Abstract
The efficient prediction of product quality is a major challenge in lithium-ion battery production, as conventional measures such as aging are time-consuming and costly. This study presents a comprehensive data mining approach to predict the quality of lithium-ion batteries using linear and non-linear support vector machines. A methodology for extracting and selecting features from data sources within production is presented, and several feature selection algorithms – as well as models – are compared with regard to their predictive power. A minimum test error of 8.8 % for the early cycle life prediction was achieved, along with a classification accuracy of 96.6 %, when dividing the lithium-ion batteries into two quality grades with high and low cycle life.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
