Online State of Health Estimation for Lithium-Ion Batteries Based on Support Vector Machine-Reference-Cited by-同舟云学术

Online State of Health Estimation for Lithium-Ion Batteries Based on Support Vector Machine

Published:2018-06-04 Issue:6 Volume:8 Page:925
ISSN:2076-3417
Container-title:Applied Sciences
language:en
Short-container-title:Applied Sciences

Author:

Chen Zheng^ORCID,Sun Mengmeng,Shu Xing,Xiao Renxin,Shen Jiangwei

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Link

http://www.mdpi.com/2076-3417/8/6/925/pdf

Reference34 articles.

1. Online state of charge estimation of lithium-ion batteries: A moving horizon estimation approach

2. A method for state-of-charge estimation of Li-ion batteries based on multi-model switching strategy

3. A robust state-of-charge estimator for multiple types of lithium-ion batteries using adaptive extended Kalman filter

4. State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking

5. Battery SOH Management Research in the US-China Clean Energy Research Center-Clean Vehicle Consortium

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