Multi-objective optimization with Q-learning for cruise and power allocation control parameters of connected fuel cell hybrid vehicles-Reference-Cited by-同舟云学术

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Multi-objective optimization with Q-learning for cruise and power allocation control parameters of connected fuel cell hybrid vehicles

Published:2024-11 Issue: Volume:373 Page:123910
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Zhang Baodi,Chang Liang,Teng Teng,Chen Qifang,Li Qiangwei,Cao Yaoguang,Yang Shichun,Zhang Xin

Funder

National Natural Science Foundation of China

Hebei Provincial Natural Science Foundation

Ministry of Science and Technology of the People's Republic of China

Publisher

Elsevier BV

Reference51 articles.

1. Development strategy of vehicle-energy-road-cloud collaboration in the new technological situation;Chao;Strategic Study of CAE,2024

2. Energy management strategies of connected HEVs and PHEVs: recent progress and outlook;Zhang;Prog Energy Combust Sci,2019

3. Co-optimization of speed trajectory and power management for a fuel-cell/battery electric vehicle;Kim;Appl Energy,2020

4. Global optimization energy management for multi-energy source vehicles based on “information layer-physical layer-energy layer-dynamic programming”(IPE-DP);Xu;Appl Energy,2022

5. An optimal control strategy design for plug-in hybrid electric vehicles based on internet of vehicles;Zhang;Energy,2021

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