  Ubiquitous use of lithium-ion batteries across multiple industries presents
an opportunity to explore cost saving initiatives as the price to performance
ratio continually decreases in a competitive environment. Manufacturers using
lithium-ion batteries ranging in applications from mobile phones to electric
vehicles need to know how long batteries will last for a given service life. To
understand 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 presents
another method to compare predicted results vs. actual test data for battery
capacity fade. The fundamental effort is to find out if machine learning
techniques may be trained to use early life cycle data in order to accurately
predict battery capacity over the battery life cycle. Results show comparison
of methods between Gaussian Process Regression (GPR) and Elastic Net Regression
(ENR) and highlight key data features used from the extensive dataset found in
the work of Severson, et al.
