Journal
APPLIED ENERGY
Volume 324, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2022.119678
Keywords
Lithium-ion battery safety; Inconsistency; Connection fault; External short circuit; Fault diagnosis
Categories
Funding
- National Natural Science Foundation of China [U2033204]
- Key Research and Development Plan of Anhui Province [202104a07020003]
- University Synergy Innovation Program of Anhui Province [GXXT-2020-079]
- Youth Innovation Promotion Association CAS [Y201768]
- National Construction of High-Level University Public Graduate Project
Ask authors/readers for more resources
This study proposes a multi-fault diagnosis strategy focusing on detecting and isolating different types of faults in lithium-ion batteries. By using principal component analysis (PCA) and parallel kernel principal component analysis (KPCA), the method accurately detects faults and reconstructs fault waveforms to improve fault diagnosis reliability, as verified by tested data.
Various faults of the lithium-ion battery threaten the safety and performance of the battery system. The early faults are difficult to detect and isolate owing to unobvious abnormality and the nonlinear time-varying characteristics of the battery. Herein, a multi-fault diagnosis strategy is proposed that focuses on detecting and isolating different types of faults, and estimating fault waveforms of the battery, including inconsistency evaluation, virtual connection fault, and external short circuit. First, the principal component analysis (PCA) model of the battery is established and the contribution is employed to detect the abnormity in the battery pack. Once the fault is detected, the parallel kernel principal component analysis (KPCA) technology is adopted to reconstruct the fault waveform of the battery parameters, including ohmic resistance, terminal voltage, and opencircuit voltage. These parameters are jointly taken as fault indexes improving the reliability of fault diagnosis. Finally, the proposed method is verified using amounts of tested data of eight cells in series. The results indicate that the contribution-based PCA method can accurately detect the fault. Furthermore, the reconstruction-based parallel PCA-KPCA can accurately estimate the fault waveform of the faulty battery, which helps investigate the fault degree and causes.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available