Document Type : Research Article
Authors
1
Post-Doctoral Research Scholar, Department of Computer Science and Engineering, Manipur International University, Manipur, India-795140. & Associate Professor, MAI -NEFHI College of Engineering & Technology, Asmara, Eritrea.
2
Dean School of Physical Science and Engineering, Manipur International University, Manipur, India- 795140.
3
Department of Electrical and Electronics Engineering, Sri Sai Ram Engineering College, Chennai, Tamil Nadu, India-600044.
4
Department of Electrical and Electronics Engineering, Sriram Engineering College, Perumalpattu, Chennai, Tamil Nadu, India-602024.
5
Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Avinashi - Coimbatore Road, Arasur, Tamil Nadu, India-641407.
6
Department of Electrical and Electronics Engineering, SASI Institute of Technology and Engineering, Tadepalligudem, Kadakatla, Andhra Pradesh, India-534101.
10.22060/eej.2026.25681.5987
Abstract
As solar-powered electric vehicles (EVs) become more prevalent, intelligent energy management techniques are necessary to optimise the use of renewable energy and boost system efficiency. For sustainable EV operation, this paper proposes an AI-enabled energy management architecture that combines an optimised maximum power point tracking (MPPT) controller, a Coupled Inductor SEPIC Clamp (CISC) converter and intelligent route prediction. By diminishing input current ripple, lowering switch voltage stress and increasing conversion efficiency, the CISC converter is employed for increasing the Photovoltaic (PV) voltage. A Superb Fairy Optimization-based Radial Basis Function Inference System (SFO-RBFIS) MPPT controller is developed for optimising solar energy extraction under dynamic irradiance and temperature circumstances. It offers quick convergence, excellent tracking accuracy and few steady-state oscillations. Furthermore, an Artificial Neural Network (ANN) and the SFO Algorithm are employed in a routing strategy that anticipates energy-efficient routing. The developed system attains a converter efficiency of 97.3%, greater MPPT performance and superior energy management when compared to traditional approaches, according to outcomes in the MATLAB tool. The developed framework offers a sustainable and energy-efficient solution for future solar-powered EV systems.
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