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2. Remaining useful life prediction of lithium-ion batteries based on autoregression with exogenous variables model. (2024). Ma, Zhihua ; Huang, Zhelin.
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3. Health prognostics of lithium-ion batteries based on universal voltage range features mining and adaptive multi-Gaussian process regression with Harris Hawks optimization algorithm. (2024). Guo, Yongfang ; Wang, Yashuang ; Yu, Xiangyuan ; Huang, Kai.
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4. Precise modeling of lithium-ion battery in industrial applications using Walrus optimization algorithm. (2024). Fahmy, Hend M ; Hasanien, Hany M ; Alqahtani, Ayedh H.
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5. A feature fusion-based convolutional neural network for battery state-of-health estimation with mining of partial voltage curve. (2024). Fei, Zicheng ; Lu, Zhenfeng ; Yang, Fangfang ; Wang, Benfei.
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6. Driving behavior-guided battery health monitoring for electric vehicles using extreme learning machine. (2024). Ren, Yao ; Jiang, Weiran ; Lin, Jing ; Zhang, Jiawei ; Khoo, Edwin ; Song, Ziyou.
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7. A Review of SOH Prediction of Li-Ion Batteries Based on Data-Driven Algorithms. (2023). Zhang, Ming ; Li, Liwei ; Wang, Licheng ; Du, Jiaxuan ; Sun, Hanlei ; Yang, Dongfang.
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8. Lithium-Ion Battery Life Prediction Method under Thermal Gradient Conditions. (2023). Zhang, Weijian ; Wang, Shiqian ; Di, LI ; Song, Dawei.
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9. 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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10. Long short-term memory network with Bayesian optimization for health prognostics of lithium-ion batteries based on partial incremental capacity analysis. (2023). Han, Te ; Geng, Mengyao ; Meng, Huixing.
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11. State of health estimation of lithium-ion batteries based on fine-tuning or rebuilding transfer learning strategies combined with new features mining. (2023). Lv, Ziteng ; Guo, Yongfang ; Yao, Kaixin ; Huang, Kai.
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12. Rapid online health estimation for lithium-ion batteries based on partial constant-voltage charging segment. (2023). Deng, Zhongwei ; Bian, Xiaolei ; Guo, Wenchao ; Yang, Lin ; Li, Jilin.
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13. Hybrid deep neural network with dimension attention for state-of-health estimation of Lithium-ion Batteries. (2023). Bao, Xinyuan ; Li, Xin ; Xie, Siqiang ; Chen, Liping ; Lopes, Antonio M.
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14. State of health estimation for lithium-ion batteries on few-shot learning. (2023). Liu, Zhitao ; Zhang, Shuxin ; Su, Hongye.
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15. A Bayesian method for capacity degradation prediction of lithium-ion battery considering both within and cross group heterogeneity. (2023). Hu, Zhiyong ; Xie, Yuguang ; Mao, Lei ; Li, Jinzhong ; Zhang, Jiarui ; Wang, Chao.
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16. Machine learning based swift online capacity prediction of lithium-ion battery through whole cycle life. (2022). Xue, Qiao ; Li, Junqiu.
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17. State of health estimation of power batteries based on multi-feature fusion models using stacking algorithm. (2022). Liu, Zhiming ; Zhang, Xiangwen.
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18. A fast state-of-health estimation method using single linear feature for lithium-ion batteries. (2022). Mei, Xuesong ; Shi, Mingjie ; Xu, Jun ; Lin, Chuanping.
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19. Constant current charging time based fast state-of-health estimation for lithium-ion batteries. (2022). Mei, Xuesong ; Shi, Mingjie ; Xu, Jun ; Lin, Chuanping.
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20. A hybrid method with cascaded structure for early-stage remaining useful life prediction of lithium-ion battery. (2022). Liu, Yongjie ; Yan, Lisen ; Peng, Jun ; Gao, Dianzhu ; Huang, Zhiwu ; Wu, Yue.
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21. State of charge estimation of lithium-ion batteries based on cubature Kalman filters with different matrix decomposition strategies. (2022). Huang, Zhijia ; Tian, Yong ; Li, Xiaoyu.
