Enhancing Fuel Cell Electric Vehicle Efficiency With an Information-Bridged Hierarchical Reinforcement Learning Method

The intelligent transportation system furnishes electrified vehicles with multi-source traffic information, thereby enhancing the potential for greater energy efficiency. Eco-driving and internal energy management represent dual pathways to achieving these efficiencies. Departing from existing studies that typically investigate these pathways independently, this paper introduces a collaborative hierarchical reinforcement learning (RL) method that synchronizes the optimization of both eco-driving and energy management in an integrated solution. To advance RL performance, we propose a novel information bridge scheme at the methodological level. This scheme optimizes the actor-critic RL algorithm's learning mechanism, facilitating improved information exchange between the dual agents: eco-driving (upper layer) and energy management (lower layer). The critic value of the lower-level agent as a conduit for transmitting condensed state information into the upper, improving the holistic performance. Furthermore, convolutional networks are employed to enhance traffic information extraction. The effectiveness of our method is demonstrated through SUMO simulations, showing a 30.28% improvement in energy efficiency with only a 7.53% compromise in timeliness compared to prevailing Krauss eco-driving. Results also indicate improved training stability and adaptability of our method. This research not only contributes to the optimization of eco-driving for electrified vehicles but also has values in other multi-agent collaborative optimization fields.