Arinze, Stella N. and Nwajana, Augustine O. ORCID: https://orcid.org/0000-0001-6591-5269 (2026) AI-Driven hybrid battery–supercapacitor systems for electric vehicles: performance analysis and opportunities. World Electric Vehicle Journal, 17 (7):380. pp. 1-74. ISSN 2032-6653 (Online) (doi:10.3390/wevj17070380)

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Abstract

The rapid adoption of electric vehicles (EVs) has intensified the demand for advanced energy storage technologies capable of delivering high energy density, high power density, enhanced safety, and extended service life. Although lithium-ion batteries remain the dominant energy storage technology for EVs, their limited power capability, thermal degradation, and accelerated aging under high transient loads constrain vehicle performance. Battery–supercapacitor hybrid energy storage systems (HESSs) have emerged as a promising solution by combining the high energy density of batteries with the high-power density and rapid charge–discharge capability of supercapacitors. However, the increasing complexity of HESS architecture requires intelligent energy management strategies to optimize power allocation, battery protection, thermal regulation, and overall system efficiency. Existing review papers primarily address individual aspects of HESS architecture, battery management, or artificial intelligence (AI)-based control, leaving a lack of a unified review integrating these topics. This paper addresses this gap by reviewing 181 publications published between 2020 and 2026, covering HESS architectures, conventional and AI-driven energy management strategies, machine learning, deep learning, reinforcement learning, battery state estimation, diagnostics, prognostics, thermal management, and fault diagnosis. The reviewed studies are critically analyzed to assess the impact of AI on battery lifetime, regenerative braking, charging performance, thermal behavior, and energy efficiency. The review further identifies emerging research directions, including explainable AI, digital twins, federated learning, edge intelligence, vehicle-to-grid integration, and cybersecurity-aware energy management. The findings indicate that AI-based approaches generally demonstrate greater adaptability, predictive capability, and battery protection than conventional methods under dynamic operating conditions, although challenges related to computational complexity, real-time implementation, data availability, explainability, cybersecurity, and standardization remain significant barriers to large-scale deployment.

Item Type:	Article
Uncontrolled Keywords:	battery management system, energy management strategies, deep reinforcement learning, state-of-charge estimation, battery digital twin, vehicle-to-grid; intelligent, transportation systems
Subjects:	T Technology > T Technology (General) T Technology > TL Motor vehicles. Aeronautics. Astronautics T Technology > TS Manufactures
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Engineering (ENG) Faculty of Engineering & Science > School of Science (SCI)
Related URLs:	Publisher
Last Modified:	24 Aug 2026 16:01
URI:	https://gala.gre.ac.uk/id/eprint/54296

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