Data Driven Prediction of Battery Cycle Life Before Capacity Degradation

by   Anmol Singh, et al.

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.



There are no comments yet.


page 6

page 19

page 20

page 21


Gaussian process regression for forecasting battery state of health

Accurately predicting the future capacity and remaining useful life of b...

Pay Attention: Leveraging Sequence Models to Predict the Useful Life of Batteries

We use data on 124 batteries released by Stanford University to first tr...

Promise and Challenges of a Data-Driven Approach for Battery Lifetime Prognostics

Recent data-driven approaches have shown great potential in early predic...

A Dynamic Battery State-of-Health Forecasting Model for Electric Trucks: Li-Ion Batteries Case-Study

It is of extreme importance to monitor and manage the battery health to ...

Interpretable Battery Cycle Life Range Prediction Using Early Degradation Data at Cell Level

Battery cycle life prediction using early degradation data has many pote...

Modified Gaussian Process Regression Models for Cyclic Capacity Prediction of Lithium-ion Batteries

This paper presents the development of machine learning-enabled data-dri...

Analysis of NARXNN for State of Charge Estimation for Li-ion Batteries on various Drive Cycles

Electric Vehicles (EVs) are rapidly increasing in popularity as they are...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.