Data Driven Prediction of Battery Cycle Life Before Capacity Degradation

• URL: http://arxiv.org/abs/2110.09687v1
• Date: Tue, 19 Oct 2021 01:35:12 GMT
• Title: Data Driven Prediction of Battery Cycle Life Before Capacity Degradation
• Authors: Anmol Singh, Caitlin Feltner, Jamie Peck, Kurt I. Kuhn
• Abstract summary: This paper utilizes the data and methods implemented by Kristen A. Severson, et al, to explore the methodologies that the research team used. 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.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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