  This paper proposes the online parameters identification of semi-empirical
models of Proton Exchange Membrane Fuel Cell (PEMFC). The covariance of unknown
measurement noise is also estimated simultaneously. The actual data are fed to
Kalman (for linear-in-parameters models) or extended Kalman filter (for
nonlinear ones) which have been adapted for parameter identification. These
filters suffer from the fact that the noise of the measurements is unknown. In
order to tackle this conundrum, the measurement-noise is simultaneously
estimated, and the estimation is used in the filters. The ultimate consequence
of estimating measurement-noise is error reduction which has been demonstrated
by simulation results.
