Multi-constrained route optimization for Electric Vehicles (EVs) using Particle Swarm Optimization (PSO)

• Published in: 2011 11th International Conference on Intelligent Systems Design and Applications pp. 391 - 396
• Main Authors: Siddiqi, U. F., Shiraishi, Y., Sait, S. M.
• Format: Conference Proceeding
• Language: English; Japanese
• Published: IEEE 01.11.2011
• Subjects: Arrays; Batteries; Electric Vehicles (EVs); Genetic algorithms; Multi Constrained Optimal Path; Optimization; Quality of service; Roads; Route Optimization; Simulated Evolution (SimE); Vehicles
• ISSN: 2164-7143
• DOI: 10.1109/ISDA.2011.6121687

Route optimization (RO) is an important feature of the Electric Vehicles (EVs) which is responsible for finding optimized paths between any source and destination nodes in the road network. In this paper, the RO problem of EVs is solved by using the Multi Constrained Optimal Path (MCOP) approach. The proposed MCOP problem aims to minimize the length of the path and meets constraints on total travelling time, total time delay due to signals, total recharging time, and total recharging cost. The Penalty Function method is used to transform the MCOP problem into unconstrained optimization problem. The unconstrained optimization is performed by using a Particle Swarm Optimization (PSO) based algorithm. The proposed algorithm has innovative methods for finding the velocity of the particles and updating their positions. The performance of the proposed algorithm is compared with two previous heuristics: H_MCOP and Genetic Algorithm (GA). The time of optimization is varied between 1 second (s) and 5s. The proposed algorithm has obtained the minimum value of the objective function in at-least 9.375% more test instances than the GA and H_MCOP.