{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T03:32:08Z","timestamp":1784863928887,"version":"3.55.0"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T00:00:00Z","timestamp":1708905600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T00:00:00Z","timestamp":1708905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Energy Inform"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The growing energy demand and population raising require alternative, clean, and sustainable energy systems. During the last few years, hydrogen energy has proven to be a crucial factor under the current conditions. Although the energy conversion process in polymer electrolyte fuel cells (PEFCs) is clean and noiseless since the only by-products are heat and water, the inside phenomena are not simple. As a result, correct monitoring of the health situation of the device is required to perform efficiently. This paper aims to explore and evaluate the machine learning (ML) and deep learning (DL) models for predicting classification fault detection in PEFCs. It represents a support for decision-making by the fuel cell operator or user. Seven ML and DL model classifiers are considered. A database comprising 182,156 records and 20 variables arising from the fuel cell's energy conversion process and operating conditions is considered. This dataset is unbalanced; therefore, techniques to balance are applied and analyzed in the training and testing of several models. The results showed that the logistic regression (LR), k-nearest neighbor (KNN), decision tree (DT), random forest (RF), and Naive Bayes (NB) models present similar and optimal trends in terms of performance indicators and computational cost; unlike support vector machine (SMV) and multi-layer perceptron (MLP) whose performance is affected when the data is balanced and even presents a higher computational cost. Therefore, it is a novel approach for fault detection analysis in PEFC that combines the interpretability of different ML and DL algorithms while addressing data imbalance, so common in the real world, using resampling techniques. This methodology provides clear information for the model decision-making process, improving confidence and facilitating further optimization; in contrast to traditional physics-based models, paving the way for data-driven control strategies.<\/jats:p>","DOI":"10.1186\/s42162-024-00318-2","type":"journal-article","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T11:02:59Z","timestamp":1708945379000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Enhanced fault detection in polymer electrolyte fuel cells via integral analysis and machine learning"],"prefix":"10.1186","volume":"7","author":[{"given":"Ester","family":"Melo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julio","family":"Barzola-Monteses","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Holguer H.","family":"Noriega","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mayken","family":"Espinoza-Andaluz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,2,26]]},"reference":[{"key":"318_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.121637","volume":"238","author":"M Aldubyan","year":"2022","unstructured":"Aldubyan M, Krarti M (2022) Impact of stay home living on energy demand of residential buildings: Saudi Arabian case study. Energy 238:121637. https:\/\/doi.org\/10.1016\/j.energy.2021.121637","journal-title":"Energy"},{"key":"318_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/s40031-024-00994-4","author":"S Awasthi","year":"2024","unstructured":"Awasthi S, Singh G, Ahamad N (2024) Classifying electrical faults in a distribution system using k-nearest neighbor (knn) model in presence of multiple distributed generators. J Inst Eng India Ser B. https:\/\/doi.org\/10.1007\/s40031-024-00994-4","journal-title":"J Inst Eng India Ser B"},{"key":"318_CR3","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1007\/s00530-021-00817-2","volume":"28","author":"C Azad","year":"2022","unstructured":"Azad C, Bhushan B, Sharma R, Shankar A, Singh KK, Khamparia A (2022) Prediction model using SMOTE, genetic algorithm and decision tree (PMSGD) for