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Stacked bidirectional long short-term memory networks for state-of-charge estimation of lithium-ion batteries

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Cited by:

  1. Biao Yang & Yinshuang Wang & Yuedong Zhan, 2022. "Lithium Battery State-of-Charge Estimation Based on a Bayesian Optimization Bidirectional Long Short-Term Memory Neural Network," Energies, MDPI, vol. 15(13), pages 1-18, June.
  2. Huang, Haichi & Bian, Chong & Wu, Mengdan & An, Dong & Yang, Shunkun, 2024. "A novel integrated SOC–SOH estimation framework for whole-life-cycle lithium-ion batteries," Energy, Elsevier, vol. 288(C).
  3. Ma, Qiuhui & Zheng, Ying & Yang, Weidong & Zhang, Yong & Zhang, Hong, 2021. "Remaining useful life prediction of lithium battery based on capacity regeneration point detection," Energy, Elsevier, vol. 234(C).
  4. Oyewole, Isaiah & Chehade, Abdallah & Kim, Youngki, 2022. "A controllable deep transfer learning network with multiple domain adaptation for battery state-of-charge estimation," Applied Energy, Elsevier, vol. 312(C).
  5. Kuang, Pan & Zhou, Fei & Xu, Shuai & Li, Kangqun & Xu, Xiaobin, 2024. "State-of-charge estimation hybrid method for lithium-ion batteries using BiGRU and AM co-modified Seq2Seq network and H-infinity filter," Energy, Elsevier, vol. 300(C).
  6. Zhao, Hongqian & Chen, Zheng & Shu, Xing & Shen, Jiangwei & Liu, Yonggang & Zhang, Yuanjian, 2023. "Multi-step ahead voltage prediction and voltage fault diagnosis based on gated recurrent unit neural network and incremental training," Energy, Elsevier, vol. 266(C).
  7. Feng, Xiong & Chen, Junxiong & Zhang, Zhongwei & Miao, Shuwen & Zhu, Qiao, 2021. "State-of-charge estimation of lithium-ion battery based on clockwork recurrent neural network," Energy, Elsevier, vol. 236(C).
  8. Wenxian Duan & Chuanxue Song & Silun Peng & Feng Xiao & Yulong Shao & Shixin Song, 2020. "An Improved Gated Recurrent Unit Network Model for State-of-Charge Estimation of Lithium-Ion Battery," Energies, MDPI, vol. 13(23), pages 1-19, December.
  9. Saleh Mohammed Shahriar & Erphan A. Bhuiyan & Md. Nahiduzzaman & Mominul Ahsan & Julfikar Haider, 2022. "State of Charge Estimation for Electric Vehicle Battery Management Systems Using the Hybrid Recurrent Learning Approach with Explainable Artificial Intelligence," Energies, MDPI, vol. 15(21), pages 1-26, October.
  10. Yang, Kuo & Tang, Yugui & Zhang, Shujing & Zhang, Zhen, 2022. "A deep learning approach to state of charge estimation of lithium-ion batteries based on dual-stage attention mechanism," Energy, Elsevier, vol. 244(PB).
  11. Sun, Daoming & Yu, Xiaoli & Wang, Chongming & Zhang, Cheng & Huang, Rui & Zhou, Quan & Amietszajew, Taz & Bhagat, Rohit, 2021. "State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator," Energy, Elsevier, vol. 214(C).
  12. Wu, Lifeng & Zhang, Yu, 2023. "Attention-based encoder-decoder networks for state of charge estimation of lithium-ion battery," Energy, Elsevier, vol. 268(C).
  13. Ni, Zichuan & Xiu, Xianchao & Yang, Ying, 2022. "Towards efficient state of charge estimation of lithium-ion batteries using canonical correlation analysis," Energy, Elsevier, vol. 254(PC).
  14. Chen, Junxiong & Feng, Xiong & Jiang, Lin & Zhu, Qiao, 2021. "State of charge estimation of lithium-ion battery using denoising autoencoder and gated recurrent unit recurrent neural network," Energy, Elsevier, vol. 227(C).
  15. 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).
  16. Siyi Tao & Bo Jiang & Xuezhe Wei & Haifeng Dai, 2023. "A Systematic and Comparative Study of Distinct Recurrent Neural Networks for Lithium-Ion Battery State-of-Charge Estimation in Electric Vehicles," Energies, MDPI, vol. 16(4), pages 1-17, February.
