Electric Vehicle Charging Station Search in Stochastic Environments

Marianne Guillet (), Gerhard Hiermann (), Alexander Kröller () and Maximilian Schiffer ()
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Marianne Guillet: TomTom Location Technology Germany GmbH, 12435 Berlin, Germany; School of Management, Technical University of Munich, 80333 Munich, Germany; Operations Management, RWTH Aachen University, 52072 Aachen, Germany
Gerhard Hiermann: School of Management, Technical University of Munich, 80333 Munich, Germany
Alexander Kröller: TomTom Location Technology Germany GmbH, 12435 Berlin, Germany
Maximilian Schiffer: School of Management, Technical University of Munich, 80333 Munich, Germany; Munich Data Science Institute, Technical University of Munich, 85748 Munich, Germany

Transportation Science, 2022, vol. 56, issue 2, 483-500

Abstract: Electric vehicles are a central component of future mobility systems as they promise to reduce local noxious and fine dust emissions, as well as CO 2 emissions, if fed by clean energy sources. However, the adoption of electric vehicles so far fell short of expectations despite significant governmental incentives. One reason for this slow adoption is the drivers’ perceived range anxiety, especially for individually owned vehicles. Here, bad user experiences (e.g., conventional cars blocking charging stations or inconsistent real-time availability data) manifest the drivers’ range anxiety. Against this background, we study stochastic search algorithms that can be readily deployed in today’s navigation systems in order to minimize detours to reach an available charging station. We model such a search as a finite-horizon Markov decision process and present a comprehensive framework that considers different problem variants, speedup techniques, and three solution algorithms: an exact labeling algorithm, a heuristic labeling algorithm, and a rollout algorithm. Extensive numerical studies show that our algorithms significantly decrease the expected time to find a free charging station while increasing the solution-quality robustness and the likelihood that a search is successful compared with myopic approaches.

Keywords: stochastic search; Markov decision process; dynamic programming; electric vehicle charging (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:56:y:2022:i:2:p:483-500

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