Constrained extended Kalman filter design and application for on-line state estimation of high-order polymer electrolyte membrane fuel cell systems

In this paper an alternative approach to extended Kalman filtering (EKF) for polymer electrolyte membrane fuel cell (FC) systems is proposed. The goal is to obtain robust realtime capable state estimations of a high-order FC model for observer applications mixed with control or fault detection. The introduced formulation resolves dependencies on operating conditions by successive linearization and constraints, allowing to run the nonlinear FC model at significantly lower sampling rates than with standard approaches. The proposed method provides state estimates for challenging operating conditions such as shut-down and start-up of the fuel cell for which the unconstrained EKF fails. A detailed comparison with the unscented Kalman filter shows that the proposed EKF reconstructs the outputs equally accurate but nine times faster. An application to measured data from an FC powered passenger car is presented, yielding state estimates of a real FC system, which are validated based on the applied model. (c) 2021 The Authors. Published by Elsevier Ltd on behalf of Hydrogen Energy Publications LLC. This is an open access article under the CC BY license (http://creativecommons.org/ licenses/by/4.0/).

Automated summary

This paper presents an alternative approach to extended Kalman filtering for polymer electrolyte membrane fuel cell systems, providing robust real-time state estimations and achieving faster computational speed compared to standard approaches. The method resolves dependencies on operating conditions and offers accurate state estimates even in challenging scenarios, making it a viable option for control and fault detection applications.