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This paper presents a comprehensive solution for condition monitoring of PEM fuel cell systems. It comprises a modular DC-DC converter, a 90-channel fuel cell voltage monitor, and embedded diagnostics algorithms. Besides providing its basic functionality, the DC-DC converter is designed to perform diagnostic probing of fuel cells by injecting current excitation waveforms. The designed voltage monitor enables the precise voltage measurement of all individual cells in the stack. The interconnection between the DC-DC converter and the voltage monitor provides a platform for an embedded condition monitoring system. It employs a novel algorithm for fast electrochemical impedance estimation and automatic tuning of the thresholds used in the fault detection algorithm. The final output is a unitfree condition indicator that describes the overall condition of the fuel cell stack. As such, the designed condition monitoring system allows the seamless integration and optimal exploitation of fuel cell power systems. The complete solution has been evaluated on an 8.5 kW PEM fuel cell power system.

Proton Exchange Membrane (PEM) fuel cells are currently seen as the most suitable choice for implementation into daily-use applications. However, the PEM technology does not yet fulfil all the necessary requirements that the mass-market demands and proper strides towards elimination of remaining issues have to be taken. Hence, in this paper, the focus is made on water management faults, i.e. flooding and drying. More precisely, it deals with detection of them with the use of Electrochemical Impedance Spectroscopy (EIS). The EIS was successfully applied as a diagnostic tool to a fuel cell stack consisted of 80 cells without usage of any special purpose measurement equipment, where, in addition to the stack current, only voltage of the complete stack was measured. The paper describes the modifications that were made on the EIS to make it capable of handling the diagnostics of fuel cell stacks. The results of the experimental study show that the approach is successful in detecting the flooding and drying faults and that for detection only excitation signals with frequencies between 30 and 300 Hz are required. Based on the experimental data and conclusions, a diagnostic decision algorithm is proposed.

This paper presents an alternative computational method for on‐line estimation and tracking of the impedance of PEM fuel cell systems. The method is developed in order to provide the information to diagnostics and health management system. Proper water management remains the main issue influencing the reliability and durability of PEM fuel cell technology. While literature reviews reveal the thorough understanding of the underlying processes and extensive experimental work, the existing implementations rely on expensive hardware or time consuming computational methods. In this scope, we will show how the characteristic values of the fuel cell impedance, required by the diagnostic system, can be computed by robust and computationally efficient algorithms, which are suitable for implementation in embedded systems. The methods under consideration include continuous‐time wavelet transform (CWT) and extended Kalman filter (EKF). The CWT is a time‐frequency technique, which is suitable for tracking transient signal components. The EKF is a stochastic signal processing method, which provides confidence measures for the estimates. The paper shows, that both methods provide accurate estimates for diagnostics of FCS and can perform on‐line tracking of these features. The performance of the algorithms is validated on experimental data from a commercial fuel cell stack.