QDQN-ThermoNet: A quantum-driven dual deep Q-Network framework for intelligent thermal regulation in solid-state and hydrogen fuel cell systems of future electric vehicles

This paper presents QDQN-ThermoNet, a novel Quantum-Driven Dual Deep Q-Network framework for intelligent thermal regulation in next-generation electric vehicles with hybrid energy systems. Our approach introduces a dual-agent architecture where a classical DQN governs solid-state battery thermal management while a quantum-enhanced DQN regulates proton exchange membrane fuel cell dynamics, both sharing a unified quantum-enhanced experience replay buffer to facilitate cross-system information transfer. Hardware-in-the-Loop validation across diverse operational scenarios demonstrates significant performance improvements compared to classical methods, including enhanced thermal stability (95.1 % vs. 82.3 %), faster thermal response (2.1 s vs. 4.7 s), reduced overheating events (0.3 vs. 3.2), and superior energy efficiency (22.4 % energy savings). The quantum-enhanced components deliver 38.7 % greater sample efficiency and maintain robust performance under sparse data conditions (33.9 % improvement), while material-adaptive control strategies leveraging MXene-enhanced phase change materials achieve a 50.3 % reduction in peak temperature rise during transients. Component lifetime analysis reveals a 33.2 % extension in battery service life through optimized thermal management. These results establish QDQN-ThermoNet as a significant advancement in AI-driven thermal management for future electric vehicle platforms, effectively addressing the complex challenges of coordinating thermal regulation across divergent energy sources with different optimal operating temperatures.