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

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...

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Veröffentlicht in:Sustainability Jg. 12; H. 6; S. 2464
Hauptverfasser: Li, Xinran, Kan, Haoxuan, Hua, Xuedong, Wang, Wei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 20.03.2020
Schlagworte:
Algorithms
Climate change
Dynamic programming
Electric vehicles
Energy consumption
Markov analysis
Research methodology
Sustainability
Travel
ISSN:2071-1050, 2071-1050
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:2071-1050
2071-1050
DOI:10.3390/su12062464