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State-of-health estimation of lithium-ion batteries based on semi-supervised transfer component analysis

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Cited by:

  1. Zhou, Quan & Li, Yanfei & Zhao, Dezong & Li, Ji & Williams, Huw & Xu, Hongming & Yan, Fuwu, 2022. "Transferable representation modelling for real-time energy management of the plug-in hybrid vehicle based on k-fold fuzzy learning and Gaussian process regression," Applied Energy, Elsevier, vol. 305(C).
  2. Biao Yang & Yinshuang Wang & Yuedong Zhan, 2022. "Lithium Battery State-of-Charge Estimation Based on a Bayesian Optimization Bidirectional Long Short-Term Memory Neural Network," Energies, MDPI, vol. 15(13), pages 1-18, June.
  3. Wang, Qiao & Ye, Min & Cai, Xue & Sauer, Dirk Uwe & Li, Weihan, 2023. "Transferable data-driven capacity estimation for lithium-ion batteries with deep learning: A case study from laboratory to field applications," Applied Energy, Elsevier, vol. 350(C).
  4. Diego Castanho & Marcio Guerreiro & Ludmila Silva & Jony Eckert & Thiago Antonini Alves & Yara de Souza Tadano & Sergio Luiz Stevan & Hugo Valadares Siqueira & Fernanda Cristina Corrêa, 2022. "Method for SoC Estimation in Lithium-Ion Batteries Based on Multiple Linear Regression and Particle Swarm Optimization," Energies, MDPI, vol. 15(19), pages 1-21, September.
  5. Cristobal Morales & Augusto Lismayes & Hector Chavez & Harold R. Chamorro & Lorenzo Reyes-Chamorro, 2021. "The Impact of Aging-Preventive Algorithms on BESS Sizing under AGC Performance Standards," Energies, MDPI, vol. 14(21), pages 1-13, November.
  6. Wei Li & Hang Li & Zheng He & Weijie Ji & Jing Zeng & Xue Li & Yiyong Zhang & Peng Zhang & Jinbao Zhao, 2022. "Electrochemical Failure Results Inevitable Capacity Degradation in Li-Ion Batteries—A Review," Energies, MDPI, vol. 15(23), pages 1-28, December.
  7. Fang Guo & Guangshan Huang & Wencan Zhang & An Wen & Taotao Li & Hancheng He & Haolin Huang & Shanshan Zhu, 2023. "Lithium Battery State-of-Health Estimation Based on Sample Data Generation and Temporal Convolutional Neural Network," Energies, MDPI, vol. 16(24), pages 1-15, December.
  8. Giovane Ronei Sylvestrin & Joylan Nunes Maciel & Marcio Luís Munhoz Amorim & João Paulo Carmo & José A. Afonso & Sérgio F. Lopes & Oswaldo Hideo Ando Junior, 2025. "State of the Art in Electric Batteries’ State-of-Health (SoH) Estimation with Machine Learning: A Review," Energies, MDPI, vol. 18(3), pages 1-77, February.
  9. Braco, Elisa & San Martín, Idoia & Sanchis, Pablo & Ursúa, Alfredo, 2023. "Fast capacity and internal resistance estimation method for second-life batteries from electric vehicles," Applied Energy, Elsevier, vol. 329(C).
  10. Jiang, Fusheng & Ren, Yi & Tang, Ting & Wu, Zeyu & Xia, Quan & Sun, Bo & Yang, Dezhen, 2024. "An adaptive semi-supervised self-learning method for online state of health estimation of lithium-ion batteries," Energy, Elsevier, vol. 305(C).
  11. Theissler, Andreas & Pérez-Velázquez, Judith & Kettelgerdes, Marcel & Elger, Gordon, 2021. "Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
  12. Sadiqa Jafari & Zeinab Shahbazi & Yung-Cheol Byun & Sang-Joon Lee, 2022. "Lithium-Ion Battery Estimation in Online Framework Using Extreme Gradient Boosting Machine Learning Approach," Mathematics, MDPI, vol. 10(6), pages 1-17, March.
  13. Lin Chen & Deqian Chen & Manping He & Haihong Pan & Bing Ji, 2025. "A Novel Online State-of-Health Estimation Method for Lithium-Ion Batteries with Multi-Input Metabolic Long Short-Term Memory Framework," Energies, MDPI, vol. 18(5), pages 1-18, February.
