Active disturbance rejection control for voltage stabilization in open-cathode fuel cells through temperature regulation. (November 2016)

• Record Type: Journal Article
• Authors: Lotfi, Nima; Zomorodi, Hesam; Landers, Robert G.
• Is Part Of: Control engineering practice. Volume 56(2016)
• Page Start: 92
• Page End: 100
• Publication Date: 2016-11
• Subjects: Open-cathode PEM fuel cell -- Temperature control and estimation -- Observer design -- Uncertainty estimation -- Adaptive voltage regulation; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2016.08.006
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: Temperature regulation is an important control challenge in open-cathode fuel cell systems. In this paper, a feedback controller, combined with a novel output-injection observer, is designed and implemented for fuel cell stack temperature control. The first functionality of the observer is to smooth the noisy temperature measurements. To this end, the observer gain is calculated based on Kalman filter theory which, in turn, results in a robust temperature estimation despite temperature model uncertainties and measurement noise. Furthermore, the observer is capable of estimating the output voltage model uncertainties. It is shown that temperature control not only ensures the fuel cell temperature reference is properly tracked, but, along with the uncertainty estimator, can also be used to stabilize the output voltage. Voltage regulation is of great importance for open-cathode fuel cells, which typically suffer from gradual voltage decay over time due to their dead-end anode operation. Moreover, voltage control ensures predictable and fixed fuel cell output voltages for given current values, even in the presence of disturbances. The observer stability is proved using Lyapunov theory, and the observer's effectiveness in combination with the controller is validated experimentally. The results show promising controller performances in regulating fuel cell temperature and voltage in the presence of model uncertainties and disturbances.