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PhD defence by Christian Jeppesen on Online Fault Detection for High Temperature Proton Exchange Membrane Fuel Cells - A Data Driven Impedance Approach


01.06.2017 kl. 13.00 - 16.00


Christian Jeppesen, Department of Energy Technology, will defend the thesis "Online Fault Detection for High Temperature Proton Exchange Membrane Fuel Cells - A Data Driven Impedance Approach".


Online Fault Detection for High Temperature Proton Exchange Membrane Fuel Cells - A Data Driven Impedance Approach


Christian Jeppesen


Professor Søren Knudsen Kær


Associate Professor Samuel Simon Araya 


Associate Professor Zhenyu Yang, Dept. of Energy Technology, Aalborg University (Chairman)
Professor Daniel Hissel, University of Franche-Comte, Belfort, France
Dr. Holger Janssen, Research Center Jülich, Germany


An increasing share of fluctuating energy sources are being introduced in the Danish electricity grid. This is a result of a pursuit of greener energy system, where renewable energy sources produce the electricity. However, this introduces new problems related to balancing the supply and demand, at all times. In Denmark, this problem has so far been addressed by building new high voltage electricity transmission lines to surrounding countries, but with an increasing amount of renewable energy this solution is not feasible in long term. One possible solution could be to introduce electricity storage solutions, that can store the energy from surplus capacity periods and use it in low capacity periods. One way of storing electricity is to produce hydrogen using electrolyzers and utilize it in fuel cells to produce electricity whenever electricity is needed.

For fuel cells to become ready for large scale commercialization, prices need to come down and the durability needs to be improved. One method to improve durability and availability is by designing fault detection and isolation (FDI) algorithms, which can commence mitigation strategies for preventing down time and to ensure smooth fuel cell operation with minimal degradation.

In this dissertation, FDI algorithms for detecting five common faults in high temperature proton exchange membrane fuel cells are investigated. The five faults investigated are related to anode and cathode gas supply. For the anode, the considered faults are carbon monoxide (CO) contamination, methanol vapor contamination and hydrogen starvation. For the cathode, oxidant starvation and too high flow of oxidant are considered.

The FDI algorithms are based on a data-driven impedance approach, where databases containing data from healthy and non-healthy operations are constructed. The fault detection and isolation process has been divided in to three steps: characterization, feature extracting and change detecting & isolation.

For characterization of the fuel cell impedance, two techniques are considered, electrochemical impedance spectroscopy (EIS) and current pulse injection (CPI).

In the CPI method, small current pulses are added to the DC fuel cell current, and based on the corresponding voltage, the parameters of a simple equivalent electrical circuit (EEC) model can be estimated. The parameters of the EEC model can be used as features for fault detection. The advantage of this method is that it can be implemented simply, using a transistor and a resistor, and although the estimated EEC model is more simple, it might be useful for some FDI applications.

When using the EIS method for fuel cell impedance characterization, a small sinusoidal current is superimposed on the DC current, and based on the corresponding phase shift and amplitude difference, the impedance can be estimated. Based on the fuel cell impedance, two feature extraction methods are analyzed in this dissertation. First, fitting an EEC model to the impedance spectrum and utilizing the EEC model parameters as features. Second, extracting internal relationships of the impedance spectrum, such as angles and magnitudes as features. Knowing the behavior of the features in healthy and non-healthy operation, algorithms are designed for FDI.

For change detection and isolation of the faults, two methods are considered in this dissertation. Firstly, based on an extracted feature, a squared error is calculated and compared to a threshold. Based on this a general likelihood ratio test is designed for detecting an increased level of CO in the anode gas, for a change in the value of a resistor in the EEC model. The algorithm demonstrated the ability to detect CO contamination with very low probability of false alarm. As a second method, an artificial neural network classifier is trained based on a database containing healthy and non-healthy data. This approach is demonstrated in this dissertation, resulting in a global accuracy of 94.6 %, and the algorithm is reported to yield a good detectability for four of the five faults investigated, with the exception of methanol vapor contamination in the anode gas, where it showed difficulties distinguishing between healthy operation and the faulty operation, for the investigated methanol vapour concentration.


PhD defence by Christian Jeppesen on Online Fault Detection for High Temperature Proton Exchange Membrane Fuel Cells - A Data Driven Impedance Approach





Department of Energy Technology


Pontoppidanstræde 111, auditorium

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