首页|Robustness enhanced estimation strategy using Kalman filter for oxygen excess ratio in air supply system of vehicular PEMFC
Robustness enhanced estimation strategy using Kalman filter for oxygen excess ratio in air supply system of vehicular PEMFC
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NETL
NSTL
Elsevier
The reliability and stability of proton exchange membrane fuel cells (PEMFC) critically depend on the accurateair supply. Limitations in sensor technology make it challenging to directly measure the internal state of the airsupply system in an automotive environment, affecting the output performance of PEMFCs. To this end, thispaper proposes a state estimation strategy using the Kalman filter for real-time reconstruction of the oxygenexcess ratio (OER) in PEMFCs. A nonlinear dynamic system model of the air supply process is firstly establishedand parameterized using the trust region method based on experimental data. The influence of key system parameterson the dynamic response is analyzed to identify primary factors. Additionally, a nonlinear observerbased on the cubature Kalman filter (CKF) is designed, and an augmented state observer is proposed followingsensitivity analysis. To enhance robustness, real-time model mismatch judgment and adjustment is implementedusing normalized innovation squared (NIS) and interval type-2 fuzzy logic systems. Comparative analyses undervariable load and parameter mismatch scenarios show that the proposed strategy reduces the cumulative error ofreconstructed OER by 24.87 % compared to the standard CKF under large load variations and demonstratessuperior estimation accuracy and stability in various model uncertainties.
Air supply systemParameter identificationState estimationSensitivity analysis
Hongwei Yue、Hongwen He、Xuyang Zhao
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National Key Laboratory of Advanced Vehicle Integration and Control, National Engineering Research Center for Electric Vehicles, School of Mechanical Engineering,Beijing Institute of Technology, Beijing 100081, China
NETL
NSTL
Elsevier
