Performance degradation prediction of proton exchange membrane fuel cell using a hybrid prognostic approach

Abstract The proton exchange membrane fuel cell has been widely used for industrial systems; however, its performance gradually degrades during use. Therefore, the study on the performance degradation prediction of fuel cells is helpful to extend its lifespan. In this paper, a novel hybrid approach using a combination of model-based adaptive Kalman filter and data-driven NARX neural network is proposed to predict the degradation of fuel cells. The overall degradation trend (i.e., irreversible degradation process) is captured by an empirical aging model and adaptive Kalman filter. Meanwhile, the detail degradation information (i.e., reversible degradation process) is depicted by the NARX neural network. Moreover, the correlation analysis of the reversible voltage time series is carried out to obtain the number of delays of the NARX neural network based on the autocorrelation function and the partial autocorrelation function. Then, the total degradation prediction is the sum of the overall degradation prediction and the detail degradation prediction. Finally, the prognostic capability of the proposed method is verified by two aging datasets, and the results show the effectiveness and superiority of the proposed method which can provide accurate degradation forecasting and remaining useful life.

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

[2]  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.

[3]  Ali Cheknane,et al.  Forecasting hourly global solar radiation using hybrid k-means and nonlinear autoregressive neural network models , 2013 .

[4]  Salah Laghrouche,et al.  Degradation prediction of proton exchange membrane fuel cell based on grey neural network model and particle swarm optimization , 2019, Energy Conversion and Management.

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

[6]  Noureddine Zerhouni,et al.  A hybrid remaining useful life prognostic method for proton exchange membrane fuel cell , 2018, International Journal of Hydrogen Energy.

[7]  Bo-Hyung Cho,et al.  State-of-health diagnosis based on hamming neural network using output voltage pattern recognition for a PEM fuel cell , 2012 .

[8]  Daniel Hissel,et al.  Degradation prediction of PEM fuel cell based on artificial intelligence , 2020 .

[9]  Jin Huang,et al.  Energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle based on fuzzy logic , 2012 .

[10]  Noureddine Zerhouni,et al.  Prognostics of Proton Exchange Membrane Fuel Cell stack in a particle filtering framework including characterization disturbances and voltage recovery , 2014, 2014 International Conference on Prognostics and Health Management.

[11]  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 .

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

[13]  Salah Laghrouche,et al.  Fuel cell health prognosis using Unscented Kalman Filter: Postal fuel cell electric vehicles case study , 2019, International Journal of Hydrogen Energy.

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

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

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

[17]  S. Laghrouche,et al.  Proton Exchange Membrane Fuel Cell Prognostics Using Genetic Algorithm and Extreme Learning Machine , 2020, Fuel Cells.

[18]  Xianguo Li,et al.  A review of polymer electrolyte membrane fuel cell durability for vehicular applications: Degradation modes and experimental techniques , 2019, Energy Conversion and Management.

[19]  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 .

[20]  Xiong Shusheng,et al.  Research and development of on-board hydrogen-producing fuel cell vehicles , 2020 .

[21]  Wen-Yeau Chang.  Estimating equivalent circuit parameters of proton exchange membrane fuel cell using the current change method , 2013 .

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

[23]  Chenbin Zhang,et al.  An adaptive remaining energy prediction approach for lithium-ion batteries in electric vehicles , 2016 .

[24]  Yujie Wang,et al.  Health degradation assessment of proton exchange membrane fuel cell based on an analytical equivalent circuit model , 2020 .

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

[26]  Kung-Sik Chan,et al.  Time Series Analysis: With Applications in R , 2010 .

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

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

[29]  Hao Liu,et al.  Prognostics methods and degradation indexes of proton exchange membrane fuel cells: A review , 2020 .

[30]  Liangfei Xu,et al.  A reconstructed fuel cell life-prediction model for a fuel cell hybrid city bus , 2018 .

[31]  Zonghai Chen,et al.  Power capability prediction for lithium-ion batteries based on multiple constraints analysis , 2017 .

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

[33]  Yujie Wang,et al.  Energy management strategy for battery/supercapacitor/fuel cell hybrid source vehicles based on finite state machine , 2019, Applied Energy.

[34]  E. Zio,et al.  An ensemble of models for integrating dependent sources of information for the prognosis of the remaining useful life of Proton Exchange Membrane Fuel Cells , 2019, Mechanical Systems and Signal Processing.

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

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

[37]  Jianqiu Li,et al.  A novel diagnostic methodology for fuel cell stack health: Performance, consistency and uniformity , 2019, Energy Conversion and Management.

[38]  Mohammad Jafar Kermani,et al.  Degradation forecast for PEMFC cathode-catalysts under cyclic loads , 2017 .

[39]  Abdellatif Miraoui,et al.  Degradation Prediction of PEM Fuel Cell Stack Based on Multiphysical Aging Model With Particle Filter Approach , 2017, IEEE Transactions on Industry Applications.

[40]  Luís Guilherme Barbosa Rolim,et al.  A Control-Oriented Model of a PEM Fuel Cell Stack Based on NARX and NOE Neural Networks , 2015, IEEE Transactions on Industrial Electronics.

[41]  Reinhard Tatschl,et al.  Membrane degradation model for 3D CFD analysis of fuel cell performance as a function of time , 2016 .

[42]  Daniel Hissel,et al.  A multi-scale hybrid degradation index for proton exchange membrane fuel cells , 2019, Journal of Power Sources.

[43]  Abdesslem Djerdir,et al.  Aging prognosis model of proton exchange membrane fuel cell in different operating conditions , 2020, International Journal of Hydrogen Energy.