Optimized multi-objective energy management strategy for solar-fuel cell hybrid electric vehicles using RSM and SFOA

Abstract

This study presents an advanced energy management strategy for solar-assisted fuel cell hybrid electric vehicles (FCHEVs), integrating lithium-ion battery storage, proton exchange membrane fuel cells (PEMFC), and photovoltaic (PV) panels. A comprehensive system model was developed, encompassing PV modules, FC stacks, power converters, and traction motors under both static and dynamic driving conditions. To optimize key operational parameters—namely, power split ratio, converter duty cycle, and load demand—Response Surface Methodology (RSM) was employed. This approach significantly enhanced system performance: energy efficiency improved to 88 %, surpassing the baseline range of 78–80 %, and battery state-of-charge (SoC) gain reached 2.4 %, compared to 1.1–1.8 % without optimization. Furthermore, fuel cell degradation was effectively minimized to 0.04, a substantial reduction from 0.1 observed in suboptimal conditions. These results highlight the potential of integrating statistical modeling with bio-inspired optimization techniques to achieve intelligent, real-time energy management in FCHEVs, leading to greater energy utilization, extended driving range, and improved component longevity.