Life Prediction Based on D-S ELM for PEMFC

Abstract

The proton exchange membrane fuel cell (PEMFC) is an extremely clean and efficient power generation device. However, its limited lifespan has restricted the large-scale commercial development of PEMFCs. Life prediction is a promising solution for the further life extension of PEMFCs. In this paper, D-S ELM(DWT-SaDE ELM), define as, an enhanced extreme learning machine (ELM) optimized by discrete wavelet transform (DWT) and self-adaptive differential evolutionary algorithm (SaDE), is proposed to predict the remaining useful life (RUL) of PEMFCs. In D-S ELM, DWT is employed to extract available features from multi-input data with stochastic noise. Then, SaDE explores the optimal parameter configuration for the ELM neural network. Moreover, the influence of training data sizes on the prediction results is discussed. Simulations show that D-S ELM has obvious advantages in prediction accuracy. Furthermore, the superiority of D-S ELM in small sample applicability, prediction speed and robustness make it more suitable for the online prediction of PEMFCs.