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Ubiquitous use of lithium-ion batteries across multiple industries presentsan opportunity to explore cost saving initiatives as the price to performanceratio continually decreases in a competitive environment. Manufacturers usinglithium-ion batteries ranging in applications from mobile phones to electricvehicles need to know how long batteries will last for a given service life. Tounderstand this, expensive testing is required.  This paper utilizes the data and methods implemented by Kristen A. Severson,et al, to explore the methodologies that the research team used and presentsanother method to compare predicted results vs. actual test data for batterycapacity fade. The fundamental effort is to find out if machine learningtechniques may be trained to use early life cycle data in order to accuratelypredict battery capacity over the battery life cycle. Results show comparisonof methods between Gaussian Process Regression (GPR) and Elastic Net Regression(ENR) and highlight key data features used from the extensive dataset found inthe work of Severson, et al.

Data Driven Prediction of Battery Cycle Life Before Capacity Degradation