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Developing an online data-driven approach for prognostics and health management of lithium-ion batteries

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  1. Wei, Meng & Ye, Min & Zhang, Chuanwei & Li, Yan & Zhang, Jiale & Wang, Qiao, 2023. "A multi-scale learning approach for remaining useful life prediction of lithium-ion batteries based on variational mode decomposition and Monte Carlo sampling," Energy, Elsevier, vol. 283(C).
  2. 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).
  3. Zhang, Huixin & Xi, Xiaopeng & Pan, Rong, 2023. "A two-stage data-driven approach to remaining useful life prediction via long short-term memory networks," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
  4. Karimi, Danial & Behi, Hamidreza & Berecibar, Maitane & Van Mierlo, Joeri, 2023. "A comprehensive coupled 0D-ECM to 3D-CFD thermal model for heat pipe assisted-air cooling thermal management system under fast charge and discharge," Applied Energy, Elsevier, vol. 339(C).
  5. Md Sazzad Hosen & Poonam Yadav & Joeri Van Mierlo & Maitane Berecibar, 2023. "A Post-Mortem Study Case of a Dynamically Aged Commercial NMC Cell," Energies, MDPI, vol. 16(3), pages 1-14, January.
  6. Zhou, Yifei & Wang, Shunli & Xie, Yanxing & Shen, Xianfeng & Fernandez, Carlos, 2023. "Remaining useful life prediction and state of health diagnosis for lithium-ion batteries based on improved grey wolf optimization algorithm-deep extreme learning machine algorithm," Energy, Elsevier, vol. 285(C).
  7. Kumar, Roushan & Das, Kaushik & Krishna, Anurup, 2024. "Comparative analysis of data-driven electric vehicle battery health models across different operating conditions," Energy, Elsevier, vol. 309(C).
  8. Wei, Yupeng & Wu, Dazhong, 2023. "Prediction of state of health and remaining useful life of lithium-ion battery using graph convolutional network with dual attention mechanisms," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
  9. Lyu, Dongzhen & Liu, Enhui & Chen, Huiling & Zhang, Bin & Xiang, Jiawei, 2025. "Transfer-driven prognosis from battery cells to packs: An application with adaptive differential model decomposition," Applied Energy, Elsevier, vol. 377(PA).
  10. Wang, Fujin & Zhao, Zhibin & Zhai, Zhi & Shang, Zuogang & Yan, Ruqiang & Chen, Xuefeng, 2023. "Explainability-driven model improvement for SOH estimation of lithium-ion battery," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
  11. Meng, Huixing & Hu, Mengqian & Kong, Ziyan & Niu, Yiming & Liang, Jiali & Nie, Zhenyu & Xing, Jinduo, 2024. "Risk analysis of lithium-ion battery accidents based on physics-informed data-driven Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
  12. Lai, Xin & Yao, Yi & Tang, Xiaopeng & Zheng, Yuejiu & Zhou, Yuanqiang & Sun, Yuedong & Gao, Furong, 2023. "Voltage profile reconstruction and state of health estimation for lithium-ion batteries under dynamic working conditions," Energy, Elsevier, vol. 282(C).
  13. Hong, Jichao & Zhang, Huaqin & Zhang, Xinyang & Yang, Haixu & Chen, Yingjie & Wang, Facheng & Huang, Zhongguo & Wang, Wei, 2024. "Online accurate voltage prediction with sparse data for the whole life cycle of Lithium-ion batteries in electric vehicles," Applied Energy, Elsevier, vol. 369(C).
  14. 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).
  15. Shunli Wang & Pu Ren & Paul Takyi-Aninakwa & Siyu Jin & Carlos Fernandez, 2022. "A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries," Energies, MDPI, vol. 15(14), pages 1-27, July.
  16. Shi, Mingjie & Xu, Jun & Lin, Chuanping & Mei, Xuesong, 2022. "A fast state-of-health estimation method using single linear feature for lithium-ion batteries," Energy, Elsevier, vol. 256(C).
  17. Dapai Shi & Jingyuan Zhao & Chika Eze & Zhenghong Wang & Junbin Wang & Yubo Lian & Andrew F. Burke, 2023. "Cloud-Based Artificial Intelligence Framework for Battery Management System," Energies, MDPI, vol. 16(11), pages 1-21, May.
  18. Khaleghi, Sahar & Hosen, Md Sazzad & Van Mierlo, Joeri & Berecibar, Maitane, 2024. "Towards machine-learning driven prognostics and health management of Li-ion batteries. A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
  19. Chen, Kui & Luo, Yang & Long, Zhou & Li, Yang & Nie, Guangbo & Liu, Kai & Xin, Dongli & Gao, Guoqiang & Wu, Guangning, 2025. "Big data-driven prognostics and health management of lithium-ion batteries:A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 214(C).
  20. Haochen Qin & Xuexin Fan & Yaxiang Fan & Ruitian Wang & Qianyi Shang & Dong Zhang, 2023. "A Computationally Efficient Approach for the State-of-Health Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 16(14), pages 1-23, July.
  21. Danial Karimi & Hamidreza Behi & Joeri Van Mierlo & Maitane Berecibar, 2022. "An Experimental Study on Thermal Performance of Graphite-Based Phase-Change Materials for High-Power Batteries," Energies, MDPI, vol. 15(7), pages 1-13, March.
  22. 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).
  23. Peng, Simin & Zhu, Junchao & Wu, Tiezhou & Tang, Aihua & Kan, Jiarong & Pecht, Michael, 2024. "SOH early prediction of lithium-ion batteries based on voltage interval selection and features fusion," Energy, Elsevier, vol. 308(C).
  24. Zhang, Zhen & Zhu, Yuhao & Gong, Yichang & Wang, Teng & Cui, Naxin & Shang, Yunlong, 2025. "Insight into the whole from the part: Redefined state of health for lithium-ion batteries based on optimal charging fragment search," Energy, Elsevier, vol. 320(C).
  25. Qian, Cheng & Guan, Hongsheng & Xu, Binghui & Xia, Quan & Sun, Bo & Ren, Yi & Wang, Zili, 2024. "A CNN-SAM-LSTM hybrid neural network for multi-state estimation of lithium-ion batteries under dynamical operating conditions," Energy, Elsevier, vol. 294(C).
  26. Dai, Houde & Wang, Jiaxin & Huang, Yiyang & Lai, Yuan & Zhu, Liqi, 2024. "Lightweight state-of-health estimation of lithium-ion batteries based on statistical feature optimization," Renewable Energy, Elsevier, vol. 222(C).
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