Long-Term Performance Prediction of PEMFC Based on LASSO-ESN

In recent years, with wide application of proton exchange membrane fuel cell (PEMFC) in vehicles and portable applications, researches regarding PEMFC lifetime behavior and associated prognostic techniques receive more interest. In this article, a least absolute shrinkage and selection operator-echo state network (LASSO-ESN)-based prognostic strategy is proposed for the optimization of input parameters and long-term PEMFC behavior prediction. In the analysis, ESN is selected to predict PEMFC long-term behavior iteratively, while input parameters to ESN are optimized using LASSO. With LASSO, the contribution of input parameters to PEMFC prediction can he evaluated, and those with the minimum weight are eliminated iteratively during the prediction. From the findings, the most accurate predictions and corresponding optimized input parameters can be determined. Furthermore, effectiveness of proposed strategy is investigated using PEMFC data at different operating conditions. Results demonstrate that with proposed strategy, optimized input parameters at different operating conditions can be determined, and accurate PEMFC predictions can be provided.

Automated summary

A prognostic strategy based on LASSO-ESN is proposed for optimizing input parameters and predicting long-term PEMFC behavior accurately, demonstrating the effectiveness of the strategy in providing optimized input parameters and accurate PEMFC predictions at different operating conditions.