Lithium battery state-of-health estimation and remaining useful lifetime prediction based on non-parametric aging model and particle filter algorithm-Reference-Cited by-同舟云学术

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Lithium battery state-of-health estimation and remaining useful lifetime prediction based on non-parametric aging model and particle filter algorithm

Published:2022-02 Issue: Volume:11 Page:100156
ISSN:2590-1168
Container-title:eTransportation
language:en
Short-container-title:eTransportation

Author:

Li Xiaoyu,Yuan Changgui,Wang Zhenpo,He Jiangtao,Yu Shike

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology,Transportation,Automotive Engineering

Reference33 articles.

1. Machine learning for continuous innovation in battery technologies;Aykol;Nat Rev Mater,2020

2. A review on the key issues of the lithium ion battery degradation among the whole life cycle;Han;eTransportation,2019

3. Multiphysical modeling for life analysis of lithium-ion battery pack in electric vehicles;Xia;Renew Sustain Energy Rev,2020

4. Towards a smarter battery management system: a critical review on battery state of health monitoring methods;Xiong;J Power Sources,2018

5. S. Hong, T. Yue, H. Liu, “Vehicle energy system active defense: a health assessment of lithium-ion batteries,” Int J Intell Syst, vol. 2020, pp. 1− 19.

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