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Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction

Citations

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Cited by:

  1. Luping Chen & Liangjun Xu & Yilin Zhou, 2018. "Novel Approach for Lithium-Ion Battery On-Line Remaining Useful Life Prediction Based on Permutation Entropy," Energies, MDPI, vol. 11(4), pages 1-15, April.
  2. Yang Zhang & Bo Guo, 2015. "Online Capacity Estimation of Lithium-Ion Batteries Based on Novel Feature Extraction and Adaptive Multi-Kernel Relevance Vector Machine," Energies, MDPI, vol. 8(11), pages 1-19, November.
  3. Ng, Selina S.Y. & Xing, Yinjiao & Tsui, Kwok L., 2014. "A naive Bayes model for robust remaining useful life prediction of lithium-ion battery," Applied Energy, Elsevier, vol. 118(C), pages 114-123.
  4. Yao Ahoutou & Adrian Ilinca & Mohamad Issa, 2022. "Electrochemical Cells and Storage Technologies to Increase Renewable Energy Share in Cold Climate Conditions—A Critical Assessment," Energies, MDPI, vol. 15(4), pages 1-30, February.
  5. Shuai Wang & Lingling Zhao & Xiaohong Su & Peijun Ma, 2014. "Prognostics of Lithium-Ion Batteries Based on Battery Performance Analysis and Flexible Support Vector Regression," Energies, MDPI, vol. 7(10), pages 1-17, October.
  6. Pan, Haihong & Lü, Zhiqiang & Wang, Huimin & Wei, Haiyan & Chen, Lin, 2018. "Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine," Energy, Elsevier, vol. 160(C), pages 466-477.
  7. Zhao, Dao & Zhou, Zhijie & Tang, Shuaiwen & Cao, You & Wang, Jie & Zhang, Peng & Zhang, Yijun, 2022. "Online estimation of satellite lithium-ion battery capacity based on approximate belief rule base and hidden Markov model," Energy, Elsevier, vol. 256(C).
  8. Khaleghi, Sahar & Karimi, Danial & Beheshti, S. Hamidreza & Hosen, Md. Sazzad & Behi, Hamidreza & Berecibar, Maitane & Van Mierlo, Joeri, 2021. "Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network," Applied Energy, Elsevier, vol. 282(PA).
  9. Wenshuo Tang & Darius Roman & Ross Dickie & Valentin Robu & David Flynn, 2020. "Prognostics and Health Management for the Optimization of Marine Hybrid Energy Systems," Energies, MDPI, vol. 13(18), pages 1-29, September.
  10. Liu, Kai & Wang, Jiangbo & Yamamoto, Toshiyuki & Morikawa, Takayuki, 2018. "Exploring the interactive effects of ambient temperature and vehicle auxiliary loads on electric vehicle energy consumption," Applied Energy, Elsevier, vol. 227(C), pages 324-331.
  11. Taichun Qin & Shengkui Zeng & Jianbin Guo & Zakwan Skaf, 2016. "A Rest Time-Based Prognostic Framework for State of Health Estimation of Lithium-Ion Batteries with Regeneration Phenomena," Energies, MDPI, vol. 9(11), pages 1-18, November.
  12. Wei, Yupeng & Wu, Dazhong & Terpenny, Janis, 2021. "Learning the health index of complex systems using dynamic conditional variational autoencoders," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
  13. Cheng, Yujie & Song, Dengwei & Wang, Zhenya & Lu, Chen & Zerhouni, Noureddine, 2020. "An ensemble prognostic method for lithium-ion battery capacity estimation based on time-varying weight allocation," Applied Energy, Elsevier, vol. 266(C).
  14. Jinsong Yu & Baohua Mo & Diyin Tang & Jie Yang & Jiuqing Wan & Jingjing Liu, 2017. "Indirect State-of-Health Estimation for Lithium-Ion Batteries under Randomized Use," Energies, MDPI, vol. 10(12), pages 1-19, December.
  15. Ma, Guijun & Zhang, Yong & Cheng, Cheng & Zhou, Beitong & Hu, Pengchao & Yuan, Ye, 2019. "Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  16. Wei, Meng & Balaya, Palani & Ye, Min & Song, Ziyou, 2022. "Remaining useful life prediction for 18650 sodium-ion batteries based on incremental capacity analysis," Energy, Elsevier, vol. 261(PA).
  17. Yu Peng & Yandong Hou & Yuchen Song & Jingyue Pang & Datong Liu, 2018. "Lithium-Ion Battery Prognostics with Hybrid Gaussian Process Function Regression," Energies, MDPI, vol. 11(6), pages 1-20, June.
  18. Kuo-Hsin Tseng & Jin-Wei Liang & Wunching Chang & Shyh-Chin Huang, 2015. "Regression Models Using Fully Discharged Voltage and Internal Resistance for State of Health Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 8(4), pages 1-19, April.
  19. Lin Zou & Baoyi Wen & Yiying Wei & Yong Zhang & Jie Yang & Hui Zhang, 2022. "Online Prediction of Remaining Useful Life for Li-Ion Batteries Based on Discharge Voltage Data," Energies, MDPI, vol. 15(6), pages 1-16, March.
  20. Muhammad Waseem & Jingyuan Huang & Chak-Nam Wong & C. K. M. Lee, 2023. "Data-Driven GWO-BRNN-Based SOH Estimation of Lithium-Ion Batteries in EVs for Their Prognostics and Health Management," Mathematics, MDPI, vol. 11(20), pages 1-27, October.
  21. Shaojun Wang & Datong Liu & Jianbao Zhou & Bin Zhang & Yu Peng, 2016. "A Run-Time Dynamic Reconfigurable Computing System for Lithium-Ion Battery Prognosis," Energies, MDPI, vol. 9(8), pages 1-19, July.
  22. Shengjin Tang & Chuanqiang Yu & Xue Wang & Xiaosong Guo & Xiaosheng Si, 2014. "Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error," Energies, MDPI, vol. 7(2), pages 1-28, January.
  23. Jiangbo Wang & Kai Liu & Toshiyuki Yamamoto, 2017. "Improving Electricity Consumption Estimation for Electric Vehicles Based on Sparse GPS Observations," Energies, MDPI, vol. 10(1), pages 1-12, January.
  24. Song, Yuchen & Liu, Datong & Liao, Haitao & Peng, Yu, 2020. "A hybrid statistical data-driven method for on-line joint state estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 261(C).
  25. Jun Peng & Zhiyong Zheng & Xiaoyong Zhang & Kunyuan Deng & Kai Gao & Heng Li & Bin Chen & Yingze Yang & Zhiwu Huang, 2020. "A Data-Driven Method with Feature Enhancement and Adaptive Optimization for Lithium-Ion Battery Remaining Useful Life Prediction," Energies, MDPI, vol. 13(3), pages 1-20, February.
  26. Zhonghua Yun & Wenhu Qin & Weipeng Shi & Peng Ping, 2020. "State-of-Health Prediction for Lithium-Ion Batteries Based on a Novel Hybrid Approach," Energies, MDPI, vol. 13(18), pages 1-22, September.
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