Search

Your search keyword '"Ni Meng"' showing total 23 results
Start Over Author "Ni Meng" Journal energy
23 results on '"Ni Meng"'

11. Sulfur-tolerant Fe-doped La0·3Sr0·7TiO3 perovskite as anode of direct carbon solid oxide fuel cells

Author

Cai, Weizi, primary, Cao, Dan, additional, Zhou, Mingyang, additional, Yan, Xiaomin, additional, Li, Yuzhi, additional, Wu, Zhen, additional, Lü, Shengping, additional, Mao, Caiyun, additional, Xie, Yongmin, additional, Zhao, Caiwen, additional, Yu, Jialing, additional, Ni, Meng, additional, Liu, Jiang, additional, and Wang, Hailin, additional

Published
2020
Full Text
View/download PDF

23. Online energy management strategy for ammonia-hydrogen hybrid electric vehicles harnessing deep reinforcement learning.

Author

Chen, Fujun, Wang, Bowen, Ni, Meng, Gong, Zhichao, and Jiao, Kui

Subjects
*DEEP reinforcement learning, *REINFORCEMENT learning, *HYBRID electric vehicles, *ENERGY management, *HYBRID systems, *INTERNAL combustion engines
Abstract

Powertrain electrification and fuel decarbonization play pivotal roles in the pursuit of carbon peak and carbon neutrality within the realm of transportation. Ammonia and hydrogen are essential carbon-neutral fuels suited for deployment in heavy-duty, long-haul transportation applications. The technological complementarity of ammonia and hydrogen can be realized in the current context by combining various energy conversion devices. Energy management strategy (EMS) is one of the critical technologies in hybrid systems. In this study, a novel EMS based on deep reinforcement learning (DRL) is proposed for a heavy-duty automotive hybrid system containing an ammonia-hydrogen internal combustion engine (ICE), a fuel cell system (FCS), an ammonia electrolysis cell (AEC), and Li-ion batteries (LB). To address the protracted training times and convergence challenges intrinsic to DRL, a fuzzy logic control (FLC) is introduced to provide expert demonstrations during the DRL agent's learning process. The incorporation of FLC not only facilitates the convergence speed of the DRL algorithm, but also strikes a good balance between the operation efficiency improvement and battery SOC maintenance. The simulation results show that the proposed EMS improves average efficiency by 2 % with a modest 0.75 % SOC reduction across four operational scenarios compared to the FLC-based EMS. After thorough validation of the proposed EMS, a parametric study is conducted to examine three key parameters: charge mode threshold, FCS power change rate limit, and hybrid mode threshold. The study aims to offer valuable insights for formulating the EMS of the ammonia-hydrogen hybrid system. • Energy management strategy for ammonia-hydrogen hybrid system is proposed. • The online learning performance of deep reinforcement learning is enhanced. • The efficiency improvement and state of charge retention are integrated into reward. • Three parameters affecting Energy management strategy design are further explored. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results