Remaining useful life prediction of PEMFC systems based on the multi-input echo state network

Abstract The limited durability is one of the key barriers of Proton Exchange Membrane Fuel Cell (PEMFC) to large-scale commercial applications. The data-driven prognostic method aims to estimate the Remaining Useful Life (RUL) without the need for complete knowledge about the system’s physical phenomena. As an improved structure of the recurrent neural network, the Echo State Network (ESN) has demonstrated better performances, especially in reducing the computational complexity and accelerating the convergence rate. The traditional prognostic methods utilize only the previous state, e.g. stack voltage, for prediction. Nevertheless, the current operating conditions, such as stack current, stack temperature and the pressures of the reactants (i.e. oxygen and hydrogen) can also contain important degradation information in practice. Especially, the stack current is a crucial operating parameter, since it is normally taken as the scheduling variable and it could reflect the operating conditions. Compared with the single-input and single-output (SISO-ESN) structure, the ESN with multiple inputs and multiple outputs (MIMO-ESN) is proposed in this paper to improve the RUL prediction accuracy. Stack voltage, stack current, stack temperature and the pressures of the reactants are combinedly used to predict the RUL. After the mathematical modeling and the parameter designing, the prediction performance of SISO-ESN and MIMO-ESN are verified and compared on a 1 kW electrical power test bench developed in the laboratory. Results show that the MIMO-ESN method has a better performance than the SISO-ESN method under both static and quasi-dynamic operating conditions.

[1]  Qi Li,et al.  Optimal Energy Management and Control in Multimode Equivalent Energy Consumption of Fuel Cell/Supercapacitor of Hybrid Electric Tram , 2019, IEEE Transactions on Industrial Electronics.

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

[3]  Mantas Lukosevicius,et al.  A Practical Guide to Applying Echo State Networks , 2012, Neural Networks: Tricks of the Trade.

[4]  Jay Lee,et al.  Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications , 2014 .

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

[6]  Yun Wang,et al.  A review of polymer electrolyte membrane fuel cells: Technology, applications,and needs on fundamental research , 2011 .

[7]  Jian Chen,et al.  Remaining useful life estimation for proton exchange membrane fuel cells using a hybrid method , 2019, Applied Energy.

[8]  Henry Markram,et al.  Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.

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

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

[11]  Huicui Chen,et al.  Main factors affecting the lifetime of Proton Exchange Membrane fuel cells in vehicle applications: A review , 2014 .

[12]  Noureddine Zerhouni,et al.  Estimating the end-of-life of PEM fuel cells: Guidelines and metrics , 2016 .

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

[14]  Yang Zhou,et al.  Multi-mode predictive energy management for fuel cell hybrid electric vehicles using Markov driving pattern recognizer , 2020 .

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

[16]  Damien Paire,et al.  Nonlinear Performance Degradation Prediction of Proton Exchange Membrane Fuel Cells Using Relevance Vector Machine , 2016, IEEE Transactions on Energy Conversion.

[17]  Abdellatif Miraoui,et al.  A Modified Relevance Vector Machine for PEM Fuel-Cell Stack Aging Prediction , 2016 .

[18]  Weirong Chen,et al.  Remaining useful life prediction of PEMFC based on long short-term memory recurrent neural networks , 2019, International Journal of Hydrogen Energy.

[19]  Fei Gao,et al.  Data-driven proton exchange membrane fuel cell degradation predication through deep learning method , 2018, Applied Energy.

[20]  Bhaskar Saha,et al.  An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries , 2010 .

[21]  M. Gerard,et al.  Multi-scale coupling between two dynamical models for PEMFC aging prediction , 2013 .

[22]  Jihong Wang,et al.  Overview of current development in electrical energy storage technologies and the application potential in power system operation , 2015 .

[23]  Rachid Outbib,et al.  Adaptive Prognostic of Fuel Cells by Implementing Ensemble Echo State Networks in Time-Varying Model Space , 2020, IEEE Transactions on Industrial Electronics.