Optimal selection of electric vehicle charging stations considering value of energy and consumer behavior
Stukey, Rachel Melagoda, Adithya Shanthanam, Sangar Aravinthan, Visvakumar Clark, C. Brendan
Stukey, Rachel
Melagoda, Adithya
Shanthanam, Sangar
Aravinthan, Visvakumar
Clark, C. Brendan
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Original Date
Digitization Date
Issue Date
2025
Type
Conference paper
Genre
Keywords
Charging station,Electric vehicles (EV),Firefly algorithm,Optimization,Social behavior,Value for energy
Subjects (LCSH)
Citation
Stukey, Rachel & Melagoda, Adithya & Shanthanam, Sangar & Aravinthan, Visvakumar & Clark, Brendan. (2025). Optimal Selection of Electric Vehicle Charging Stations Considering Value of Energy and Consumer Behavior. 1-6. 10.1109/NAPS66256.2025.11272319.
Abstract
The growing adoption of electric vehicles (EVs) introduces pressing challenges in optimizing charging station placement and utilization across highway networks. Infrastructure planning must address diverse user behaviors and systemic constraints, especially as EV sales continue to rise worldwide. This paper presents an integrated framework combining Agent-Based Modeling (ABM) and a nature-inspired Firefly Algorithm, developed in MATLAB, to simulate and optimize EV driver charging decisions. The model incorporates primary factors such as travel distance, wait time, charging duration, energy need, and grid conditions, along with secondary influences including charging anxiety, location desirability, and recreational proximity. Unlike prior studies that isolate behavioral, pricing, or grid considerations, our framework dynamically evaluates optimal charging stations based on a comprehensive charging station score that captures the value of energy, locational impact, and traveler preferences. By selecting optimal stations aligned with grid constraints and traveler context, the system improves charging load distribution and prevents local grid overloads. Case studies using real-world travel data from Kansas City to Denver confirm that our method supports scalable EV infrastructure planning, enhances driver experience, and strengthens utility readiness for increasing demand. These findings demonstrate how agent-based simulation and metaheuristic optimization can be effectively integrated to support responsive, grid-aware EV charging strategies. © 2025 IEEE.
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Publisher
Institute of Electrical and Electronics Engineers
Journal
2025 57th North American Power Symposium, NAPS 2025