Balanced Electric Vehicle Charging Under Uncertainty: From User-Centric to System-Centric Approaches

Marianne Guillet ()
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Marianne Guillet: Technical University of Munich

Chapter Chapter 49 in Operations Research Proceedings 2023, 2025, pp 383-388 from Springer

Abstract: Abstract Electric vehicles (EVs) are an essential lever for decarbonizing the transportation sector. However, many drivers remain reluctant to purchase an EV as they do not trust the public charging infrastructure, due to charging station congestion risks and unreliable charging station availability. This thesis contributes novel models and solution methods, that aim to reliably help EV drivers find a free public charging station in the presence of uncertainties. From a short-term user perspective, the objective is to provide reliable in-car guidance instructions that explicitly account for charging station availability uncertainty. In a single-agent setting, an algorithmic framework composed of a rollout and a labeling algorithm can save up to 44% of a driver’s search time. In a multi-agent setting, efficient driver coordination strategies through information-sharing and charging requests centralization can decrease the multi-agent search cost by up to 38%, compared to uncoordinated greedy searches. From a long-term perspective, the objective is to avoid public charging station utilization conflicts of drivers that get guidance instructions through self-interested navigation platforms. A mechanism design approach allows gearing the behavior of such platforms towards an outcome that benefits all EV drivers, by decreasing the total cost by up to 52% in an offline setting and by up to 42% in an online setting.

Keywords: Electric vehicle; Stochastic search; Multi-agent system; Mechanism design (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-031-58405-3_49

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