  14. Zhang, Xugang & Gao, Xiyuan & Duan, Linchao & Gong, Qingshan & Wang, Yan & Ao, Xiuyi, 2025. "A novel method for state of health estimation of lithium-ion batteries based on fractional-order differential voltage-capacity curve," Applied Energy, Elsevier, vol. 377(PA).
  15. Zhang, Shuzhi & Jiang, Shiyong & Wang, Hongxia & Zhang, Xiongwen, 2022. "A novel dual time-scale voltage sensor fault detection and isolation method for series-connected lithium-ion battery pack," Applied Energy, Elsevier, vol. 322(C).
  16. Shen, Dongxu & Wu, Lifeng & Kang, Guoqing & Guan, Yong & Peng, Zhen, 2021. "A novel online method for predicting the remaining useful life of lithium-ion batteries considering random variable discharge current," Energy, Elsevier, vol. 218(C).
  17. Zuo, Hongyan & Liang, Jingwei & Zhang, Bin & Wei, Kexiang & Zhu, Hong & Tan, Jiqiu, 2023. "Intelligent estimation on state of health of lithium-ion power batteries based on failure feature extraction," Energy, Elsevier, vol. 282(C).
  18. Ardeshiri, Reza Rouhi & Liu, Ming & Ma, Chengbin, 2022. "Multivariate stacked bidirectional long short term memory for lithium-ion battery health management," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
  19. Han, Xiaojuan & Wang, Zuran & Wei, Zixuan, 2021. "A novel approach for health management online-monitoring of lithium-ion batteries based on model-data fusion," Applied Energy, Elsevier, vol. 302(C).
  20. Soo, Yin-Yi & Wang, Yujie & Xiang, Haoxiang & Chen, Zonghai, 2024. "Machine learning based battery pack health prediction using real-world data," Energy, Elsevier, vol. 308(C).
  21. Noman Khan & Ijaz Ul Haq & Fath U Min Ullah & Samee Ullah Khan & Mi Young Lee, 2021. "CL-Net: ConvLSTM-Based Hybrid Architecture for Batteries’ State of Health and Power Consumption Forecasting," Mathematics, MDPI, vol. 9(24), pages 1-22, December.
  22. Xue, Qiao & Li, Junqiu & Xu, Peipei, 2022. "Machine learning based swift online capacity prediction of lithium-ion battery through whole cycle life," Energy, Elsevier, vol. 261(PA).
  23. Kurucan, Mehmet & Özbaltan, Mete & Yetgin, Zeki & Alkaya, Alkan, 2024. "Applications of artificial neural network based battery management systems: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
  24. He, Jiabei & Wu, Lifeng, 2023. "Cross-conditions capacity estimation of lithium-ion battery with constrained adversarial domain adaptation," Energy, Elsevier, vol. 277(C).
  25. Nagulapati, Vijay Mohan & Lee, Hyunjun & Jung, DaWoon & Brigljevic, Boris & Choi, Yunseok & Lim, Hankwon, 2021. "Capacity estimation of batteries: Influence of training dataset size and diversity on data driven prognostic models," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
  26. Wang, Fujin & Zhao, Zhibin & Zhai, Zhi & Guo, Yanjie & Xi, Huan & Wang, Shibin & Chen, Xuefeng, 2023. "Feature disentanglement and tendency retainment with domain adaptation for Lithium-ion battery capacity estimation," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
  27. Ma, Yan & Shan, Ce & Gao, Jinwu & Chen, Hong, 2023. "Multiple health indicators fusion-based health prognostic for lithium-ion battery using transfer learning and hybrid deep learning method," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
  28. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2023. "Wind power forecasting: A hybrid forecasting model and multi-task learning-based framework," Energy, Elsevier, vol. 278(PA).
  29. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2022. "Photovoltaic power forecasting: A hybrid deep learning model incorporating transfer learning strategy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
  30. Li, Yihuan & Li, Kang & Liu, Xuan & Wang, Yanxia & Zhang, Li, 2021. "Lithium-ion battery capacity estimation — A pruned convolutional neural network approach assisted with transfer learning," Applied Energy, Elsevier, vol. 285(C).
  31. 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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