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Energy-efficient routing for electri...
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Abousleiman, Rami Dib.
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Energy-efficient routing for electric vehicles using metaheuristic optimization methods.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Energy-efficient routing for electric vehicles using metaheuristic optimization methods./
作者:
Abousleiman, Rami Dib.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2014,
面頁冊數:
127 p.
附註:
Source: Dissertation Abstracts International, Volume: 78-05(E), Section: B.
Contained By:
Dissertation Abstracts International78-05B(E).
標題:
Electrical engineering. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10304423
ISBN:
9781369480917
Energy-efficient routing for electric vehicles using metaheuristic optimization methods.
Abousleiman, Rami Dib.
Energy-efficient routing for electric vehicles using metaheuristic optimization methods.
- Ann Arbor : ProQuest Dissertations & Theses, 2014 - 127 p.
Source: Dissertation Abstracts International, Volume: 78-05(E), Section: B.
Thesis (Ph.D.)--Oakland University, 2014.
Environmental concerns, energy dependency, and unstable fuel prices have led to an increased market share of electric vehicles. Currently, vehicle routing algorithms are designed for fossil-fueled vehicles. Energy-efficient routing for electric vehicles, on the other hand, require different solutions. "Negative path" costs generated by regenerative braking, battery power and energy limits, and vehicle parameters that are only available at query time, make the task of electric vehicle energy-efficient routing a challenging problem. In this work, a model representing electric vehicles is presented. The developed model covers most factors affecting the load on the electric vehicle's battery including traffic and ambient temperatures. The model was validated using a 2013 Fiat 500e electric vehicle. The average error was reported to be 1.03% after 306 kilometers of test-driving. After the model was validated, 2 metaheuristic search approaches were used to study the energy efficient routing problem for electric vehicles. Ant Colony Optimization and Particle Swarm Optimization are presented and implemented. Real-world test results show improvements in the energy consumption of electric vehicles. A real-world test was conducted on an 18 kilometer routing problem and the results where compared to the routes generated by Google Maps and MapQuest. The alternative route that was generated by the developed algorithms proved to be more than 9.3% energy efficient than the most energy-efficient route that was generated by the traditional online routing services. Finally, the developed algorithms were tested against the Bellman-Ford algorithm, which is one of the few deterministic algorithms that supports negative paths. The metaheuristic methods were able to outperform Bellman-Ford and generate much faster solutions mainly because of its high computational complexity.
ISBN: 9781369480917Subjects--Topical Terms:
649834
Electrical engineering.
Energy-efficient routing for electric vehicles using metaheuristic optimization methods.
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Environmental concerns, energy dependency, and unstable fuel prices have led to an increased market share of electric vehicles. Currently, vehicle routing algorithms are designed for fossil-fueled vehicles. Energy-efficient routing for electric vehicles, on the other hand, require different solutions. "Negative path" costs generated by regenerative braking, battery power and energy limits, and vehicle parameters that are only available at query time, make the task of electric vehicle energy-efficient routing a challenging problem. In this work, a model representing electric vehicles is presented. The developed model covers most factors affecting the load on the electric vehicle's battery including traffic and ambient temperatures. The model was validated using a 2013 Fiat 500e electric vehicle. The average error was reported to be 1.03% after 306 kilometers of test-driving. After the model was validated, 2 metaheuristic search approaches were used to study the energy efficient routing problem for electric vehicles. Ant Colony Optimization and Particle Swarm Optimization are presented and implemented. Real-world test results show improvements in the energy consumption of electric vehicles. A real-world test was conducted on an 18 kilometer routing problem and the results where compared to the routes generated by Google Maps and MapQuest. The alternative route that was generated by the developed algorithms proved to be more than 9.3% energy efficient than the most energy-efficient route that was generated by the traditional online routing services. Finally, the developed algorithms were tested against the Bellman-Ford algorithm, which is one of the few deterministic algorithms that supports negative paths. The metaheuristic methods were able to outperform Bellman-Ford and generate much faster solutions mainly because of its high computational complexity.
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