Kalman Filters versus Neural Networks in Battery State-of-Charge Estimation: A Comparative Study

Journals Menu

Follow SCIRP

	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

	Paper Publishing WeChat

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

Citations h5-index & Ranking*

Journals Menu

"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):

Google Scholar
CrossRef

[1]	Machine learning: an advanced platform for materials development and state prediction in lithium‐ion batteries
	Advanced …, 2022

[2]	Computationally Efficient State-of-Charge Estimation in Li-Ion Batteries Using Enhanced Dual-Kalman Filter
	Hafez, AA Hussein - Energies, 2022

[3]	Methods for estimating lithium-ion battery state of charge for use in electric vehicles: a review
	Energy Harvesting and …, 2022

[4]	Application of Neural Networks in a Sodium-Nickel Chloride Battery Management System
	Journal of Control …, 2022

[5]	Predictive Modeling of Charge Levels for Battery Electric Vehicles using CNN EfficientNet and IGTD Algorithm
	arXiv preprint arXiv:2206.03612, 2022

[6]	Kalman Filter Is All You Need: Optimization Works When Noise Estimation Fails
	2021

[7]	Development of a Dynamic Model of Lithium Ion Battery Pack for Battery System Monitoring Algorithms in Electric Vehicles
	2021 23rd European …, 2021

[8]	Electro-thermal modelling of LFP prismatic cell along with the SOC estimation model
	2020

[9]	Battery State of Charge of a Plugged-In Hybrid Electric Vehicle
	2020

[10]	Analysis of NARXNN for State of Charge Estimation for Li-ion Batteries on various Drive Cycles
	2020

[11]	State of Charge Estimation of Lead-Acid Battery with Coulomb Counting and Feed-Forward Neural Network Method
	2020

[12]	Modeling and Estimation of Lithium-ion Battery State of Charge Using Intelligent Techniques
	2020

[13]	Predicting the state of charge and health of batteries using data-driven machine learning
	2020

[14]	Robust Artificial Neural Network-Based Models for Accurate Surface Temperature Estimation of Batteries
	2020

[15]	Predicting the Current and Future State of Batteries using Data-Driven Machine Learning
	2020

[16]	Fotovoltaik enerji üretim tesisleri için batarya yönetim sistemi tasarımı
	2019

[17]	A Neural Network-Based Robust Online SOC and SOH Estimation for Sealed Lead-Acid Batteries in Renewable Systems.
	2019

[18]	Unmanned Aerial Vehicle (UAV) Attitude Estimation Using Artificial Neural Network Approach
	2019

[19]	Estimation of state of charge for lithium-ion batteries-A Review
	2019

[20]	Development of a comprehensive electro-thermal battery model for energy management in microgrid systems
	2019

[21]	Modelling of Ultracapacitors Using Recurrent Artificial Neural Network
	Automation 2018, 2018

[22]	A Neural Network-Based Robust Online SOC and SOH Estimation for Sealed Lead–Acid Batteries in Renewable Systems
	Arabian Journal for Science and Engineering, 2018

[23]	Experimental evaluation of mathematical and artificial neural network modeling of energy storage system
	Dynamical Systems in Applications, 2017

[24]	Lithium Ion Battery Cell Modelling
	2017

[25]	Monitoring techniques for 12-V lead–acid batteries in automobiles
	Lead-Acid Batteries for Future Automobiles, 2017

[26]	The wavelet-based artificial neural network for state of charge estimation in lithium ion battery
	2017

[27]	Comparative study of SOC estimation techniques for Li-ion batteries
	ProQuest Dissertations Publishing, 2016

[28]	ONLINE MODELLING AND STATE-OF-CHARGE ESTIMATION FOR LITHIUM-TITANATE BATTERY
	2016

[29]	Capacity Fade Estimation in Electric Vehicle Li-Ion Batteries Using Artificial Neural Networks
	2015

[1]	State of Charge Estimation for Electric Vehicles Using Random Forest Green Energy and Intelligent Transportation, 2024 DOI:10.1016/j.geits.2024.100177

[2]	Optimal battery state of charge parameter estimation and forecasting using non-linear autoregressive exogenous Materials Science for Energy Technologies, 2023 DOI:10.1016/j.mset.2023.05.003

[3]	Artificial Intelligence and Industrial Applications Lecture Notes in Networks and Systems, 2023 DOI:10.1007/978-3-031-43520-1_6

