Multi-fault diagnosis for series-connected lithium-ion battery pack with reconstruction-based contribution based on parallel PCA-KPCA
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DOI: 10.1016/j.apenergy.2022.119678
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- Khaleghi, S. & Firouz, Y. & Van Mierlo, J. & Van den Bossche, P., 2019. "Developing a real-time data-driven battery health diagnosis method, using time and frequency domain condition indicators," Applied Energy, Elsevier, vol. 255(C).
- J.-M. Tarascon & M. Armand, 2001. "Issues and challenges facing rechargeable lithium batteries," Nature, Nature, vol. 414(6861), pages 359-367, November.
- Woojin Soh & Heeyoung Kim & Bong-Jin Yum, 2018. "Application of kernel principal component analysis to multi-characteristic parameter design problems," Annals of Operations Research, Springer, vol. 263(1), pages 69-91, April.
- Xiong, Rui & Sun, Wanzhou & Yu, Quanqing & Sun, Fengchun, 2020. "Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles," Applied Energy, Elsevier, vol. 279(C).
- Banguero, Edison & Correcher, Antonio & Pérez-Navarro, Ángel & García, Emilio & Aristizabal, Andrés, 2020. "Diagnosis of a battery energy storage system based on principal component analysis," Renewable Energy, Elsevier, vol. 146(C), pages 2438-2449.
- Liu, Zhentong & He, Hongwen, 2017. "Sensor fault detection and isolation for a lithium-ion battery pack in electric vehicles using adaptive extended Kalman filter," Applied Energy, Elsevier, vol. 185(P2), pages 2033-2044.
- Varga, Bogdan Ovidiu, 2013. "Electric vehicles, primary energy sources and CO2 emissions: Romanian case study," Energy, Elsevier, vol. 49(C), pages 61-70.
- Ma, Mina & Wang, Yu & Duan, Qiangling & Wu, Tangqin & Sun, Jinhua & Wang, Qingsong, 2018. "Fault detection of the connection of lithium-ion power batteries in series for electric vehicles based on statistical analysis," Energy, Elsevier, vol. 164(C), pages 745-756.
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Cited by:
- Lin, Yu-Hsiu & Shen, Ting-Yu, 2023. "Novel cell screening and prognosing based on neurocomputing-based multiday-ahead time-series forecasting for predictive maintenance of battery modules in frequency regulation-energy storage systems," Applied Energy, Elsevier, vol. 351(C).
- Ren, Song & Sun, Jing, 2024. "Multi-fault diagnosis strategy based on a non-redundant interleaved measurement circuit and improved fuzzy entropy for the battery system," Energy, Elsevier, vol. 292(C).
- Xu, Yiming & Ge, Xiaohua & Shen, Weixiang, 2024. "Multi-objective nonlinear observer design for multi-fault detection of lithium-ion battery in electric vehicles," Applied Energy, Elsevier, vol. 362(C).
- Xin Liu & Haihong Huang & Wenjing Chang & Yongqi Cao & Yuhang Wang, 2024. "Enhanced Wavelet Transform Dynamic Attention Transformer Model for Recycled Lithium-Ion Battery Anomaly Detection," Energies, MDPI, vol. 17(20), pages 1-15, October.
- Seunghwan Jung & Minseok Kim & Eunkyeong Kim & Baekcheon Kim & Jinyong Kim & Kyeong-Hee Cho & Hyang-A Park & Sungshin Kim, 2024. "The Early Detection of Faults for Lithium-Ion Batteries in Energy Storage Systems Using Independent Component Analysis with Mahalanobis Distance," Energies, MDPI, vol. 17(2), pages 1-23, January.
- Julan Chen & Guangheng Qi & Kai Wang, 2023. "Synergizing Machine Learning and the Aviation Sector in Lithium-Ion Battery Applications: A Review," Energies, MDPI, vol. 16(17), pages 1-22, August.
- Song, Youngbin & Park, Shina & Kim, Sang Woo, 2023. "Model-free quantitative diagnosis of internal short circuit for lithium-ion battery packs under diverse operating conditions," Applied Energy, Elsevier, vol. 352(C).
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Keywords
Lithium-ion battery safety; Inconsistency; Connection fault; External short circuit; Fault diagnosis;All these keywords.
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