classification of diabetes mellitus. Multimedia Syst 28:1289\u20131307. https:\/\/doi.org\/10.1007\/s00530-021-00817-2","journal-title":"Multimedia Syst"},{"key":"318_CR4","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.neucom.2014.05.096","volume":"172","author":"L Bao","year":"2016","unstructured":"Bao L, Juan C, Li J, Zhang Y (2016) Boosted Near-miss Under-sampling on SVM ensembles for concept detection in large-scale imbalanced datasets. Neurocomputing 172:198\u2013206. https:\/\/doi.org\/10.1016\/j.neucom.2014.05.096","journal-title":"Neurocomputing"},{"key":"318_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.123827","volume":"250","author":"F Calili-Cankir","year":"2022","unstructured":"Calili-Cankir F, Ismail MS, Ingham DB, Hughes KJ, Ma L, Pourkashanian M (2022) Air-breathing versus conventional polymer electrolyte fuel cells: a parametric numerical study. Energy 250:123827. https:\/\/doi.org\/10.1016\/j.energy.2022.123827","journal-title":"Energy"},{"key":"318_CR6","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1002\/1439-7641(20001215)1:4<162::AID-CPHC162>3.0.CO;2-Z","volume":"1","author":"L Carrette","year":"2000","unstructured":"Carrette L, Friedrich KA, Stimming U (2000) Fuel cells: principles, types, fuels, and applications. ChemPhysChem 1:162\u2013193. https:\/\/doi.org\/10.1002\/1439-7641(20001215)1:4%3c162::AID-CPHC162%3e3.0.CO;2-Z","journal-title":"ChemPhysChem"},{"key":"318_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.envpol.2023.123091","volume":"342","author":"N Chen","year":"2024","unstructured":"Chen N, Ma L-L, Zhang Y, Yan Y-X (2024) Association of household solid fuel use and long-term exposure to ambient air pollution with estimated 10-year high cardiovascular disease risk among postmenopausal women. Environ Pollut 342:123091. https:\/\/doi.org\/10.1016\/j.envpol.2023.123091","journal-title":"Environ Pollut"},{"key":"318_CR8","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"T Cover","year":"1967","unstructured":"Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13:21\u201327. https:\/\/doi.org\/10.1109\/TIT.1967.1053964","journal-title":"IEEE Trans Inf Theory"},{"key":"318_CR9","doi-asserted-by":"publisher","unstructured":"Demidova L, Klyueva I (2017) SVM classification: Optimization with the SMOTE algorithm for the class imbalance problem. In: 2017 6th Mediterranean Conference on Embedded Computing (MECO). pp 1\u20134. https:\/\/doi.org\/10.1109\/MECO.2017.7977136.","DOI":"10.1109\/MECO.2017.7977136"},{"key":"318_CR10","doi-asserted-by":"publisher","unstructured":"Detti AH, Jemei S, Morando S, Steiner NY (2017) Classification Based Method Using Fast Fourier Transform (FFT) and Total Harmonic Distortion (THD) Dedicated to Proton Exchange Membrane Fuel Cell (PEMFC) Diagnosis. In: 2017 IEEE Vehicle Power and Propulsion Conference (VPPC). pp 1\u20136. https:\/\/doi.org\/10.1109\/VPPC.2017.8331040.","DOI":"10.1109\/VPPC.2017.8331040"},{"key":"318_CR11","doi-asserted-by":"publisher","unstructured":"Dudani, S.A.: The distance-weighted k-nearest-neighbor rule. IEEE Transactions on Systems, Man, and Cybernetics. SMC-6, 325\u2013327 (1976). https:\/\/doi.org\/10.1109\/TSMC.1976.5408784.","DOI":"10.1109\/TSMC.1976.5408784"},{"key":"318_CR12","doi-asserted-by":"publisher","first-page":"32126","DOI":"10.1039\/D1RA05324H","volume":"11","author":"MJ Eslamibidgoli","year":"2021","unstructured":"Eslamibidgoli MJ, Tipp FP, Jitsev J, Jankovic J, Eikerling MH, Malek K (2021) Convolutional neural networks for high throughput screening of catalyst layer inks for polymer electrolyte fuel cells. RSC Adv 11:32126\u201332134. https:\/\/doi.org\/10.1039\/D1RA05324H","journal-title":"RSC Adv"},{"key":"318_CR13","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1613\/jair.1.11192","volume":"61","author":"A Fernandez","year":"2018","unstructured":"Fernandez A, Garcia S, Herrera F, Chawla NV (2018) SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary. J Artif Intell Res 61:863\u2013905. https:\/\/doi.org\/10.1613\/jair.1.11192","journal-title":"J Artif Intell Res"},{"key":"318_CR14","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1109\/TIT.1975.1055330","volume":"21","author":"K