A Comprehensive Review of Topologies and Energy Management Methods for Hybrid Energy Storage Systems in Electric Vehicle Applications

Hybrid energy storage systems (HESSs), combining batteries and supercapacitors (SCs), have emerged as a promising solution to address the conflicting demands of high energy density, power density, and cycle life in electric vehicles (EVs). This paper presents a comprehensive and up-to-date review of power converter topologies and energy management strategies (EMSs) for HESS integration in EVs. First, the paper systematically classifies converter architectures into dual-stage, single stage, and quasi-stage topologies to analyze their operational principles, control flexibility, efficiency, and suitability for modern EVapplications. Detailed comparisons of semi-active, fully active, multi-input, and reconfigurable dual-stage converters, as well as advanced single-stage multi-source inverter (MSI) configurations along with quasi-single-stage and quasi-dual-stage topologies, are provided. Next, the EMSs applicable to HESSs in EVs are reviewed and categorized into rule-based, optimization based, machine learning (ML)-based, and hybrid methodologies. Recent advances in ML, particularly reinforcement learning (RL) algorithms, are critically examined for their potential in enabling adaptive, model-free energy management under complex and uncertain driving conditions. Comprehensive tables and comparative analyses highlight the strengths, limitations, and validation methods of leading techniques. Finally, the paper identifies key trends, technical challenges, and research gaps, and discusses future research directions to guide the co-design of converter topologies and intelligent EMSs for next-generation EVs.