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1. SOC prediction for electric buses based on interpretable transformer model: Impact of traffic conditions and feature importance. (2025). Tang, Jinjun ; Hu, Lipeng ; Liang, Xiao ; Xu, Fuqiao.
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2. Generating comprehensive lithium battery charging data with generative AI. (2025). Jiang, Lidang ; Hu, Changyan ; Zhao, Hang ; Chen, Junxiong.
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3. An embedding layer-based quantum long short-term memory model with transfer learning for proton exchange membrane fuel stack remaining useful life prediction. (2024). Woldegiorgis, Bereket Haile ; Lo, Shih-Che ; Kebede, Getnet Awoke ; Wang, Fu-Kwun.
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4. A novel battery SOC estimation method based on random search optimized LSTM neural network. (2024). Chai, Xuqing ; Li, Shihao ; Liang, Fengwei.
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5. Exploring large language model for generic and robust state-of-charge estimation of Li-ion batteries: A mixed prompt learning method. (2024). Duan, Zhiyu ; Hao, Yaqian ; Yang, Shunkun ; Feng, Junlan ; Bian, Chong.
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6. State-of-charge estimation hybrid method for lithium-ion batteries using BiGRU and AM co-modified Seq2Seq network and H-infinity filter. (2024). Xu, Xiaobin ; Kuang, Pan ; Li, Kangqun ; Zhou, Fei.
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7. Battery state of health estimation across electrochemistry and working conditions based on domain adaptation. (2024). Deng, Zhongwei ; Zhang, Xiaohong ; Bao, Huanhuan ; Cheng, Duanqian ; Liu, Chenghao.
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8. State of charge estimation for Li-ion battery during dynamic driving process based on dual-channel deep learning methods and conditional judgement. (2024). Liu, Xiaori ; Zhang, Qiang ; Wan, Guangwei.
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9. A hybrid deep learning model for lithium-ion batteries state of charge estimation based on quantile regression and attention. (2024). Zeng, Ziqing ; Long, Xinlin ; Fu, Lijun ; Liu, Lang.
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10. A novel integrated SOC–SOH estimation framework for whole-life-cycle lithium-ion batteries. (2024). Yang, Shunkun ; Wu, Mengdan ; Huang, Haichi ; Bian, Chong.
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11. FedCBE: A federated-learning-based collaborative battery estimation system with non-IID data. (2024). Tian, Yong ; Lai, Rucong ; Wang, Jie.
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12. Data-Driven Methods for the State of Charge Estimation of Lithium-Ion Batteries: An Overview. (2023). Giazitzis, Spyridon ; Leva, Sonia ; Eleftheriadis, Panagiotis ; Ogliari, Emanuele.
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13. Temperature-Based State-of-Charge Estimation Using Neural Networks, Gradient Boosting Machine and a Jetson Nano Device for Batteries. (2023). Kim, Min Chan ; Lee, Jonghyun ; Hwang, Jihwan.
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14. A Systematic and Comparative Study of Distinct Recurrent Neural Networks for Lithium-Ion Battery State-of-Charge Estimation in Electric Vehicles. (2023). Wei, Xuezhe ; Tao, Siyi ; Dai, Haifeng ; Jiang, BO.
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15. Cloud-Based Artificial Intelligence Framework for Battery Management System. (2023). Wang, Zhenghong ; Lian, Yubo ; Zhao, Jingyuan ; Shi, Dapai ; Eze, Chika ; Burke, Andrew F.
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16. Bioinspired spiking spatiotemporal attention framework for lithium-ion batteries state-of-health estimation. (2023). Zhang, Ying ; Li, Yan-Fu ; Wang, Huan.
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17. 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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18. Robust state-of-charge estimation method for lithium-ion batteries based on the fusion of time series relevance vector machine and filter algorithm. (2023). Zhang, Xin ; Fan, Xingming ; Yun, Xiang ; Meng, Xiangfei ; Wang, Chao.
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19. Attention-based encoder-decoder networks for state of charge estimation of lithium-ion battery. (2023). Zhang, YU ; Wu, Lifeng.
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20. Multi-step ahead voltage prediction and voltage fault diagnosis based on gated recurrent unit neural network and incremental training. (2023). Chen, Zheng ; Zhang, Yuanjian ; Liu, Yonggang ; Shu, Xing ; Shen, Jiangwei ; Zhao, Hongqian.
