Deep reinforcement learning-based vehicle energy efficiency autonomous learning system

To mitigate air pollution problems and reduce greenhouse gas emissions (GHG), plug-in hybrid electric vehicles (PHEV) have been developed to achieve higher fuel efficiency. The Energy Management System (EMS) is a very important component of a PHEV in achieving better fuel economy and it is a very active research area. So far, most of the existing EMS strategies just simple follow predefined rules that are not adaptive to changing driving conditions; other strategies as starting to incorporate accurate prediction of future traffic conditions. In this study, a deep reinforcement learning based PHEV energy management system is designed to autonomously learn the optimal fuel use from its own historical driving record. It is a fully data-driven and learning-enabled model that does not rely on any prediction or predefined rules. The experiment results show that the proposed model is able to achieve 16.3% energy savings comparing to conventional binary control strategies.