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Deep learning to estimate lithium-ion battery state of health without additional degradation experiments

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  1. Yong Wang & Jingda Wu & Hongwen He & Zhongbao Wei & Fengchun Sun, 2025. "Data-driven energy management for electric vehicles using offline reinforcement learning," Nature Communications, Nature, vol. 16(1), pages 1-16, December.
  2. Wu, Jian & Meng, Jinhao & Lin, Mingqiang & Wang, Wei & Wu, Ji & Stroe, Daniel-Ioan, 2024. "Lithium-ion battery state of health estimation using a hybrid model with electrochemical impedance spectroscopy," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
  3. 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).
  4. Zhang, Xudong & Fan, Jie & Zou, Yuan & Sun, Wei, 2023. "Realizing accurate battery capacity estimation using 4 min 1C discharging data," Energy, Elsevier, vol. 282(C).
  5. Yang, Minxing & Sun, Xiaofei & Liu, Rui & Wang, Lingzhi & Zhao, Fei & Mei, Xuesong, 2024. "Predict the lifetime of lithium-ion batteries using early cycles: A review," Applied Energy, Elsevier, vol. 376(PA).
  6. Liu, Zhongyong & Sun, Yuning & Tang, Xiawei & Mao, Lei, 2024. "Enabling unsupervised fault diagnosis of proton exchange membrane fuel cell stack: Knowledge transfer from single-cell to stack," Applied Energy, Elsevier, vol. 360(C).
  7. Wang, Huan & Li, Yan-Fu & Zhang, Ying, 2023. "Bioinspired spiking spatiotemporal attention framework for lithium-ion batteries state-of-health estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
  8. Hongao Liu & Chang Li & Xiaosong Hu & Jinwen Li & Kai Zhang & Yang Xie & Ranglei Wu & Ziyou Song, 2025. "Multi-modal framework for battery state of health evaluation using open-source electric vehicle data," Nature Communications, Nature, vol. 16(1), pages 1-12, December.
  9. 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).
  10. Zhang, Zhengjie & Cao, Rui & Zheng, Yifan & Zhang, Lisheng & Guang, Haoran & Liu, Xinhua & Gao, Xinlei & Yang, Shichun, 2024. "Online state of health estimation for lithium-ion batteries based on gene expression programming," Energy, Elsevier, vol. 294(C).
  11. Dou, Bowen & Hou, Shujuan & Li, Hai & Zhao, Yanpeng & Fan, Yue & Sun, Lei & Chen, Hao-sen, 2025. "Cross-domain state of health estimation for lithium-ion battery based on latent space consistency using few-unlabeled data," Energy, Elsevier, vol. 320(C).
  12. 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).
  13. Sun, Wenjie & Wu, Chengke & Xie, Chengde & Wang, Xikang & Guo, Yuanjun & Tang, Yongbing & Zhang, Yanhui & Li, Kang & Du, Guanhao & Yang, Zhile & Yao, Wenjiao, 2025. "Fine-tuning enables state of health estimation for lithium-ion batteries via a time series foundation model," Energy, Elsevier, vol. 318(C).
  14. 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).
  15. 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).
  16. Tang, Aihua & Xu, Yuchen & Hu, Yuanzhi & Tian, Jinpeng & Nie, Yuwei & Yan, Fuwu & Tan, Yong & Yu, Quanqing, 2024. "Battery state of health estimation under dynamic operations with physics-driven deep learning," Applied Energy, Elsevier, vol. 370(C).
  17. 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).
  18. Liu, Donglei & Wang, Shunli & Fan, Yongcun & Fernandez, Carlos & Blaabjerg, Frede, 2024. "An optimized multi-segment long short-term memory network strategy for power lithium-ion battery state of charge estimation adaptive wide temperatures," Energy, Elsevier, vol. 304(C).
  19. 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).
  20. Qiu, Xianghui & Yan, Wentao & Wang, Shuangfeng & Chen, Kai, 2024. "A general multi-source ensemble transfer learning framework for health prognostic of lithium-ion batteries," Applied Energy, Elsevier, vol. 376(PA).
  21. Yifan, Zheng & Sida, Zhou & Zhengjie, Zhang & Xinan, Zhou & Rui, Cao & Qiangwei, Li & Zichao, Gao & Chengcheng, Fan & Shichun, Yang, 2024. "A capacity fade reliability model for lithium-ion battery packs based on real-vehicle data," Energy, Elsevier, vol. 307(C).
  22. Feng, Xinhong & Zhang, Yongzhi & Xiong, Rui & Wang, Chun, 2024. "Comprehensive performance comparison among different types of features in data-driven battery state of health estimation," Applied Energy, Elsevier, vol. 369(C).
  23. Zhang, Dayu & Wang, Zhenpo & Liu, Peng & She, Chengqi & Wang, Qiushi & Zhou, Litao & Qin, Zian, 2024. "A multi-step fast charging-based battery capacity estimation framework of real-world electric vehicles," Energy, Elsevier, vol. 294(C).
  24. Tang, Aihua & Xu, Yuchen & Tian, Jinpeng & Zou, Hang & Liu, Kailong & Yu, Quanqing, 2025. "Adaptive engineering-assisted deep learning for battery module health monitoring across dynamic operations," Energy, Elsevier, vol. 322(C).
  25. Liu, Chenghao & Deng, Zhongwei & Zhang, Xiaohong & Bao, Huanhuan & Cheng, Duanqian, 2024. "Battery state of health estimation across electrochemistry and working conditions based on domain adaptation," Energy, Elsevier, vol. 297(C).
  26. 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).
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