{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T16:36:58Z","timestamp":1780331818746,"version":"3.54.1"},"reference-count":25,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,22]],"date-time":"2024-09-22T00:00:00Z","timestamp":1726963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62303257"],"award-info":[{"award-number":["62303257"]}]},{"name":"National Natural Science Foundation of China","award":["ZR2023QF008"],"award-info":[{"award-number":["ZR2023QF008"]}]},{"name":"National Natural Science Foundation of China","award":["2023KJ119"],"award-info":[{"award-number":["2023KJ119"]}]},{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["62303257"],"award-info":[{"award-number":["62303257"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["ZR2023QF008"],"award-info":[{"award-number":["ZR2023QF008"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["2023KJ119"],"award-info":[{"award-number":["2023KJ119"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shandong Province Higher Education Youth Innovation Team Project","award":["62303257"],"award-info":[{"award-number":["62303257"]}]},{"name":"Shandong Province Higher Education Youth Innovation Team Project","award":["ZR2023QF008"],"award-info":[{"award-number":["ZR2023QF008"]}]},{"name":"Shandong Province Higher Education Youth Innovation Team Project","award":["2023KJ119"],"award-info":[{"award-number":["2023KJ119"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s 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 R2, compared to other models, such as Partial Least Squares Regression (PLS), Support Vector Machine (SVM), and back propagation (BP) neural networks, when predicting power output for electric stacks of the same type. However, the algorithm\u2019s performance decreased when applied to electric stacks with varying material compositions, highlighting the need for more sophisticated models to handle such diversity. These results underscore the potential of the BP-AdaBoost algorithm to improve PEMFC efficiency while also emphasizing the necessity for further research to develop models capable of accurately predicting power output across different types of PEMFC stacks.<\/jats:p>","DOI":"10.3390\/s24186120","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T08:56:06Z","timestamp":1727168166000},"page":"6120","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Data-Driven Power Prediction for Proton Exchange Membrane Fuel Cell Reactor Systems"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8021-005X","authenticated-orcid":false,"given":"Shuai","family":"He","sequence":"first","affiliation":[{"name":"School of Mechanical & Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuejing","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Mechanical & Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zexu","family":"Bai","sequence":"additional","affiliation":[{"name":"Qingdao Chuangqi Xinde New Energy Technology Co., Ltd., Qingdao 266100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiyao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Qingdao Chuangqi Xinde New Energy Technology Co., Ltd., Qingdao 266100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shinee","family":"Lou","sequence":"additional","affiliation":[{"name":"School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoqing","family":"Mu","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.apenergy.2013.01.001","article-title":"Use of metamodeling optimal approach promotes the performance of proton exchange membrane fuel cell (PEMFC)","volume":"105","author":"Cheng","year":"2013","journal-title":"Appl. Energy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"19865","DOI":"10.1016\/j.ijhydene.2021.12.251","article-title":"Advancements and current technologies on hydrogen fuel cell applications for marine vehicles","volume":"47","author":"Arat","year":"2022","journal-title":"Int. J. Hydrogen Energy"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Sagong, H., Jeong, S., and Lee, H. (2024). Analysis of Failure Mechanism and Reliability Enhancement of Silicon Strain Gauge-Based Pressure Sensor for Automotive Applications. Sensors, 24.","DOI":"10.3390\/s24030975"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Tang, X., Shi, L., Zhang, Y., Li, B., Xu, S., and Song, Z. (2024). Degradation adaptive energy management strategy for FCHEV based on the Rule-DDPG method: Tailored to the current SOH of the powertrain. IEEE Trans. Transp. Electrif.","DOI":"10.1109\/TTE.2024.3399054"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ariza, E., Correcher, A., and Vargas-Salgado, C.J.S. (2023). PEMFCs Model-Based Fault Diagnosis: A Proposal Based on Virtual and Real Sensors Data Fusion. Sensors, 23.","DOI":"10.3390\/s23177383"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1016\/j.jpowsour.2006.06.007","article-title":"Experimental investigation of coupling phenomena in polymer electrolyte fuel cell stacks","volume":"161","author":"Santis","year":"2006","journal-title":"J. Power Sources"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5728","DOI":"10.1016\/j.ijhydene.2008.07.017","article-title":"Evaluation of an on-site cell voltage monitor for fuel cell systems","volume":"33","author":"Mulder","year":"2008","journal-title":"Int. J. Hydrogen Energy"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"8006","DOI":"10.1016\/j.jpowsour.2010.06.054","article-title":"A robust cell voltage monitoring system for analysis and diagnosis of fuel cell or battery systems","volume":"195","author":"Brunner","year":"2010","journal-title":"J. Power Sources"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.apenergy.2014.03.048","article-title":"Main factors affecting the lifetime of Proton Exchange Membrane fuel cells in vehicle applications: A review","volume":"125","author":"Pei","year":"2014","journal-title":"Appl. Energy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"112051","DOI":"10.1016\/j.enconman.2019.112051","article-title":"Three-dimensional multi-phase model of PEM fuel cell coupled with improved agglomerate sub-model of catalyst layer","volume":"199","author":"Xie","year":"2019","journal-title":"Energy Convers. