Genetic Algorithm-based Control of a Modular On-Board Charger for Electric Vehicle Applications
Year: 2025
Type: conference-paper
Abstract: This paper presents the operation and control of a modular on-board charger (OBC) for electric vehicle (EV) applications. The modular design enhances fault tolerance, scalability, and thermal management, making it suitable for high-power EV applications. Singlestage isolated Cuk-based converters are used as submodules, supporting bidirectional power flow for enhanced energy efficiency. Additionally, the modular OBC operates efficiently in various modes, including normal driving, regenerative braking, and grid-connected charging, thanks to its integrated structure. A key innovation is the implementation of genetic algorithm (GA)-based controllers, which optimise control parameters to address the right-half-plane (RHP) zero challenges inherent in Cuk converters, thereby improving system stability and dynamic performance. Simulation and experimental results in charging mode demonstrate robust performance, highlighting precise grid current control, effective power factor correction, and stable battery charging under both normal and partial fault conditions.
Authors: Esfahani, Fatemeh Nasr, Parvizimosaed, Mohammadreza, Ebrahimi, Javad, Bakhshai, Alireza, Ma, Xiadong, Darwish, Ahmed
Institutions: Lancaster University, Queen's University, Queens University
Language: English
Topic: Advanced DC-DC Converters
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6908961986
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