Lithium-ion battery life prognostic health management system using particle filtering framework
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DOI: 10.1177/1748006XJRR342
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Cited by:
- Wang, Yiwei & Gogu, Christian & Kim, Nam H. & Haftka, Raphael T. & Binaud, Nicolas & Bes, Christian, 2019. "Noise-dependent ranking of prognostics algorithms based on discrepancy without true damage information," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 86-100.
- Lin Li & Alfredo Alan Flores Saldivar & Yun Bai & Yun Li, 2019. "Battery Remaining Useful Life Prediction with Inheritance Particle Filtering," Energies, MDPI, vol. 12(14), pages 1-18, July.
- Li, Junfu & Wang, Lixin & Lyu, Chao & Wang, Dafang & Pecht, Michael, 2019. "Parameter updating method of a simplified first principles-thermal coupling model for lithium-ion batteries," Applied Energy, Elsevier, vol. 256(C).
- Jianxun Zhang & Xiao He & Xiaosheng Si & Changhua Hu & Donghua Zhou, 2017. "A Novel Multi-Phase Stochastic Model for Lithium-Ion Batteries’ Degradation with Regeneration Phenomena," Energies, MDPI, vol. 10(11), pages 1-24, October.
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Keywords
lithium-ion battery; particle filtering; prognostics; end-of-discharge; end-of-life;All these keywords.
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