International Journal of Modern Nonlinear Theory and Application

International Journal of Modern Nonlinear Theory and Application

ISSN Print: 2167-9479
ISSN Online: 2167-9487
www.scirp.org/journal/ijmnta
E-mail: ijmnta@scirp.org
"Kalman Filters versus Neural Networks in Battery State-of-Charge Estimation: A Comparative Study"
written by Ala A. Hussein,
published by International Journal of Modern Nonlinear Theory and Application, Vol.3 No.5, 2014
has been cited by the following article(s):
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[4] Predicting the Current and Future State of Batteries using Data-Driven Machine Learning
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[11] A Neural Network-Based Robust Online SOC and SOH Estimation for Sealed Lead-Acid Batteries in Renewable Systems.
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[14] Modelling of Ultracapacitors Using Recurrent Artificial Neural Network
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[15] A Neural Network-Based Robust Online SOC and SOH Estimation for Sealed Lead–Acid Batteries in Renewable Systems
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[16] Experimental evaluation of mathematical and artificial neural network modeling of energy storage system
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[17] Lithium Ion Battery Cell Modelling
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[18] Monitoring techniques for 12-V lead–acid batteries in automobiles
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[19] The wavelet-based artificial neural network for state of charge estimation in lithium ion battery
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[20] Comparative study of SOC estimation techniques for Li-ion batteries
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[21] ONLINE MODELLING AND STATE-OF-CHARGE ESTIMATION FOR LITHIUM-TITANATE BATTERY
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[22] Capacity Fade Estimation in Electric Vehicle Li-Ion Batteries Using Artificial Neural Networks
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