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Lithium-ion battery capacity estimation — A pruned convolutional neural network approach assisted with transfer learning

Citations

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  1. Li, Yihuan & Li, Kang & Liu, Xuan & Li, Xiang & Zhang, Li & Rente, Bruno & Sun, Tong & Grattan, Kenneth T.V., 2022. "A hybrid machine learning framework for joint SOC and SOH estimation of lithium-ion batteries assisted with fiber sensor measurements," Applied Energy, Elsevier, vol. 325(C).
  2. Zhang, Meng & Hu, Tao & Wu, Lifeng & Kang, Guoqing & Guan, Yong, 2021. "A method for capacity estimation of lithium-ion batteries based on adaptive time-shifting broad learning system," Energy, Elsevier, vol. 231(C).
  3. Diego Castanho & Marcio Guerreiro & Ludmila Silva & Jony Eckert & Thiago Antonini Alves & Yara de Souza Tadano & Sergio Luiz Stevan & Hugo Valadares Siqueira & Fernanda Cristina Corrêa, 2022. "Method for SoC Estimation in Lithium-Ion Batteries Based on Multiple Linear Regression and Particle Swarm Optimization," Energies, MDPI, vol. 15(19), pages 1-21, September.
  4. Chen, Junxiong & Hu, Yuanjiang & Zhu, Qiao & Rashid, Haroon & Li, Hongkun, 2023. "A novel battery health indicator and PSO-LSSVR for LiFePO4 battery SOH estimation during constant current charging," Energy, Elsevier, vol. 282(C).
  5. Yao, Jiachi & Han, Te, 2023. "Data-driven lithium-ion batteries capacity estimation based on deep transfer learning using partial segment of charging/discharging data," Energy, Elsevier, vol. 271(C).
  6. Wang, Fu-Kwun & Amogne, Zemenu Endalamaw & Chou, Jia-Hong & Tseng, Cheng, 2022. "Online remaining useful life prediction of lithium-ion batteries using bidirectional long short-term memory with attention mechanism," Energy, Elsevier, vol. 254(PB).
  7. Cao, Mengda & Zhang, Tao & Liu, Yajie & Zhang, Yajun & Wang, Yu & Li, Kaiwen, 2022. "An ensemble learning prognostic method for capacity estimation of lithium-ion batteries based on the V-IOWGA operator," Energy, Elsevier, vol. 257(C).
  8. Fan, Guodong & Zhang, Xi, 2023. "Battery capacity estimation using 10-second relaxation voltage and a convolutional neural network," Applied Energy, Elsevier, vol. 330(PA).
  9. Braco, Elisa & San Martín, Idoia & Sanchis, Pablo & Ursúa, Alfredo, 2023. "Fast capacity and internal resistance estimation method for second-life batteries from electric vehicles," Applied Energy, Elsevier, vol. 329(C).
  10. Zhang, Yijie & Ma, Tao & Yang, Hongxing, 2022. "Grid-connected photovoltaic battery systems: A comprehensive review and perspectives," Applied Energy, Elsevier, vol. 328(C).
  11. Jiang, Bo & Zhu, Jiangong & Wang, Xueyuan & Wei, Xuezhe & Shang, Wenlong & Dai, Haifeng, 2022. "A comparative study of different features extracted from electrochemical impedance spectroscopy in state of health estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 322(C).
  12. Emil Petkovski & Iacopo Marri & Loredana Cristaldi & Marco Faifer, 2023. "State of Health Estimation Procedure for Lithium-Ion Batteries Using Partial Discharge Data and Support Vector Regression," Energies, MDPI, vol. 17(1), pages 1-14, December.
  13. 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).
  14. Fu, Shiyi & Tao, Shengyu & Fan, Hongtao & He, Kun & Liu, Xutao & Tao, Yulin & Zuo, Junxiong & Zhang, Xuan & Wang, Yu & Sun, Yaojie, 2024. "Data-driven capacity estimation for lithium-ion batteries with feature matching based transfer learning method," Applied Energy, Elsevier, vol. 353(PA).
  15. Molla Shahadat Hossain Lipu & Tahia F. Karim & Shaheer Ansari & Md. Sazal Miah & Md. Siddikur Rahman & Sheikh T. Meraj & Rajvikram Madurai Elavarasan & Raghavendra Rajan Vijayaraghavan, 2022. "Intelligent SOX Estimation for Automotive Battery Management Systems: State-of-the-Art Deep Learning Approaches, Open Issues, and Future Research Opportunities," Energies, MDPI, vol. 16(1), pages 1-31, December.
