EXECUTING ADVANCED ALGORITHMS TO EXTRAPOLATE UNIQUE ELECTRIC VEHICLE USERS’ CHARGING BEHAVIORS
Date of Award
Doctor of Philosophy in Industrial and Manufacturing Engineering
Mechanical, Industrial and Systems Engineering
Gretchen A. Macht
Policymakers have structured legislation and targets to increase the adoption of electric vehicles and the deployment of charging stations. As questions arise on how to expand the electric vehicle infrastructure, advanced computational models are attempting to quantify the future placement of charging stations. These models, however, do not necessarily incorporate the variability of user behavior, as a subgroup of consumer behavior, as this information is not well-understood. This research aims to execute advanced algorithms and statistical analysis to explore the users’ charging behaviors that are insinuated in the existing literature. The results support the user-centric design of public charging infrastructure, which assists in reducing the electric vehicle adoption threshold in the long run and facilitating the transition to a low-carbon, sustainable transportation system.
Throughout this work, charging session data is analyzed to investigate users’ charging behavior differences from the two perspectives of charging station usage and users themselves. Quantifying these users’ unique patterns supports the human variability knowledge required for more comprehensive and holistic models of electric vehicle charging station placement.
The findings of this work indicate that the prominent known charging behavior, range anxiety, has somewhat shifted among users due to the battery technology and charging infrastructure improvements. With these major system improvements, it is further found that procrastination charging behavior has emerged among users. Overall, the results can provide a reflection on the effectiveness of previously established strategies, policies, and actions by the decision makers (i.e., policymakers, automotive industry, infrastructure developers, and scholars); additionally, it can assist them with future decision making as they strive to achieve a fully electric transportation system.
This work aims to point out that the importance of understanding unique and unexplored charging behaviors among electric vehicle users as responsible parties persist in reducing the EV adoption threshold by providing a reliable and ubiquitous charging network and stable power grid. This work creates the opportunity to raise awareness among hesitant potential EV consumers to reconsider their purchasing decisions to accelerate the acceptance of electric vehicles nationally and internationally.
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Khaleghikarahrodi, Mehrsa, "EXECUTING ADVANCED ALGORITHMS TO EXTRAPOLATE UNIQUE ELECTRIC VEHICLE USERS’ CHARGING BEHAVIORS" (2023). Open Access Dissertations. Paper 1500.
Available for download on Thursday, May 08, 2025