Online state of health estimation on NMC cells based on predictive analytics

By Maitane Berecibar, Floris Devriendt, Matthieu Dubarry, I. Villarreal, Noshin Omar, Wouter Verbeke, J. Van Mierlo. 2016

193 citations recorded by the source corpus.

Subfields: Automotive Engineering, Biomedical Engineering, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/ea25872811e20f79edf1ffb6cdfed3d6e78d8476

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Kailong Liu
2. David A. Howey
3. Christoph R. Birkl
4. Maitane Berecibar
5. Aoife M. Foley
6. Elise Nanini-Maury
7. Michael A. Osborne
8. J. Van Mierlo

In Biomedical Engineering

1. Edward H. Sargent
2. Xiaoxia Zhou

In Control and Systems Engineering

1. Michael A. Osborne
2. David A. Howey
3. Robert R. Richardson
4. Kailong Liu
5. Xiaoxia Zhou
6. Yi Li

Citations in the corpus

• Machine learning for a sustainable energy future (2022, background): “38 and redox active electrolyte for RFBs 39 ML has also aided battery management, for example, through state of charge determination, 40 state of health evaluation 41,42 and remaining life prediction 43,44 .”
• Gaussian Process Regression for In Situ Capacity Estimation of Lithium-Ion Batteries (2017, methodology): “2SVMs are, like GPs, nonparametric, but they do not provide confidence estimates in their predictions.”
• Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review (2019, background): “Processed external features are normally extracted from differential charging curves under constant current rate, such as IC/DV curves [77,78] and voltage gradient curves (dV/dt) [79,80].”