Battery lifetime prediction and performance assessment of different modeling approaches

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.

Automated summary

This study utilized a comprehensive aging dataset of nickel-manganese-cobalt oxide (NMC) cells to develop and train different capacity fade models, with the nonlinear autoregressive network (NARXnet) being able to most accurately predict capacity degradation while minimizing computational effort. The research highlights the importance of selecting the lifetime method and model performance in understanding the complex and nonlinear aging behavior of Li-ion batteries.