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21. State of Charge Estimation for Electric Vehicle Battery Management Systems Using the Hybrid Recurrent Learning Approach with Explainable Artificial Intelligence. (2022). Shahriar, Saleh Mohammed ; Ahsan, Mominul ; Nahiduzzaman, MD ; Haider, Julfikar ; Bhuiyan, Erphan A.
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22. Lithium Battery State-of-Charge Estimation Based on a Bayesian Optimization Bidirectional Long Short-Term Memory Neural Network. (2022). Zhan, Yuedong ; Wang, Yinshuang ; Yang, Biao.
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23. Prognostics and health management of Lithium-ion battery using deep learning methods: A review. (2022). Zhang, Ying ; Li, Yan-Fu.
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24. An optimized relevant long short-term memory-squared gain extended Kalman filter for the state of charge estimation of lithium-ion batteries. (2022). Xu, Wenhua ; Wang, Shunli ; Takyi-Aninakwa, Paul ; Li, Huan ; Zhang, Hongying ; Fernandez, Carlos.
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25. Towards efficient state of charge estimation of lithium-ion batteries using canonical correlation analysis. (2022). Ni, Zichuan ; Yang, Ying ; Xiu, Xianchao.
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26. A deep learning approach to state of charge estimation of lithium-ion batteries based on dual-stage attention mechanism. (2022). Zhang, Zhen ; Tang, Yugui ; Yang, Kuo.
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27. Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning. (2022). Chen, Zhenhang ; Wang, Ya-Xiong ; Zhang, Wei.
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28. Effect analysis on SOC values of the power lithium manganate battery during discharging process and its intelligent estimation. (2022). Huang, Zhonghua ; Wei, Kexiang ; Tan, Jiqiu ; Zhu, Hong ; Zuo, Hongyan ; Zhang, Bin.
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29. Effects analysis on active equalization control of lithium-ion batteries based on intelligent estimation of the state-of-charge. (2022). Wen, Ming ; Wei, Kexiang ; Chen, Jingwei ; Zhu, Hao ; Zhang, Bin ; Huang, Zhonghua ; Deng, Yuanwang ; Zeng, Yan.
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30. An optimized long short-term memory-weighted fading extended Kalman filtering model with wide temperature adaptation for the state of charge estimation of lithium-ion batteries. (2022). Yang, Xiaoyong ; Wang, Shunli ; Takyi-Aninakwa, Paul ; Zhang, Hongying ; Fernandez, Carlos.
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31. A controllable deep transfer learning network with multiple domain adaptation for battery state-of-charge estimation. (2022). Kim, Youngki ; Oyewole, Isaiah ; Chehade, Abdallah.
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32. State-of-charge estimation of lithium-ion battery based on clockwork recurrent neural network. (2021). Zhang, Zhongwei ; Feng, Xiong ; Miao, Shuwen ; Zhu, Qiao ; Chen, Junxiong.
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33. A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM. (2021). Ren, Xiaoqing ; Yu, Xiaodong ; Dong, Xia ; Liu, Shulin.
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34. 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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35. Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter. (2021). Chen, Zheng ; Zhang, Yuanjian ; Liu, Yonggang ; Shu, Xing ; Shen, Jiangwei ; Zhao, Hongqian.
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36. State of charge estimation of lithium-ion battery using denoising autoencoder and gated recurrent unit recurrent neural network. (2021). Feng, Xiong ; Zhu, Qiao ; Jiang, Lin ; Chen, Junxiong.
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37. Prognostics of battery cycle life in the early-cycle stage based on hybrid model. (2021). Peng, Zhen ; Zhang, YU ; Guan, Yong ; Wu, Lifeng.
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38. State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator. (2021). Huang, Rui ; Bhagat, Rohit ; Zhou, Quan ; Sun, Daoming ; Wang, Chongming ; Zhang, Cheng ; Yu, Xiaoli ; Amietszajew, Taz.
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39. An Improved Gated Recurrent Unit Network Model for State-of-Charge Estimation of Lithium-Ion Battery. (2020). Shao, Yulong ; Duan, Wenxian ; Peng, Silun ; Song, Shixin ; Xiao, Feng.
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