Towards a Probabilistic Fusion Approach for Robust Battery Prognostics

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Published Jun 27, 2024
Jokin Alcibar Jose I. Aizpurua Ekhi Zugasti

Abstract

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.

How to Cite

Alcibar, J., Aizpurua, J. I. ., & Zugasti, E. . (2024). Towards a Probabilistic Fusion Approach for Robust Battery Prognostics. PHM Society European Conference, 8(1), 13. https://doi.org/10.36001/phme.2024.v8i1.3981
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Keywords

Bayesian Neural Network, Uncertainty Quantification, Bayesian Ensemble, Battery Health Management

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Technical Papers