Fukunaga","year":"1975","unstructured":"Fukunaga K, Hostetler L (1975) The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans Inf Theory 21:32\u201340. https:\/\/doi.org\/10.1109\/TIT.1975.1055330","journal-title":"IEEE Trans Inf Theory"},{"key":"318_CR15","doi-asserted-by":"publisher","first-page":"746","DOI":"10.1016\/j.ijhydene.2023.03.335","volume":"52","author":"SP Ghorbanzade Zaferani","year":"2024","unstructured":"Ghorbanzade Zaferani SP, Amiri MK, Sarmasti Emami MR, Zahmatkesh S, Hajiaghaei-Keshteli M, Panchal H (2024) Prediction and optimization of sustainable fuel cells behavior using artificial intelligence algorithms. Int J Hydrogen Energy 52:746\u2013766. https:\/\/doi.org\/10.1016\/j.ijhydene.2023.03.335","journal-title":"Int J Hydrogen Energy"},{"key":"318_CR16","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1080\/14786443908649684","volume":"14","author":"WR Grove","year":"1839","unstructured":"Grove WR (1839) XXIV. On voltaic series and the combination of gases by platinum. London Edinburgh Dublin Philo Magaz J Sci 14:127\u2013130. https:\/\/doi.org\/10.1080\/14786443908649684","journal-title":"London Edinburgh Dublin Philo Magaz J Sci"},{"key":"318_CR17","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1016\/j.ijhydene.2023.07.115","volume":"52","author":"T Hai","year":"2024","unstructured":"Hai T, Alenizi FA, Mohammed AH, Goyal V, Marjan RK, Quzwain K, Mohammed Metwally AS (2024) Solid oxide fuel cell energy system with absorption-ejection refrigeration optimized using a neural network with multiple objectives. Int J Hydrogen Energy 52:954\u2013972. https:\/\/doi.org\/10.1016\/j.ijhydene.2023.07.115","journal-title":"Int J Hydrogen Energy"},{"key":"318_CR18","doi-asserted-by":"publisher","first-page":"7023","DOI":"10.1016\/j.ijhydene.2017.01.131","volume":"42","author":"I-S Han","year":"2017","unstructured":"Han I-S, Chung C-B (2017) A hybrid model combining a support vector machine with an empirical equation for predicting polarization curves of PEM fuel cells. Int J Hydrogen Energy 42:7023\u20137028. https:\/\/doi.org\/10.1016\/j.ijhydene.2017.01.131","journal-title":"Int J Hydrogen Energy"},{"key":"318_CR19","doi-asserted-by":"publisher","unstructured":"Hatti M, Tioursi M, Nouibat W (2006) Static modelling by neural networks of a PEM fuel cell. In: IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics. pp 2121\u20132126. https:\/\/doi.org\/10.1109\/IECON.2006.347589.","DOI":"10.1109\/IECON.2006.347589"},{"key":"318_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2021.114367","volume":"243","author":"W Huo","year":"2021","unstructured":"Huo W, Li W, Zhang Z, Sun C, Zhou F, Gong G (2021) Performance prediction of proton-exchange membrane fuel cell based on convolutional neural network and random forest feature selection. Energy Convers Manag 243:114367. https:\/\/doi.org\/10.1016\/j.enconman.2021.114367","journal-title":"Energy Convers Manag"},{"key":"318_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2021.116441","volume":"285","author":"P Jiang","year":"2021","unstructured":"Jiang P, Fan YV, Kleme\u0161 JJ (2021) Impacts of COVID-19 on energy demand and consumption: challenges, lessons and emerging opportunities. Appl Energy 285:116441. https:\/\/doi.org\/10.1016\/j.apenergy.2021.116441","journal-title":"Appl Energy"},{"key":"318_CR22","doi-asserted-by":"publisher","unstructured":"Kamal M, Yu D (2014) Fault diagnosis for fuel cell stack using independent MLP neural network. Presented at the 2nd International Conference on Innovations in Engineering and Technology (ICCET\u20192014) , Penang https:\/\/doi.org\/10.15242\/iie.e0914059.","DOI":"10.15242\/iie.e0914059"},{"key":"318_CR23","doi-asserted-by":"publisher","unstructured":"Klell M, Eichlseder H, Trattner A (2023) Fuel Cells. In: Klell, M., Eichlseder, H., and Trattner, A. (eds.) Hydrogen in automotive engineering: production, storage, application. pp 137\u2013192. Springer Fachmedien, Wiesbaden. https:\/\/doi.org\/10.1007\/978-3-658-35061-1_6.","DOI":"10.1007\/978-3-658-35061-1_6"},{"key":"318_CR24","doi-asserted-by":"publisher","unstructured":"Kulkarni Y, Ramamritham K, Somu N (2021) EnsembleNTLDetect: