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The Gaussian Mixture Optimal Transport Ensemble Kalman Filter and its application to predict the capacity fade of lithium-ion batteries | IEEE Conference Publication | IEEE Xplore

The Gaussian Mixture Optimal Transport Ensemble Kalman Filter and its application to predict the capacity fade of lithium-ion batteries


Abstract:

In this paper, we propose a novel algorithm, the Gaussian Mixture Optimal Transport Ensemble Kalman Filter (GM-OT-EnKF), which combines the Gaussian mixture (GM) with the...Show More

Abstract:

In this paper, we propose a novel algorithm, the Gaussian Mixture Optimal Transport Ensemble Kalman Filter (GM-OT-EnKF), which combines the Gaussian mixture (GM) with the optimal transport Ensemble Kalman filter (OT-EnKF). We utilize an ensemble of state realizations to demonstrate state propagation, followed by clustering the ensemble to recover the GM of the propagated uncertainty. The posterior density is updated using the OT-EnKF, recognized for its optimality among quadratic functions minimizing the Monge-Kantorovich dual problem in optimal transport. The accuracy of the GMOT-EnKF is validated through its application in estimating and predicting capacity fade in lithium-ion batteries. It outperforms the EnKF, OT-EnKF, and the particle Gaussian mixture filter.
Date of Conference: 01-04 July 2024
Date Added to IEEE Xplore: 18 October 2024
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Conference Location: Vallette, Malta

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