  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.
