Unraveling the Degradation Mechanisms of Lithium-Ion Batteries
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- Ma, Guijun & Zhang, Yong & Cheng, Cheng & Zhou, Beitong & Hu, Pengchao & Yuan, Ye, 2019. "Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
- Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
- Kang, LiuWang & Zhao, Xuan & Ma, Jian, 2014. "A new neural network model for the state-of-charge estimation in the battery degradation process," Applied Energy, Elsevier, vol. 121(C), pages 20-27.
- Sieg, Johannes & Schmid, Alexander U. & Rau, Laura & Gesterkamp, Andreas & Storch, Mathias & Spier, Bernd & Birke, Kai Peter & Sauer, Dirk Uwe, 2022. "Fast-charging capability of lithium-ion cells: Influence of electrode aging and electrolyte consumption," Applied Energy, Elsevier, vol. 305(C).
- Richard Schmuch & Ralf Wagner & Gerhard Hörpel & Tobias Placke & Martin Winter, 2018. "Performance and cost of materials for lithium-based rechargeable automotive batteries," Nature Energy, Nature, vol. 3(4), pages 267-278, April.
- Antônio Rufino Júnior, Carlos & Sanseverino, Eleonora Riva & Gallo, Pierluigi & Koch, Daniel & Schweiger, Hans-Georg & Zanin, Hudson, 2022. "Blockchain review for battery supply chain monitoring and battery trading," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
- Tang, Xiaopeng & Liu, Kailong & Lu, Jingyi & Liu, Boyang & Wang, Xin & Gao, Furong, 2020. "Battery incremental capacity curve extraction by a two-dimensional Luenberger–Gaussian-moving-average filter," Applied Energy, Elsevier, vol. 280(C).
- Gao, Tianfeng & Bai, Jinlong & Ouyang, Dongxu & Wang, Zhirong & Bai, Wei & Mao, Ning & Zhu, Yu, 2023. "Effect of aging temperature on thermal stability of lithium-ion batteries: Part A – High-temperature aging," Renewable Energy, Elsevier, vol. 203(C), pages 592-600.
- Hongxin Yu & Yaohui Jiang & Zhaowen Zhang & Wen-Long Shang & Chunjia Han & Yuanjun Zhao, 2022. "The impact of carbon emission trading policy on firms’ green innovation in China," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-24, December.
- Zheng, Yuejiu & Qin, Chao & Lai, Xin & Han, Xuebing & Xie, Yi, 2019. "A novel capacity estimation method for lithium-ion batteries using fusion estimation of charging curve sections and discrete Arrhenius aging model," Applied Energy, Elsevier, vol. 251(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.
- Kai Song & Yu Lan & Xian Zhang & Jinhai Jiang & Chuanyu Sun & Guang Yang & Fengshuo Yang & Hao Lan, 2023. "A Review on Interoperability of Wireless Charging Systems for Electric Vehicles," Energies, MDPI, vol. 16(4), pages 1-22, February.
- Björn Nykvist & Måns Nilsson, 2015. "Rapidly falling costs of battery packs for electric vehicles," Nature Climate Change, Nature, vol. 5(4), pages 329-332, April.
- Steven Chu & Arun Majumdar, 2012. "Opportunities and challenges for a sustainable energy future," Nature, Nature, vol. 488(7411), pages 294-303, August.
- Tang, Rui & Dore, Jonathon & Ma, Jin & Leong, Philip H.W., 2021. "Interpolating high granularity solar generation and load consumption data using super resolution generative adversarial network," Applied Energy, Elsevier, vol. 299(C).
- Che, Yunhong & Zheng, Yusheng & Wu, Yue & Sui, Xin & Bharadwaj, Pallavi & Stroe, Daniel-Ioan & Yang, Yalian & Hu, Xiaosong & Teodorescu, Remus, 2022. "Data efficient health prognostic for batteries based on sequential information-driven probabilistic neural network," Applied Energy, Elsevier, vol. 323(C).
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Keywords
lithium-ion batteries; electric vehicles’ batteries; degradation; ageing; failure;All these keywords.
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