Intelligence Based Electric Vehicle Route Planning System
Safeera N1, Chitharanjan K2

1Safeera N, Department of Computer Science, SCT College of Engineering, University of Kerala, Trivandrum (Kerala), India.
2Mr. Chitharanjan K, Department of Computer Science, SCT College of Engineering, University of Kerala, Trivandrum (Kerala), India.

Manuscript received on 15 August 2015 | Revised Manuscript received on 25 August 2015 | Manuscript Published on 30 August 2015 | PP: 308-312 | Volume-4 Issue-6, August 2015 | Retrieval Number: F4238084615/15©BEIESP
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Abstract: Now a day’s Electric Vehicles (EVs) are popular all over the world. The drift towards electric vehicles is a result of severe environmental problems caused by the Internal Combustion Engine Vehicles (ICVs). EV posses performance weaknesses in case of transportation efficiency, such as low energy density of batteries, scarcity of public charging stops, long waiting and charging time, wastage of energy due to traffic, accident and blocking conditions. EV Routing Problem (EVRP) is relevant in the recent scenario to get the efficient route, assisted by coordinating distance travelled and availability of charging stops. Besides, it incorporates the traffic parameters, blocking conditions and accidents to bring this application in real world logistics. To make EVs as the future of personal transportation and to increase the user’s acceptance, these problems should be considered. In congested areas, the concurrent and frequent recharging demands lead to high waiting time at the charging area, thus affecting both charging network and vehicle travel time. In this work, optimal route for the electric vehicles is computed that minimizes the associated cost, which is a combination of travel time, charging time and the energy consumption along the route. Inputs to the route planning system are the distance to be travelled, vehicle speed, states of charge and even sometimes the information about traffic conditions, blocks and accidents. The output of the energy management controller is to provide an optimal route that achieves best performance and overall system efficiency. As the stated problem is non-polynomial, the proposed work uses metaheuristic algorithms for finding an optimal route in a reasonable time. Genetic algorithm(GA) and Particle Swarm Optimization (PSO) are then used to solve the energy efficient routing problem for electric vehicles. These two metaheuristic methods are analyzed and studied and the results and performance of each are then compared and contrasted.
Keywords: EVRP, Charging Stations, GA, PSO

Scope of the Article: Routing, Switching and Addressing Techniques