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State-of-charge estimation of LiFePO4 batteries in electric vehicles: A deep-learning enabled approach

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Cited by:

  1. Cui, Binghan & Wang, Han & Li, Renlong & Xiang, Lizhi & Zhao, Huaian & Xiao, Rang & Li, Sai & Liu, Zheng & Yin, Geping & Cheng, Xinqun & Ma, Yulin & Huo, Hua & Zuo, Pengjian & Lu, Taolin & Xie, Jingyi, 2024. "Ultra-early prediction of lithium-ion battery performance using mechanism and data-driven fusion model," Applied Energy, Elsevier, vol. 353(PA).
  2. Yaoyidi Wang & Niansheng Chen & Guangyu Fan & Dingyu Yang & Lei Rao & Songlin Cheng & Xiaoyong Song, 2023. "DLPformer: A Hybrid Mathematical Model for State of Charge Prediction in Electric Vehicles Using Machine Learning Approaches," Mathematics, MDPI, vol. 11(22), pages 1-21, November.
  3. Guo, Ruohan & Shen, Weixiang, 2022. "A data-model fusion method for online state of power estimation of lithium-ion batteries at high discharge rate in electric vehicles," Energy, Elsevier, vol. 254(PA).
  4. Tian, Yong & Huang, Zhijia & Tian, Jindong & Li, Xiaoyu, 2022. "State of charge estimation of lithium-ion batteries based on cubature Kalman filters with different matrix decomposition strategies," Energy, Elsevier, vol. 238(PC).
  5. Jichao Hong & Fengwei Liang & Xun Gong & Xiaoming Xu & Quanqing Yu, 2022. "Accurate State of Charge Estimation for Real-World Battery Systems Using a Novel Grid Search and Cross Validated Optimised LSTM Neural Network," Energies, MDPI, vol. 15(24), pages 1-14, December.
  6. Bai, Mingliang & Yang, Xusheng & Liu, Jinfu & Liu, Jiao & Yu, Daren, 2021. "Convolutional neural network-based deep transfer learning for fault detection of gas turbine combustion chambers," Applied Energy, Elsevier, vol. 302(C).
  7. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiao & Fernandez, Carlos, 2023. "A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures," Energy, Elsevier, vol. 273(C).
  8. Yan Cheng & Xuesen Zhang & Xiaoqiang Wang & Jianhua Li, 2022. "Battery State of Charge Estimation Based on Composite Multiscale Wavelet Transform," Energies, MDPI, vol. 15(6), pages 1-16, March.
  9. Jiang, Bo & Tao, Siyi & Wang, Xueyuan & Zhu, Jiangong & Wei, Xuezhe & Dai, Haifeng, 2023. "Mechanics-based state of charge estimation for lithium-ion pouch battery using deep learning technique," Energy, Elsevier, vol. 278(PA).
  10. Kurucan, Mehmet & Özbaltan, Mete & Yetgin, Zeki & Alkaya, Alkan, 2024. "Applications of artificial neural network based battery management systems: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
  11. Wang, Qiao & Ye, Min & Wei, Meng & Lian, Gaoqi & Li, Yan, 2023. "Deep convolutional neural network based closed-loop SOC estimation for lithium-ion batteries in hierarchical scenarios," Energy, Elsevier, vol. 263(PB).
  12. Tian, Yu & Lin, Cheng & Li, Hailong & Du, Jiuyu & Xiong, Rui, 2021. "Detecting undesired lithium plating on anodes for lithium-ion batteries – A review on the in-situ methods," Applied Energy, Elsevier, vol. 300(C).
  13. Ouyang, Quan & Fang, Ruyi & Xu, Guotuan & Liu, Yonggang, 2022. "User-involved charging control for lithium-ion batteries with economic cost optimization," Applied Energy, Elsevier, vol. 314(C).
  14. Hu, Chunsheng & Ma, Liang & Guo, Shanshan & Guo, Gangsheng & Han, Zhiqiang, 2022. "Deep learning enabled state-of-charge estimation of LiFePO4 batteries: A systematic validation on state-of-the-art charging protocols," Energy, Elsevier, vol. 246(C).
  15. Lai, Rucong & Wang, Jie & Tian, Yong & Tian, Jindong, 2024. "FedCBE: A federated-learning-based collaborative battery estimation system with non-IID data," Applied Energy, Elsevier, vol. 368(C).
  16. Hsu, Chia-Wei & Xiong, Rui & Chen, Nan-Yow & Li, Ju & Tsou, Nien-Ti, 2022. "Deep neural network battery life and voltage prediction by using data of one cycle only," Applied Energy, Elsevier, vol. 306(PB).
  17. Gao, Yizhao & Sun, Ziqiang & Zhang, Dong & Shi, Dapai & Zhang, Xi, 2023. "Determination of half-cell open-circuit potential curve of silicon-graphite in a physics-based model for lithium-ion batteries," Applied Energy, Elsevier, vol. 349(C).