An Intelligent Framework for Electricity Theft Detection in Smart Grid. In: 2021 International Conference on Data Mining Workshops (ICDMW). pp 527\u2013536 (2021). https:\/\/doi.org\/10.1109\/ICDMW53433.2021.00070.","DOI":"10.1109\/ICDMW53433.2021.00070"},{"key":"318_CR25","doi-asserted-by":"publisher","first-page":"252","DOI":"10.7316\/KHNES.2017.28.3.252","volume":"28","author":"W-Y Lee","year":"2017","unstructured":"Lee W-Y, Park G-G, Sohn Y-J, Kim S-G, Kim M (2017) Fault detection and diagnosis methods for polymer electrolyte fuel cell system. Trans Korean Hydrogen New Energy Soc 28:252\u2013272. https:\/\/doi.org\/10.7316\/KHNES.2017.28.3.252","journal-title":"Trans Korean Hydrogen New Energy Soc"},{"key":"318_CR26","doi-asserted-by":"publisher","first-page":"339","DOI":"10.7316\/KHNES.2018.29.4.339","volume":"29","author":"W-Y Lee","year":"2018","unstructured":"Lee W-Y, Kim M, Oh H, Sohn Y-J, Kim S-G (2018) A review on prognostics of polymer electrolyte fuel cells. Trans Korean Hydrogen New Energy Soc 29:339\u2013356. https:\/\/doi.org\/10.7316\/KHNES.2018.29.4.339","journal-title":"Trans Korean Hydrogen New Energy Soc"},{"key":"318_CR27","unstructured":"Lema\u00eetre, G., Nogueira, F., Aridas, C.: Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. J Mach Learn Res. 18, (2016)."},{"key":"318_CR28","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1002\/fuce.201300197","volume":"14","author":"Z Li","year":"2014","unstructured":"Li Z, Giurgea S, Outbib R, Hissel D (2014a) Online diagnosis of PEMFC by combining support vector machine and fluidic model. Fuel Cells 14:448\u2013456. https:\/\/doi.org\/10.1002\/fuce.201300197","journal-title":"Fuel Cells"},{"key":"318_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.conengprac.2014.02.019","volume":"28","author":"Z Li","year":"2014","unstructured":"Li Z, Outbib R, Hissel D, Giurgea S (2014b) Data-driven diagnosis of PEM fuel cell: a comparative study. Control Eng Pract 28:1\u201312. https:\/\/doi.org\/10.1016\/j.conengprac.2014.02.019","journal-title":"Control Eng Pract"},{"key":"318_CR30","doi-asserted-by":"publisher","first-page":"1435","DOI":"10.1016\/j.renene.2018.09.077","volume":"135","author":"Z Li","year":"2019","unstructured":"Li Z, Outbib R, Giurgea S, Hissel D, Giraud A, Couderc P (2019) Fault diagnosis for fuel cell systems: a data-driven approach using high-precise voltage sensors. Renew Energy 135:1435\u20131444. https:\/\/doi.org\/10.1016\/j.renene.2018.09.077","journal-title":"Renew Energy"},{"key":"318_CR31","doi-asserted-by":"publisher","first-page":"5488","DOI":"10.1016\/j.ijhydene.2018.09.085","volume":"44","author":"R-H Lin","year":"2019","unstructured":"Lin R-H, Xi X-N, Wang P-N, Wu B-D, Tian S-M (2019) Review on hydrogen fuel cell condition monitoring and prediction methods. Int J Hydrogen Energy 44:5488\u20135498. https:\/\/doi.org\/10.1016\/j.ijhydene.2018.09.085","journal-title":"Int J Hydrogen Energy"},{"key":"318_CR32","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.1016\/j.ijhydene.2023.10.215","volume":"55","author":"B Liu","year":"2024","unstructured":"Liu B, Wei X, Sun C, Wang B, Huo W (2024) A controllable neural network-based method for optimal energy management of fuel cell hybrid electric vehicles. Int J Hydrogen Energy 55:1371\u20131382. https:\/\/doi.org\/10.1016\/j.ijhydene.2023.10.215","journal-title":"Int J Hydrogen Energy"},{"key":"318_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2024.122743","volume":"359","author":"G Lu","year":"2024","unstructured":"Lu G, Liu M, Su X, Zheng T, Luan Y, Fan W, Cui H, Liu Z (2024) Study on counter-flow mass transfer characteristics and performance optimization of commercial large-scale proton exchange membrane fuel cells. Appl Energy 359:122743. https:\/\/doi.org\/10.1016\/j.apenergy.2024.122743","journal-title":"Appl Energy"},{"key":"318_CR34","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1016\/j.isatra.2022.04.045","volume":"131","author":"Y Ma","year":"2022","unstructured":"Ma Y, Li C, Wang S (2022) Multi-objective energy management strategy for fuel cell hybrid electric vehicle based on stochastic model predictive control. ISA Trans 