Validation of Proton Exchange Membrane Fuel Cell Lifetime Prediction Based on Artificial Intelligent Methodology

Abstract

The use of renewable energy has conceptually become the world's mainstream Therefore fuel cells with low noise low operating temperature zero CO2 NOx emissions high energy efficiency and fast start-up have become the most potential energy options However membrane electrode assembly will have a degradation phenomenon after use In addition to affecting the performance and efficiency of the fuel cell it will also affect the lifetime of the fuel cell Since the long-term test is time-consuming this study proposes a rapid prediction method for the aging of the membrane electrode assembly This method will be a vigorous development of the green energy industry This study focuses on predicting the degradation of fuel cell lifetime and performance through machine learning Machine learning can effectively predict the lifetime of fuel cell with less time and limited fuel The optimized operating parameters of the PEMFC and LSTM algorithms are introduced to achieve verification of predicted fuel cell lifetime The preliminary model of this study has a good prediction effect on the test data Five-stage graphite fuel cell stack is used in this study the hydrogen flow rate is 7 L/min the humidification temperature is 80 oC after long-term testing degradation data is introduced in machine learning Error rate (mean absolute error MAE) can be within 2% The results of this study verify that machine learning can be effectively applied to fuel cell lifetime prediction

Date of Award	2020
Original language	English
Supervisor	Wei-Hsiang Lai (Supervisor)