Supervisory long-term prediction of state of available power for lithium-ion batteries in electric vehicles-Reference-Cited by-同舟云学术

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Supervisory long-term prediction of state of available power for lithium-ion batteries in electric vehicles

Published:2020-01 Issue: Volume:257 Page:114006
ISSN:0306-2619
Container-title:Applied Energy
language:en
Short-container-title:Applied Energy

Author:

Yang Lin,Cai Yishan,Yang Yixin,Deng Zhongwei

Funder

National Natural Science Foundation of China

National Key Technology R&D Program

Publisher

Elsevier BV

Subject

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

Reference45 articles.

1. Coordinated operation of electric vehicle charging and wind power generation as a virtual power plant: A multi-stage risk constrained approach;Abbasi;Appl Energy,2019

2. Estimation of state-of-charge and state-of-power capability of lithium-ion battery considering varying health conditions;Sun;J Power Sources,2014

3. A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries;Xiong;Appl Energy,2017

4. 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,2013

5. Power capability evaluation for lithium iron phosphate batteries based on multi-parameter constraints estimation;Wang;J Power Sources,2018

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