The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Adaptive Nonlinear Model-Based Fault Diagnosis of Li-Ion Batteries
Open accessAbstract
In this paper, an adaptive fault diagnosis technique is used in Li-ion batteries. The diagnosis process consists of multiple nonlinear models representing signature faults, such as overcharge and overdischarge, causing significant model parameter variation. The impedance spectroscopy of a Li-ion LiFePO4cell is used, along with the equivalent circuit methodology, to construct nonlinear battery signature-fault models. Extended Kalman filters are utilized to estimate the terminal voltage of each model and to generate residual signals. The residual signals are used in the multiple-model adaptive estimation technique to generate probabilities that determine the signature faults. It can be seen that, by using this method, signature faults can be detected accurately, thus providing an effective way of diagnosing Li-ion battery failure.
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Cites
Accurate electrical battery model capable of predicting runtime and I-V performance
Chen, M., & Rincon-Mora, G. A. (2006) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2006.874229 · Cited by 2,427
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
Plett, G. L. (2004) · Journal of Power Sources · doi:10.1016/j.jpowsour.2004.02.033 · Cited by 1,768
A review on prognostics and health monitoring of Li-ion battery
Zhang, J., & Lee, J. (2011) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.03.101 · Cited by 792
Prognostics in Battery Health Management
Goebel, K., Saha, B., Saxena, A., Celaya, J., & Christophersen, J. (2008) · IEEE Instrumentation & Measurement Magazine · doi:10.1109/mim.2008.4579269 · Cited by 594
Cited by
Hu, X., Zhang, K., Liu, K., Lin, X., Dey, S., & Onori, S. (2020) · IEEE Industrial Electronics Magazine · doi:10.1109/mie.2020.2964814 · Cited by 580
Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods
Zhao, Y., Liu, P., Wang, Z., Zhang, L., & Hong, J. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.05.139 · Cited by 375
A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges
Tran, M.-K., & Fowler, M. (2020) · Algorithms · doi:10.3390/a13030062 · Cited by 300
Implementation for a cloud battery management system based on the CHAIN framework
Yang, S., Zhang, Z., Cao, R., Wang, M., Cheng, H., Zhang, L., Jiang, Y., Li, Y., Chen, B., Ling, H., et al. (2021) · Energy and AI · doi:10.1016/j.egyai.2021.100088 · Cited by 178
Qahouq, J. A. A., & Xia, Z. (2017) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2686324 · Cited by 174
Metadata
- Authors
- Sidhu, A., Izadian, A., & Anwar, S.
- Venue
- IEEE Transactions on Industrial Electronics
- Published
- 2015-02
- Volume
- 62
- Issue
- 2
- Pages
- 1002-1011
- ISSN
- 0278-0046 (ISSN-L)
- 1557-9948
- OpenAlex
- W2033969310
- Cited by
- 283
- References
- 4
- FWCI
- 25.74
- Type
- journal article
- Language
- en
- Open access
- green
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- Sidhu, A., Izadian, A., & Anwar, S. (2015). Adaptive Nonlinear Model-Based Fault Diagnosis of Li-Ion Batteries. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/tie.2014.2336599
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