Deep Reinforcement Learning Based Transferable EMS for Hybrid Electric Trains

The hybrid electric train which operate without overhead wires or traditional power sources rely on hydrogen fuel cells (FC) and batteries for power. These fuel cell-based hybrid electric trains (FCHETs) are more efficient than those powered by diesel or electricity because they do not produce any tailpipe emissions making them an eco-friendly mode of transport. The target of this paper is to proposes low-budget FCHETs that prioritizes energy efficiency to reduce operating costs and minimize its impact on the environment. To this end, an energy management strategy (EMS) has been developed that optimizes the distribution of energy to reduce the amount of hydrogen required to power the train. The EMS achieves this by balancing battery charging and discharging. To enhance the performance of the EMS, proposes to use of a deep reinforcement learning (DRL) algorithm specifically the deep deterministic policy gradient (DDPG) combined with transfer learning (TL) which can improve the system's efficiency when driving cycles are changed. DRL-based strategies are commonly used in energy management and they suffer from unstable convergence, slow learning speed, insufficient constraint capability. To address these limitations, proposes an action masking technique to stop the DDPG-based approach from producing incorrect actions that go against the system's physical limits and prevent them from being generated. The DDPG+TL agent consumes up to ۳.۹% less energy than conventional rule-based EMS while maintaining the battery's charge level within a predetermined range. The results show that DDPG+TL can sustain battery charge at minimal hydrogen consumption with minimal training time for agent.

Keywords:

fuel cell , State of Charge , Energy management strategy , Deep Reinforcement Learning , Deep Deterministic Policy Gradient , transfer learning