Parallel reinforcement learning-based energy efficiency improvement for a cyber-physical system

Abstract

As a complex and critical cyber-physical system &#x0028 CPS &#x0029 , the hybrid electric powertrain is significant to mitigate air pollution and improve fuel economy. Energy management strategy &#x0028 EMS &#x0029 is playing a key role to improve the energy efficiency of this CPS. This paper presents a novel bidirectional long short-term memory &#x0028 LSTM &#x0029 network based parallel reinforcement learning &#x0028 PRL &#x0029 approach to construct EMS for a hybrid tracked vehicle &#x0028 HTV &#x0029 . This method contains two levels. The high-level establishes a parallel system first, which includes a real powertrain system and an artificial system. Then, the synthesized data from this parallel system is trained by a bidirectional LSTM network. The lower-level determines the optimal EMS using the trained action state function in the model-free reinforcement learning &#x0028 RL &#x0029 framework. PRL is a fully data-driven and learning-enabled approach that does not depend on any prediction and predefined rules. Finally, real vehicle testing is implemented and relevant experiment data is collected and calibrated. Experimental results validate that the proposed EMS can achieve considerable energy efficiency improvement by comparing with the conventional RL approach and deep RL.