Routing of Electric Vehicles in a Stochastic Network with Non-recurrent Incidents

Authors

  • Mohammad Arani University of Arkansas at Little Rock, Address, 2801 S. University Ave., Little Rock, AR, 72204, USA
  • Mohammad Mehdi Rezvani Louisiana State University, Baton Rouge, LA, 70803, USA
  • Hamzeh Davarikia McNeese State University, 4205 Ryan St, Lake Charles, LA 70605, USA
  • Yupo Chan University of Arkansas at Little Rock, Address, 2801 S. University Ave., Little Rock, AR, 72204, USA

Keywords:

Electric vehicles routing, FIFO, non-FIFO, stochastic networks

Abstract

An approach for mapping an electric vehicle (EV) driver’s travel time constraints and risk-taking behavior to real-time routing in a probabilistic, time-dependent (or stochastic) network is proposed in this paper. The proposed approach is based on a heuristic algorithm that finds the shortest path according to the driver’s preferences. Accounting for en-route delays and alternate routes, the EV routing problem in stochastic networks is shown to exhibit other than the First-In-First-Out (FIFO) property; i.e., the traveling time for those who depart earlier may not reach sooner of those who depart later or wait en-route in the charging stations. The proposed approach provides EV drivers the option to manage their trip and reach the destination on time, while by taking advantage of the non-FIFO characteristics of the traffic network, charge their cars en-route. The proposed routing algorithm is tested on a given stochastic transportation network. The best routes based on the driver’s preferences are identified while accounting for the best-planned delays at the charging stations or en-route.

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Published

2020-04-08

How to Cite

Arani, M., Rezvani, M. M. ., Davarikia , H. ., & Chan, Y. . (2020). Routing of Electric Vehicles in a Stochastic Network with Non-recurrent Incidents. American Scientific Research Journal for Engineering, Technology, and Sciences, 67(1), 25–35. Retrieved from https://www.asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/5764

Issue

Vol. 67 No. 1 (2020)

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Articles

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