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State of charge estimation of lithium‐ion battery based on extended Kalman filter and unscented Kalman filter techniques

https://doi.org/10.1002/est2.408
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/adfm.201805978openalex-snapshot
An Exploration of New Energy Storage System: High Energy Density, High Safety, and Fast Charging Lithium Ion Battery
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Estimation of State of Charge and State of Health of Lithium‐Ion Batteries Based on a New Adaptive Nonlinear Observer
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Estimation of Lithium‐Ion Battery State of Charge for Electric Vehicles Using an Adaptive Joint Algorithm
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Estimation of lithium‐ion battery state of charge for electric vehicles using a nonlinear state observer
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Data Science-Based Full-Lifespan Management of Lithium-Ion Battery
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Modelling of Lithium-Ion Battery Using MATLAB/Simulink for Electric Vehicle Applications
inferredcites →https://doi.org/10.1007/s11771-020-4485-9openalex-snapshot
Simulation of second-order RC equivalent circuit model of lithium battery based on variable resistance and capacitance
inferredcites →https://doi.org/10.1016/j.apenergy.2016.08.016openalex-snapshot
Co-estimation of state-of-charge, capacity and resistance for lithium-ion batteries based on a high-fidelity electrochemical model
inferredcites →https://doi.org/10.1016/j.apenergy.2020.114932openalex-snapshot
A noise-tolerant model parameterization method for lithium-ion battery management system
inferredcites →https://doi.org/10.1016/j.conengprac.2022.105176openalex-snapshot
Electrochemical modeling and parameterization towards control-oriented management of lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.conengprac.2022.105202openalex-snapshot
Interpretable machine learning for battery capacities prediction and coating parameters analysis
inferredcites →https://doi.org/10.1016/j.electacta.2017.10.153openalex-snapshot
A comparative study of different equivalent circuit models for estimating state-of-charge of lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.electacta.2018.08.076openalex-snapshot
An online method to simultaneously identify the parameters and estimate states for lithium ion batteries
inferredcites →https://doi.org/10.1016/j.energy.2018.09.101openalex-snapshot
Lithium-ion battery modeling and parameter identification based on fractional theory
inferredcites →https://doi.org/10.1016/j.energy.2019.03.059openalex-snapshot
State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network
inferredcites →https://doi.org/10.1016/j.energy.2020.119529openalex-snapshot
A compact and optimized neural network approach for battery state-of-charge estimation of energy storage system
inferredcites →https://doi.org/10.1016/j.energy.2020.119603openalex-snapshot
A state-of-charge estimation method of the power lithium-ion battery in complex conditions based on adaptive square root extended Kalman filter
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Classification, summarization and perspectives on state-of-charge estimation of lithium-ion batteries used in electric vehicles: A critical comprehensive survey
inferredcites →https://doi.org/10.1016/j.est.2021.102594openalex-snapshot
Sequent extended Kalman filter capacity estimation method for lithium-ion batteries based on discrete battery aging model and support vector machine
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An extended Kalman filter based data-driven method for state of charge estimation of Li-ion batteries
inferredcites →https://doi.org/10.1016/j.est.2022.104124openalex-snapshot
A novel method of parameter identification and state of charge estimation for lithium-ion battery energy storage system
inferredcites →https://doi.org/10.1016/j.est.2022.104307openalex-snapshot
An execution time optimized state of charge estimation method for lithium-ion battery
inferredcites →https://doi.org/10.1016/j.ijepes.2019.02.046openalex-snapshot
Hybrid state of charge estimation for lithium-ion battery under dynamic operating conditions
inferredcites →https://doi.org/10.1016/j.jclepro.2018.09.065openalex-snapshot
A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations
inferredcites →https://doi.org/10.1016/j.jclepro.2019.119147openalex-snapshot
Online simultaneous identification of parameters and order of a fractional order battery model
inferredcites →https://doi.org/10.1016/j.jpowsour.2018.09.034openalex-snapshot
Comparative study of methods for integrated model identification and state of charge estimation of lithium-ion battery
inferredcites →https://doi.org/10.1016/j.rser.2019.109334openalex-snapshot
State estimation for advanced battery management: Key challenges and future trends
inferredcites →https://doi.org/10.1016/j.rser.2020.109994openalex-snapshot
Combined internal resistance and state-of-charge estimation of lithium-ion battery based on extended state observer
