Simulation-Based Electric Vehicle Sustainable Routing with Time-Dependent Stochastic Information

Xinran Li, Haoxuan Kan, Xuedong Hua and Wei Wang
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Xinran Li: Jiangsu Key Laboratory of Urban ITS, Southeast University, No. 2 Southeast University Road, Jiangning District, Nanjing 211189, China
Haoxuan Kan: School of Mechanical Engineering, Southeast University, No. 2 Southeast University Road, Jiangning District, Nanjing 211189, China
Xuedong Hua: Jiangsu Key Laboratory of Urban ITS, Southeast University, No. 2 Southeast University Road, Jiangning District, Nanjing 211189, China
Wei Wang: Jiangsu Key Laboratory of Urban ITS, Southeast University, No. 2 Southeast University Road, Jiangning District, Nanjing 211189, China

Sustainability, 2020, vol. 12, issue 6, 1-16

Abstract: We propose a routing method for electric vehicles that finds a route with minimal expected travel time in time-dependent stochastic networks. The method first estimates whether the vehicle can reach the destination with the current battery level and selects potential reasonable charging stations if needed. Then, the route-search problem is formulated as a shortest path problem with time-dependent stochastic disruptions, using a Markov decision process. The shortest path problem is solved by an approximate dynamic programming algorithm to improve calculation efficiency in complex networks. Several simulation cases and a scenario-based example are given to prove the validity of the method.

Keywords: electric vehicle; routing method; stochastic information; Markov decision process; approximate dynamic programming algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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