TY - JOUR
T1 - An adaptive Kalman filter-based estimation method for online oxygen flow measurement in PEMFCs with mismatch detection
AU - Yue, Hongwei
AU - He, Hongwen
AU - Wu, Jingda
AU - Chen, Jinzhou
AU - Zhao, Xuyang
AU - Chang, Yuhua
N1 - Publisher Copyright:
© 2025
PY - 2025/10/1
Y1 - 2025/10/1
N2 - Accurate airflow monitoring is critical for optimizing the performance of PEMFCs in dynamic environments. However, existing sensor techniques face significant limitations due to safety risks and leakage concerns, making direct measurement impractical. Furthermore, the complex interactions among system parameters challenge traditional observer techniques, as parameter mismatches often compromise estimation accuracy. To address these issues, this paper proposes a novel state estimation method to achieve robust online oxygen flow estimation across diverse scenarios. The proposed method uses a square root cubature Kalman filter to fuse the predictive model with limited sensor signals, enabling precise estimation of unmeasurable states in the cathode channel. To deal with model uncertainties, a Mahalanobis distance-based metric is introduced to assess the occurrence of mismatches, while a cascade classifier identifies specific parameters that influence estimation performance. Subsequently, the corresponding observer combined with an augmented mechanism is activated to correct the estimated oxygen flow, considering the influence of mismatched parameters. Additionally, an event-triggered mechanism is employed to minimize unnecessary computational requirements. Simulation results demonstrate that the proposed method significantly outperforms traditional estimation methods, improving estimation accuracy and reducing the mean absolute error of oxygen flow estimation by over 31 %, 70 %, and 83 % when the three uncertain parameters are mismatched, respectively. This method represents a significant advancement in monitoring unmeasurable states, further driving the application of advanced estimation technologies.
AB - Accurate airflow monitoring is critical for optimizing the performance of PEMFCs in dynamic environments. However, existing sensor techniques face significant limitations due to safety risks and leakage concerns, making direct measurement impractical. Furthermore, the complex interactions among system parameters challenge traditional observer techniques, as parameter mismatches often compromise estimation accuracy. To address these issues, this paper proposes a novel state estimation method to achieve robust online oxygen flow estimation across diverse scenarios. The proposed method uses a square root cubature Kalman filter to fuse the predictive model with limited sensor signals, enabling precise estimation of unmeasurable states in the cathode channel. To deal with model uncertainties, a Mahalanobis distance-based metric is introduced to assess the occurrence of mismatches, while a cascade classifier identifies specific parameters that influence estimation performance. Subsequently, the corresponding observer combined with an augmented mechanism is activated to correct the estimated oxygen flow, considering the influence of mismatched parameters. Additionally, an event-triggered mechanism is employed to minimize unnecessary computational requirements. Simulation results demonstrate that the proposed method significantly outperforms traditional estimation methods, improving estimation accuracy and reducing the mean absolute error of oxygen flow estimation by over 31 %, 70 %, and 83 % when the three uncertain parameters are mismatched, respectively. This method represents a significant advancement in monitoring unmeasurable states, further driving the application of advanced estimation technologies.
KW - Data fusion
KW - Event-triggered mechanism
KW - Kalman filter
KW - Mismatch identification
KW - State estimation
UR - http://www.scopus.com/inward/record.url?scp=105006853389&partnerID=8YFLogxK
U2 - 10.1016/j.measurement.2025.118011
DO - 10.1016/j.measurement.2025.118011
M3 - Article
AN - SCOPUS:105006853389
SN - 0263-2241
VL - 254
JO - Measurement: Journal of the International Measurement Confederation
JF - Measurement: Journal of the International Measurement Confederation
M1 - 118011
ER -