Power battery removes redundant design considering the intelligence of fuel cell vehicles

Donghai Hu, Yan Sun, Chunxiao Feng and Fengyan Yi

Renewable Energy, 2025, vol. 247, issue C

Abstract: Power batteries are usually designed with larger capacities to ensure the normal operation of fuel cell vehicles under complex conditions, leading to redundancy and increased costs. Existing methods typically do not consider the intelligent prediction and regulation of operating conditions, necessitating designs that account for extreme cases and failing to eliminate redundant battery capacity effectively. This paper takes a fuel cell urban logistics vehicle as an example for research. It proposes a power cell matching selection method for fuel cell vehicles that considers the intelligence of working condition prediction. A neural network is trained to adjust its hidden nodes, affecting the prediction results, and the concept of intelligence is defined through these results. The study investigates the matching selection of power batteries under six working conditions, concluding that higher intelligence reduces the required battery capacity. Hardware-in-the-loop verification shows that the optimal battery capacity under 95 % intelligence is 60Ah, which is 28Ah, 25Ah, and 14Ah lower than under 0 %, 33 %, and 66 % intelligence, respectively. The impact of power batteries on their capacity under different levels of intelligence has a certain regularity. This paper provides practical insights for optimizing power battery capacity, offering a solution to reduce costs associated with oversized batteries.

Keywords: Fuel cell vehicle; Power battery capacity; Intelligent prediction; Redundant design (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148125007360
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:247:y:2025:i:c:s0960148125007360

DOI: 10.1016/j.renene.2025.123074

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().