Integrated Thermal and Energy Management of Connected Hybrid Electric Vehicles Using Deep Reinforcement Learning

The climate-adaptive mymargin energy management system (EMS) holds promising potential for harnessing the concealed energy-saving capabilities of connected plug-in hybrid electric vehicles (PHEVs). This research focuses on exploring the synergistic effects of artificial intelligence control and traffic preview to enhance the performance of the EMS. A high-fidelity model of a multimode connected PHEV is calibrated using the experimental data as a foundation. Subsequently, a model-free multistate deep reinforcement learning (DRL) algorithm is proposed to develop the integrated thermal and energy management (ITEM) system, incorporating the features of engine smart warm-up and engine-assisted heating for cold climate conditions. The optimality and adaptability of the proposed system are evaluated through both offline tests and online hardware-in-the-loop (HIL) tests, encompassing a homologation driving cycle and a real-world driving cycle in China with real-time traffic data. The results demonstrate that ITEM achieves a close to dynamic programming (DP) fuel economy performance with a margin of 93.7%, while reducing fuel consumption ranging from 2.2% to 9.6% as ambient temperature decreases from 15 degrees C to -15 degrees C in comparison to the state-of-the-art DRL-based EMS solutions.