Data Driven Prediction of Battery Cycle Life Before Capacity Degradation

TL;DR

This paper investigates machine learning approaches, specifically Gaussian Process Regression and Elastic Net Regression, to predict lithium-ion battery capacity degradation over its lifecycle using early cycle data, aiming to reduce costly testing.

Contribution

It introduces a novel comparison of GPR and ENR methods for battery life prediction using early cycle data, building on previous datasets and methodologies.

Findings

GPR outperforms ENR in prediction accuracy

Key early cycle features significantly influence capacity fade predictions

The proposed methods reduce the need for extensive battery testing

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.