Data-Driven Power Prediction for Proton Exchange Membrane Fuel Cell Reactor Systems.

Shuai He, Xuejing Wu, Zexu Bai, Jiyao Zhang, Shinee Lou, Guoqing Mu

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

A Trend Forecasting Method for the Vibration Signals of Aircraft Engines Combining Enhanced Slice-Level Adaptive Normalization Using Long Short-Term Memory Under Multi-Operating Conditions.Sensors (Basel, Switzerland) · 2025
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Shuai HeSchool of Mechanical & Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China.ORCID 0009-0006-8021-005X

Xuejing WuSchool of Mechanical & Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China.

Zexu BaiQingdao Chuangqi Xinde New Energy Technology Co., Ltd., Qingdao 266100, China.

Jiyao ZhangQingdao Chuangqi Xinde New Energy Technology Co., Ltd., Qingdao 266100, China.

Shinee LouSchool of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China.

Guoqing MuSchool of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China.

Funding

NSF China Grant 62303257Shandong Provincial Natural Science Foundation ZR2023QF008

6 · The paper itself

Abstract

Enhancing high-performance proton exchange membrane fuel cell (PEMFC) technology is crucial for the widespread adoption of hydrogen energy, a leading renewable resource. In this research, we introduce an innovative and cost-effective data-driven approach using the BP-AdaBoost algorithm to accurately predict the power output of hydrogen fuel cell stacks. The algorithm's effectiveness was validated with experimental data obtained from an advanced fuel cell testing platform, where the predicted power outputs closely matched the actual results. Our findings demonstrate that the BP-AdaBoost algorithm achieved lower RMSE and MAE, along with higher R

Indexed as

BP-AdaBoostdata-drivenPEMFC stackspower prediction

Identifiers

PMID39338872

DOI10.3390/s24186120

PMCPMC11435624

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.