Generating Comprehensive Lithium Battery Charging Data with Generative AI

arXiv:2404.07577v1 Announce Type: new
Abstract: In optimizing performance and extending the lifespan of lithium batteries, accurate state prediction is pivotal. Traditional regression and classification methods have achieved some success in battery state prediction. However, the efficacy of these data-driven approaches heavily relies on the availability and quality of public datasets. Additionally, generating electrochemical data predominantly through battery experiments is a lengthy and costly process, making it challenging to acquire high-quality electrochemical data. This difficulty, coupled with data incompleteness, significantly impacts …

abstract arxiv availability batteries battery charging classification cs.lg data data-driven datasets eess.sp generative however lithium performance pivotal prediction public quality regression state success type