Real-time personalized health status prediction of lithium-ion batteries using deep transfer learning-Reference-Cited by-同舟云学术

Real-time personalized health status prediction of lithium-ion batteries using deep transfer learning

Published:2022 Issue:10 Volume:15 Page:4083-4094
ISSN:1754-5692
Container-title:Energy & Environmental Science
language:en
Short-container-title:Energy Environ. Sci.

Author:

Ma Guijun¹^ORCID,Xu Songpei²^ORCID,Jiang Benben³,Cheng Cheng²^ORCID,Yang Xin⁴,Shen Yue⁵^ORCID,Yang Tao⁶,Huang Yunhui⁵^ORCID,Ding Han¹,Yuan Ye²^ORCID

Affiliation:

1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

2. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China

3. Department of Automation, Tsinghua University, Beijing 100084, China

4. College of Electrical and Information Engineering, Hunan University, Changsha 410082, China

5. School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

6. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China

Abstract

We have developed a high-performance deep transfer learning framework to predict the health status of lithium-ion batteries for each end-user at any charge–discharge cycle.

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Subject

Pollution,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment,Environmental Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/EE/D2EE01676A

Reference54 articles.

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2. Formation Challenges of Lithium-Ion Battery Manufacturing

3. Ultrasonic Scanning to Observe Wetting and “Unwetting” in Li-Ion Pouch Cells

4. Recycling lithium-ion batteries from electric vehicles

5. Challenges and opportunities towards fast-charging battery materials

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