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22. Compensation Method for Estimating the State of Charge of Li-Polymer Batteries Using Multiple Long Short-Term Memory Networks Based on the Extended Kalman Filter. (2021). Yoo, Seungryeol ; Shin, Donghoon ; Yoon, Beomjin.
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23. Capacity estimation of batteries: Influence of training dataset size and diversity on data driven prognostic models. (2021). Choi, Yun Seok ; Nagulapati, Vijay Mohan ; Lee, Hyunjun ; Lim, Hankwon ; Jung, Dawoon ; Brigljevic, Boris.
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24. Remaining useful life prediction of lithium battery based on capacity regeneration point detection. (2021). Zhang, Yong ; Zheng, Ying ; Yang, Weidong ; Ma, Qiuhui.
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25. Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network. (2021). He, Yurong ; Wang, Xinzhi ; Cheng, Gong.
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26. Research on state of health prediction model for lithium batteries based on actual diverse data. (2021). Wang, Tiancheng ; Chen, Shaohui ; Fu, Ping ; Zhu, Hongyu ; Zhou, DI ; Song, Bai ; Qu, Xisong ; Zheng, Wenbin.
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27. A method to estimate battery SOH indicators based on vehicle operating data only. (2021). Pelissier, S ; Venet, P ; Vichard, L ; Harel, F ; Hissel, D ; Ravey, A.
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28. Estimation and prediction of state of health of electric vehicle batteries using discrete incremental capacity analysis based on real driving data. (2021). Xu, Zhicheng ; Zhang, Yaoming ; Lund, Peter D ; Wang, Jun.
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29. Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries. (2021). Peng, Zhike ; Zhang, XI ; Kong, Jin-Zhen ; Yang, Fangfang ; Pan, Ershun ; Wang, Dong.
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30. A novel data-driven method for predicting the circulating capacity of lithium-ion battery under random variable current. (2021). Peng, Zhen ; Xu, Tingting ; Wu, Lifeng.
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31. Capacity estimation of lithium-ion cells by combining model-based and data-driven methods based on a sequential extended Kalman filter. (2021). Han, Xuebing ; Lai, Xin ; Yi, Wei ; Sun, Tao ; Zhou, Long ; Qin, Chao ; Zheng, Yuejiu ; Cui, Yifan.
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32. Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation. (2021). Ding, Yunhui ; Wang, Yijue ; Chen, Lin ; Pan, Haihong ; Liu, Bohao.
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33. A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery. (2021). Sui, Xin ; Vilsen, Soren B ; He, Shan ; Teodorescu, Remus ; Meng, Jinhao ; Stroe, Daniel-Ioan.
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34. Ensemble Gradient Boosted Tree for SoH Estimation Based on Diagnostic Features. (2020). Firouz, Yousef ; Khaleghi, Sahar ; van den Bossche, Peter ; Berecibar, Maitane ; van Mierlo, Joeri.
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36. An adaptive multi-state estimation algorithm for lithium-ion batteries incorporating temperature compensation. (2020). Lei, Zhenzhen ; Chen, Zheng ; Liu, Yonggang ; Shu, Xing ; Shen, Jiangwei.
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37. A uniform estimation framework for state of health of lithium-ion batteries considering feature extraction and parameters optimization. (2020). Lei, Zhenzhen ; Chen, Zheng ; Liu, Yonggang ; Shu, Xing ; Shen, Jiangwei.
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38. State of health estimation for Li-ion battery via partial incremental capacity analysis based on support vector regression. (2020). Wang, Zhenpo ; Li, Xiaoyu ; Yuan, Changgui.
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39. Battery incremental capacity curve extraction by a two-dimensional Luenberger–Gaussian-moving-average filter. (2020). Wang, Xin ; Tang, Xiaopeng ; Liu, Kailong ; Lu, Jingyi ; Gao, Furong.
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40. State-of-health estimation of lithium-ion batteries based on semi-supervised transfer component analysis. (2020). Cheng, Yuhua ; Li, Yuanyuan ; Teodorescu, Remus ; Sheng, Hanmin ; Stroe, Daniel-Ioan.
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41. SOC and SOH Joint Estimation of the Power Batteries Based on Fuzzy Unscented Kalman Filtering Algorithm. (2019). Shi, Ying ; Yang, Yang ; Zhang, Peng ; Zeng, Miaomiao ; Xie, Changjun.
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