The rapid growth of renewable energy systems and electric vehicles (EVs) is driving the development of advanced power electronic converters with higher efficiency, reliability, flexibility, and dynamic performance. Power converters serve as a critical interface between renewable energy sources, energy storage systems, electric grids, and EV charging infrastructure. However, the nonlinear, time-varying, and uncertain characteristics of these systems create significant challenges for conventional control techniques. Recent advances in Artificial Intelligence (AI), machine learning, deep learning, fuzzy logic, neural networks, and reinforcement learning provi de new opportunities for developing intelligent control strategies capable of adapting to changing operating conditions and improving converter performance.

This Special Issue focuses on recent advances in AI-driven intelligent control techniques for power converter-based renewable energy and electric vehicle applications. The primary objective is to bring together researchers, academics, and industry professionals to present innovative methodologies, control architectures, modeling approaches, and experimental developments that enhance the performance of modern power conversion systems. Particular emphasis is placed on intelligent control strategies that impro ve efficiency, power quality, stability, reliability, fault tolerance, and dynamic response.

Potential topics include, but are not limited to: AI-based control of DC-DC, AC-DC, and DC-AC converters; intelligent maximum power point tracking for photovoltaic and wind energy systems; neural network, fuzzy logic, ANFIS, and hybrid intelligent controllers; reinforcement learning and deep reinforcement learning for power converters; intelligent control of grid-connected inverters; bidirectional converters for battery energy storage; AI-enabled EV charging and vehicle-to-grid systems; power quality and harmonic mitigation; fault diagnosis and fault -tolerant control; model predictive and adaptive intelligent control; digital control and real -time implementation; and AI-based optimization of renewable energy and EV power conversion systems.

The Special Issue welcomes original research articles, review articles, case studies, and experimental or simulation - based studies that contribute to advancing intelligent control methodologies for next -generation renewable energy and electric vehicle systems.

The primary goal of this special issue is to bring together recent advances in AI-driven intelligent control techniques for power converter-based renewable energy and electric vehicle applications. We invite contributions that explore advanced intelligent control strategies for power electronic converters, renewable energy systems, energy storage systems, and electric vehicle charging infrastructure, with particular interest in AI-based converter control, machine learning and deep learning, reinforcement le arning, intelligent MPPT techniques, grid -connected inverters, bidirectional converters, EV charging and vehicle -to-grid systems, power quality improvement, fault -tolerant control, and real-time implementation of intelligent control techniques.