Practical Application-Oriented Energy Management for a Plug-In Hybrid Electric Bus Using a Dynamic SOC Design Zone Plan Method

The main problem in current energy management is the ability of practical application. To address the problem, this paper proposes a reinforcement learning (RL)-based energy management by combining Tubule Q-learning and Pontryagin's Minimum Principle (PMP) algorithms for a plug-in hybrid electric bus (PHEB). The main innovation distinguished from the existing energy management strategies is that a dynamic SOC design zone plan method is proposed. It is characterized by two aspects: (1) a series of fixed locations are defined in the city bus route and a linear SOC reference trajectory is re-planned at fixed locations; (2) a triangle zone will be re-planned based on the linear SOC reference trajectory. Additionally, a one-dimensional state space is also designed to ensure the real-time control. The off-line trainings demonstrate that the agent of the RL-based energy management can be well trained and has good generalization performance. The results of hardware in loop simulation (HIL) demonstrate that the trained energy management has good real-time performance, and its fuel consumption can be decreased by 12.92%, compared to a rule-based control strategy.

Automated summary

This paper proposes a reinforcement learning-based energy management method for plug-in hybrid electric buses, which combines different algorithms and introduces a dynamic SOC design zone plan method. The experimental results demonstrate that this method performs well in energy management and fuel consumption.