Robustness enhanced estimation strategy using Kalman filter for oxygen excess ratio in air supply system of vehicular PEMFC

Abstract

The reliability and stability of proton exchange membrane fuel cells (PEMFC) critically depend on the accurate air supply. Limitations in sensor technology make it challenging to directly measure the internal state of the air supply system in an automotive environment, affecting the output performance of PEMFCs. To this end, this paper proposes a state estimation strategy using the Kalman filter for real-time reconstruction of the oxygen excess ratio (OER) in PEMFCs. A nonlinear dynamic system model of the air supply process is firstly established and parameterized using the trust region method based on experimental data. The influence of key system parameters on the dynamic response is analyzed to identify primary factors. Additionally, a nonlinear observer based on the cubature Kalman filter (CKF) is designed, and an augmented state observer is proposed following sensitivity analysis. To enhance robustness, real-time model mismatch judgment and adjustment is implemented using normalized innovation squared (NIS) and interval type-2 fuzzy logic systems. Comparative analyses under variable load and parameter mismatch scenarios show that the proposed strategy reduces the cumulative error of reconstructed OER by 24.87 % compared to the standard CKF under large load variations and demonstrates superior estimation accuracy and stability in various model uncertainties.