A Systematic Review On Integrated Optimization Of Solar PV Tilt, Supercapacitor Buffering, And AI-Driven Energy Management In Fchevs
DOI:
https://doi.org/10.29284/45nx3p91Keywords:
Fuel cell hybrid electric vehicles (FCHEVs), Vehicle-integrated photovoltaics (VIPV), Solar PV tilt angle optimization, Particle swarm optimization (PSO), Artificial neural networks (ANN), Supercapacitors (SCs), Battery supercapacitor hybrid energy storage systems, Lithium-ion battery degradation, Battery protection and peak shaving, Energy management strategies (EMS), Model predictive control (MPC), Reinforcement learning (RL), Hardware-in-the-loop (HIL) simulationAbstract
Fuel Cell Hybrid Electric Vehicles (FCHEVs) with vehicle-integrated photovoltaics (VIPV), supercapacitor-based hybrid energy storage systems, and intelligent energy management strategies (EMS) have emerged as potentially viable remedies to sustainable mobility. Nevertheless, their mass scale deployment is limited by inefficient harvesting of photovoltaic energy, rapid battery degradation, and inefficient power allocation in the changing driving conditions. This paper is a systematic review of the three interconnected optimisation areas, namely, (i) optimisation of tilt-angle in solar PV systems, (ii) capacity enhancement of supercapacitors in battery protection, and (iii) the planning of energy in FCHEVs. Peer-reviewed articles published since 2018 using the PRISMA 2020 were analyzed and synthesized in a comparative way. The review states that adaptive tilt optimisation and AI-supported EMS consistently outperform non-active ones and rule-driven systems, and buffering with supercapacitors significantly mitigates high-rate battery stress. A unified supervisory optimisation model reported in the recent literature is presented to identify system-level synergies and implementation issues. Lastly, research gaps that are not addressed and future directions are provided to advance renewable-hybrid FCHEVs toward commercial viability.References
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