 5 – Conclusion
LIMITATIONS
Findings
FUTURE WORK
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call