Simultaneously Parameter Identification and Measurement-Noise Covariance estimation of a Proton Exchange Membrane Fuel Cell

TL;DR

This paper introduces an online method for simultaneously identifying PEMFC model parameters and estimating measurement noise covariance, improving accuracy through adaptive Kalman filtering techniques.

Contribution

It presents a novel approach that jointly estimates model parameters and measurement noise covariance in real-time for PEMFC systems.

Findings

Error reduction demonstrated in simulations

Effective simultaneous parameter and noise covariance estimation

Enhanced accuracy of PEMFC modeling

Abstract

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.