Review on hydrogen fuel cell condition monitoring and prediction methods

Abstract A hydrogen fuel cell combines oxygen and hydrogen to generate electricity, which becomes a promising power source. The conditions of the fuel cell, such as health status, and faults, are essential for ensuring the continuous power supplying. There are technologies and algorithms carried out for fuel cell health monitoring, fault diagnosis, and prediction, lifespan prediction. For a comprehensive understanding, this paper proposed a comprehensive overview of the work on fuel cell conditions monitoring technology. It reviewed the works of literature from two different points of view, technology, and scenario. For technology view, there are model-based method, filter-based method, and data motivated method. For scenario views, it proposed a 5 × 5 tables for detail comparison. Based on this review, readers can easily understand the current status of condition monitoring technology for the fuel cell.

[1]  Daniel Hissel,et al.  Diagnostic tools for PEMFCs: from conception to implementation , 2014 .

[2]  Niusha Shafiabady,et al.  Dynamic modelling of PEM fuel cell of power electric bicycle system , 2016 .

[3]  Hicham Chaoui,et al.  Overview and benchmark analysis of fuel cell parameters estimation for energy management purposes , 2018 .

[4]  Marco Sorrentino,et al.  A review on model-based diagnosis methodologies for PEMFCs , 2013 .

[5]  Peng Yu,et al.  Online adaptive status prediction strategy for data-driven fault prognostics of complex systems , 2011, 2011 Prognostics and System Health Managment Confernece.

[6]  Fatiha Nejjari,et al.  On-line model-based fault detection and isolation for PEM fuel cell stack systems , 2014 .

[7]  Daniel Hissel,et al.  A Non‐Intrusive Signal‐Based Method for a Proton Exchange Membrane Fuel Cell Fault Diagnosis , 2017 .

[8]  Noureddine Zerhouni,et al.  Prognostics of Proton Exchange Membrane Fuel Cells stack using an ensemble of constraints based connectionist networks , 2016 .

[9]  Marco Sorrentino,et al.  Model-based development of a fault signature matrix to improve solid oxide fuel cell systems on-site diagnosis , 2015 .

[10]  Horng-Wen Wu.  A review of recent development: Transport and performance modeling of PEM fuel cells , 2016 .

[11]  Søren Knudsen Kær,et al.  Modeling and experimental validation of water mass balance in a PEM fuel cell stack , 2016 .

[12]  Denis Candusso,et al.  On the issue of the PEMFC operating fault identification: Generic analysis tool based on voltage pointwise singularity strengths , 2017, International Journal of Hydrogen Energy.

[13]  Thamo Sutharssan,et al.  A review on prognostics and health monitoring of proton exchange membrane fuel cell , 2017 .

[14]  Yunqi Li,et al.  A statistical study of proton conduction in Nafion®-based composite membranes: Prediction, filler selection and fabrication methods , 2018 .

[15]  Daniel Hissel,et al.  Data-driven diagnosis of PEM fuel cell: A comparative study , 2014 .

[16]  L. Mao,et al.  Investigation of PEMFC fault diagnosis with consideration of sensor reliability , 2017, International Journal of Hydrogen Energy.

[17]  Lisa M. Jackson,et al.  Failure Mode and Effect Analysis, and Fault Tree Analysis of Polymer Electrolyte Membrane Fuel Cells , 2016 .

[18]  Latifa Oukhellou,et al.  PEMFC stack voltage singularity measurement and fault classification , 2014 .

[19]  Daniel Hissel,et al.  Wavelet-Based Approach for Online Fuel Cell Remaining Useful Lifetime Prediction , 2016, IEEE Transactions on Industrial Electronics.

[20]  Michel Benne,et al.  Polymer electrolyte membrane fuel cell fault diagnosis based on empirical mode decomposition , 2015 .

[21]  Kai Sundmacher,et al.  Understanding PEM fuel cell dynamics: The reversal curve , 2017 .

[22]  Daniel Hissel,et al.  Non intrusive diagnosis of polymer electrolyte fuel cells by wavelet packet transform , 2011 .

[23]  Dacheng Zhang,et al.  Some Improvements of Particle Filtering Based Prognosis for PEM Fuel Cells , 2016 .

[24]  Lisa M. Jackson,et al.  Expert diagnosis of polymer electrolyte fuel cells , 2017 .

[25]  Tianyu Li,et al.  Predictive energy management of fuel cell supercapacitor hybrid construction equipment , 2018 .

