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   RePEc:eee:appene:v:381:y:2025:i:c:s0306261924024814.

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1. Multi-modal framework for battery state of health evaluation using open-source electric vehicle data. (2025). Li, Chang ; Liu, Hongao ; Song, Ziyou ; Wu, Ranglei ; Xie, Yang ; Zhang, Kai ; Hu, Xiaosong.
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2. Safety and reliability analysis of lithium-ion batteries with real-time health monitoring. (2025). Pecht, Michael ; Kong, Lingxi ; Boles, Steven Tyler ; Lin, Chun-Pang ; Bhargava, Cherry ; Naqvi, Ijaz Haider ; Khan, Ayesha.
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3. Toward the ensemble consistency: Condition-driven ensemble balance representation learning and nonstationary anomaly detection for battery energy storage system. (2025). Chen, XU ; Yang, Jiayang ; Zhao, Chunhui.
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4. Life prediction of on-board supercapacitor energy storage system based on gate recurrent unit neural network using sparse monitoring data. (2025). Huang, Xuelin ; Yan, Rong ; Wei, LI ; Wang, YU ; Lin, Tingrun.
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5. Mathematical modelling of electrochemical, thermal and degradation processes in lithium-ion cells—A comprehensive review. (2024). Mehta, Rohit ; Gupta, Amit.
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6. Deep transfer learning enables battery state of charge and state of health estimation. (2024). Yang, Yongsong ; Liu, Shizhuo ; Yu, Quanqing ; Zhao, Lijun ; Zhang, Chengming ; Nie, Yuwei ; Xu, Yuchen.
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7. A novel method of battery pack energy health estimation based on visual feature learning. (2024). Zhang, Yanru ; Fan, Xinyuan ; Zhao, BO ; Sun, Bingxiang.
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8. Lithium-ion battery capacity estimation based on fragment charging data using deep residual shrinkage networks and uncertainty evaluation. (2024). Li, Qingbo ; Pan, Long ; Xie, Jingying ; Lai, Chunyan ; Ma, Changjun ; Zhu, Yunpeng ; Lu, Taolin.
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9. A hybrid deep learning approach for remaining useful life prediction of lithium-ion batteries based on discharging fragments. (2024). Chen, Zhongwei ; Mao, Zhiyu ; Liu, Yunpeng ; Ahmed, Moin ; Feng, Jiangtao ; Hou, BO.
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10. Improved electric-thermal-aging multi-physics domain coupling modeling and identification decoupling of complex kinetic processes based on timescale quantification in lithium-ion batteries. (2024). Zhang, Mengyun ; Wang, Liping ; Liang, Jianhong ; Shi, Haotian ; Qi, Chuangshi ; Fernandez, Carlos ; Huang, QI.
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11. Data-driven capacity estimation for lithium-ion batteries with feature matching based transfer learning method. (2024). Zhang, Xuan ; Fu, Shiyi ; Tao, Shengyu ; He, Kun ; Zuo, Junxiong ; Fan, Hongtao ; Wang, YU ; Sun, Yaojie ; Liu, Xutao.
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12. Data-Driven GWO-BRNN-Based SOH Estimation of Lithium-Ion Batteries in EVs for Their Prognostics and Health Management. (2023). Waseem, Muhammad ; Huang, Jingyuan ; Wong, Chak-Nam ; C. K. M. Lee, .
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13. Electrochemical Impedance Spectroscopy: A New Chapter in the Fast and Accurate Estimation of the State of Health for Lithium-Ion Batteries. (2023). Liu, Yanshuo ; Cui, Xiaoli ; Zhang, Ming ; Wang, Licheng.
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14. Deep feature extraction in lifetime prognostics of lithium-ion batteries: Advances, challenges and perspectives. (2023). Bai, Yun ; Yang, Shuai ; Ding, Ping ; Zhang, Huahua ; Li, Chuan.
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15. Optimal dispatch approach for second-life batteries considering degradation with online SoH estimation. (2023). Zhang, Xuan ; Cheng, Ming ; Sun, Hongbin ; Wei, Guodan ; Ran, Aihua.
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16. State of health estimation of the lithium-ion power battery based on the principal component analysis-particle swarm optimization-back propagation neural network. (2023). Wang, Yuqing ; Zhong, Yiming ; Wu, Muyao.
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17. State of health estimation of the LiFePO4 power battery based on the forgetting factor recursive Total Least Squares and the temperature correction. (2023). Wu, Muyao ; Wang, LI.
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18. State of health estimation with attentional long short-term memory network for lithium-ion batteries. (2023). Wang, Wei ; Lin, Mingqiang ; Wu, JI ; Meng, Jinhao.
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19. An efficient and robust method for lithium-ion battery capacity estimation using constant-voltage charging time. (2023). Cai, Yingfeng ; Li, Xin ; Sun, Xiaodong ; Yang, Jufeng ; Mi, Chris.
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20. Neural network extended state-observer for energy system monitoring. (2023). Zhang, Zuoyi ; Cheng, Zhonghua ; Dong, Zhe ; Zhu, Yunlong ; Huang, Xiaojin.
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21. State-of-health estimation for lithium-ion batteries with hierarchical feature construction and auto-configurable Gaussian process regression. (2023). Cui, Ningmin ; Cai, Lei ; Li, Junxin ; Meng, Jinhao ; Peng, Jichang ; Jin, Haiyan ; Zhao, Xinchao.
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22. Transferable data-driven capacity estimation for lithium-ion batteries with deep learning: A case study from laboratory to field applications. (2023). Cai, Xue ; Li, Weihan ; Wang, Qiao ; Ye, Min ; Sauer, Dirk Uwe.
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23. Electrochemical Impedance Spectroscopy Based on the State of Health Estimation for Lithium-Ion Batteries. (2022). Li, Liwei ; Wang, Licheng ; Yang, Dongfang.
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24. Battery capacity trajectory prediction by capturing the correlation between different vehicles. (2022). Deng, Zhongwei ; Xie, YI ; Li, Jinwen ; Liu, Hongao ; Lu, Chen.
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    RePEc:eee:energy:v:260:y:2022:i:c:s0360544222020175.

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25. Data efficient health prognostic for batteries based on sequential information-driven probabilistic neural network. (2022). Yang, Yalian ; Sui, Xin ; Hu, Xiaosong ; Teodorescu, Remus ; Bharadwaj, Pallavi ; Stroe, Daniel-Ioan ; Zheng, Yusheng ; Che, Yunhong ; Wu, Yue.
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