Lithium Ion Batteries Impedance Approximation and Model Parameterization Using Artificial Neural Networks

Author:

Pohlmann Sebastian1,Mashayekh Ali2,Karnehm Dominic1,Kuder Manuel2,Neve Antje1,Weyh Thomas1

Affiliation:

1. Institute of Distributed Intelligent Systems, University of the Bundeswehr,Munich,Germany

2. Institute of Electrical Energy Systems, University of the Bundeswehr,Munich,Germany

Publisher

IEEE

Reference24 articles.

1. Total CO2-equivalent life-cycle emissions from commercially available passenger cars

2. Early quality classification and prediction of battery cycle life in production using machine learning;Stock;Journal of Energy Storage,2022

3. The lithium-ion battery: State of the art and fu-ture perspectives;Zubi,2018

4. Review on state of health estimation methodologies for lithium-ion batteries in the context of circular economy;Basia,2021

5. A comprehensive equivalent circuit model for lithium-ion batteries, incorporating the effects of state of health, state of charge, and temperature on model parameters;Tran,2021

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