[26]  Chang-Bock Chung,et al.  Performance prediction and analysis of a PEM fuel cell operating on pure oxygen using data-driven models: A comparison of artificial neural network and support vector machine , 2016 .

[27]  Siti Najibah Abd Rahman,et al.  Overview biohydrogen technologies and application in fuel cell technology , 2016 .

[28]  Jianqiu Li,et al.  Comprehensive analysis of galvanostatic charge method for fuel cell degradation diagnosis , 2018 .

[29]  G. Molaeimanesh,et al.  Lattice Boltzmann simulation of proton exchange membrane fuel cells – A review on opportunities and challenges , 2016 .

[30]  Noureddine Zerhouni,et al.  PEMFC aging modeling for prognostics and health assessment , 2015 .

[31]  Noureddine Zerhouni,et al.  Degradations analysis and aging modeling for health assessment and prognostics of PEMFC , 2016, Reliab. Eng. Syst. Saf..

[32]  Yongdong Li,et al.  A double-fuzzy diagnostic methodology dedicated to online fault diagnosis of proton exchange membrane fuel cell stacks , 2014 .

[33]  Junghui Chen,et al.  Prognostics of PEM fuel cells based on Gaussian process state space models , 2018 .

[34]  Gabriele Moser,et al.  Fault diagnosis in fuel cell systems using quantitative models and support vector machines , 2014 .

[35]  Daniel Hissel,et al.  Proton exchange membrane fuel cell degradation prediction based on Adaptive Neuro-Fuzzy Inference Systems . , 2014 .

[36]  Cesare Pianese,et al.  Control algorithm design for degradation mitigation and lifetime improvement of Polymer Electrolyte Membrane Fuel Cells , 2017 .

[37]  R. Gouriveau,et al.  Data-driven Prognostics of Proton Exchange Membrane Fuel Cell Stack with constraint based Summation-Wavelet Extreme Learning Machine. , 2015 .

[38]  Marcelo Godoy Simões,et al.  On-line fault diagnostic system for proton exchange membrane fuel cells , 2008 .

[39]  N. Rajasekar,et al.  A comprehensive review on parameter estimation techniques for Proton Exchange Membrane fuel cell modelling , 2018, Renewable and Sustainable Energy Reviews.

[40]  Yongdong Li,et al.  Fault detection and isolation for Polymer Electrolyte Membrane Fuel Cell systems by analyzing cell voltage generated space , 2015 .

[41]  D. S. Falcão,et al.  1D + 3D two-phase flow numerical model of a proton exchange membrane fuel cell , 2017 .

[42]  Hongye Su,et al.  A Review on Prognostics of Proton Exchange Membrane Fuel Cells , 2016, 2016 IEEE Vehicle Power and Propulsion Conference (VPPC).

[43]  Chuan Lyu,et al.  A novel health indicator for PEMFC state of health estimation and remaining useful life prediction , 2017 .

[44]  Chris Develder,et al.  Quantitive analysis of electric vehicle flexibility : a data-driven approach , 2018 .

[45]  Nicholas Jenkins,et al.  A data-driven approach for characterising the charging demand of electric vehicles: A UK case study , 2016 .

[46]  N. Rajasekar,et al.  Critical Evaluation of Genetic Algorithm Based Fuel Cell Parameter Extraction , 2015 .

[47]  Werner Lehnert,et al.  Parameter extraction and uncertainty analysis of a proton exchange membrane fuel cell system based on Monte Carlo simulation , 2017 .

[48]  Marco Sorrentino,et al.  A model-based diagnostic technique to enhance faults isolability in Solid Oxide Fuel Cell systems , 2017 .

[49]  Christophe Varnier,et al.  Decision process to manage useful life of multi-stacks fuel cell systems under service constraint , 2017 .

[50]  Jérémi Régnier,et al.  Fuel cell flooding diagnosis based on time-constant spectrum analysis , 2016 .

[51]  Àngela Nebot,et al.  PEM fuel cell fault diagnosis via a hybrid methodology based on fuzzy and pattern recognition techniques , 2014, Eng. Appl. Artif. Intell..

[52]  Noureddine Zerhouni,et al.  Proton exchange membrane fuel cell behavioral model suitable for prognostics. , 2015 .

[53]  J. García-Villalobos,et al.  Fuel cell-based CHP system modelling using Artificial Neural Networks aimed at developing techno-economic efficiency maximization control systems , 2017 .

