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Optimized Energy Control Scheme for Electric Drive of EV Powertrain Using Genetic Algorithms

Author

Listed:
  • S. M. Nawazish Ali

    (School of Engineering, Macquarie University, Sydney, NSW 2109, Australia
    Current address: School of Engineering, Macquarie University, Sydney, NSW 2109, Australia.
    These authors contributed equally to this work.)

  • Vivek Sharma

    (School of Engineering, Macquarie University, Sydney, NSW 2109, Australia
    These authors contributed equally to this work.)

  • M. J. Hossain

    (School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia
    These authors contributed equally to this work.)

  • Subhas C. Mukhopadhyay

    (School of Engineering, Macquarie University, Sydney, NSW 2109, Australia
    These authors contributed equally to this work.)

  • Dong Wang

    (Department of Energy Technology, Aalborg University, DK-9220 Aalborg, Denmark
    These authors contributed equally to this work.)

Abstract

Automotive applications often experience conflicting-objective optimization problems focusing on performance parameters that are catered through precisely developed cost functions. Two such conflicting objectives which substantially affect the working of traction machine drive are maximizing its speed performance and minimizing its energy consumption. In case of an electric vehicle (EV) powertrain, drive energy is bounded by battery dynamics (charging and capacity) which depend on the consumption of drive voltage and current caused by driving cycle schedules, traffic state, EV loading, and drive temperature. In other words, battery consumption of an EV depends upon its drive energy consumption. A conventional control technique improves the speed performance of EV at the cost of its drive energy consumption. However, the proposed optimized energy control (OEC) scheme optimizes this energy consumption by using robust linear parameter varying (LPV) control tuned by genetic algorithms which significantly improves the EV powertrain performance. The analysis of OEC scheme is conducted on the developed vehicle simulator through MATLAB/Simulink based simulations as well as on an induction machine drive platform. The accuracy of the proposed OEC is quantitatively assessed to be 99.3% regarding speed performance which is elaborated by the drive speed, voltage, and current results against standard driving cycles.

Suggested Citation

  • S. M. Nawazish Ali & Vivek Sharma & M. J. Hossain & Subhas C. Mukhopadhyay & Dong Wang, 2021. "Optimized Energy Control Scheme for Electric Drive of EV Powertrain Using Genetic Algorithms," Energies, MDPI, vol. 14(12), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3529-:d:574628
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