Qinglin Zhu

A deep reinforcement learning framework for hybrid electric vehicle energy management: Integrating real-world data augmentation and multi-scale perception

A fresh enhanced training set covering all-round driving cycles based on real driving data is constructed, thus breaking through the training restrictions brought by standard driving cycles, and improves fuel economy by 5.2% on average across various driving cycles compared to adaptive ECMS (AECMS).