[54]  Noureddine Zerhouni,et al.  Prognostics and Health Management of PEMFC – State of the art and remaining challenges , 2013 .

[55]  Marco Sorrentino,et al.  On the Use of Neural Networks and Statistical Tools for Nonlinear Modeling and On-field Diagnosis of Solid Oxide Fuel Cell Stacks , 2014 .

[56]  Sergio Toscani,et al.  Low-Cost PEM Fuel Cell Diagnosis Based on Power Converter Ripple With Hysteresis Control , 2015, IEEE Transactions on Instrumentation and Measurement.

[57]  A. Urquia,et al.  Proton exchange membrane fuel cell failure mode early diagnosis with wavelet analysis of electrochemical noise , 2016 .

[58]  Daniel Hissel,et al.  Determination of the health state of fuel cell vehicle for a clean transportation , 2018 .

[59]  Tamer Khatib,et al.  A comparative study of evolutionary algorithms and adapting control parameters for estimating the parameters of a single-diode photovoltaic module's model , 2016 .

[60]  Daniel Hissel,et al.  SOFC modelling based on discrete Bayesian network For system diagnosis use , 2012 .

[61]  Sascha Wörz,et al.  A novel method for optimal fuel consumption estimation and planning for transportation systems , 2017 .

[62]  Dino Isa,et al.  Modeling of commercial proton exchange membrane fuel cell using support vector machine , 2016 .

[63]  C. Pianese,et al.  Analytical calculation of electrolyte water content of a Proton Exchange Membrane Fuel Cell for on-board modelling applications , 2018, Journal of Power Sources.

[64]  Liangcai Zeng,et al.  Diagnosis and Prognosis of Degradation Process via Hidden Semi-Markov Model , 2018, IEEE/ASME Transactions on Mechatronics.

[65]  N. Rajasekar,et al.  A novel approach for fuel cell parameter estimation using simple Genetic Algorithm , 2015 .

[66]  K. Bouzek,et al.  Three-dimensional macrohomogeneous mathematical model of an industrial-scale high-temperature PEM fuel cell stack , 2018 .

[67]  Xin-Jian Zhu,et al.  An artificial neural network ensemble method for fault diagnosis of proton exchange membrane fuel cell system , 2014 .

[68]  Omar Z. Sharaf,et al.  An overview of fuel cell technology: Fundamentals and applications , 2014 .

[69]  Wan Ramli Wan Daud,et al.  Electrode for proton exchange membrane fuel cells: A review , 2018, Renewable and Sustainable Energy Reviews.

[70]  Abdellatif Miraoui,et al.  Degradation prediction of PEM fuel cell using a moving window based hybrid prognostic approach , 2017 .

[71]  S. Martemianov,et al.  Proton exchange membrane fuel cell diagnosis by spectral characterization of the electrochemical noise , 2017 .

[72]  Sergio Toscani,et al.  PEM Fuel Cell Drying and Flooding Diagnosis With Signals Injected by a Power Converter , 2015, IEEE Transactions on Instrumentation and Measurement.

[73]  D. Depernet,et al.  Fault diagnosis methods for Proton Exchange Membrane Fuel Cell system , 2017 .

[74]  Saeid R. Dindarloo,et al.  Prediction of fuel consumption of mining dump trucks: A neural networks approach , 2015 .

[75]  Samuel Simon Araya,et al.  A comprehensive review of PBI-based high temperature PEM fuel cells , 2016 .

[76]  Siti Kartom Kamarudin,et al.  Titanium dioxide in fuel cell technology: An overview , 2015 .

[77]  Daniel Hissel,et al.  Online implementation of SVM based fault diagnosis strategy for PEMFC systems , 2015 .

[78]  Tie-Jun Cui,et al.  Deep learning of system reliability under multi-factor influence based on space fault tree , 2019, Neural Computing and Applications.

[79]  Jian Chen,et al.  Prognostics of Proton Exchange Membrane Fuel Cells Using A Model-based Method , 2017 .

[80]  Nigel M. Sammes,et al.  Model-based condition monitoring of PEM fuel cell using Hotelling T2 control limit , 2006 .

[81]  James Lam,et al.  An Improved Incremental Learning Approach for KPI Prognosis of Dynamic Fuel Cell System , 2016, IEEE Transactions on Cybernetics.

[82]  Daniel Hissel,et al.  Signal-Based Diagnostics by Wavelet Transform for Proton Exchange Membrane Fuel Cell☆ , 2015 .

