Hierarchical Optimization Based on Deep Reinforcement Learning for Connected Fuel Cell Hybrid Vehicles through Signalized Intersections

Abstract

With the advantages of non-pollution and energy-saving, hydrogen fuel cell hybrid vehicles (HFCHVs) are regarded as one of the potential traveling ways in the future. The energy management of FCHVs has a huge energy-efficient potential which is combined with the Internet of Things (IOT) and auto-driving technologies. In this paper, a hierarchical joint optimization method that combines deep deterministic policy gradient and dynamic planning (DDPG-DP) for speed planning and energy management of the HFCHV is proposed for urban road driving scenarios. The results demonstrate that when the HFCHV is operating in driving scenario 1, the traveling efficiency of the DDPG-DP algorithm is 17.8% higher than that of the IDM-DP algorithm, and the hydrogen fuel consumption is reduced by 2.7%. In contrast, the difference in the traveling efficiency and fuel economy is small among the three algorithms in driving scenario 2, the number of idling/stop situations of the DDPG-DP algorithm is reduced compared with that of the IDM-DP algorithm. This will support further research for multi-objective eco-driving optimization of fuel cell hybrid vehicles.