Model-based State-of-charge Estimation Approach of the Lithium-ion Battery Using an Improved Adaptive Particle Filter-Reference-Cited by-同舟云学术

Model-based State-of-charge Estimation Approach of the Lithium-ion Battery Using an Improved Adaptive Particle Filter

Published:2016-12 Issue: Volume:103 Page:394-399
ISSN:1876-6102
Container-title:Energy Procedia
language:en
Short-container-title:Energy Procedia

Author:

Ye Min,Guo Hui,Xiong Rui,Yang Ruixin

Publisher

Elsevier BV

Reference10 articles.

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3. A comprehensive review on estimation strategies used in hybrid and battery electric vehicles;Cuma;Renewable and Sustanable Energy Reviews,2015

4. A review on lithium-ion battery ageing mechanisms and estimations for automotive applications;Barré;J. Power Sources,2013

5. The state of charge estimation for rechargeable batteries based on artificial neural network techniques;Ismail;International Conference on Control, Decision & Information Technologies,2013

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2. Mitigation of state of charge estimation error due to noisy current input measurement;Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering;2023-06-29

3. Parameter Identification of Lithium-Ion Battery Model Based on African Vultures Optimization Algorithm;Mathematics;2023-05-08

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