Work

Battery state of health estimation under fast charging via deep transfer learning

Year: 2025

Type: article

Abstract: ) of 0.97. By integrating local and global analysis, this approach significantly enhances battery aging detection under fast-charging conditions, demonstrating strong potential for battery health management systems.

Source: iScience

DOI: 10.1016/j.isci.2025.112235

Authors: Jingyuan Zhao, Di Li, Yuqi Li, Dapai Shi, Jinrui Nan, Andrew F. Burke

Institutions: University of California, Davis, Hubei University of Arts and Science, Chinese Academy of Sciences, Institute of Physics, Beijing Institute of Technology

Language: English

Topic: Advanced Battery Technologies Research

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $3,240

Funders: Beijing Institute of Technology, Science, Technology and Innovation Commission of Shenzhen Municipality, Hubei University of Arts and Science

Awards: G3781684046, G4772175681

OpenAlex ID: W4408566570

FWCI: 12.91

Cites: 70

Cited by: 52

Related to: 10