Title: Optimised dual hybrid energy storage systems for EV powertrain based on modified genetic algorithm

Authors: Mukil Alagirisamy; Balachandra Pattanaik; Ramesh Redrouthu; Chandu V.V. Muralee Gopi

Addresses: Electrical and Electronics Department, Lincoln University College, Wisma Lincoln, Malaysia ' Department of Electrical Engineering, Lincoln University College, Wisma Lincoln, Malaysia ' Department of Chemical Engineering, College of Biological and Chemical Engineering, Addis Ababa Science and Technology University, P.O. Box 16417, Addis Ababa, Ethiopia ' Department of Electrical Engineering, University of Sharjah, P.O. Box 27272, Sharjah, United Arab Emirates

Abstract: EV has generally been recognised as a viable substitute for internal combustion engine-powered vehicle. The EV is capacity and lifetime of the energy storage system, leading to decreased drive range of the vehicle and rise in price. To overcome these drawbacks, dual-HESS is introduced in the EV. In this work, batteries and ultracapacitors are utilised as HESS. In this work, fuzzy control is also implemented, which is accountable for splitting the energy among the front and rear wheels units in a far more appropriate manner in order to meet the needs of greater performance. Utilising the MATLAB/Simulink software, the entire framework was built with the FTP-75 (urban), US06 (maximum speed as well as required acceleration) and HWFET (highway) driving cycles. When contrasted to a corresponding existing EV deployed with a solo HESS unit, the suggested dual-HESS architecture enhanced the driving range by 145.15 km as well minimising the HESS mass by 23.93%.

Keywords: electric vehicles; GOA; GA; HESS; power management control; PMC; SoC; FLC.

DOI: 10.1504/IJESMS.2023.131794

International Journal of Engineering Systems Modelling and Simulation, 2023 Vol.14 No.3, pp.148 - 157

Received: 13 Sep 2021
Accepted: 16 Nov 2021
Published online: 03 Jul 2023 *

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