Battery lifetime prediction and performance assessment of different modeling approaches

Md Sazzad Hosen; Joris Jaguemont; Joeri Van Mierlo; Maitane Berecibar

doi:10.1016/j.isci.2021.102060

Battery lifetime prediction and performance assessment of different modeling approaches

iScience. 2021 Jan 19;24(2):102060. doi: 10.1016/j.isci.2021.102060. eCollection 2021 Feb 19.

Authors

Md Sazzad Hosen^{1

2}, Joris Jaguemont^{1

2}, Joeri Van Mierlo^{1

2}, Maitane Berecibar^{1

2}

Affiliations

¹ Battery Innovation Center, MOBI Research Group, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium.
² Flanders Make, 3001 Heverlee, Belgium.

Abstract

Lithium-ion battery technologies have conquered the current energy storage market as the most preferred choice thanks to their development in a longer lifetime. However, choosing the most suitable battery aging modeling methodology based on investigated lifetime characterization is still a challenge. In this work, a comprehensive aging dataset of nickel-manganese-cobalt oxide (NMC) cell is used to develop and/or train different capacity fade models to compare output responses. The assessment is conducted for semi-empirical modeling (SeM) approach against a machine learning model and an artificial neural network model. Among all, the nonlinear autoregressive network (NARXnet) can predict the capacity degradation most precisely minimizing the computational effort as well. This research work signifies the importance of lifetime methodological choice and model performance in understanding the complex and nonlinear Li-ion battery aging behavior.

Keywords: Electrochemical Energy Storage; Electrochemistry; Energy Engineering; Energy Materials; Energy Storage; Energy Systems.