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Data efficient health prognostic for batteries based on sequential information-driven probabilistic neural network

Citations

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Cited by:

  1. 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).
  2. Shi, Haotian & Wang, Shunli & Huang, Qi & Fernandez, Carlos & Liang, Jianhong & Zhang, Mengyun & Qi, Chuangshi & Wang, Liping, 2024. "Improved electric-thermal-aging multi-physics domain coupling modeling and identification decoupling of complex kinetic processes based on timescale quantification in lithium-ion batteries," Applied Energy, Elsevier, vol. 353(PB).
  3. Lin, Yupeng & Wan, Fu & Yang, Da & Li, Shufan & Liu, Ruiqi & Yin, Wenwei & Mu, Jingyi & Chen, Weigen, 2025. "Battery degradation trajectory early prediction with degradation recognition and physics-guided under different charging strategies," Energy, Elsevier, vol. 336(C).
  4. Wu, Xiaoying & Yan, Chong & Wang, Linbing & Dou, Wenwen & Li, Yi & Gao, Guohong & Wang, Jianping & Fan, Yuqian & Tan, Xiaojun, 2025. "Data-driven SOC estimation method for power batteries under driving cycle conditions and a wide temperature range," Energy, Elsevier, vol. 340(C).
  5. Zhang, Xiaoxi & Pan, Yongjun & Xiong, Yue & Zhang, Yongzhi & Tang, Mao & Dai, Wei & Liu, Binghe & Hou, Liang, 2024. "Deep learning-based vibration stress and fatigue-life prediction of a battery-pack system," Applied Energy, Elsevier, vol. 357(C).
  6. Carlos Antônio Rufino Júnior & Eleonora Riva Sanseverino & Pierluigi Gallo & Murilo Machado Amaral & Daniel Koch & Yash Kotak & Sergej Diel & Gero Walter & Hans-Georg Schweiger & Hudson Zanin, 2024. "Unraveling the Degradation Mechanisms of Lithium-Ion Batteries," Energies, MDPI, vol. 17(14), pages 1-51, July.
  7. Cheng, Ming & Zhang, Xuan & Ran, Aihua & Wei, Guodan & Sun, Hongbin, 2023. "Optimal dispatch approach for second-life batteries considering degradation with online SoH estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
  8. Xu, Huanwei & Wu, Lingfeng & Xiong, Shizhe & Li, Wei & Garg, Akhil & Gao, Liang, 2023. "An improved CNN-LSTM model-based state-of-health estimation approach for lithium-ion batteries," Energy, Elsevier, vol. 276(C).
  9. Yuan, Zhu & Deng, Zhongwei & He, Yvxin & Ning, Zhansheng & Liu, Jincheng, 2025. "Multi-step prediction of battery state of health based on self-supervised pre-training and transfer learning using the xPatch model," Energy, Elsevier, vol. 341(C).
  10. Hou, Jing & Su, Taian & Gao, Tian & Yang, Yan & Xue, Wei, 2025. "Early prediction of battery lifetime for lithium-ion batteries based on a hybrid clustered CNN model," Energy, Elsevier, vol. 319(C).
  11. Ke, Leisi & Lin, Mingqiang & Meng, Jinhao & Wu, Ji & Wang, Wei & Wang, Fengxiang, 2026. "A sparse SOH estimation framework based on clustered voltage segments and multi-head CNN under arbitrary charging condition," Energy, Elsevier, vol. 344(C).
  12. Li, Qingbo & Zhong, Jun & Du, Jinqiao & Yi, Yong & Tian, Jie & Li, Yan & Lai, Chunyan & Lu, Taolin & Xie, Jingying, 2024. "Probabilistic neural network-based flexible estimation of lithium-ion battery capacity considering multidimensional charging habits," Energy, Elsevier, vol. 294(C).
  13. Tang, Aihua & Jiang, Yihan & Nie, Yuwei & Yu, Quanqing & Shen, Weixiang & Pecht, Michael G., 2023. "Health and lifespan prediction considering degradation patterns of lithium-ion batteries based on transferable attention neural network," Energy, Elsevier, vol. 279(C).
  14. Chen, Yuan & Li, Dongyuan & Huang, Xiaohe & Hong, Jichao & Mu, Chaoxu & Wu, Longxing & Li, Kerui, 2025. "Exploring life warning solution of lithium-ion batteries in real-world scenarios: TCN-transformer fusion model for battery pack SOH estimation," Energy, Elsevier, vol. 335(C).
  15. Wang, Zhenxi & Ma, Yan & Gao, Jinwu & Chen, Hong, 2025. "Remaining useful life prediction for solid-state lithium batteries based on spatial–temporal relations and neuronal ODE-assisted KAN," Reliability Engineering and System Safety, Elsevier, vol. 260(C).
  16. Chan, Joey & Chen, Zhen & Pan, Ershun, 2026. "Foundation models knowledge distillation for battery capacity degradation forecast," Reliability Engineering and System Safety, Elsevier, vol. 270(C).
  17. Chen, Shouxuan & Zhang, Shuting & Geng, Yuanfei & Jia, Yao & Zhang, Shuzhi, 2025. "Dynamic conditions-oriented model-data fused framework enabling state of charge and capacity accurate co-estimation of lithium-ion battery," Energy, Elsevier, vol. 317(C).
  18. Yu, Quanqing & Nie, Yuwei & Guo, Shanshan & Li, Junfu & Zhang, Chengming, 2024. "Machine learning enables rapid state of health estimation of each cell within battery pack," Applied Energy, Elsevier, vol. 375(C).
  19. 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).
  20. Zou, Yuanru & Shi, Haotian & Cao, Wen & Wang, Shunli & Nie, Shiliang & Chen, Dan, 2025. "A high-speed recurrent state network with noise reduction for multi-temperature state of energy estimation of electric vehicles lithium-ion batteries," Energy, Elsevier, vol. 322(C).
  21. 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).
  22. Wang, Qiao & Ye, Min & Wei, Meng & Lian, Gaoqi & Li, Yan, 2023. "Random health indicator and shallow neural network based robust capacity estimation for lithium-ion batteries with different fast charging protocols," Energy, Elsevier, vol. 271(C).
  23. Li, Chuan & Zhang, Huahua & Ding, Ping & Yang, Shuai & Bai, Yun, 2023. "Deep feature extraction in lifetime prognostics of lithium-ion batteries: Advances, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
  24. Bockrath, Steffen & Lorentz, Vincent & Pruckner, Marco, 2023. "State of health estimation of lithium-ion batteries with a temporal convolutional neural network using partial load profiles," Applied Energy, Elsevier, vol. 329(C).
  25. Che, Yunhong & Zheng, Yusheng & Forest, Florent Evariste & Sui, Xin & Hu, Xiaosong & Teodorescu, Remus, 2024. "Predictive health assessment for lithium-ion batteries with probabilistic degradation prediction and accelerating aging detection," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
  26. Li, Qingbo & Lu, Taolin & Lai, Chunyan & Li, Jiwei & Pan, Long & Ma, Changjun & Zhu, Yunpeng & Xie, Jingying, 2024. "Lithium-ion battery capacity estimation based on fragment charging data using deep residual shrinkage networks and uncertainty evaluation," Energy, Elsevier, vol. 290(C).
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