131:178\u2013196. https:\/\/doi.org\/10.1016\/j.isatra.2022.04.045","journal-title":"ISA Trans"},{"key":"318_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.treng.2023.100190","volume":"13","author":"S Madushani","year":"2023","unstructured":"Madushani S, Sandamal K, Meddage DPP, Pasindu HR, Gomes P (2023) Evaluating expressway traffic crash severity by using logistic regression and explainable & supervised machine learning classifiers. Transport Eng 13:100190. https:\/\/doi.org\/10.1016\/j.treng.2023.100190","journal-title":"Transport Eng"},{"key":"318_CR36","unstructured":"Mao L, Jackson L (2016) Comparative study on prediction of fuel cell performance using machine learning approaches. Loughborough University."},{"key":"318_CR37","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1002\/fuce.201600139","volume":"17","author":"L Mao","year":"2017","unstructured":"Mao L, Jackson L, Dunnett S (2017) Fault diagnosis of practical polymer electrolyte membrane (PEM) fuel cell system with data-driven approaches. Fuel Cells 17:247\u2013258. https:\/\/doi.org\/10.1002\/fuce.201600139","journal-title":"Fuel Cells"},{"key":"318_CR38","doi-asserted-by":"publisher","unstructured":"Mao L, He K, Jackson L, Wu Q (2021) Chapter 7 - Application of artificial neural networks in polymer electrolyte membrane fuel cell system prognostics. In: Mellal, M.A. and Pecht, M.G. (eds.) Nature-Inspired Computing Paradigms in Systems. pp 93\u2013109. Academic Press. https:\/\/doi.org\/10.1016\/B978-0-12-823749-6.00005-2.","DOI":"10.1016\/B978-0-12-823749-6.00005-2"},{"key":"318_CR39","doi-asserted-by":"publisher","DOI":"10.1115\/1.4007195","author":"M Meiler","year":"2012","unstructured":"Meiler M, Hofer EP, Nuhic A, Schmid O (2012) An Empirical Stationary Fuel Cell Model Using Limited Experimental Data for Identification. J Fuel Cell Sci Technol. https:\/\/doi.org\/10.1115\/1.4007195","journal-title":"J Fuel Cell Sci Technol"},{"key":"318_CR40","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1016\/j.rser.2011.09.020","volume":"16","author":"S Mekhilef","year":"2012","unstructured":"Mekhilef S, Saidur R, Safari A (2012) Comparative study of different fuel cell technologies. Renew Sustain Energy Rev 16:981\u2013989. https:\/\/doi.org\/10.1016\/j.rser.2011.09.020","journal-title":"Renew Sustain Energy Rev"},{"key":"318_CR41","doi-asserted-by":"publisher","unstructured":"Melo E, Encalada \u00c1, Espinoza M (2020) Behavior of a Polymer Electrolyte Fuel Cell from a Statistical Point of View Based on Data Analysis. Presented at the November 1. https:\/\/doi.org\/10.1007\/978-3-030-62833-8_10.","DOI":"10.1007\/978-3-030-62833-8_10"},{"key":"318_CR42","doi-asserted-by":"publisher","unstructured":"Melo E, Pe\u00f1afiel J, Barzola-Monteses J, Espinoza M(2022) An initial approach about data preprocessing techniques applied to polymer electrolyte fuel cells: a case study. Presented at the January 1. https:\/\/doi.org\/10.1007\/978-981-16-4126-8_6.","DOI":"10.1007\/978-981-16-4126-8_6"},{"key":"318_CR43","doi-asserted-by":"publisher","first-page":"894","DOI":"10.1016\/j.ijhydene.2023.12.242","volume":"56","author":"V Modanloo","year":"2024","unstructured":"Modanloo V, Mashayekhi A, Akhoundi B (2024) A comparative analysis of predictive models for estimating the formability of stamped titanium bipolar plates for proton exchange membrane fuel cells. Int J Hydrogen Energy 56:894\u2013902. https:\/\/doi.org\/10.1016\/j.ijhydene.2023.12.242","journal-title":"Int J Hydrogen Energy"},{"key":"318_CR44","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/7194728","volume":"2021","author":"NM Mqadi","year":"2021","unstructured":"Mqadi NM, Naicker N, Adeliyi T (2021) Solving misclassification of the credit card imbalance problem using near miss. Math Probl Eng 2021:e7194728. https:\/\/doi.org\/10.1155\/2021\/7194728","journal-title":"Math Probl Eng"},{"key":"318_CR45","doi-asserted-by":"publisher","first-page":"11628","DOI":"10.1016\/j.ijhydene.2013.04.135","volume":"38","author":"G Napoli","year":"2013","unstructured":"Napoli G, Ferraro M, Sergi F, Brunaccini G, Antonucci V (2013) Data driven models for a PEM fuel cell stack performance prediction. Int J