Teng Liu

TL;DR: A model predictive control framework is established to minimize the total running cost of a fuel cell/battery hybrid electric bus, inclusive of hydrogen cost and costs caused by fuel cell and battery degradation.

TL;DR: This paper proposes a heuristic planning energy management controller, based on a Dyna agent of reinforcement learning approach, for real-time fuel saving optimization of a plug-in hybrid electric vehicle (PHEV).

TL;DR: Deep learning and genetic algorithm are synthesized to derive the power split controls between the battery and internal combustion engine to promote the real-time application of the advanced energy management system in hybrid electric vehicles (HEVs).

TL;DR: An online energy management controller is presented in this paper for a plug-in hybrid electric vehicle (PHEV) which is based on driving conditions recognition and genetic algorithm (GA), which is close to the globally optimal method, dynamic programming, and is superior to the charge-depleting/charge-sustaining technique.

TL;DR: Experimental results validate that the proposed PRL approach to construct EMS for a hybrid tracked vehicle can achieve considerable energy efficiency improvement by comparing with the conventional RL approach and deep RL.