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An improved CNN-LSTM model-based state-of-health estimation approach for lithium-ion batteries

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Cited by:

  1. Sun, Jing & Fan, Chaoqun & Yan, Huiyi, 2024. "SOH estimation of lithium-ion batteries based on multi-feature deep fusion and XGBoost," Energy, Elsevier, vol. 306(C).
  2. Huang, Haichi & Bian, Chong & Wu, Mengdan & An, Dong & Yang, Shunkun, 2024. "A novel integrated SOC–SOH estimation framework for whole-life-cycle lithium-ion batteries," Energy, Elsevier, vol. 288(C).
  3. Li, Xiaopeng & Zhao, Minghang & Zhong, Shisheng & Li, Junfu & Fu, Song & Yan, Zhiqi, 2024. "BMSFormer: An efficient deep learning model for online state-of-health estimation of lithium-ion batteries under high-frequency early SOC data with strong correlated single health indicator," Energy, Elsevier, vol. 313(C).
  4. Fargalla, Mandella Ali M. & Yan, Wei & Deng, Jingen & Wu, Tao & Kiyingi, Wyclif & Li, Guangcong & Zhang, Wei, 2024. "TimeNet: Time2Vec attention-based CNN-BiGRU neural network for predicting production in shale and sandstone gas reservoirs," Energy, Elsevier, vol. 290(C).
  5. 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.
  6. S, Vignesh & Che, Hang Seng & Selvaraj, Jeyraj & Tey, Kok Soon & Lee, Jia Woon & Shareef, Hussain & Errouissi, Rachid, 2024. "State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges," Applied Energy, Elsevier, vol. 369(C).
  7. Sun, Rongli & Chen, Junsheng & Li, Benchuan & Piao, Changhao, 2025. "State of health estimation for Lithium-ion batteries based on novel feature extraction and BiGRU-Attention model," Energy, Elsevier, vol. 319(C).
  8. Gu, Pingwei & Zhang, Ying & Duan, Bin & Zhang, Chenghui & Kang, Yongzhe, 2024. "Rapid and flexible lithium-ion battery performance evaluation using random charging curve based on deep learning," Energy, Elsevier, vol. 293(C).
  9. Zhang, Chaolong & Luo, Laijin & Yang, Zhong & Du, Bolun & Zhou, Ziheng & Wu, Ji & Chen, Liping, 2024. "Flexible method for estimating the state of health of lithium-ion batteries using partial charging segments," Energy, Elsevier, vol. 295(C).
  10. Zhao, Jingyuan & Wang, Zhenghong & Wu, Yuyan & Burke, Andrew F., 2025. "Predictive pretrained transformer (PPT) for real-time battery health diagnostics," Applied Energy, Elsevier, vol. 377(PD).
  11. Zhang, Hao & Gao, Jingyi & Kang, Le & Zhang, Yi & Wang, Licheng & Wang, Kai, 2023. "State of health estimation of lithium-ion batteries based on modified flower pollination algorithm-temporal convolutional network," Energy, Elsevier, vol. 283(C).
  12. Liu, Zhi-Feng & Huang, Ya-He & Zhang, Shu-Rui & Luo, Xing-Fu & Chen, Xiao-Rui & Lin, Jun-Jie & Tang, Yu & Guo, Liang & Li, Ji-Xiang, 2025. "A collaborative interaction gate-based deep learning model with optimal bandwidth adjustment strategies for lithium-ion battery capacity point-interval forecasting," Applied Energy, Elsevier, vol. 377(PD).
  13. Khosravi, Nima & Oubelaid, Adel, 2025. "Deep learning-driven estimation and multi-objective optimization of lithium-ion battery parameters for enhanced EV/HEV performance," Energy, Elsevier, vol. 320(C).
  14. Ji, Shanling & Zhang, Zhisheng & Stein, Helge S. & Zhu, Jianxiong, 2025. "Flexible health prognosis of battery nonlinear aging using temporal transfer learning," Applied Energy, Elsevier, vol. 377(PD).
  15. Sun, Jing & Wang, Haitao, 2025. "State of health estimation for lithium-ion batteries based on optimal feature subset algorithm," Energy, Elsevier, vol. 322(C).
  16. Zhao, Xiaoyu & Wang, Zuolu & Miao, Haiyan & Yang, Wenxian & Gu, Fengshou & Ball, Andrew D., 2024. "A label-free battery state of health estimation method based on adversarial multi-domain adaptation network and relaxation voltage," Energy, Elsevier, vol. 308(C).
  17. Cui, Ying & Ye, Lin & Yao, Zhongran & Gu, Xiaoyong & Wang, Xinwang, 2024. "Performance optimization of cement calciner based on CFD simulation and machine learning algorithm," Energy, Elsevier, vol. 302(C).
  18. Zhang, Ran & Ji, ChunHui & Zhou, Xing & Liu, Tianyu & Jin, Guang & Pan, Zhengqiang & Liu, Yajie, 2024. "Capacity estimation of lithium-ion batteries with uncertainty quantification based on temporal convolutional network and Gaussian process regression," Energy, Elsevier, vol. 297(C).
  19. Wei, Meng & Ye, Min & Zhang, Chuanwei & Wang, Qiao & Lian, Gaoqi & Xia, Baozhou, 2024. "Integrating mechanism and machine learning based capacity estimation for LiFePO4 batteries under slight overcharge cycling," Energy, Elsevier, vol. 296(C).
  20. Peng, Simin & Wang, Yujian & Tang, Aihua & Jiang, Yuxia & Kan, Jiarong & Pecht, Michael, 2025. "State of health estimation joint improved grey wolf optimization algorithm and LSTM using partial discharging health features for lithium-ion batteries," Energy, Elsevier, vol. 315(C).
  21. Ran, Pengyu & Huang, Xianghua & Zhang, Zihao & Hao, Xuanzhang & Li, Lingwei, 2025. "A novel neural network with variable-activation-function for analytical redundancy of variable cycle engine sensors," Energy, Elsevier, vol. 319(C).
  22. Zhang, Zhen & Zhu, Yuhao & Zhang, Qi & Cui, Naxin & Shang, Yunlong, 2024. "Multi-cycle charging information guided state of health estimation for lithium-ion batteries based on pre-trained large language model," Energy, Elsevier, vol. 313(C).
  23. Tang, Telu & Yang, Xiangguo & Li, Muheng & Li, Xin & Huang, Hai & Guan, Cong & Huang, Jiangfan & Wang, Yufan & Zhou, Chaobin, 2025. "Deep learning model-based real-time state-of-health estimation of lithium-ion batteries under dynamic operating conditions," Energy, Elsevier, vol. 317(C).
  24. 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).
  25. Yao, Jiachi & Chang, Zhonghao & Han, Te & Tian, Jingpeng, 2024. "Semi-supervised adversarial deep learning for capacity estimation of battery energy storage systems," Energy, Elsevier, vol. 294(C).
  26. Wu, Jiang & Lei, Dong & Liu, Zelong & Zhang, Yan, 2024. "A fusion algorithm of multidimensional element space mapping architecture for SOC estimation of lithium-ion batteries under dynamic operating conditions," Energy, Elsevier, vol. 311(C).
  27. 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).
  28. Bao, Zhengyi & Nie, Jiahao & Lin, Huipin & Jiang, Jiahao & He, Zhiwei & Gao, Mingyu, 2023. "A global–local context embedding learning based sequence-free framework for state of health estimation of lithium-ion battery," Energy, Elsevier, vol. 282(C).
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