Towards a Probabilistic Fusion Approach for Robust Battery Prognostics

TL;DR

This paper presents a Bayesian ensemble learning framework that combines multiple neural networks to accurately predict battery capacity fade and quantify uncertainty, enhancing robustness for battery health prognostics.

Contribution

It introduces a novel Bayesian ensemble stacking method for battery degradation prediction, improving accuracy and uncertainty quantification over existing models.

Findings

Enhanced prediction accuracy over single models

Improved robustness compared to classical stacking

Effective uncertainty quantification in battery health

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.