Fault detection based on multiple features clustering and unsupervised scoring for lithium-ion battery packs-Reference-Cited by-同舟云学术

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Fault detection based on multiple features clustering and unsupervised scoring for lithium-ion battery packs

Published:2024-05-17 Issue: Volume: Page:1814-1818
ISSN:
Container-title:2024 IEEE 10th International Power Electronics and Motion Control Conference (IPEMC2024-ECCE Asia)
language:
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Author:

Nie Wenhao¹,Deng Zhongwei¹,He Yuxin¹,Jiang Chunlin²

Affiliation:

1. University of Electronic Science and Technology of China,School of Mechanical and Electrical Engineering,Chengdu,China,611731

2. University of Electronic Science and Technology of China,School of Computer Science and Engineering,Chengdu,China,611731

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10567049/10567050/10567713.pdf?arnumber=10567713

Reference12 articles.

1. Critical review of state of health estimation methods of Li-ion batteries for real applications

2. Early Diagnosis of Accelerated Aging for Lithium-Ion Batteries With an Integrated Framework of Aging Mechanisms and Data-Driven Methods

3. Data-driven prediction of battery cycle life before capacity degradation

4. Advanced Fault Diagnosis for Lithium-Ion Battery Systems: A Review of Fault Mechanisms, Fault Features, and Diagnosis Procedures

5. Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles

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