Analysis of PEM fuel cell experimental data using principal component analysis and multi linear regression

Abstract

Abstract:Polarisation curves performed at the Fuel Cell System Laboratory (FC LAB) at Belfort on a PEM fuel cell stack using a homemade fully instrumented test bench led to more than 100 variables depending on time. Visualising and analysing all the different test variables are complex. In this work, we show how the Principal Component Analysis (PCA) method helps to explore correlations between variables and similarities between measurements at a specific sampling time (individuals). To complete this method, an empirical model of the PEM fuel cell is proposed by linking the different input parameters to the cell voltage using Multiple Linear Regressions.

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“…The principal component analysis (PCA) aims at reducing the dimensionality of a large data set consisting of a high number of correlated variables while maintaining as much as possible of the variation present in the data set [53,54]. We used the software MiniTab 17.…”

Application of the Sensor Selection Approach in Polymer Electrolyte Membrane Fuel Cell Prognostics and Health Management

“…Although several studies employ a model-based method for fuel cell diagnostics, i.e., developing a fuel cell model, and identifying fuel cell faults from residuals between model outputs and actual measurements [4][5][6][7][8], there are complexities in developing an accurate fuel cell model containing complete sets of failure modes. Data-driven approaches are more widely used for fuel cell diagnostics, that is, extracting the features by applying signal processing techniques to the sensor data, and discriminating fuel cell faults with extracted features [9][10][11][12][13]. Compared to fuel cell diagnostics, fewer studies have been devoted to fuel cell prognostics, and among these studies, training data from a fuel cell system is required to generate the input-output relationship of the fuel cell model for the prediction of future performance [14][15][16][17][18].…”

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“…A multi-linear regression analysis (MLRA) attempts to model the relationship between two or more variables and a response by fitting a linear equation to observed data (Noori, Khakpour, Omidvar, & Farokhnia, 2010;Placca, Kouta, Candusso, Blachot, & Charon, 2010). The general purpose of MLRA is to learn about the relationship between several independent variables and a dependent variable.…”