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State of health estimation of lithium-ion batteries with a temporal convolutional neural network using partial load profiles

Citations

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  1. Yang, Simin & Xie, Hengwei & Liu, Shengzhe & Fan, Yuqian & Tan, Xiaojun, 2025. "A method for estimating the SOH of lithium-ion batteries under complex charging conditions using dilated residual temporal encoding," Energy, Elsevier, vol. 328(C).
  2. Ko, Chi-Jyun & Chen, Kuo-Ching & Chen, Chih-Hung, 2025. "Advantageous characteristics of constant voltage charging: A good option to estimate battery states for lithium-ion batteries," Energy, Elsevier, vol. 322(C).
  3. 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).
  4. Ren, Yi & Tang, Ting & Jiang, Fusheng & Xia, Quan & Zhu, Xiayu & Sun, Bo & Yang, Dezhen & Feng, Qiang & Qian, Cheng, 2025. "A novel state of health estimation method for lithium-ion battery pack based on cross generative adversarial networks," Applied Energy, Elsevier, vol. 377(PA).
  5. Liu, Ruixue & Jiang, Benben, 2025. "A multi-time-resolution attention-based interaction network for co-estimation of multiple battery states," Applied Energy, Elsevier, vol. 381(C).
  6. Zhang, Jiarui & Mao, Lei & Liu, Zhongyong & Yu, Kun & Hu, Zhiyong, 2025. "A Bayesian transfer learning framework for assessing health status of Lithium-ion batteries considering individual battery operating states," Applied Energy, Elsevier, vol. 382(C).
  7. 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).
  8. Chen, Si-Zhe & Liu, Jing & Yuan, Haoliang & Tao, Yibin & Xu, Fangyuan & Yang, Ling, 2025. "AM-MFF: A multi-feature fusion framework based on attention mechanism for robust and interpretable lithium-ion battery state of health estimation," Applied Energy, Elsevier, vol. 381(C).
  9. Chenyuan Liu & Heng Li & Kexin Li & Yue Wu & Baogang Lv, 2025. "Deep Learning for State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles: A Systematic Review," Energies, MDPI, vol. 18(6), pages 1-20, March.
  10. 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).
  11. 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).
  12. Xiong, Ran & Wang, Shunli & Huang, Qi & Yu, Chunmei & Fernandez, Carlos & Xiao, Wei & Jia, Jun & Guerrero, Josep M., 2024. "Improved cooperative competitive particle swarm optimization and nonlinear coefficient temperature decreasing simulated annealing-back propagation methods for state of health estimation of energy storage batteries," Energy, Elsevier, vol. 292(C).
  13. Wang, Tong & Wu, Yan & Zhu, Keming & Cen, Jianmeng & Wang, Shaohong & Huang, Yuqi, 2025. "Deep learning and polarization equilibrium based state of health estimation for lithium-ion battery using partial charging data," Energy, Elsevier, vol. 317(C).
  14. Fu, Shiyi & Fan, Hongtao & Jin, Zhaorui & Ji, Fan & Tao, Yulin & Dong, Yachao & Chen, Xunyuan & Shao, Minghao & Yuan, Shuyu & Wang, Yu & Sun, Yaojie, 2026. "Recent progress in state of health estimation for lithium-ion batteries: From laboratory to practical application," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PB).
  15. 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).
  16. Chen, Baoliang & Liu, Yonggui, 2025. "Data driven-based health prognostics and charge estimation for lithium-ion batteries under varying discharging patterns," Energy, Elsevier, vol. 335(C).
  17. 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.
  18. Wang, Tianyu & Ma, Zhongjing & Zou, Suli & Chen, Zhan & Wang, Peng, 2024. "Lithium-ion battery state-of-health estimation: A self-supervised framework incorporating weak labels," Applied Energy, Elsevier, vol. 355(C).
  19. Neha Bhushan & Saad Mekhilef & Kok Soon Tey & Mohamed Shaaban & Mehdi Seyedmahmoudian & Alex Stojcevski, 2024. "Dynamic K-Decay Learning Rate Optimization for Deep Convolutional Neural Network to Estimate the State of Charge for Electric Vehicle Batteries," Energies, MDPI, vol. 17(16), pages 1-16, August.
  20. Lyu, Guangzheng & Zhang, Heng & Miao, Qiang, 2024. "An adaptive and interpretable SOH estimation method for lithium-ion batteries based-on relaxation voltage cross-scale features and multi-LSTM-RFR2," Energy, Elsevier, vol. 304(C).
  21. Luc Vivien Assiene Mouodo & Petros J. Axaopoulos, 2025. "Optimization and Estimation of the State of Charge of Lithium-Ion Batteries for Electric Vehicles," Energies, MDPI, vol. 18(13), pages 1-25, June.
  22. Zhang, Liping & Chen, Caiyi & Luo, Delin, 2025. "A comprehensive framework of synchronous SOC-SOH joint estimation for lithium-ion battery with multi-depth expert networks," Energy, Elsevier, vol. 339(C).
  23. Zhu, Yuli & Jiang, Bo & Zhu, Jiangong & Wang, Xueyuan & Wang, Rong & Wei, Xuezhe & Dai, Haifeng, 2023. "Adaptive state of health estimation for lithium-ion batteries using impedance-based timescale information and ensemble learning," Energy, Elsevier, vol. 284(C).
  24. Liu, Xinghua & Li, Siqi & Tian, Jiaqiang & Wei, Zhongbao & Wang, Peng, 2023. "Health estimation of lithium-ion batteries with voltage reconstruction and fusion model," Energy, Elsevier, vol. 282(C).
  25. Duan, Linchao & Zhang, Xugang & Jiang, Zhigang & Gong, Qingshan & Wang, Yan & Ao, Xiuyi, 2023. "State of charge estimation of lithium-ion batteries based on second-order adaptive extended Kalman filter with correspondence analysis," Energy, Elsevier, vol. 280(C).
  26. Xia, Baozhou & Ye, Min & Wei, Meng & Wang, Qiao & Lian, Gaoqi & Li, Yan, 2025. "SOH estimation of lithium-ion batteries with local health indicators in multi-stage fast charging protocols," Energy, Elsevier, vol. 334(C).
  27. Zhao, Bo & Zhang, Weige & Zhang, Yanru & Zhang, Caiping & Zhang, Chi & Zhang, Junwei, 2025. "Lithium-ion battery remaining useful life prediction based on interpretable deep learning and network parameter optimization," Applied Energy, Elsevier, vol. 379(C).
  28. Wang, Yaxuan & Guo, Shilong & Cui, Yue & Deng, Liang & Zhao, Lei & Li, Junfu & Wang, Zhenbo, 2025. "A comprehensive review of machine learning-based state of health estimation for lithium-ion batteries: data, features, algorithms, and future challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 224(C).
  29. 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).
  30. Xiong, Ran & Zhao, Pengfei & Cao, Di & Zhang, Sen & Zhan, Wei & Tang, Ming & Zhang, Yuning & Hu, Weihao, 2025. "Transfer learning with composite kernel sparse Gaussian process-aided model for probabilistic state of health estimation of lithium-ion batteries against multi-source coupled harsh scenarios," Applied Energy, Elsevier, vol. 401(PC).
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