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Optimized Path Planning for Electric Vehicle Routing and Charging Station Navigation Systems

Optimized Path Planning for Electric Vehicle Routing and Charging Station Navigation Systems

Mouhcine Elgarej, Mansouri Khalifa, Mohamed Youssfi
Copyright: © 2020 |Volume: 11 |Issue: 3 |Pages: 21
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799802860|DOI: 10.4018/IJAMC.2020070103
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MLA

Elgarej, Mouhcine, et al. "Optimized Path Planning for Electric Vehicle Routing and Charging Station Navigation Systems." IJAMC vol.11, no.3 2020: pp.58-78. http://doi.org/10.4018/IJAMC.2020070103

APA

Elgarej, M., Khalifa, M., & Youssfi, M. (2020). Optimized Path Planning for Electric Vehicle Routing and Charging Station Navigation Systems. International Journal of Applied Metaheuristic Computing (IJAMC), 11(3), 58-78. http://doi.org/10.4018/IJAMC.2020070103

Chicago

Elgarej, Mouhcine, Mansouri Khalifa, and Mohamed Youssfi. "Optimized Path Planning for Electric Vehicle Routing and Charging Station Navigation Systems," International Journal of Applied Metaheuristic Computing (IJAMC) 11, no.3: 58-78. http://doi.org/10.4018/IJAMC.2020070103

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Abstract

With the increase in the number of electric vehicles (EV) on the street in the last years, the drivers of EVs are suffering from the problem of guiding themselves toward the nearest charging stations for recharging their batteries or finding the shortest routes toward their destinations. Although, the electric vehicle planning problem (EPP) is designed to achieve several transactions such as battery energy restrictions and the challenge of finding the nearest charging stations to the position of the electric vehicle. In this work, a new distributed system for electric vehicle routing is based on a novel driving strategy using a distributed Ant system algorithm (AS). The distributed architecture minimizes the total travelling path for the EV to attain the destination by proposing a set of the nearest charging stations that can be visited for recharging during his travels. Simulation result proved that our prototype is able to prepare optimal solutions within a reasonable time and forwarding EVs toward the nearest charging stations during their trips.

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