Energy management systems for solar electric vehicles: Architectures, control strategies, and future research directions

Solar Electric Vehicles (SEVs) have emerged as a promising solution for sustainable transportation by integrating photovoltaic energy harvesting with electric propulsion systems. However, their performance is constrained by intermittent solar irradiance, limited photovoltaic area, battery degradation, and the lack of unified energy management strategies. This review presents a comprehensive and structured analysis of energy management systems (EMS) for SEVs, covering photovoltaic integration, battery storage, control strategies, and system-level architectures. A quantitative comparison of Maximum Power Point Tracking techniques, battery management approaches, and EMS control strategies is provided based on metrics such as efficiency, computational complexity, and real-time applicability. The review further classifies EMS methods into rule-based, optimization-based, and artificial intelligence-driven approaches, highlighting their trade-offs and practical limitations. In addition, real-world constraints including solar variability, thermal effects, and driving cycle interactions are analyzed to evaluate system performance under realistic conditions. Recent advancements such as reinforcement learning-based EMS, distributed optimization, vehicle-to-grid integration, and digital twin frameworks are also discussed to identify emerging research directions. Based on a systematic synthesis of existing literature, key research gaps are identified, emphasizing the need for adaptive, multi-physics, and experimentally validated EMS frameworks. This review provides a comprehensive reference for developing next-generation intelligent energy management solutions for solar electric vehicles.