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

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  • Xinran Li

    (Jiangsu Key Laboratory of Urban ITS, Southeast University, No. 2 Southeast University Road, Jiangning District, Nanjing 211189, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, No. 2 Southeast University Road, Jiangning District, Nanjing 211189, China
    School of Transportation, 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
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, No. 2 Southeast University Road, Jiangning District, Nanjing 211189, China
    School of Transportation, 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
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, No. 2 Southeast University Road, Jiangning District, Nanjing 211189, China
    School of Transportation, Southeast University, No. 2 Southeast University Road, Jiangning District, Nanjing 211189, China)

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.

Suggested Citation

  • Xinran Li & Haoxuan Kan & Xuedong Hua & Wei Wang, 2020. "Simulation-Based Electric Vehicle Sustainable Routing with Time-Dependent Stochastic Information," Sustainability, MDPI, vol. 12(6), pages 1-16, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:6:p:2464-:d:335141
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    References listed on IDEAS

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    1. Fu, Liping, 2001. "An adaptive routing algorithm for in-vehicle route guidance systems with real-time information," Transportation Research Part B: Methodological, Elsevier, vol. 35(8), pages 749-765, September.
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    Cited by:

    1. Xiao Lin & Yoshinari Nishiki & Lóránt A. Tavasszy, 2020. "Performance and Intrusiveness of Crowdshipping Systems: An Experiment with Commuting Cyclists in The Netherlands," Sustainability, MDPI, vol. 12(17), pages 1-14, September.
    2. Wang, Jianxin & Lim, Ming K. & Liu, Weihua, 2024. "Promoting intelligent IoT-driven logistics through integrating dynamic demand and sustainable logistics operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).

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