Remaining useful life estimation of Lithium-ion battery based on interacting multiple model particle filter and support vector regression-Reference-Cited by-同舟云学术

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Remaining useful life estimation of Lithium-ion battery based on interacting multiple model particle filter and support vector regression

Published:2021-06 Issue: Volume:210 Page:107542
ISSN:0951-8320
Container-title:Reliability Engineering & System Safety
language:en
Short-container-title:Reliability Engineering & System Safety

Author:

Li Sai^ORCID,Fang Huajing,Shi Bing

Funder

Hubei Provincial Department of Education

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Safety, Risk, Reliability and Quality

Reference33 articles.

1. Lithium-ion batteries,2009

2. A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems;Wang;Renew Sustain Energy Rev,2020

3. State of health prediction of lithium-ion batteries: Multiscale logic regression and Gaussian process regression ensemble;Yu;Reliab Eng Syst Saf,2018

4. Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries;Shrivastava;Renew Sustain Energy Rev,2019

5. A practical lithium-ion battery model for state of energy and voltage responses prediction incorporating temperature and ageing effects;Li;IEEE Trans Ind Electron,2018

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