April 27, 2022, 1:12 a.m. | Huang Zhang, Yang Su, Faisal Altaf, Torsten Wik, Sebastien Gros

cs.LG updates on arXiv.org arxiv.org

Battery cycle life prediction using early degradation data has many potential
applications throughout the battery product life cycle. Various data-driven
methods have been proposed for point prediction of battery cycle life with
minimum knowledge of the battery degradation mechanisms. However, management of
batteries at end-of-life with lower economic and technical risk requires
prediction of cycle life with quantified uncertainty, which is still lacking.
The interpretability (i.e., the reason for high prediction accuracy) of these
advanced data-driven methods is also worthy …

arxiv battery data life prediction

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Stagista Technical Data Engineer

@ Hager Group | BRESCIA, IT

Data Analytics - SAS, SQL - Associate

@ JPMorgan Chase & Co. | Mumbai, Maharashtra, India