Towards a Probabilistic Fusion Approach for Robust Battery Prognostics

In this context, robust and reliable battery Batteries are a key enabling technology for the decarbonization prognostics models support the development of accurate of transport and energy sectors. The estimation of the state-of-health (SOH) of batteries is a In this direction, the development of accurate and robust battery key activity for the design of RUL prognostics models. SOHbased state-of-health prognostics models can unlock the potential prognostics models focus on capturing the run-to-failure of autonomous systems for complex, remote and reliable ageing dynamics and battery health state estimation (Toughzaoui operations. It is frequently used to determine age-related modelling concepts and ensemble learning strategies, form degradation that reduces energy capacity and rises safety risks, a valuable prognostics framework to combine uncertainty in including overheating and explosions (Wang et al., 2022). Accordingly, this paper introduces Therefore, accurate SOH monitoring and forecasting are key a Bayesian ensemble learning approach to predict activities to design and operate safe, reliable and effective the capacity depletion of lithium-ion batteries. SOH estimation is an ongoing area of research (Yang, Chen, The proposed Bayesian ensemble methodology employs Chen, & Huang, 2023). SOH refers to the ratio of the current a stacking technique, integrating multiple Bayesian neural maximum capacity relative to its original specified capacity networks (BNNs) as base learners, which have been trained (X. SOH can be quantified on data diversity.