FURI | Spring 2024

Lithium-ion Battery Degradation Machine Learning Model

The objective of this study is to use impedance data to develop a machine-learning model capable of accurately predicting the state of health of lithium-ion batteries. By analyzing the relationship between impedance characteristics and battery health, the model demonstrates a new, noninvasive approach to testing and extending battery life and enhancing reliability. The findings highlight the potential for improved efficiency of battery usage. Future work should focus on continuing to improve the accuracy through increased data collection and possibly applying different machine learning algorithms.