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"100014","DOI":"10.1016\/j.egyai.2020.100014","article-title":"Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology","volume":"1","author":"Wang","year":"2020","journal-title":"Energy AI"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Meng, X., Mei, J., Tang, X., Jiang, J., Sun, C., and Song, K. (2024). The Degradation Prediction of Proton Exchange Membrane Fuel Cell Performance Based on a Transformer Model. Energies, 17.","DOI":"10.3390\/en17123050"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5470","DOI":"10.1016\/j.ijhydene.2018.10.042","article-title":"Remaining useful life prediction of PEMFC based on long short-term memory recurrent neural networks","volume":"44","author":"Liu","year":"2019","journal-title":"Int. J. Hydrogen Energy"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Long, B., Wu, K., Li, P., and Li, M. (2022). A Novel Remaining Useful Life Prediction Method for Hydrogen Fuel Cells Based on the Gated Recurrent Unit Neural Network. Appl. Sci., 12.","DOI":"10.3390\/app12010432"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yang, J., Wu, Y., and Liu, X. (2023). Proton Exchange Membrane Fuel Cell Power Prediction Based on Ridge Regression and Convolutional Neural Network Data-Driven Model. Sustainability, 15.","DOI":"10.3390\/su151411010"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1016\/j.energy.2018.07.022","article-title":"Online Markov Chain-based energy management for a hybrid tracked vehicle with speedy Q-learning","volume":"160","author":"Liu","year":"2018","journal-title":"Energy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4468","DOI":"10.1016\/j.ijhydene.2018.11.226","article-title":"Evaluating the electrochemical and power performances of microbial fuel cells across physical scales: A novel numerical approach","volume":"44","author":"Krastev","year":"2019","journal-title":"Int. J. Hydrogen Energy"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"35957","DOI":"10.1109\/ACCESS.2018.2850743","article-title":"Unscented Kalman Filter-Based Battery SOC Estimation and Peak Power Prediction Method for Power Distribution of Hybrid Electric Vehicles","volume":"6","author":"Wang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"8028874","DOI":"10.1155\/2022\/8028874","article-title":"A Modern Simple Power Prediction Index for Improving Battery Life","volume":"2022","author":"Dahmardeh","year":"2022","journal-title":"Int. Trans. Electr. Energy Syst."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tang, X., Xu, S., and Sun, C. (2024). Deep Learning-Based State-of-Health Estimation of Proton-Exchange Membrane Fuel Cells under Dynamic Operation Conditions. Sensors, 24.","DOI":"10.3390\/s24144451"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"012110","DOI":"10.1088\/1755-1315\/188\/1\/012110","article-title":"A novel fault diagnosis method for photovoltaic array based on BP-Adaboost strong classifier","volume":"188","author":"Zheng","year":"2018","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Jia, Y., Wu, W., Cheng, Z., Su, X., and Lin, A. (2020). A Diagnosis Method for the Compound Fault of Gearboxes Based on Multi-Feature and BP-AdaBoost. Symmetry, 12.","DOI":"10.3390\/sym12030461"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1019845","DOI":"10.1155\/2019\/1019845","article-title":"Transformer Fault Diagnosis Based on BP-Adaboost and PNN Series Connection","volume":"2019","author":"Yan","year":"2019","journal-title":"Math. Probl. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Cui, Y., Zhang, J., and Zhong, W. (2019, January 21\u201324). Short-term Photovoltaic Output Prediction Method Based on Similar Day Selection with Grey Relational Theory. Proceedings of the 2019 IEEE Innovative Smart Grid Technologies\u2014Asia (ISGT Asia), Chengdu, China.","DOI":"10.1109\/ISGT-Asia.2019.8881413"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"108966","DOI":"10.1016\/j.epsr.2022.108966","article-title":"Short-term photovoltaic power prediction with similar-day integrated by BP-AdaBoost based on the Grey-Markov model","volume":"215","author":"Yang","year":"2023","journal-title":"Electr. Power Syst. Res."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/18\/6120\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:02:20Z","timestamp":1760112140000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/18\/6120"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,22]]},"references-count":25,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["s24186120"],"URL":"https:\/\/doi.org\/10.3390\/s24186120","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,22]]}}}