Hydrogen Energy 38:11628\u201311638. https:\/\/doi.org\/10.1016\/j.ijhydene.2013.04.135","journal-title":"Int J Hydrogen Energy"},{"key":"318_CR46","doi-asserted-by":"publisher","first-page":"2178","DOI":"10.1149\/1.2220792","volume":"140","author":"TV Nguyen","year":"1993","unstructured":"Nguyen TV, White RE (1993) A water and heat management model for proton-exchange-membrane fuel cells. J Electrochem Soc 140:2178. https:\/\/doi.org\/10.1149\/1.2220792","journal-title":"J Electrochem Soc"},{"key":"318_CR47","doi-asserted-by":"publisher","first-page":"458","DOI":"10.3390\/su10020458","volume":"10","author":"T Ogawa","year":"2018","unstructured":"Ogawa T, Takeuchi M, Kajikawa Y (2018) Comprehensive analysis of trends and emerging technologies in all types of fuel cells based on a computational method. Sustainability 10:458. https:\/\/doi.org\/10.3390\/su10020458","journal-title":"Sustainability"},{"key":"318_CR48","doi-asserted-by":"publisher","first-page":"651","DOI":"10.3182\/20120902-4-FR-2032.00114","volume":"45","author":"R Onanena","year":"2012","unstructured":"Onanena R, Oukhellou L, C\u00f4me E, Candusso D, Hissel D, Aknin P (2012) Fault-diagnosis of PEM fuel cells using electrochemical spectroscopy impedance. IFAC Proc Vol 45:651\u2013656. https:\/\/doi.org\/10.3182\/20120902-4-FR-2032.00114","journal-title":"IFAC Proc Vol"},{"key":"318_CR49","doi-asserted-by":"publisher","first-page":"4707","DOI":"10.1609\/aaai.v33i01.33014707","volume":"33","author":"M Peng","year":"2019","unstructured":"Peng M, Zhang Q, Xing X, Gui T, Huang X, Jiang Y-G, Ding K, Chen Z (2019) Trainable undersampling for class-imbalance learning. Proc AAAI Conf Artif Intell 33:4707\u20134714. https:\/\/doi.org\/10.1609\/aaai.v33i01.33014707","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"318_CR50","unstructured":"Platt JC (1998) Sequential minimal optimization: a fast algorithm for training support vector machines"},{"key":"318_CR51","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.rser.2018.05.017","volume":"93","author":"K Priya","year":"2018","unstructured":"Priya K, Sathishkumar K, Rajasekar N (2018) A comprehensive review on parameter estimation techniques for Proton Exchange Membrane fuel cell modelling. Renew Sustain Energy Rev 93:121\u2013144. https:\/\/doi.org\/10.1016\/j.rser.2018.05.017","journal-title":"Renew Sustain Energy Rev"},{"key":"318_CR52","doi-asserted-by":"publisher","first-page":"1184","DOI":"10.1016\/j.ijhydene.2023.10.019","volume":"50","author":"R Quan","year":"2024","unstructured":"Quan R, Liang W, Wang J, Li X, Chang Y (2024) An enhanced fault diagnosis method for fuel cell system using a kernel extreme learning machine optimized with improved sparrow search algorithm. Int J Hydrogen Energy 50:1184\u20131196. https:\/\/doi.org\/10.1016\/j.ijhydene.2023.10.019","journal-title":"Int J Hydrogen Energy"},{"key":"318_CR53","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1243\/09576509JPE603","volume":"222","author":"P Rama","year":"2008","unstructured":"Rama P, Chen R, Andrews J (2008) A review of performance degradation and failure modes for hydrogen-fuelled polymer electrolyte fuel cells. Proc Inst Mech Eng A J Power Energy 222:421\u2013441. https:\/\/doi.org\/10.1243\/09576509JPE603","journal-title":"Proc Inst Mech Eng A J Power Energy"},{"key":"318_CR54","doi-asserted-by":"publisher","first-page":"78621","DOI":"10.1109\/ACCESS.2021.3083638","volume":"9","author":"V Rupapara","year":"2021","unstructured":"Rupapara V, Rustam F, Shahzad HF, Mehmood A, Ashraf I, Choi GS (2021) Impact of SMOTE on imbalanced text features for toxic comments classification using RVVC model. IEEE Access 9:78621\u201378634. https:\/\/doi.org\/10.1109\/ACCESS.2021.3083638","journal-title":"IEEE Access"},{"key":"318_CR55","doi-asserted-by":"publisher","unstructured":"Saikia K, Kakati B, Boro B, Verma A (2018) Current Advances and Applications of Fuel Cell Technologies. In: Recent Advancements in Biofuels and Bioenergy Utilization. pp 303\u2013337. https:\/\/doi.org\/10.1007\/978-981-13-1307-3_13.","DOI":"10.1007\/978-981-13-1307-3_13"},{"key":"318_CR56","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1149\/09809.0279ecst","volume":"98","author":"AD