[83]  Laurent Larger,et al.  Brain-inspired computational paradigm dedicated to fault diagnosis of PEM fuel cell stack , 2017 .

[84]  Belkacem Ould Bouamama,et al.  Extended Kalman Filter for prognostic of Proton Exchange Membrane Fuel Cell , 2016 .

[85]  Carla Tagliaferri,et al.  Life cycle assessment of a polymer electrolyte membrane fuel cell system for passenger vehicles , 2017 .

[86]  Weirong Chen,et al.  A discrete hidden Markov model fault diagnosis strategy based on K-means clustering dedicated to PEM fuel cell systems of tramways , 2018, International Journal of Hydrogen Energy.

[87]  Daniel Hissel,et al.  Diagnostic & health management of fuel cell systems: Issues and solutions , 2016, Annu. Rev. Control..

[88]  Belkacem Ould Bouamama,et al.  Particle filter based hybrid prognostics of proton exchange membrane fuel cell in bond graph framework , 2016, Comput. Chem. Eng..

[89]  Arunachala Mada Kannan,et al.  Characterization techniques for gas diffusion layers for proton exchange membrane fuel cells: A review , 2012 .

[90]  Xuesong Yan,et al.  Parameter extraction of different fuel cell models with transferred adaptive differential evolution , 2015 .

[91]  Zuomin Dong,et al.  Load profile based empirical model for the lifetime prediction of an automotive PEM fuel cell , 2017 .

[92]  Marco Sorrentino,et al.  A review on non-model based diagnosis methodologies for PEM fuel cell stacks and systems , 2013 .

[93]  Gexiang Zhang,et al.  Parameter fitting of PEMFC models based on adaptive differential evolution , 2014 .

[94]  Noureddine Zerhouni,et al.  Proton exchange membrane fuel cell ageing forecasting algorithm based on Echo State Network , 2017 .

[95]  Ibrahim Dincer,et al.  A preliminary life cycle assessment of PEM fuel cell powered automobiles , 2007 .

[96]  Donghua Zhou,et al.  Diagnosis and Prognosis for Complicated Industrial Systems - Part I , 2016, IEEE Trans. Ind. Electron..

[97]  Y. Bultel,et al.  Proton exchange membrane fuel cell model for aging predictions: Simulated equivalent active surface area loss and comparisons with durability tests , 2016 .

[98]  Pierluigi Pisu,et al.  Prognostic-oriented Fuel Cell Catalyst Aging Modeling and Its Application to Health-Monitoring and Prognostics of a PEM Fuel Cell , 2020 .

[99]  Xiaozhan Yang,et al.  Robust Model-Based Fault Diagnosis for PEM Fuel Cell Air-Feed System , 2016, IEEE Transactions on Industrial Electronics.

[100]  Noureddine Zerhouni,et al.  Joint Particle Filters Prognostics for Proton Exchange Membrane Fuel Cell Power Prediction at Constant Current Solicitation , 2016, IEEE Transactions on Reliability.

[101]  B. Ould Bouamama,et al.  A Survey of Diagnostic of Fuel Cell Stack Systems , 2012 .

[102]  Noboru Katayama,et al.  Real-Time Electrochemical Impedance Diagnosis for Fuel Cells Using a DC–DC Converter , 2015, IEEE Transactions on Energy Conversion.

[103]  Noureddine Zerhouni,et al.  Prognostics of PEM fuel cell in a particle filtering framework , 2014 .

[104]  Noureddine Zerhouni,et al.  ANOVA method applied to proton exchange membrane fuel cell ageing forecasting using an echo state network , 2017, Math. Comput. Simul..

[105]  Daniel Hissel,et al.  Diagnosis for PEMFC Systems: A Data-Driven Approach With the Capabilities of Online Adaptation and Novel Fault Detection , 2015, IEEE Transactions on Industrial Electronics.

[106]  S. Martemianov,et al.  Statistical short-time analysis of electrochemical noise generated within a proton exchange membrane fuel cell , 2018, Journal of Solid State Electrochemistry.

[107]  Daming Zhou,et al.  Global Parameters Sensitivity Analysis and Development of a Two-Dimensional Real-Time Model of Proton-Exchange-Membrane Fuel Cells , 2018 .

[108]  Hongye Su,et al.  Data-based short-term prognostics for proton exchange membrane fuel cells , 2017 .