Battery lifetime prediction and performance assessment of different modeling approaches

iScience

Joeri Van Mierlo¹  Joris Jaguemont²  Maitane Berecibar³  Md Sazzad Hosen³ 

[1] Corresponding author;Flanders Make, 3001 Heverlee, Belgium;Battery Innovation Center, MOBI Research Group, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium;

关键词: Electrochemistry;    Electrochemical Energy Storage;    Energy Engineering;    Energy Storage;    Energy Systems;    Energy Materials;   

DOI  :  

来源: DOAJ

【 摘 要 】

Summary: 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.

【 授权许可】

Unknown