488 citations · 737 across the 5 of their papers we have counts for
5 papers
Fast data augmentation for battery degradation prediction
Weihan Li, Harshvardhan Samsukha, Bruis van Vlijmen +4
Degradation prediction for lithium-ion batteries using data-driven methods requires high-quality aging data. However, generating such data, whether in the laboratory or the field,…
"Knees" in lithium-ion battery aging trajectories
Peter M. Attia, Alexander Bills, Ferran Brosa Planella +14
Lithium-ion batteries can last many years but sometimes exhibit rapid, nonlinear degradation that severely limits battery lifetime. In this work, we review prior work on "knees" in…
Estimation of Li-ion degradation test sample sizes required to understand cell-to-cell variability
Philipp Dechent, Samuel Greenbank, Felix Hildenbrand +3
Ageing of lithium-ion batteries results in irreversible reduction in performance. Intrinsic variability between cells, caused by manufacturing differences, occurs throughout life a…
Piecewise-linear modelling with feature selection for Li-ion battery end of life prognosis
Samuel Greenbank, David A. Howey
The complex nature of lithium-ion battery degradation has led to many machine learning based approaches to health forecasting being proposed in literature. However, machine learnin…
Automated feature extraction and selection for data-driven models of rapid battery capacity fade and end of life
Samuel Greenbank, David A. Howey
Lithium-ion cells may experience rapid degradation in later life, especially with more extreme usage protocols. The onset of rapid degradation is called the `knee point', and forec…