2026-04-21T09:40:13Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/67742024-11-15T07:15:29Zcom_20.500.11984_1143col_20.500.11984_1148

Alcibar, Jokin Aizpurua Unanue, Jose Ignacio Zugasti, Ekhi 2024-11-14T10:06:44Z 2024-11-14T10:06:44Z 2024 978-1-936263-40-0 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=178488 https://hdl.handle.net/20.500.11984/6774 Batteries are a key enabling technology for the decarbonization of transport and energy sectors. The safe and reliable operation of batteries is crucial for battery-powered systems. In this direction, the development of accurate and robust battery state-of-health prognostics models can unlock the potential of autonomous systems for complex, remote and reliable operations. The combination of Neural Networks, Bayesian modelling concepts and ensemble learning strategies, form a valuable prognostics framework to combine uncertainty in a robust and accurate manner. Accordingly, this paper introduces a Bayesian ensemble learning approach to predict the capacity depletion of lithium-ion batteries. The approach accurately predicts the capacity fade and quantifies the uncertainty associated with battery design and degradation processes. The proposed Bayesian ensemble methodology employs a stacking technique, integrating multiple Bayesian neural networks (BNNs) as base learners, which have been trained on data diversity. The proposed method has been validated using a battery aging dataset collected by the NASA Ames Prognostics Center of Excellence. Obtained results demonstrate the improved accuracy and robustness of the proposed probabilistic fusion approach with respect to (i) a single BNN model and (ii) a classical stacking strategy based on different BNNs. eng Attribution-4.0 International http://creativecommons.org/licenses/by/4.0/ © 2024 The Authors Bayesian optimization Uncertainty analysis Towards a Probabilistic Fusion Approach for Robust Battery Prognostics http://purl.org/coar/resource_type/c_c94f