A Multi-Stage Fault Diagnosis Method for Proton Exchange Membrane Fuel Cell Based on Support Vector Machine with Binary Tree

Abstract

The reliability and durability of the proton exchange membrane (PEM) fuel cells are vital factors restricting their applications. Therefore, establishing an online fault diagnosis system is of great significance. In this paper, a multi-stage fault diagnosis method for the PEM fuel cell is proposed. First, the tests of electrochemical impedance spectroscopy under various fault conditions are conducted. Specifically, prone recoverable faults, such as flooding, membrane drying, and air starvation, are included, and different fault degrees from minor, moderate to severe, are covered. Based on this, an equivalent circuit model (ECM) is selected to fit impedance spectroscopy by the hybrid genetic particle swarm optimization algorithm, and then fault features are determined by the analysis of each model parameter under different fault conditions. Furthermore, a multi-stage fault diagnosis model is constructed with the support vector machine with the binary tree, in which fault features obtained from the ECM are used as the characteristic inputs to realize the fault classification (including fault type and fault degree) online. The results show that the accuracy of the basic fault test and subdivided fault test can reach 100% and 98.3%, respectively, which indicates that the proposed diagnosis method can effectively identify flooding, drying, and air starvation of PEM fuel cells.

Accurate Estimation of PEMFC State of Health using Modified Hybrid Artificial Neural Network Models

“…For instance. Jiaping xie et al [2] proposed a method using multi-stage fault diagnosis based on support vector machine. The fault features are selected and determined by an equivalent circuit model using the hybrid genetic particle swarm optimization algorithm to realize the fault classification online.…”

“…Numerous methods have been proposed to identify and diagnose the state of fuel cell's health, i.e. semi-empirical, empirical, physical, analytical and the black-box models [2]. In the literature, most common technique to di agnose the fuel cell is the electrochemical impedance spectroscopy (EIS) in order to make the hydration monitoring [3], which characterizes the values of the impedance parameters based on the Randles model of constant phase elements (CPE) through estimating the flooding and drying state in the fuel cell system.…”

Intelligence Techniques in Sustainable Energy: Analysis of a Decade of Advances

“…Failure diagnosis. Proton exchange membrane (PEM) fuel cells are garnering attention due to their potential in sectors like fuel cell vehicles [153,154]. However, the complexity of PEM fuel cells and the variety of potential faults they can exhibit make their reliability and durability a concern, highlighting the significance of fault diagnosis.…”

“…Siamese artificial neural networks, tailored to PEM fuel cells, distinguish features from impedance spectra [156]. Additionally, support vector machines combined with binary trees have been utilized to hasten fault categorization [153], and a novel deep learning approach marries a backpropagation neural network with an inception-based convolutional network, targeting fault identification in fuel cell tram systems [157]. In recent advancements, long short-term memory (LSTM) networks, acclaimed for processing time series data, have been pivotal for diagnosing issues like flooding in vehicle-based systems [158].…”