Date of Award


Degree Type


Degree Name

Master of Science in Systems Engineering


Mechanical, Industrial and Systems Engineering

First Advisor

Gretchen A. Macht


To reduce the threshold of EV adoption, the electric vehicle supply equipment (EVSE) infrastructure needs to fit the EV systems to its users to satisfy their expectations. There needs to be a reflection of user behavior variability in the EVSE infrastructure to improve its functionality. Individual users’ charging actions, over time, construct a charging behavioral pattern that distinguishes the users from one another. This research analyses the EV users’ unique behavioral charging patterns. Therefore, profiles of users’ charging actions are clustered to investigate the unique behavioral patterns. An unsupervised clustering algorithm (i.e., K-means) is implemented through three distance metrics (i.e., Hamming, Wasserstein, and Manhattan) to classify the Rhode Island public charging stations’ frequent users. Frequent users of these stations were established using the Pareto Principle, in which 608 users (20% of users) contributed to 89% of charging events. The results indicated five clusters through the Wasserstein distance metric, which proposed different EV charging behavior patterns. The five achieved clusters represent: 39% of anxious or opportunistic users (1); 27% of users with consistent charging regardless of their EVs state of charge (2); while 21% of users having sporadic charging behavior revealing no pattern (3); and then approximately 5% of users with procrastination tendencies with only charging when practically out of charge (4) and 8% of users who experience that rush toward running out of charge (procrastinators) but exhibit some opportunistic early charging (5). Knowing these users’ unique behaviors helps both public and private stakeholders target EV market growth through user-centric EVSE location placement.



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