How battery capacities are correctly estimated considering latent short-circuit faults
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DOI: 10.1016/j.apenergy.2024.124190
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- Tang, Aihua & Huang, Yukun & Xu, Yuchen & Hu, Yuanzhi & Yan, Fuwu & Tan, Yong & Jin, Xin & Yu, Quanqing, 2024. "Data-physics-driven estimation of battery state of charge and capacity," Energy, Elsevier, vol. 294(C).
- Kristen A. Severson & Peter M. Attia & Norman Jin & Nicholas Perkins & Benben Jiang & Zi Yang & Michael H. Chen & Muratahan Aykol & Patrick K. Herring & Dimitrios Fraggedakis & Martin Z. Bazant & Step, 2019. "Data-driven prediction of battery cycle life before capacity degradation," Nature Energy, Nature, vol. 4(5), pages 383-391, May.
- Goh, Taedong & Park, Minjun & Seo, Minhwan & Kim, Jun Gu & Kim, Sang Woo, 2017. "Capacity estimation algorithm with a second-order differential voltage curve for Li-ion batteries with NMC cathodes," Energy, Elsevier, vol. 135(C), pages 257-268.
- Jiwei Wang & Hao Li & Chunling Wu & Yujun Shi & Linxuan Zhang & Yi An, 2024. "State of Health Estimations for Lithium-Ion Batteries Based on MSCNN," Energies, MDPI, vol. 17(17), pages 1-21, August.
- Jiang, Bo & Dai, Haifeng & Wei, Xuezhe, 2020. "Incremental capacity analysis based adaptive capacity estimation for lithium-ion battery considering charging condition," Applied Energy, Elsevier, vol. 269(C).
- Zhang, Guangxu & Wei, Xuezhe & Tang, Xuan & Zhu, Jiangong & Chen, Siqi & Dai, Haifeng, 2021. "Internal short circuit mechanisms, experimental approaches and detection methods of lithium-ion batteries for electric vehicles: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
- Penelope K. Jones & Ulrich Stimming & Alpha A. Lee, 2022. "Impedance-based forecasting of lithium-ion battery performance amid uneven usage," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
- Jiangong Zhu & Yixiu Wang & Yuan Huang & R. Bhushan Gopaluni & Yankai Cao & Michael Heere & Martin J. Mühlbauer & Liuda Mereacre & Haifeng Dai & Xinhua Liu & Anatoliy Senyshyn & Xuezhe Wei & Michael K, 2022. "Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
- Tang, Aihua & Wu, Xinyu & Xu, Tingting & Hu, Yuanzhi & Long, Shengwen & Yu, Quanqing, 2024. "State of health estimation based on inconsistent evolution for lithium-ion battery module," Energy, Elsevier, vol. 286(C).
- Chen, Zeyu & Xiong, Rui & Tian, Jinpeng & Shang, Xiong & Lu, Jiahuan, 2016. "Model-based fault diagnosis approach on external short circuit of lithium-ion battery used in electric vehicles," Applied Energy, Elsevier, vol. 184(C), pages 365-374.
- Berecibar, Maitane & Garmendia, Maitane & Gandiaga, Iñigo & Crego, Jon & Villarreal, Igor, 2016. "State of health estimation algorithm of LiFePO4 battery packs based on differential voltage curves for battery management system application," Energy, Elsevier, vol. 103(C), pages 784-796.
- Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
- Yunwei Zhang & Qiaochu Tang & Yao Zhang & Jiabin Wang & Ulrich Stimming & Alpha A. Lee, 2020. "Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning," Nature Communications, Nature, vol. 11(1), pages 1-6, December.
- Yang, Qifan & Sun, Jinlei & Kang, Yongzhe & Ma, Hongzhong & Duan, Dawei, 2023. "Internal short circuit detection and evaluation in battery packs based on transformation matrix and an improved state-space model," Energy, Elsevier, vol. 276(C).
- Xing, Yinjiao & He, Wei & Pecht, Michael & Tsui, Kwok Leung, 2014. "State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures," Applied Energy, Elsevier, vol. 113(C), pages 106-115.
- Wenxiao Huang & Yusheng Ye & Hao Chen & Rafael A. Vilá & Andrew Xiang & Hongxia Wang & Fang Liu & Zhiao Yu & Jinwei Xu & Zewen Zhang & Rong Xu & Yecun Wu & Lien-Yang Chou & Hansen Wang & Junwei Xu & D, 2022. "Onboard early detection and mitigation of lithium plating in fast-charging batteries," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
- 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).
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Keywords
Capacity estimation; Short-circuit; Quantitative diagnosis; Battery module; End- cloud collaboration;All these keywords.
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