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eess.SY2025
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,…
eess.SY2021
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…
eess.SY2021★ 203 cited
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…