Multi-source domain transfer learning with small sample learning for thermal runaway diagnosis of lithium-ion battery
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DOI: 10.1016/j.apenergy.2024.123248
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- Huixing Meng & Qiaoqiao Yang & Enrico Zio & Jinduo Xing, 2023. "An integrated methodology for dynamic risk prediction of thermal runaway in lithium-ion batteries," Post-Print hal-04103786, HAL.
- Bo Zhang & Caicai Zhou & Wei Li & Shengfei Ji & Hengrui Li & Zhe Tong & See-Kiong Ng, 2022. "Intelligent Bearing Fault Diagnosis Based on Open Set Convolutional Neural Network," Mathematics, MDPI, vol. 10(21), pages 1-22, October.
- Oyewole, Isaiah & Chehade, Abdallah & Kim, Youngki, 2022. "A controllable deep transfer learning network with multiple domain adaptation for battery state-of-charge estimation," Applied Energy, Elsevier, vol. 312(C).
- Jia, Zhuangzhuang & Huang, Zonghou & Zhai, Hongju & Qin, Pen & Zhang, Yue & Li, Yawen & Wang, Qingsong, 2022. "Experimental investigation on thermal runaway propagation of 18,650 lithium-ion battery modules with two cathode materials at low pressure," Energy, Elsevier, vol. 251(C).
- Zhao, Hongqian & Chen, Zheng & Shu, Xing & Shen, Jiangwei & Liu, Yonggang & Zhang, Yuanjian, 2023. "Multi-step ahead voltage prediction and voltage fault diagnosis based on gated recurrent unit neural network and incremental training," Energy, Elsevier, vol. 266(C).
- Xia, Quan & Ren, Yi & Wang, Zili & Yang, Dezhen & Yan, Peiyu & Wu, Zeyu & Sun, Bo & Feng, Qiang & Qian, Cheng, 2023. "Safety risk assessment method for thermal abuse of lithium-ion battery pack based on multiphysics simulation and improved bisection method," Energy, Elsevier, vol. 264(C).
- Shen, Sheng & Sadoughi, Mohammadkazem & Li, Meng & Wang, Zhengdao & Hu, Chao, 2020. "Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 260(C).
- Hong, Jichao & Wang, Zhenpo & Qu, Changhui & Zhou, Yangjie & Shan, Tongxin & Zhang, Jinghan & Hou, Yankai, 2022. "Investigation on overcharge-caused thermal runaway of lithium-ion batteries in real-world electric vehicles," Applied Energy, Elsevier, vol. 321(C).
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Keywords
Thermal runaway; Multi-source domain transfer learning; meta-learning; Adversarial learning; Small samples;All these keywords.
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