Reliability enhancement of state of health assessment model of lithium-ion battery considering the uncertainty with quantile distribution of deep features

Zhang, Ying; Zhang, Ming; Liu, Chao; Feng, Zhipeng; Xu, Yuchun

doi:10.1016/j.ress.2024.110002

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…(Y. Zhang, Zhang, Liu, Feng, & Xu, 2024) introduced a SOH assessment method that estimates uncertainty through the quantile distribution of deep features, which are inferred from a Residual Neural Network (ResNet) architecture. This approach generates SOH values accompanied by confidence intervals.…”

Section: Introductionmentioning

confidence: 99%

Towards a Probabilistic Fusion Approach for Robust Battery Prognostics

Alcibar,

Aizpurua,

Zugasti

2024

PHME_CONF

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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 differentBNNs.

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Section: Introductionmentioning

confidence: 99%