TY - JOUR
T1 - Electric Vehicle Charging Station Search in Stochastic Environments
AU - Guillet, Marianne
AU - Hiermann, Gerhard
AU - Kröller, Alexander
AU - Schiffer, Maximilian
N1 - Publisher Copyright:
© 2022 INFORMS
PY - 2022/3
Y1 - 2022/3
N2 - Electric vehicles are a central component of future mobility systems as they promise to reduce local noxious and fine dust emissions, as well as CO2 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.
AB - Electric vehicles are a central component of future mobility systems as they promise to reduce local noxious and fine dust emissions, as well as CO2 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.
KW - Markov decision process
KW - dynamic programming
KW - electric vehicle charging
KW - stochastic search
UR - http://www.scopus.com/inward/record.url?scp=85130419063&partnerID=8YFLogxK
U2 - 10.1287/trsc.2021.1102
DO - 10.1287/trsc.2021.1102
M3 - Article
AN - SCOPUS:85130419063
SN - 0041-1655
VL - 56
SP - 483
EP - 500
JO - Transportation Science
JF - Transportation Science
IS - 2
ER -