Santamaria","year":"2020","unstructured":"Santamaria AD, Mortazavi M, Chauhan V, Benner J, Philbrick O, Clemente R, Jia H, Ling C (2020) Applications of artificial intelligence for analysis of two-phase flow in PEM fuel cell flow fields. ECS Trans 98:279. https:\/\/doi.org\/10.1149\/09809.0279ecst","journal-title":"ECS Trans"},{"key":"318_CR57","doi-asserted-by":"publisher","DOI":"10.1149\/1945-7111\/abfa5c","volume":"168","author":"AD Santamaria","year":"2021","unstructured":"Santamaria AD, Mortazavi M, Chauhan V, Benner J, Philbrick O, Clemente R, Jia H, Ling C (2021) Machine learning applications of two-phase flow data in polymer electrolyte fuel cell reactant channels. J Electrochem Soc 168:054505. https:\/\/doi.org\/10.1149\/1945-7111\/abfa5c","journal-title":"J Electrochem Soc"},{"key":"318_CR58","doi-asserted-by":"publisher","DOI":"10.1016\/j.envc.2024.100838","volume":"14","author":"N Saxena","year":"2024","unstructured":"Saxena N, Kumar R, Rao YKSS, Mondloe DS, Dhapekar NK, Sharma A, Yadav AS (2024) Hybrid KNN-SVM machine learning approach for solar power forecasting. Environ Challenges 14:100838. https:\/\/doi.org\/10.1016\/j.envc.2024.100838","journal-title":"Environ Challenges"},{"key":"318_CR59","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1016\/0167-9473(95)00032-1","volume":"21","author":"M Schumacher","year":"1996","unstructured":"Schumacher M, Ro\u00dfner R, Vach W (1996) Neural networks and logistic regression: part I. Comput Stat Data Anal 21:661\u2013682. https:\/\/doi.org\/10.1016\/0167-9473(95)00032-1","journal-title":"Comput Stat Data Anal"},{"key":"318_CR60","doi-asserted-by":"publisher","unstructured":"Sokolova M, Japkowicz N, Szpakowicz S (2006) Beyond Accuracy, F-Score and ROC: A Family of Discriminant Measures for Performance Evaluation. In: Sattar, A. and Kang, B. (eds.) AI 2006: Advances in Artificial Intelligence. pp 1015\u20131021. Springer, Berlin, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11941439_114.","DOI":"10.1007\/11941439_114"},{"key":"318_CR61","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1039\/C8EE01157E","volume":"12","author":"I Staffell","year":"2019","unstructured":"Staffell I, Scamman D, Abad AV, Balcombe PE, Dodds P, Ekins P, Shah NR, Ward K (2019) The role of hydrogen and fuel cells in the global energy system. Energy Environ Sci 12:463\u2013491. https:\/\/doi.org\/10.1039\/C8EE01157E","journal-title":"Energy Environ Sci"},{"key":"318_CR62","doi-asserted-by":"publisher","unstructured":"Tyagi S, Mittal S (2020) Sampling Approaches for Imbalanced Data Classification Problem in Machine Learning. In: Singh, P.K., Kar, A.K., Singh, Y., Kolekar, M.H., and Tanwar, S. (eds.) Proceedings of ICRIC 2019. pp 209\u2013221. Springer International Publishing, Cham. https:\/\/doi.org\/10.1007\/978-3-030-29407-6_17.","DOI":"10.1007\/978-3-030-29407-6_17"},{"key":"318_CR63","unstructured":"US Department of Energy: Spotlight: Artificial Intelligence, https:\/\/www.energy.gov\/technologytransitions\/articles\/spotlight-artificial-intelligence. Accessed 14 Jun 2023"},{"key":"318_CR64","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1016\/0167-9473(95)00033-X","volume":"21","author":"W Vach","year":"1996","unstructured":"Vach W, Ro\u00dfner R, Schumacher M (1996) Neural networks and logistic regression: part II. Comput Stat Data Anal 21:683\u2013701. https:\/\/doi.org\/10.1016\/0167-9473(95)00033-X","journal-title":"Comput Stat Data Anal"},{"key":"318_CR65","doi-asserted-by":"publisher","first-page":"2762","DOI":"10.1038\/s41467-019-10399-3","volume":"10","author":"BJ van Ruijven","year":"2019","unstructured":"van Ruijven BJ, De Cian E, Sue Wing I (2019) Amplification of future energy demand growth due to climate change. Nat Commun 10:2762. https:\/\/doi.org\/10.1038\/s41467-019-10399-3","journal-title":"Nat Commun"},{"key":"318_CR66","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.jechem.2023.02.027","volume":"81","author":"N Vaz","year":"2023","unstructured":"Vaz N, Choi J, Cha Y, Kong J, Park Y, Ju H (2023) Multi-objective optimization of the cathode catalyst layer micro-composition of polymer electrolyte