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Optimal Calibration of Kalman Filters for State of Charge Estimation of Lithium-Ion Cells

https://doi.org/10.2139/ssrn.5371653
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Modeling, state of charge estimation, and charging of lithium‐ion battery in electric vehicle: A review
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Battery Management Systems
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State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures
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Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries
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Comparative study of reduced order equivalent circuit models for on-board state-of-available-power prediction of lithium-ion batteries in electric vehicles
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Trial-and-error or avoiding a guess? Initialization of the Kalman filter
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Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles
inferredcites →https://doi.org/10.1016/j.energy.2020.119025openalex-snapshot
State of charge estimation for lithium-ion battery based on an Intelligent Adaptive Extended Kalman Filter with improved noise estimator
inferredcites →https://doi.org/10.1016/j.est.2020.101980openalex-snapshot
An improved adaptive unscented kalman filtering for state of charge online estimation of lithium-ion battery
inferredcites →https://doi.org/10.1016/j.est.2021.102457openalex-snapshot
State-of-charge estimation of lithium-ion battery pack by using an adaptive extended Kalman filter for electric vehicles
inferredcites →https://doi.org/10.1016/j.est.2021.102559openalex-snapshot
Co-estimation for capacity and state of charge for lithium-ion batteries using improved adaptive extended Kalman filter
inferredcites →https://doi.org/10.1016/j.est.2022.104007openalex-snapshot
State of charge estimation of Lithium-ion battery using an improved fractional-order extended Kalman filter
inferredcites →https://doi.org/10.1016/j.est.2022.104396openalex-snapshot
Estimation for state of charge of lithium-ion batteries by adaptive fractional-order unscented Kalman filters
inferredcites →https://doi.org/10.1016/j.est.2023.108392openalex-snapshot
Battery state-of-charge estimation using data-driven Gaussian process Kalman filters
inferredcites →https://doi.org/10.1016/j.est.2023.110389openalex-snapshot
A guide to equivalent circuit fitting for impedance analysis and battery state estimation
inferredcites →https://doi.org/10.1016/j.ijepes.2014.04.059openalex-snapshot
State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation
inferredcites →https://doi.org/10.1016/j.jclepro.2019.06.273openalex-snapshot
State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter
inferredcites →https://doi.org/10.1016/j.jpowsour.2004.02.031openalex-snapshot
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
inferredcites →https://doi.org/10.1016/j.jpowsour.2004.02.032openalex-snapshot
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
inferredcites →https://doi.org/10.1016/j.jpowsour.2004.02.033openalex-snapshot
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
inferredcites →https://doi.org/10.1016/j.jpowsour.2006.06.003openalex-snapshot
Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs
inferredcites →https://doi.org/10.1016/j.jpowsour.2010.11.074openalex-snapshot
Challenges for rechargeable batteries
inferredcites →https://doi.org/10.1016/j.rser.2019.06.040openalex-snapshot
Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries
inferredcites →https://doi.org/10.1021/acsmeasuresciau.2c00070openalex-snapshot
Electrochemical Impedance Spectroscopy─A Tutorial
inferredcites →https://doi.org/10.1080/00401706.2000.10485979openalex-snapshot
A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code
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/access.2019.2942213openalex-snapshot
State of Charge Estimation for Lithium-Ion Batteries Using Model-Based and Data-Driven Methods: A Review
inferredcites →https://doi.org/10.1109/access.2023.3305950openalex-snapshot
State-of-Charge Estimation Method for Lithium-Ion Batteries Using Extended Kalman Filter With Adaptive Battery Parameters
inferredcites →https://doi.org/10.1109/icca.2007.4376458openalex-snapshot
Two Efficient Implementation Forms of Unscented Kalman Filter
inferredcites →https://doi.org/10.1109/iccep.2009.5212025openalex-snapshot
A dynamic lithium-ion battery model considering the effects of temperature and capacity fading
inferredcites →https://doi.org/10.1109/icnn.1995.488968openalex-snapshot
Particle swarm optimization
inferredcites →https://doi.org/10.1109/iecon.2010.5675134openalex-snapshot
Enhanced model for Lithium-Polymer cells including temperature effects
inferredcites →https://doi.org/10.1109/ievc.2014.7056126openalex-snapshot
State of charge estimation using extended Kalman filters for battery management system
inferredcites →https://doi.org/10.1109/intlec.2011.6099803openalex-snapshot
A practical circuit-based model for Li-ion battery cells in electric vehicle applications
inferredcites →https://doi.org/10.1109/pesc.2008.4592119openalex-snapshot
Electrical battery model for use in dynamic electric vehicle simulations
inferredcites →https://doi.org/10.1109/pesgm.2017.8273755openalex-snapshot
Adaptive adjustment of noise covariance in Kalman filter for dynamic state estimation
inferredcites →https://doi.org/10.1109/tac.1972.1100100openalex-snapshot
Approaches to adaptive filtering
inferredcites →https://doi.org/10.1109/tec.2006.874229openalex-snapshot
Accurate electrical battery model capable of predicting runtime and I-V performance
inferredcites →https://doi.org/10.1109/tie.2018.2880668openalex-snapshot
A Simplified Model-Based State-of-Charge Estimation Approach for Lithium-Ion Battery With Dynamic Linear Model
inferredcites →https://doi.org/10.1111/j.1467-985x.2010.00646_6.xopenalex-snapshot
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
inferredcites →https://doi.org/10.2307/1271432openalex-snapshot
A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
inferredcites →https://doi.org/10.56021/9781421407944openalex-snapshot
Matrix Computations
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