Novel Hybrid Optimization for MPPT-Based EV Charging Using Cat-Mouse and Honey Badger Algorithms

Abstract

To mitigate carbon emissions and curb the greenhouse gas effect, many countries are transitioning toward renewable energy-based power generation while simultaneously replacing conventional transportation with hybrid and electric vehicles. Given the critical need for efficient charging solutions for electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs), this research explores the growing demand on the traditional power grid, which results in increased costs and operational stress. To address these challenges, the integration of local renewable energy sources, such as solar photovoltaic (PV) and wind energy, into the grid is proposed. However, due to the inherent intermittency of PV power generation, battery storage systems (BSS) are necessary to enhance grid stability.
This paper presents a hybrid charging station concept that integrates solar and wind energy with the grid, ensuring efficient and safe charging for various EV scenarios. The proposed system incorporates on-site PV power generation, utilizing BSS to manage load fluctuations and reduce grid dependency. Additionally, the use of interleaved buck-boost converters within the BSS enhances power conversion efficiency. A battery charging model for solar PV-based systems is developed, featuring a Maximum Power Point Tracking (MPPT) mechanism implemented through a buck-boost converter to optimize efficiency. To further enhance MPPT performance, a novel hybrid optimization algorithm combining the Cat-Mouse and Honey Badger algorithms is introduced. The proposed approach ensures improved efficiency and stability throughout the charging process. Simulation results validate the effectiveness of the hybrid MPPT algorithm, achieving an efficiency rate of 99.99%, thereby optimizing battery charging while minimizing energy losses.