An efficient TSAGTO-MPPT framework for PV-powered electric vehicles using interleaved DC–DC SEPIC converter
Savitha P B1, Madhu S2, Priyashree S2, Shruti V Joshi2, Deekshitha Arasa1 and Kavitha K1
Department of Electrical and Electronics Engineering,Dayananda Sagar College of Engineering, Bengaluru,Karnataka,India1
Department of Electrical and Electronics Engineering,BNM Institute of Technology, Bengaluru,Karnataka,India2
Department of Electrical and Electronics Engineering,BNM Institute of Technology, Bengaluru,Karnataka,India2
Corresponding Author : Savitha P B
Recieved : 29-April-2025; Revised : 23-April-2026; Accepted : 24-April-2026
Abstract
An electric vehicle (EV) is a vehicle that utilizes one or more electric motors for propulsion. It is powered by electricity from external sources and operates using an onboard battery. However, EV charging systems suffer from power conversion losses due to inefficiencies in electronic components such as switches and inductors, leading to reduced overall performance. In this research, a tri-strategy artificial gorilla troops optimization-maximum power point tracking (TSAGTO-MPPT) method is proposed for EV applications using an interleaved direct current-direct current (DC–DC) single-ended primary-inductor converter (SEPIC). The conventional artificial gorilla troops optimization (AGTO) algorithm is enhanced using a tri-strategy approach that incorporates the Halton sequence, information-sharing search, and the golden sine strategy (GSS). These enhancements improve population diversity, avoid local optima, and enhance accuracy by preventing premature convergence. A photovoltaic (PV) system is integrated with the EV to convert solar energy into electrical power, thereby assisting in battery recharging. Maximum power point tracking (MPPT) is employed to optimize system parameters and extract the maximum available energy from the PV system. To validate the effectiveness of the proposed TSAGTO-MPPT method, it is compared with existing approaches, such as the unified firefly ersatz neural network (UFENN) with MPPT. The proposed TSAGTO-MPPT achieves an efficiency of 98.76%, outperforming existing methods such as UFENN with MPPT.
Keywords
Electric vehicles (EVs), Maximum power point tracking (MPPT), Artificial Gorilla Troops Optimization (AGTO), SEPIC Converter, Photovoltaic (PV) system, Energy efficiency.
Cite this article
PB Savitha, S M, S P, Joshi SV, Arasa D, K K. An efficient TSAGTO-MPPT framework for PV-powered electric vehicles using interleaved DC–DC SEPIC converter. International Journal of Advanced Technology and Engineering Exploration. 2026;13(137):595-610. DOI : 10.19101/IJATEE.2025.121220568
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