Effective Energy Management Technique for Fuel Cell Hybrid Electric Vehicle using Deep Reinforcement Learning Technique

Fuel Cell Hybrid Electric Vehicles (FCHEVs) offer an environmentally friendly alternative to conventional vehicles, yet their performance heavily relies on the efficiency of their Energy Management Systems (EMS). Traditional energy management strategies often fall short in handling the dynamic nature of real-world driving conditions. This paper presents aneffective energy management technique for FCHEVs based on Deep Reinforcement Learning (DRL), which leverages the power of deep neural networks and reinforcement learning to autonomously optimize energy distribution between the fuel cell, battery, and super capacitor. The proposed DRL-based EMS continuously learns from its environment, adapting to varying power demands and driving conditions in real-time, ultimately reducing fuel consumption and enhancing system efficiency. This approach effectively balances power allocation while maintaining system longevity, thereby offering a robust solution for real-time, adaptive energy management in FCHEVs. The findings of this study suggest that DRL can significantly enhance the overall efficiency of FCHEVs, paving the way for more sustainable and high-performance electric vehicle systems.