Date of Completion
Chao Hu; Jason Lee
The ability to understand and predict the state of health (SOH) of lithium-ion batteries is an integral component of their widespread commercial use. There are various methods through which SOH can be analyzed and predicted, and this paper discusses these different methods, and the strengths and weaknesses of each. This paper also details an analysis of lithium-ion battery SOH through two data-driven machine learning methods: XGBoost and Gaussian process regression. A comparison is made between each method’s accuracy in predicting next-cycle discharge capacity using electrochemical impedance spectroscopy (EIS) readings and battery charge and discharge rates, from a dataset given in a Nature Communications Journal article. In this application, both methods show similar results in prediction accuracy, and the use of one over the other depends heavily on individual needs, including a need for a confidence interval of the prediction, the allotted amount of time, and prior knowledge of the data.
Zaretsky, Charli, "Probabilistic Machine Learning for Battery State of Health Prognostics" (2023). Honors Scholar Theses. 969.