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

Seyd Muhammad Nawazish Ali*, Vivek Sharma, Md. Jahangir Hossain, Subhas Mukhopadhyay, Dong Wang

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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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
Original languageEnglish
Article number3529
Issue number12
Number of pages16
Publication statusPublished - Jun 2021


  • Drive energy consumption
  • EV powertrain
  • Genetic algorithms
  • Induction machine drive
  • Linear parameter varying control


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