Data-driven prediction of battery failure for electric vehicles.

ABSTRACT: Despite great progress in battery safety modeling, accurately predicting the evolution of multiphysics systems is extremely challenging. The question on how to ensure safety of billions of automotive batteries during their lifetime remains unanswered. In this study, we overcome the challenge by developing machine learning techniques based on the recorded data that are uploaded to the cloud. Using charging voltage and temperature curves from early cycles that are yet to exhibit symptoms of battery failure, we apply data-driven models to both predict and classify the sample data by health condition based on the observational, empirical, physical, and statistical understanding of the multiscale systems. The best well-integrated machine learning models achieve a verified classification accurac

SUBMITTER: Zhao J 

PROVIDER: S-EPMC9010759 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature