Predicting State of Health of Lithium Ion Batteries

The paper focuses on predicting the state of health of Lithium Ion batteries used in Electric Vehicles to make predictive analytics on Remaining Useful Life as well as understand the battery dynamics for recycling enabling sustainable development. The work makes use of three different datasets, namely the Calce Dataset, the lithium-ion battery model dataset, and the Oxford Battery Degradation dataset. Multiple machine learning and deep learning algorithms are adopted to predict the state of the health of the batteries in the datasets and are compared with respect to various metrics.