Abstract
State estimation is widely used in the field of process system engineering. There are several available technologies regarding this topic, e.g. Extended Kalman Filter (EKF). The EKF is the variant of the standard Kalman filter and is successfully applied on the nonlinear systems for state estimation. As well known, the PEMFC is a typical nonlinear system, and some of the internal states are obtained costly, and even cannot be measured directly. Hence, in order to obtain these internal states effectively via collecting measurable variables, the EKF is applied in this study. The goal of this paper is to demonstrate the implementation of the EKF based on a PEMFC model which is taken in a literature, in order to estimate the following internal states: concentrations of vapor and oxygen in cathode chamber, as well as cell temperature. The corresponding results show that the EKF can serve as a ‘software sensor’ for the control design or the supervision in the fuel cell system.
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Liu, J., Ji, G., Zhou, S. (2013). Extended Kalman Filter (EKF) with Sensitive Equation for the PEMFC Internal State Estimation. In: Yang, GC., Ao, SI., Huang, X., Castillo, O. (eds) IAENG Transactions on Engineering Technologies. Lecture Notes in Electrical Engineering, vol 186. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5651-9_23
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DOI: https://doi.org/10.1007/978-94-007-5651-9_23
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