Behavior and state-of-health monitoring of Li-ion batteries using impedance spectroscopy and recurrent neural networks
By A. Eddahech, O. Briat, N. Bertrand, J. Delétage, J. Vinassa. 2012
426 citations recorded by the source corpus.
Subfields: Automotive Engineering, Control and Systems Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/bda375c0002706ca4e7a9ec8a1b2d519e97a0c4f
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
- P. Kollmeyer
- Ali Emadi
- David A. Howey
- Carlos Vidal
- Pawel Malysz
- Weihan Li
- Philipp Dechent
- N. Sengupta
In Control and Systems Engineering
- Ali Emadi
- Delphine M. Riu
- David A. Howey
- Carlos Vidal
- Anuradha M. Annaswamy
- Yong Guan
- Sébastien Grolleau
- Lifeng Wu
In Electrical and Electronic Engineering
- P. Kollmeyer
- Ali Emadi
- David A. Howey
- Carlos Vidal
- Pawel Malysz
- Weihan Li
- Philipp Dechent
- N. Sengupta
Citations in the corpus
- Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art (2020, methodology): “Theauthors in [61] have used EIS to determine and validate the parameters of an ECM capable of finding correlations with the Li-ion SOC, then developed an RNN to predict the SOH of the Li-ion battery based on both the battery capacity fade and increase of its equivalent series resistance.”