A Novel Deep Belief Network and Extreme Learning Machine Based Performance Degradation Prediction Method for Proton Exchange Membrane Fuel Cell

Yucen Xie,Zhongliang Li,Jianxiao Zou,Chao Peng,Fei Gao

doi:10.1109/access.2020.3026487

Abstract

Lifetime and reliability seriously affect the applications of proton exchange membrane fuel cell (PEMFC). Performance degradation prediction of PEMFC is the basis for improving the lifetime and reliability of PEMFC. To overcome the lower prediction accuracy caused by uncertainty and nonlinearity characteristics of degradation voltage data, this article proposes a novel deep belief network (DBN) and extreme learning machine (ELM) based performance degradation prediction method for PEMFC. A DBN based fuel cell degradation features extraction model is designed to extract high-quality degradation features in the original degradation data by layer-wise learning. To tackle the issues of overfitting and instability in fuel cell performance degradation prediction, an ELM with good generalization performance is introduced as a nonlinear prediction model, which can get some enhancement of prediction precision and reliability. Based on the designed DBN-ELM model, the particle swarm optimization (PSO) algorithm is used in the model training process to optimize the basic network structure of DBN-ELM further to improve the prediction accuracy of the hybrid neural network. Finally, the proposed prediction method is experimentally validated by using actual data collected from the 5-cells PEMFC stack. The results demonstrate that the proposed approach always has better prediction performance compared with the existing conventional methods, whether in the cases of various training phase or the cases of multi-step-ahead prediction.

Highlights

The proton exchange membrane fuel cells (PEMFC) have been taken as a potential power generation system for many fields, including electric vehicles, aerospace electronics, and aircrafts [1], [2], due to its high conversion efficiency, low operation temperature, and clean reaction products [3], [4].The associate editor coordinating the review of this manuscript and approving it for publication was Jenny Mahoney.the fuel cell system is affected by multiple factors during operation, which reduces its reliability and shortens its lifetime [5]
In this article, a novel degradation prediction approach based on deep belief network (DBN) and extreme learning machine (ELM) is proposed for PEMFC stack performance degradation prediction
The proposed hybrid method combines the benefits of DBN and ELM, which can accurately extract the high nonlinearity features exhibited in degradation voltage data and improve the accuracy and reliability of degradation prediction

Summary

Introduction

The proton exchange membrane fuel cells (PEMFC) have been taken as a potential power generation system for many fields, including electric vehicles, aerospace electronics, and aircrafts [1], [2], due to its high conversion efficiency, low operation temperature, and clean reaction products [3], [4]. The fuel cell system is affected by multiple factors during operation, which reduces its reliability and shortens its lifetime [5]. Predicting the performance degradation can effectively indicate the health status of PEMFCs, which could provide a maintenance plan to reduce the failures and downtimes of PEMFCs, thereby extending their lifetime and increasing their reliability [6], [7].

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Results

Conclusion