Square Root Unscented Kalman Filter-Based Multiple-Model Fault Diagnosis of PEM Fuel Cells
Harsh operating conditions imposed by vehicular applications significantly limit the utilization of proton exchange membrane fuel cells (PEMFCs) in electric propulsion systems. Improper/poor management and supervision of rapidly varying current demands can lead to undesired electrochemical reactions...
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MDPI AG
2024-12-01
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| author | Abdulrahman Allam Michael Mangold Ping Zhang |
| author_facet | Abdulrahman Allam Michael Mangold Ping Zhang |
| author_sort | Abdulrahman Allam |
| collection | DOAJ |
| description | Harsh operating conditions imposed by vehicular applications significantly limit the utilization of proton exchange membrane fuel cells (PEMFCs) in electric propulsion systems. Improper/poor management and supervision of rapidly varying current demands can lead to undesired electrochemical reactions and critical cell failures. Among other failures, flooding and catalytic degradation are failure mechanisms that directly impact the composition of the membrane electrode assembly and can cause irreversible cell performance deterioration. Due to the functional significance and high manufacturing costs of the catalyst layer, monitoring internal fuel cell states is crucial. For this purpose, a diagnostic-oriented multi-scale PEMFC catalytic degradation model is developed which incorporates the failure effects of catalytic degradation on cell dynamics and global stack performance. Embedded to the multi-scale model is a square root unscented Kalman filter (SRUKF)-based multiple-model fault diagnosis scheme. In this approach, multiple models are used to estimate specific internal PEMFC system parameters, such as the mass transfer coefficient of the gas diffusion layer or the exchange current density, which are treated as additional system states. Online state estimates are provided by the SRUKF, which additionally propagates model-conditioned statistical information to update a Bayesian framework for model selection. The Bayesian model selection method carries fault indication signals that are interpreted by a derived decision logic to obtain reliable information on the current-operating system regime. The proposed diagnosis scheme is evaluated through simulations using the LA 92 and NEDC driving cycles. |
| format | Article |
| id | doaj-art-94daf6c2704f4dff9f5a665bb873bfbb |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-94daf6c2704f4dff9f5a665bb873bfbb2025-01-10T13:20:36ZengMDPI AGSensors1424-82202024-12-012512910.3390/s25010029Square Root Unscented Kalman Filter-Based Multiple-Model Fault Diagnosis of PEM Fuel CellsAbdulrahman Allam0Michael Mangold1Ping Zhang2Institute of Applied Mathematics, Bingen University of Applied Sciences, 55411 Bingen am Rhein, GermanyInstitute of Applied Mathematics, Bingen University of Applied Sciences, 55411 Bingen am Rhein, GermanyInstitute of Autmatic Control, University of Kaiserslautern-Landau, 67653 Kaiserslautern, GermanyHarsh operating conditions imposed by vehicular applications significantly limit the utilization of proton exchange membrane fuel cells (PEMFCs) in electric propulsion systems. Improper/poor management and supervision of rapidly varying current demands can lead to undesired electrochemical reactions and critical cell failures. Among other failures, flooding and catalytic degradation are failure mechanisms that directly impact the composition of the membrane electrode assembly and can cause irreversible cell performance deterioration. Due to the functional significance and high manufacturing costs of the catalyst layer, monitoring internal fuel cell states is crucial. For this purpose, a diagnostic-oriented multi-scale PEMFC catalytic degradation model is developed which incorporates the failure effects of catalytic degradation on cell dynamics and global stack performance. Embedded to the multi-scale model is a square root unscented Kalman filter (SRUKF)-based multiple-model fault diagnosis scheme. In this approach, multiple models are used to estimate specific internal PEMFC system parameters, such as the mass transfer coefficient of the gas diffusion layer or the exchange current density, which are treated as additional system states. Online state estimates are provided by the SRUKF, which additionally propagates model-conditioned statistical information to update a Bayesian framework for model selection. The Bayesian model selection method carries fault indication signals that are interpreted by a derived decision logic to obtain reliable information on the current-operating system regime. The proposed diagnosis scheme is evaluated through simulations using the LA 92 and NEDC driving cycles.https://www.mdpi.com/1424-8220/25/1/29proton exchange membrane fuel cellsfloodingcatalytic degradationmultiple-model fault diagnosissquare root unscented kalman filter |
| spellingShingle | Abdulrahman Allam Michael Mangold Ping Zhang Square Root Unscented Kalman Filter-Based Multiple-Model Fault Diagnosis of PEM Fuel Cells Sensors proton exchange membrane fuel cells flooding catalytic degradation multiple-model fault diagnosis square root unscented kalman filter |
| title | Square Root Unscented Kalman Filter-Based Multiple-Model Fault Diagnosis of PEM Fuel Cells |
| title_full | Square Root Unscented Kalman Filter-Based Multiple-Model Fault Diagnosis of PEM Fuel Cells |
| title_fullStr | Square Root Unscented Kalman Filter-Based Multiple-Model Fault Diagnosis of PEM Fuel Cells |
| title_full_unstemmed | Square Root Unscented Kalman Filter-Based Multiple-Model Fault Diagnosis of PEM Fuel Cells |
| title_short | Square Root Unscented Kalman Filter-Based Multiple-Model Fault Diagnosis of PEM Fuel Cells |
| title_sort | square root unscented kalman filter based multiple model fault diagnosis of pem fuel cells |
| topic | proton exchange membrane fuel cells flooding catalytic degradation multiple-model fault diagnosis square root unscented kalman filter |
| url | https://www.mdpi.com/1424-8220/25/1/29 |
| work_keys_str_mv | AT abdulrahmanallam squarerootunscentedkalmanfilterbasedmultiplemodelfaultdiagnosisofpemfuelcells AT michaelmangold squarerootunscentedkalmanfilterbasedmultiplemodelfaultdiagnosisofpemfuelcells AT pingzhang squarerootunscentedkalmanfilterbasedmultiplemodelfaultdiagnosisofpemfuelcells |