Adaptive Hybrid Energy Management for Vehicle-Mounted Wind-Solar Energy Harvesting Systems

Abstract

The growing demand for self-sustaining electric vehicles highlights the limited adaptability of conventional onboard renewable energy systems to dynamic environmental and vehicular conditions. This study proposes a Vehicle-Mounted Wind–Solar Hybrid Renewable Energy System (VMW-WEH++) integrating an Adaptive Resonant Energy Converter (AREC) with a Predictive Hybrid Energy Management (P-HEM) framework for intelligent multi-source power coordination. The system operates in a state-aware manner: solar energy is prioritized during vehicle motion to avoid aerodynamic losses, while wind energy is maximized during stationary or idle states based on predicted availability. AREC replaces conventional Maximum Power Point Tracking (MPPT) converters by dynamically tuning its resonant LC network to maintain impedance matching for both Photovoltaic (PV) and micro-turbine inputs, achieving conversion efficiencies exceeding 95% with reduced switching losses. The P-HEM framework combines medium-term forecasting using Prophet and short-term prediction via a Temporal Convolutional Network (TCN), enabling proactive energy scheduling. A hybrid battery–supercapacitor storage architecture ensures voltage stability and reduced battery stress. MATLAB/Simulink results demonstrate improved efficiency, higher usable harvested energy, and enhanced SoC stability and adaptive energy management.