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Application of Evolutionary Algorithm for Triobjective Optimization: Electric Vehicle

Author

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  • Naourez Ben Hadj

    (National Engineering School of Sfax, University of Sfax, Sfax, Tunisia)

  • Jalila Kaouthar Kammoun

    (National Engineering School of Sfax, University of Sfax, Sfax, Tunisia)

  • Rafik Neji

    (National Engineering School of Sfax, University of Sfax, Sfax, Tunisia)

Abstract

For Electric Vehicles (EVs), Weight and losses reduction are important factors not only in reducing the energy consumption and cost but also in increasing autonomy. This paper describes the application of an evolutionary algorithm for multiobjective optimization in the traction chain (TC) of pure EV. In this study, the optimisation algorithm is based on the Strength Pareto Evolutionary Algorithm (SPEA-II) and the fitness function is defined so as to minimize the electric vehicle cost (EVC), the electric vehicle weight (EVW) and the losses in the electric vehicle (EVL). Also, in this study, different requirements are considered as constraints like the efficiency of the permanent magnets engine, the number of conductor in the slots, the winding temperature…The simulation results show the effectiveness of the approach and reduction in EVC, EVW and EVL while ensuring that the electric vehicle performance is not sacrificed.

Suggested Citation

  • Naourez Ben Hadj & Jalila Kaouthar Kammoun & Rafik Neji, 2014. "Application of Evolutionary Algorithm for Triobjective Optimization: Electric Vehicle," International Journal of Energy Optimization and Engineering (IJEOE), IGI Global, vol. 3(3), pages 1-19, July.
    • Handle: RePEc:igg:jeoe00:v:3:y:2014:i:3:p:1-19
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