Energies, Vol. 19, Pages 830: Hybrid CryStAl and Random Decision Forest Algorithm Control for Ripple Reduction and Efficiency Optimization in Vienna Rectifier-Based EV Charging Systems

Energies, Vol. 19, Pages 830: Hybrid CryStAl and Random Decision Forest Algorithm Control for Ripple Reduction and Efficiency Optimization in Vienna Rectifier-Based EV Charging Systems

Energies doi: 10.3390/en19030830

Authors:
Mohammed Abdullah Ravindran
Kalaiarasi Nallathambi
Mohammed Alruwaili
Ahmed Emara
Narayanamoorthi Rajamanickam

The rapid growth of electric vehicle (EV) deployment has created a strong demand for charging systems capable of handling higher power levels while preserving grid stability and maintaining satisfactory energy quality. In this work, a fast-charging architecture for 400 V battery systems is developed using a Vienna rectifier on the AC front end and a DC–DC buck converter on the DC stage. To enhance the performance of this topology, two complementary control techniques are combined: the Crystal Structure Algorithm (CryStAl), used for offline optimization of switching behavior, and a Random Decision Forest (RDF) model, employed for real-time adaptation to operating conditions. A clear, step-oriented derivation of the converter state–space equations is included to support controller design and ensure reproducibility. This control framework improves the key performance indices, including Total Harmonic Distortion (THD), ripple suppression, efficiency, and power factor correction. Specifically, the Vienna rectifier works on input current shaping and enhances the power quality, while the buck converter maintains a constant DC output appropriate for reliable battery charging. The simulation studies show that the combined CryStAl–RDF approach outperforms the conventional PI- and Particle Swarm Optimization (PSO)-based controllers. The proposed method achieves THD less than 2%, conversion efficiency higher than 97.5%, and a power factor close to unity. The voltage and current ripples are also significantly reduced, which justifies the extended life of the batteries and reliable charging performance. Overall, the results portray the potential of the combined metaheuristic optimization with machine learning-based decision techniques to enhance the behavior of power electronic converters for EV fast-charging applications. The proposed control method offers a practical and scalable route for next-generation EV charging infrastructure.