Transfer learning prediction on lithium-ion battery heat release under thermal runaway condition
Year: 2024
Type: article
Abstract: Accurately predicting the variability of thermal runaway (TR) behavior in lithium-ion (Li-ion) batteries is critical for designing safe and reliable energy storage systems. Unfortunately, traditional calorimetry-based experiments to measure heat release during TR are time-consuming and expensive. Herein, we highlight an exciting transfer learning approach that leverages mass ejection data and metadata from cells to predict heat output variability during TR events. This approach significantly reduces the effort and time to assess thermal risks associated with Li-ion batteries.
Source: Nano Research Energy
Authors: Changmin Shi, Di Zhu, Liwen Zhang, Siyuan Song, Brian W. Sheldon
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4405039012