A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion polymer battery in electric vehicles-Reference-Cited by-同舟云学术

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A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion polymer battery in electric vehicles

Published:2014-01 Issue: Volume:113 Page:463-476
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Xiong Rui,Sun Fengchun,Chen Zheng,He Hongwen

Funder

the National High Technology Research and Development Program of China

the National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Mechanical Engineering,General Energy,Building and Construction

Reference31 articles.

1. A comparative study of state of charge estimation algorithms for LiFePO4 batteries used in electric vehicles;Li;J Power Sources,2013

2. Battery cell state-of-charge estimation using linear parameter varying system techniques;Hu;J Power Sources,2012

3. Li-ion battery SOC estimation method based on the reduced order extended Kalman filtering;Lee;J Power Sources,2007

4. State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge;Lee;J Power Sources,2008

5. Model-based State of Charge and peak power capability joint estimation of Lithium-Ion battery in plug-in hybrid electric vehicles;Xiong;J Power Sources,2012

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