Signal Processing and Stochastic Filtering for EIS Based PHM of Fuel Cell Systems

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.