@inproceedings{2d6d73f447b740a38a960c8151fcea84,
title = "Adaptive Eco-driving of Fuel Cell Vehicles Based on Multi-light Trained Deep Reinforcement Learning",
abstract = "With the advent of modern communication technology, future driving environment information such as the coming traffic lights can help vehicles make more intelligent planning and decision-making. This study proposed an adaptive eco-driving method of Fuel Cell Vehicles (FCVs) based on multi-light trained Deep Reinforcement Learning (DRL), aiming to improve the fuel economy and comfort of FCVs. By setting a reasonable reward function, the DRL agent takes actions to maximize the cumulative reward according to the environmental information received. An application method was further studied to fulfill the eco-driving of vehicles in complex arterial road scenarios. The result shows that the proposed method can adapt to variable scenarios with traffic lights, and quickly solve the approximate optimal speed trajectory. Hydrogen consumption of multi-light trained DRL models is reduced by more than 10% on average compared with that of the single-light trained DRL model.",
keywords = "Adaptive speed planning, Deep reinforcement learning, Eco-driving, Fuel cell vehicle, Signalized intersection, TD3",
author = "Bo Liu and Chao Sun and Qiang Ren and Xiaodong Wei and Qingyun Min and Biao Liang",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 18th IEEE Vehicle Power and Propulsion Conference, VPPC 2021 ; Conference date: 25-10-2021 Through 28-10-2021",
year = "2021",
doi = "10.1109/VPPC53923.2021.9699178",
language = "English",
series = "2021 IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - ProceedingS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 IEEE Vehicle Power and Propulsion Conference, VPPC 2021 - ProceedingS",
address = "United States",
}