  17. Ren, Xiaoqing & Liu, Shulin & Yu, Xiaodong & Dong, Xia, 2021. "A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM," Energy, Elsevier, vol. 234(C).
  18. Zhang, Ying & Li, Yan-Fu, 2022. "Prognostics and health management of Lithium-ion battery using deep learning methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
  19. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Li, Huan & Xu, Wenhua & Fernandez, Carlos, 2022. "An optimized relevant long short-term memory-squared gain extended Kalman filter for the state of charge estimation of lithium-ion batteries," Energy, Elsevier, vol. 260(C).
  20. Donghun Wang & Jihwan Hwang & Jonghyun Lee & Minchan Kim & Insoo Lee, 2023. "Temperature-Based State-of-Charge Estimation Using Neural Networks, Gradient Boosting Machine and a Jetson Nano Device for Batteries," Energies, MDPI, vol. 16(6), pages 1-17, March.
  21. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiaoyong & Fernandez, Carlos, 2022. "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," Applied Energy, Elsevier, vol. 326(C).
  22. Wang, Ya-Xiong & Chen, Zhenhang & Zhang, Wei, 2022. "Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning," Energy, Elsevier, vol. 244(PB).
  23. Panagiotis Eleftheriadis & Spyridon Giazitzis & Sonia Leva & Emanuele Ogliari, 2023. "Data-Driven Methods for the State of Charge Estimation of Lithium-Ion Batteries: An Overview," Forecasting, MDPI, vol. 5(3), pages 1-24, September.
  24. Wang, Chao & Zhang, Xin & Yun, Xiang & Meng, Xiangfei & Fan, Xingming, 2023. "Robust state-of-charge estimation method for lithium-ion batteries based on the fusion of time series relevance vector machine and filter algorithm," Energy, Elsevier, vol. 285(C).
  25. Li, Hao & Fu, Lijun & Long, Xinlin & Liu, Lang & Zeng, Ziqing, 2024. "A hybrid deep learning model for lithium-ion batteries state of charge estimation based on quantile regression and attention," Energy, Elsevier, vol. 294(C).
  26. E, Jiaqiang & Zhang, Bin & Zeng, Yan & Wen, Ming & Wei, Kexiang & Huang, Zhonghua & Chen, Jingwei & Zhu, Hao & Deng, Yuanwang, 2022. "Effects analysis on active equalization control of lithium-ion batteries based on intelligent estimation of the state-of-charge," Energy, Elsevier, vol. 238(PB).
  27. Li, Chuan & Zhang, Huahua & Ding, Ping & Yang, Shuai & Bai, Yun, 2023. "Deep feature extraction in lifetime prognostics of lithium-ion batteries: Advances, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
  28. Zhang, Qiang & Wan, Guangwei & Li, Chaoran & Li, Jianke & Liu, Xiaori & Li, Menghan, 2024. "State of charge estimation for Li-ion battery during dynamic driving process based on dual-channel deep learning methods and conditional judgement," Energy, Elsevier, vol. 294(C).
  29. Zuo, Hongyan & Zhang, Bin & Huang, Zhonghua & Wei, Kexiang & Zhu, Hong & Tan, Jiqiu, 2022. "Effect analysis on SOC values of the power lithium manganate battery during discharging process and its intelligent estimation," Energy, Elsevier, vol. 238(PB).
  30. Dapai Shi & Jingyuan Zhao & Chika Eze & Zhenghong Wang & Junbin Wang & Yubo Lian & Andrew F. Burke, 2023. "Cloud-Based Artificial Intelligence Framework for Battery Management System," Energies, MDPI, vol. 16(11), pages 1-21, May.
  31. 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).
  32. Chen, Zheng & Zhao, Hongqian & Shu, Xing & Zhang, Yuanjian & Shen, Jiangwei & Liu, Yonggang, 2021. "Synthetic state of charge estimation for lithium-ion batteries based on long short-term memory network modeling and adaptive H-Infinity filter," Energy, Elsevier, vol. 228(C).
  33. Zhang, Yu & Peng, Zhen & Guan, Yong & Wu, Lifeng, 2021. "Prognostics of battery cycle life in the early-cycle stage based on hybrid model," Energy, Elsevier, vol. 221(C).
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