  18. Chun Wang & Chaocheng Fang & Aihua Tang & Bo Huang & Zhigang Zhang, 2022. "A Novel Ultracapacitor State-of-Charge Fusion Estimation Method for Electric Vehicles Considering Temperature Uncertainty," Energies, MDPI, vol. 15(12), pages 1-16, June.
  19. Jingwei Hu & Bing Lin & Mingfen Wang & Jie Zhang & Wenliang Zhang & Yu Lu, 2022. "State of Charge Centralized Estimation of Road Condition Information Based on Fuzzy Sunday Algorithm," Energies, MDPI, vol. 15(8), pages 1-15, April.
  20. Zafar, Muhammad Hamza & Mansoor, Majad & Abou Houran, Mohamad & Khan, Noman Mujeeb & Khan, Kamran & Raza Moosavi, Syed Kumayl & Sanfilippo, Filippo, 2023. "Hybrid deep learning model for efficient state of charge estimation of Li-ion batteries in electric vehicles," Energy, Elsevier, vol. 282(C).
  21. Chen, Junxiong & Zhang, Yu & Wu, Ji & Cheng, Weisong & Zhu, Qiao, 2023. "SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output," Energy, Elsevier, vol. 262(PA).
  22. Wu, Lifeng & Zhang, Yu, 2023. "Attention-based encoder-decoder networks for state of charge estimation of lithium-ion battery," Energy, Elsevier, vol. 268(C).
  23. Wang, Chenxu & Xiong, Rui & Tian, Jinpeng & Lu, Jiahuan & Zhang, Chengming, 2022. "Rapid ultracapacitor life prediction with a convolutional neural network," Applied Energy, Elsevier, vol. 305(C).
  24. Takyi-Aninakwa, Paul & Wang, Shunli & Liu, Guangchen & Bage, Alhamdu Nuhu & Bobobee, Etse Dablu & Appiah, Emmanuel & Huang, Qi, 2024. "Enhanced extended-input LSTM with an adaptive singular value decomposition UKF for LIB SOC estimation using full-cycle current rate and temperature data," Applied Energy, Elsevier, vol. 363(C).
  25. Guo, Ruohan & Shen, Weixiang, 2022. "Online state of charge and state of power co-estimation of lithium-ion batteries based on fractional-order calculus and model predictive control theory," Applied Energy, Elsevier, vol. 327(C).
  26. Tang, Aihua & Huang, Yukun & Liu, Shangmei & Yu, Quanqing & Shen, Weixiang & Xiong, Rui, 2023. "A novel lithium-ion battery state of charge estimation method based on the fusion of neural network and equivalent circuit models," Applied Energy, Elsevier, vol. 348(C).
  27. Ruan, Haokai & Wei, Zhongbao & Shang, Wentao & Wang, Xuechao & He, Hongwen, 2023. "Artificial Intelligence-based health diagnostic of Lithium-ion battery leveraging transient stage of constant current and constant voltage charging," Applied Energy, Elsevier, vol. 336(C).
  28. Liu, Yunpeng & Hou, Bo & Ahmed, Moin & Mao, Zhiyu & Feng, Jiangtao & Chen, Zhongwei, 2024. "A hybrid deep learning approach for remaining useful life prediction of lithium-ion batteries based on discharging fragments," Applied Energy, Elsevier, vol. 358(C).
  29. He, Lin & Hu, Xingwen & Yin, Guangwei & Wang, Guoqiang & Shao, Xingguo & Liu, Jichao, 2024. "A current dynamics model and proportional–integral observer for state-of-charge estimation of lithium-ion battery," Energy, Elsevier, vol. 288(C).
  30. Valentina Lucaferri & Michele Quercio & Antonino Laudani & Francesco Riganti Fulginei, 2023. "A Review on Battery Model-Based and Data-Driven Methods for Battery Management Systems," Energies, MDPI, vol. 16(23), pages 1-19, November.
  31. Shanshan Guo & Zhiqiang Han & Jun Wei & Shenggang Guo & Liang Ma, 2022. "A Novel DC-AC Fast Charging Technology for Lithium-Ion Power Battery at Low-Temperatures," Sustainability, MDPI, vol. 14(11), pages 1-10, May.
  32. Xiong, Rui & Duan, Yanzhou & Zhang, Kaixuan & Lin, Da & Tian, Jinpeng & Chen, Cheng, 2023. "State-of-charge estimation for onboard LiFePO4 batteries with adaptive state update in specific open-circuit-voltage ranges," Applied Energy, Elsevier, vol. 349(C).
  33. Zhang, Cetengfei & Zhou, Quan & Hua, Min & Xu, Hongming & Bassett, Mike & Zhang, Fanggang, 2023. "Cuboid equivalent consumption minimization strategy for energy management of multi-mode plug-in hybrid vehicles considering diverse time scale objectives," Applied Energy, Elsevier, vol. 351(C).
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