Optimal Energy Management for Multi-Stack Fuel Cell Vehicles based on Hybrid Quantum Reinforcement Learning

This paper proposes a driving condition recognition-based (DCR-based) Hybrid Quantum Deep Deterministic Policy Gradient (HQDDPG) method for energy management in multi-stack fuel cell vehicle hybrid power systems (MFCV HPSs) and its quantum simulation setup on Digital Signal Processors (DSPs). Driving conditions are initially segmented into micro-trips and clustered into three types. The DCR method, using a Learning Vector Quantization Neural Network (LVQNN), is then developed, thus accurately and efficiently identifying driving condition types. Subsequently, quantum reinforcement learning is proposed to achieve optimal energy manaement of MFCV HPSS, i.e., power allocation among the multiple fuel cells to minimize the economic metrics based on the DCR results. Compared to classical large-scale neural networks, quantum reinforcement learning reduces parameters by combining a Parameterized Quantum Circuit (PQC) with a single-layer classical neural network. The PQC encodes and processes state information through quantum mechanics for enhanced computational expressiveness, while the classical neural network transforms the quantum measurement expectations into actionable outputs for energy management. The trained hybrid quantum circuits are implemented on DSPs through quantum simulations. The method is validated through Controller Hardware-in-the-Loop (CHIL) experiments, demonstrating superior performance in optimizing economic metrics compared to conventional methods.