Title: Multi-objective metaheuristic optimised PI gains of model reference adaptive controlled induction motor drive for electric vehicle

Authors: Nitesh Tiwari; Shekhar Yadav; Sabha Raj Arya

Addresses: Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, 273010, UP, India ' Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, 273010, UP, India ' Department of Electrical Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, 395007, India

Abstract: The state of the art of this paper is to provide the model reference adaptive control (MRAC) induction motor (IM) drives for electric vehicle (EV) applications. The MRAC technique is utilised in this paper to overcome the problem of direct torque control and indirect vector control (IVC). Two PI controllers are required for designing the MRAC. The gains of both controllers are depending on each other. Hence in this paper, multi-PI gain tunning is done by teaching learning based optimisation (TLBO), DE, GA, particle swarm optimisation (PSO), artificial bee colony (ABC), gray wolf optimization (GWO), Jellyfish optimisation (JFO), Krill herd Optimisation (KHO), Harris hawk's optimisation (HHO), moth flam optimisation (MFO), sparrow search optimisation (SSO), and WO techniques. All optimisation techniques with MRAC are tested by applying static, dynamic, and predefined driving cycles. Every optimisation technique is provided a better result in a specific speed range and driving cycle but JFO optimised technique gives better performance for every driving condition and all speed ranges of vehicles. The overall proposed system is tested by using MATLAB and Simulink.

Keywords: MRAC; differential evolution; driving cycle; electric vehicle; Gray wolf optimisation; jellyfish optimisation; multi-objective optimisation; performance indices.

DOI: 10.1504/IJVP.2023.131973

International Journal of Vehicle Performance, 2023 Vol.9 No.3, pp.272 - 289

Received: 21 Jun 2022
Accepted: 15 Sep 2022
Published online: 05 Jul 2023 *

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