Department
Department of Electrical and Computer Engineering
Document Type
Article
Publication Date
11-24-2025
Embargo Period
9-28-2026
Abstract
Hybrid energy storage systems (HESS) integrating batteries and supercapacitors offer a promising solution to overcome the limitations of battery-only architectures in electric vehicles (EVs). By leveraging the high energy density of batteries and the high power density of supercapacitors, HESS can enhance power delivery, improve energy efficiency, and extend battery lifespan. The effectiveness of HESS, however, is largely determined by the energy management system (EMS) that coordinates power flow under dynamic driving conditions. Unlike existing reviews, this work addresses common gaps in the literature, including the lack of industrial energy storage component examples, limited coverage of recently developed EMS strategies, and insufficient analysis of renewable energy integration. This paper presents a comprehensive review of energy systems in EVs, focusing on EMS strategies and solar PV integration for battery–supercapacitor HESS. Rule-based, optimization-based, model predictive control (MPC), fuzzy logic control (FLC), and reinforcement learning (RL) methods are investigated and compared in terms of control performance, computational cost, adaptability, and real-time feasibility. The review also identifies research gaps and outlines future directions, emphasizing the need for intelligent, adaptive, and multi-objective EMS frameworks for next-generation EVs.
Journal Title
IEEE Access
Journal ISSN
2169-3536
Volume
13
First Page
200555
Last Page
200583
Digital Object Identifier (DOI)
10.1109/ACCESS.2025.3636410
Comments
This article received funding through Kennesaw State University's Faculty Open Access Publishing Fund, supported by the KSU Libraries and KSU Office of Research.