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

S. M. Nawazish Ali, Vivek Sharma, M. J. Hossain, Subhas C. Mukhopadhyay and Dong Wang
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S. M. Nawazish Ali: School of Engineering, Macquarie University, Sydney, NSW 2109, Australia
Vivek Sharma: School of Engineering, Macquarie University, Sydney, NSW 2109, Australia
M. J. Hossain: School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia
Subhas C. Mukhopadhyay: School of Engineering, Macquarie University, Sydney, NSW 2109, Australia
Dong Wang: Department of Energy Technology, Aalborg University, DK-9220 Aalborg, Denmark

Energies, 2021, vol. 14, issue 12, 1-16

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.

Keywords: induction machine drive; drive energy consumption; linear parameter varying control; EV powertrain; genetic algorithms (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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