Title: Optimised electric vehicle drive using ANN-controlled induction motor and single inductor multi-port power converter utilising solar and battery sources

Authors: Nitin B. Sawant; A.S. Veerendra; R. Shivarudrawamy; Yogesh V. Mahadik; Aymen Flah; Ch. Punya Sekhar

Addresses: Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal – 576104, Karnataka, India ' Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal – 576104, Karnataka, India ' Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal – 576104, Karnataka, India ' Department of Electrical Engineering, Government Polytechnic, Malvan, Maharashtra, India ' Processes, Energy, Environment and Electrical Systems (Code: LR18ES34), National Engineering School of Gabes, University of Gabes, Gabes – 6072, Tunisia; Centre for Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India; Applied Science Research Center, Applied Science Private University, Amman – 11931, Jordan; Chitkara Centre for Research and Development, Chitkara University, Baddi, Himachal Pradesh, 174103, India ' Acharya Nagarjuna University, Guntur, India

Abstract: This paper presents a single inductor multi-port power converter and an artificial neural network (ANN) controller for improving electric vehicle (EV) drive systems. In this research, the converter ensures seamless power flow and improved energy utilisation, supporting stable operation under varying loads and environmental conditions. The proposed controller dynamically adjusts motor speed and handles power electronics conversions, providing superior performance in managing nonlinearities and transient responses compared to conventional control methods. Furthermore, the increment of the motor speed from 500 rad/sec to 1,000 rad/sec between 0 and 0.35 seconds, the suggested method demonstrated that the ANN controller can handle sudden changes in load references and speed conditions while maintaining stable operation with minimal oscillations or overshoot for 20% increases in the reference voltage. Finally, compared to a PI controller, the results indicate that an ANN controller made speedy adjustments and maintained the intended speed with few variations.

Keywords: artificial neural network; ANN; induction motor; electric vehicle; multi-port power converter; solar power; battery; energy efficiency.

DOI: 10.1504/IJPT.2024.144520

International Journal of Powertrains, 2024 Vol.13 No.4, pp.389 - 416

Received: 03 Sep 2024
Accepted: 19 Oct 2024
Published online: 17 Feb 2025 *

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