inferredcites →https://doi.org/10.1016/j.trpro.2019.07.029openalex-snapshot
Overview of batteries State of Charge estimation methods
inferredcites →https://doi.org/10.1088/1757-899x/937/1/012046openalex-snapshot
Battery modelling and state of charge estimation methods for Energy Management in Electric Vehicle-A review
inferredcites →https://doi.org/10.1109/access.2017.2780258openalex-snapshot
Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles
inferredcites →https://doi.org/10.1109/icirca.2018.8597234openalex-snapshot
Electric Vehicle Li-Ion Battery State of Charge Estimation Using Artificial Neural Network
inferredcites →https://doi.org/10.1109/ievc.2012.6183271openalex-snapshot
High fidelity electrical model with thermal dependence for characterization and simulation of high power lithium battery cells
inferredcites →https://doi.org/10.1109/jas.2022.105599openalex-snapshot
Towards Long Lifetime Battery: AI-Based Manufacturing and Management
inferredcites →https://doi.org/10.1109/siitme50350.2020.9292232openalex-snapshot
A Comparison between State of Charge Estimation Methods: Extended Kalman Filter and Unscented Kalman Filter
inferredcites →https://doi.org/10.1109/tie.2017.2677319openalex-snapshot
Model Parameter Identification for Lithium Batteries Using the Coevolutionary Particle Swarm Optimization Method
inferredcites →https://doi.org/10.1109/tmech.2020.3049046openalex-snapshot
Feature Analyses and Modeling of Lithium-Ion Battery Manufacturing Based on Random Forest Classification
inferredcites →https://doi.org/10.1115/1.4053361openalex-snapshot
A Hybrid Method for Lithium-Ion Batteries State-of-Charge Estimation Based on Gated Recurrent Unit Neural Network and an Adaptive Unscented Kalman Filter
inferredcites →https://doi.org/10.1155/2021/2643092openalex-snapshot
Estimation of State of Charge of Lithium-Ion Batteries Based on Wide and Deep Neural Network Model
inferredcites →https://doi.org/10.1177/0020294019827338openalex-snapshot
An equivalent circuit model analysis for the lithium-ion battery pack in pure electric vehicles
inferredcites →https://doi.org/10.3390/app7101002openalex-snapshot
Comparative Research on RC Equivalent Circuit Models for Lithium-Ion Batteries of Electric Vehicles
inferredcites →https://doi.org/10.3390/app8050659openalex-snapshot
Overview of Lithium-Ion Battery Modeling Methods for State-of-Charge Estimation in Electrical Vehicles
inferredcites →https://doi.org/10.3390/en10070987openalex-snapshot
State-of-Charge Estimation with State-of-Health Calibration for Lithium-Ion Batteries
inferredcites →https://doi.org/10.3390/en13112825openalex-snapshot
Battery Management Systems—Challenges and Some Solutions
inferredcites →https://doi.org/10.3390/en14144074openalex-snapshot
A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries
inferredcites →https://doi.org/10.3390/en6084134openalex-snapshot
Adaptive State of Charge Estimation for Li-Ion Batteries Based on an Unscented Kalman Filter with an Enhanced Battery Model
inferredcites →https://doi.org/10.3390/pr9091685openalex-snapshot
Review on the Battery Model and SOC Estimation Method
inferredcites →https://doi.org/10.3934/energy.2019.2.186openalex-snapshot
Estimation of state of charge for lithium-ion batteries - A Review
inferredcites →https://doi.org/10.5772/intechopen.94655openalex-snapshot
Management and Applications of Energy Storage Devices
inferredcites →https://doi.org/10.6113/jpe.2018.18.3.910openalex-snapshot
Enhanced Coulomb Counting Method for State-of-Charge Estimation of Lithium-ion Batteries based on Peukert’s Law and Coulombic Efficiency

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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inferred← citeshttps://doi.org/10.1002/cta.3788openalex-snapshot
High‐precision state of charge estimation of lithium‐ion batteries based on improved particle swarm optimization‐backpropagation neural network‐dual extended Kalman filtering
inferred← citeshttps://doi.org/10.1002/est2.70146openalex-snapshot
Machine Learning‐Based Surrogate Model Development for the Estimation of State‐of‐Charge and Minimization of Charging Time for Batteries of Lithium‐Ion in Electric Vehicles
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A Systematic Review of AI-Based Digital Twins for Lithium-Ion Battery Sustainability
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Robust GenUT-UKF for state of charge estimation of Li-ion battery against data and model uncertainty
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Electric vehicle battery state of charge estimation using metaheuristic-optimized CatBoost algorithms
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Algorithm-oriented benchmarking of deep learning and hybrid architectures for robust SOC estimation in electric vehicle batteries
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Comprehensive review of battery management systems for electric vehicles: Thermal management, charging strategies, and emerging technologies
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Overview of Techniques for State of Charge Estimation in Lithium-Ion Batteries and Future Directions
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