  16. Huang, Yaodi & Zhang, Pengcheng & Lu, Jiahuan & Xiong, Rui & Cai, Zhongmin, 2024. "A transferable long-term lithium-ion battery aging trajectory prediction model considering internal resistance and capacity regeneration phenomenon," Applied Energy, Elsevier, vol. 360(C).
  17. Mehta, Rohit & Gupta, Amit, 2024. "Mathematical modelling of electrochemical, thermal and degradation processes in lithium-ion cells—A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
  18. Zhu, Yunlong & Dong, Zhe & Cheng, Zhonghua & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Neural network extended state-observer for energy system monitoring," Energy, Elsevier, vol. 263(PA).
  19. Xue, Qiao & Li, Junqiu & Xu, Peipei, 2022. "Machine learning based swift online capacity prediction of lithium-ion battery through whole cycle life," Energy, Elsevier, vol. 261(PA).
  20. Shunli Wang & Pu Ren & Paul Takyi-Aninakwa & Siyu Jin & Carlos Fernandez, 2022. "A Critical Review of Improved Deep Convolutional Neural Network for Multi-Timescale State Prediction of Lithium-Ion Batteries," Energies, MDPI, vol. 15(14), pages 1-27, July.
  21. Wang, Qiao & Ye, Min & Wei, Meng & Lian, Gaoqi & Li, Yan, 2023. "Random health indicator and shallow neural network based robust capacity estimation for lithium-ion batteries with different fast charging protocols," Energy, Elsevier, vol. 271(C).
  22. 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).
  23. Li, Shuangqi & He, Hongwen & Zhao, Pengfei & Cheng, Shuang, 2022. "Data cleaning and restoring method for vehicle battery big data platform," Applied Energy, Elsevier, vol. 320(C).
  24. Liu, Yisheng & Fan, Guodong & Zhou, Boru & Chen, Shun & Sun, Ziqiang & Wang, Yansong & Zhang, Xi, 2023. "Rapid and flexible battery capacity estimation using random short-time charging segments based on residual convolutional networks," Applied Energy, Elsevier, vol. 351(C).
  25. Li, Shuangqi & He, Hongwen & Zhao, Pengfei & Cheng, Shuang, 2022. "Health-Conscious vehicle battery state estimation based on deep transfer learning," Applied Energy, Elsevier, vol. 316(C).
  26. Ma, Yan & Li, Jiaqi & Gao, Jinwu & Chen, Hong, 2024. "State of health prediction of lithium-ion batteries under early partial data based on IWOA-BiLSTM with single feature," Energy, Elsevier, vol. 295(C).
  27. Park, Shina & Song, Youngbin & Kim, Sang Woo, 2024. "Simultaneous diagnosis of cell aging and internal short circuit faults in lithium-ion batteries using average leakage interval," Energy, Elsevier, vol. 290(C).
  28. Wang, Fujin & Zhao, Zhibin & Zhai, Zhi & Guo, Yanjie & Xi, Huan & Wang, Shibin & Chen, Xuefeng, 2023. "Feature disentanglement and tendency retainment with domain adaptation for Lithium-ion battery capacity estimation," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
  29. Ma, Yan & Shan, Ce & Gao, Jinwu & Chen, Hong, 2022. "A novel method for state of health estimation of lithium-ion batteries based on improved LSTM and health indicators extraction," Energy, Elsevier, vol. 251(C).
  30. Wang, Zhe & Yang, Fangfang & Xu, Qiang & Wang, Yongjian & Yan, Hong & Xie, Min, 2023. "Capacity estimation of lithium-ion batteries based on data aggregation and feature fusion via graph neural network," Applied Energy, Elsevier, vol. 336(C).
  31. Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).
  32. Yu Wang & Hongyi Bai & Laijun Sun & Yan Tang & Yonglong Huo & Rui Min, 2022. "The Rapid and Accurate Detection of Kidney Bean Seeds Based on a Compressed Yolov3 Model," Agriculture, MDPI, vol. 12(8), pages 1-21, August.
  33. Che, Yunhong & Zheng, Yusheng & Wu, Yue & Sui, Xin & Bharadwaj, Pallavi & Stroe, Daniel-Ioan & Yang, Yalian & Hu, Xiaosong & Teodorescu, Remus, 2022. "Data efficient health prognostic for batteries based on sequential information-driven probabilistic neural network," Applied Energy, Elsevier, vol. 323(C).
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