Towards a Probabilistic Fusion Approach for Robust Battery Prognostics
<|reference_start|>Towards a Probabilistic Fusion Approach for Robust Battery Prognostics: 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.<|reference_end|>
@article{alcibar2024towards,
    title={Towards a Probabilistic Fusion Approach for Robust Battery Prognostics},
    author={Jokin Alcibar, Jose I. Aizpurua, Ekhi Zugasti},
    journal={PHM Society European Conference, 8(1), 13},
    year={2024},
    doi={10.36001/phme.2024.v8i1.4143},
    archivePrefix={arXiv},
    eprint={2405.15292},
    primaryClass={cs.LG cs.AI}
}