Genetic Algorithm-based Control of a Modular On-Board Charger for Electric Vehicle Applications

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. Single-stage 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.

Publication Details

Journal
Lancaster EPrints (Lancaster University)
Published
2025-06-06
Primary Topic
Magnetic Field Sensors Techniques
Type
article
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article

Genetic Algorithm-based Control of a Modular On-Board Charger for Electric Vehicle Applications

Lancaster EPrints (Lancaster University)
Magnetic Field Sensors Techniques
article

Genetic Algorithm-based Control of a Modular On-Board Charger for Electric Vehicle Applications

article en

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. Single-stage 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.

Lancaster EPrints (Lancaster University)
Affordable and clean energy
Openalex Percentile: Top 53%
Magnetic Field Sensors Techniques
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Genetic Algorithm-based Control of a Modular On-Board Charger for Electric Vehicle Applications · Lancaster EPrints (Lancaster University) (2025) | TGRS Research Map | TGRS