membrane fuel cells using a multi-scale, two-phase fuel cell model and data-driven surrogates. J Energy Chem 81:28\u201341. https:\/\/doi.org\/10.1016\/j.jechem.2023.02.027","journal-title":"J Energy Chem"},{"key":"318_CR67","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyai.2020.100014","volume":"1","author":"Y Wang","year":"2020","unstructured":"Wang Y, Seo B, Wang B, Zamel N, Jiao K, Adroher XC (2020) Fundamentals, materials, and machine learning of polymer electrolyte membrane fuel cell technology. Energy AI 1:100014. https:\/\/doi.org\/10.1016\/j.egyai.2020.100014","journal-title":"Energy AI"},{"key":"318_CR68","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpowsour.2021.229932","volume":"500","author":"J Wang","year":"2021","unstructured":"Wang J, Yang B, Zeng C, Chen Y, Guo Z, Li D, Ye H, Shao R, Shu H, Yu T (2021) Recent advances and summarization of fault diagnosis techniques for proton exchange membrane fuel cell systems: a critical overview. J Power Sources 500:229932. https:\/\/doi.org\/10.1016\/j.jpowsour.2021.229932","journal-title":"J Power Sources"},{"key":"318_CR69","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1002\/fuce.202200083","volume":"22","author":"F Xiao","year":"2022","unstructured":"Xiao F, Chen T, Peng Y, Zhang R (2022) Fault diagnosis method for proton exchange membrane fuel cells based on EIS measurement optimization. Fuel Cells 22:140\u2013152. https:\/\/doi.org\/10.1002\/fuce.202200083","journal-title":"Fuel Cells"},{"key":"318_CR70","doi-asserted-by":"publisher","first-page":"1589","DOI":"10.1109\/TEC.2022.3143163","volume":"37","author":"Y Xing","year":"2022","unstructured":"Xing Y, Wang B, Gong Z, Hou Z, Xi F, Mou G, Du Q, Gao F, Jiao K (2022) Data-driven fault diagnosis for pem fuel cell system using sensor pre-selection method and artificial neural network model. IEEE Trans Energy Convers 37:1589\u20131599. https:\/\/doi.org\/10.1109\/TEC.2022.3143163","journal-title":"IEEE Trans Energy Convers"},{"key":"318_CR71","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2021.111180","volume":"146","author":"M Yue","year":"2021","unstructured":"Yue M, Lambert H, Pahon E, Roche R, Jemei S, Hissel D (2021) Hydrogen energy systems: a critical review of technologies, applications, trends and challenges. Renew Sustain Energy Rev 146:111180. https:\/\/doi.org\/10.1016\/j.rser.2021.111180","journal-title":"Renew Sustain Energy Rev"},{"key":"318_CR72","doi-asserted-by":"publisher","first-page":"13483","DOI":"10.1016\/j.ijhydene.2020.03.035","volume":"45","author":"X Zhang","year":"2020","unstructured":"Zhang X, Zhou J, Chen W (2020) Data-driven fault diagnosis for PEMFC systems of hybrid tram based on deep learning. Int J Hydrogen Energy 45:13483\u201313495. https:\/\/doi.org\/10.1016\/j.ijhydene.2020.03.035","journal-title":"Int J Hydrogen Energy"},{"key":"318_CR73","doi-asserted-by":"publisher","first-page":"5410","DOI":"10.1016\/j.ijhydene.2016.11.043","volume":"42","author":"Z Zheng","year":"2017","unstructured":"Zheng Z, Morando S, Pera M-C, Hissel D, Larger L, Martinenghi R, Baylon Fuentes A (2017) Brain-inspired computational paradigm dedicated to fault diagnosis of PEM fuel cell stack. Int J Hydrogen Energy 42:5410\u20135425. https:\/\/doi.org\/10.1016\/j.ijhydene.2016.11.043","journal-title":"Int J Hydrogen Energy"}],"container-title":["Energy Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42162-024-00318-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s42162-024-00318-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42162-024-00318-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T11:06:50Z","timestamp":1708945610000},"score":1,"resource":{"primary":{"URL":"https:\/\/energyinformatics.springeropen.com\/articles\/10.1186\/s42162-024-00318-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,26]]},"references-count":73,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["318"],"URL":"https:\/\/doi.org\/10.1186\/s42162-024-00318-2","relation":{},"ISSN":["2520-8942"],"issn-type":[{"value":"2520-8942","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,26]]},"assertion":[{"value":"8 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"10"}}