[4]	Establishment of a Lithium-Ion Battery Model Considering Environmental Temperature for Battery State of Charge Estimation Journal of The Electrochemical Society, 2023 DOI:10.1149/1945-7111/ad11af

[5]	Optimal battery state of charge parameter estimation and forecasting using non-linear autoregressive exogenous Materials Science for Energy Technologies, 2023 DOI:10.1016/j.mset.2023.05.003

[6]	Methods for estimating lithium-ion battery state of charge for use in electric vehicles: a review Energy Harvesting and Systems, 2022 DOI:10.1515/ehs-2021-0039

[7]	Machine Learning: An Advanced Platform for Materials Development and State Prediction in Lithium‐Ion Batteries Advanced Materials, 2022 DOI:10.1002/adma.202101474

[8]	Application of Neural Networks in a Sodium-Nickel Chloride Battery Management System Journal of Control, Automation and Electrical Systems, 2022 DOI:10.1007/s40313-021-00847-1

[9]	Methods for estimating lithium-ion battery state of charge for use in electric vehicles: a review Energy Harvesting and Systems, 2022 DOI:10.1515/ehs-2021-0039

[10]	Computationally Efficient State-of-Charge Estimation in Li-Ion Batteries Using Enhanced Dual-Kalman Filter Energies, 2022 DOI:10.3390/en15103717

[11]	Methods for estimating lithium-ion battery state of charge for use in electric vehicles: a review Energy Harvesting and Systems, 2022 DOI:10.1515/ehs-2021-0039

[12]	Development of a Dynamic Model of Lithium Ion Battery Pack for Battery System Monitoring Algorithms in Electric Vehicles 2021 23rd European Conference on Power Electronics and Applications (EPE'21 ECCE Europe), 2021 DOI:10.23919/EPE21ECCEEurope50061.2021.9570709

[13]	Predicting the state of charge and health of batteries using data-driven machine learning Nature Machine Intelligence, 2020 DOI:10.1038/s42256-020-0156-7

[14]	Advances in Power and Control Engineering Lecture Notes in Electrical Engineering, 2020 DOI:10.1007/978-981-15-0313-9_12

[15]	Predicting the state of charge and health of batteries using data-driven machine learning Nature Machine Intelligence, 2020 DOI:10.1038/s42256-020-0156-7

[16]	Robust Artificial Neural Network-Based Models for Accurate Surface Temperature Estimation of Batteries IEEE Transactions on Industry Applications, 2020 DOI:10.1109/TIA.2020.3001256

[17]	State of Charge Estimation of Lead-Acid Battery with Coulomb Counting and Feed-Forward Neural Network Method 2020 FORTEI-International Conference on Electrical Engineering (FORTEI-ICEE), 2020 DOI:10.1109/FORTEI-ICEE50915.2020.9249870

[18]	Analysis of NARXNN for State of Charge Estimation for Li-ion Batteries on various Drive Cycles 2020 8th International Conference on Power Electronics Systems and Applications (PESA), 2020 DOI:10.1109/PESA50370.2020.9344002

[19]	Unmanned Aerial Vehicle (UAV) Attitude Estimation Using Artificial Neural Network Approach 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), 2019 DOI:10.1109/HNICEM48295.2019.9072841

[20]	Estimation of state of charge for lithium-ion batteries - A Review AIMS Energy, 2019 DOI:10.3934/energy.2019.2.186

[21]	A Neural Network-Based Robust Online SOC and SOH Estimation for Sealed Lead–Acid Batteries in Renewable Systems Arabian Journal for Science and Engineering, 2019 DOI:10.1007/s13369-018-3200-8

[22]	Automation 2018 Advances in Intelligent Systems and Computing, 2018 DOI:10.1007/978-3-319-77179-3_70

[23]	Dynamical Systems in Applications Springer Proceedings in Mathematics & Statistics, 2018 DOI:10.1007/978-3-319-96601-4_5

[24]	Lead-Acid Batteries for Future Automobiles 2017 DOI:10.1016/B978-0-444-63700-0.00014-3

Follow SCIRP

	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

	Paper Publishing WeChat

International Journal of Modern Nonlinear Theory and Application

International Journal of Modern Nonlinear Theory and Application

Journals Menu

Home

About SCIRP

Service

Policies