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

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International Journal of Modern Nonlinear Theory and Application

ISSN Print: 2167-9479
ISSN Online: 2167-9487
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"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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CrossRef

[1]	Optimization or Architecture: How to Hack Kalman Filtering
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[2]	State of Charge Estimation for Electric Vehicles Using Random Forest
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[3]	Optimal battery state of charge parameter estimation and forecasting using non-linear autoregressive exogenous
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[4]	Establishment of a Lithium-Ion Battery Model Considering Environmental Temperature for Battery State of Charge Estimation
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[5]	Optimization or Architecture: What Matters in Non-Linear Filtering?
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[6]	State of Charge Estimation of Lithium-Ion Batteries Using Extended Kalman Filter and Multi-layer Perceptron Neural Network
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[7]	Computationally Efficient State-of-Charge Estimation in Li-Ion Batteries Using Enhanced Dual-Kalman Filter. Energies 2022, 15, 3717
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[8]	Data-driven state estimation of large-scale battery systems
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[9]	Machine learning: an advanced platform for materials development and state prediction in lithium‐ion batteries
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[10]	Computationally Efficient State-of-Charge Estimation in Li-Ion Batteries Using Enhanced Dual-Kalman Filter
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[11]	Methods for estimating lithium-ion battery state of charge for use in electric vehicles: a review
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[12]	Application of Neural Networks in a Sodium-Nickel Chloride Battery Management System
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[13]	Predictive Modeling of Charge Levels for Battery Electric Vehicles using CNN EfficientNet and IGTD Algorithm
	arXiv preprint arXiv:2206.03612, 2022

[14]	Kalman Filter Is All You Need: Optimization Works When Noise Estimation Fails
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[15]	Development of a Dynamic Model of Lithium Ion Battery Pack for Battery System Monitoring Algorithms in Electric Vehicles
	2021 23rd European …, 2021

[16]	The fragility of noise estimation in Kalman filter: Optimization can handle model-misspecification
	arXiv preprint arXiv:2104.02372, 2021

[17]	ETUDE ET DIAGNOSTIC DES BATTERIES DEDIES AUX SYSTEMES PHOTOVOLTAIQUES
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[18]	Electro-thermal modelling of LFP prismatic cell along with the SOC estimation model
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[19]	Battery State of Charge of a Plugged-In Hybrid Electric Vehicle
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[20]	Analysis of NARXNN for State of Charge Estimation for Li-ion Batteries on various Drive Cycles
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[21]	State of Charge Estimation of Lead-Acid Battery with Coulomb Counting and Feed-Forward Neural Network Method
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[22]	Modeling and Estimation of Lithium-ion Battery State of Charge Using Intelligent Techniques
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[23]	Predicting the state of charge and health of batteries using data-driven machine learning
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[24]	Robust Artificial Neural Network-Based Models for Accurate Surface Temperature Estimation of Batteries
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[25]	Predicting the Current and Future State of Batteries using Data-Driven Machine Learning
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[26]	State of Charge (SOC) Estimation of Lithium-Ion Batteries using Machine Learning
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[27]	Fast charging strategies of a lithium-ion battery using aging model
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[28]	Fotovoltaik enerji üretim tesisleri için batarya yönetim sistemi tasarımı
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[29]	A Neural Network-Based Robust Online SOC and SOH Estimation for Sealed Lead-Acid Batteries in Renewable Systems.
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[30]	Unmanned Aerial Vehicle (UAV) Attitude Estimation Using Artificial Neural Network Approach
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[31]	Estimation of state of charge for lithium-ion batteries-A Review
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[32]	Development of a comprehensive electro-thermal battery model for energy management in microgrid systems
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[33]	Modelling of Ultracapacitors Using Recurrent Artificial Neural Network
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[34]	A Neural Network-Based Robust Online SOC and SOH Estimation for Sealed Lead–Acid Batteries in Renewable Systems
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[35]	Experimental evaluation of mathematical and artificial neural network modeling of energy storage system
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[36]	Lithium Ion Battery Cell Modelling
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[37]	Monitoring techniques for 12-V lead–acid batteries in automobiles
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[38]	The wavelet-based artificial neural network for state of charge estimation in lithium ion battery
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[39]	Comparative study of SOC estimation techniques for Li-ion batteries
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[40]	ONLINE MODELLING AND STATE-OF-CHARGE ESTIMATION FOR LITHIUM-TITANATE BATTERY
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[41]	Capacity Fade Estimation in Electric Vehicle Li-Ion Batteries Using Artificial Neural Networks
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[42]	FOTOVOLTAİK ENERJİ ÜRETİM TESİSLERİ İÇİN BATARYA YÖNETİM SİSTEMİ TASARIMI

[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

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	customer@scirp.org
	+86 18163351462(WhatsApp)
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