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https://doi.org/10.2139/ssrn.4680107

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inferredcites →https://doi.org/10.1002/er.3530openalex-extracted
Least-squares based coulomb counting method and its application for state-of-charge (SOC) estimation in electric vehicles
inferredcites →https://doi.org/10.1016/j.apenergy.2008.11.021openalex-extracted
Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.apenergy.2011.08.002openalex-extracted
A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation
inferredcites →https://doi.org/10.1016/j.apenergy.2013.07.061openalex-extracted
A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion polymer battery in electric vehicles
inferredcites →https://doi.org/10.1016/j.apenergy.2014.01.066openalex-extracted
A new neural network model for the state-of-charge estimation in the battery degradation process
inferredcites →https://doi.org/10.1016/j.energy.2016.05.047openalex-extracted
A combination Kalman filter approach for State of Charge estimation of lithium-ion battery considering model uncertainty
inferredcites →https://doi.org/10.1016/j.energy.2016.06.130openalex-extracted
Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery
inferredcites →https://doi.org/10.1016/j.energy.2018.08.071openalex-extracted
State of charge estimation for electric vehicle power battery using advanced machine learning algorithm under diversified drive cycles
inferredcites →https://doi.org/10.1016/j.energy.2022.125375openalex-extracted
SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output
inferredcites →https://doi.org/10.1016/j.energy.2022.125718openalex-extracted
Deep convolutional neural network based closed-loop SOC estimation for lithium-ion batteries in hierarchical scenarios
inferredcites →https://doi.org/10.1016/j.est.2016.03.003openalex-extracted
Accuracy improvement of SOC estimation in lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.est.2016.07.002openalex-extracted
Battery state of charge estimation using a load-classifying neural network
inferredcites →https://doi.org/10.1016/j.est.2021.103518openalex-extracted
State-of-charge estimation for the lithium-ion battery based on adaptive extended Kalman filter using improved parameter identification
inferredcites →https://doi.org/10.1016/j.ijepes.2013.05.038openalex-extracted
Estimation of the state of charge for a LFP battery using a hybrid method that combines a RBF neural network, an OLS algorithm and AGA
inferredcites →https://doi.org/10.1016/j.ijepes.2019.02.046openalex-extracted
Hybrid state of charge estimation for lithium-ion battery under dynamic operating conditions
inferredcites →https://doi.org/10.1016/j.jpowsour.2004.09.020openalex-extracted
Support vector based battery state of charge estimator
inferredcites →https://doi.org/10.1016/j.jpowsour.2010.06.098openalex-extracted
Dynamic electric behavior and open-circuit-voltage modeling of LiFePO4-based lithium ion secondary batteries
inferredcites →https://doi.org/10.1016/j.jpowsour.2012.12.057openalex-extracted
A comparative study of state of charge estimation algorithms for LiFePO4 batteries used in electric vehicles
inferredcites →https://doi.org/10.1016/j.jpowsour.2013.08.039openalex-extracted
A novel approach for state of charge estimation based on adaptive switching gain sliding mode observer in electric vehicles
inferredcites →https://doi.org/10.1016/j.jpowsour.2014.07.016openalex-extracted
State-of-charge estimation for battery management system using optimized support vector machine for regression
inferredcites →https://doi.org/10.1016/j.jpowsour.2015.01.145openalex-extracted
Electric vehicle state of charge estimation: Nonlinear correlation and fuzzy support vector machine
inferredcites →https://doi.org/10.1016/j.jpowsour.2018.06.104openalex-extracted
State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach
inferredcites →https://doi.org/10.1016/s0378-7753(01)00560-2openalex-extracted
Methods for state-of-charge determination and their applications
inferredcites →https://doi.org/10.1109/access.2019.2913078openalex-extracted
Accurate State-of-Charge Estimation Approach for Lithium-Ion Batteries by Gated Recurrent Unit With Ensemble Optimizer
inferredcites →https://doi.org/10.1109/access.2022.3170093openalex-extracted
An Adaptive State of Charge Estimation Method of Lithium-ion Battery Based on Residual Constraint Fading Factor Unscented Kalman Filter
inferredcites →https://doi.org/10.1109/access.2022.3187193openalex-extracted
Adaptive Integral Correction-Based State of Charge Estimation Strategy for Lithium-Ion Cells
inferredcites →https://doi.org/10.1109/pesgm.2016.7741980openalex-extracted
Online battery state-of-charge estimation based on sparse gaussian process regression
inferredcites →https://doi.org/10.1109/tie.2010.2043035openalex-extracted
State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF
inferredcites →https://doi.org/10.1109/tie.2017.2764869openalex-extracted
Battery State-of-Charge Estimation Based on Regular/Recurrent Gaussian Process Regression
inferredcites →https://doi.org/10.1109/tie.2017.2787586openalex-extracted
Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries
inferredcites →https://doi.org/10.1109/tiv.2022.3161301openalex-extracted
Real-Time State of Charge Estimation of Lithium-Ion Batteries Using Optimized Random Forest Regression Algorithm
inferredcites →https://doi.org/10.1109/tpel.2013.2243918openalex-extracted
Support Vector Machines Used to Estimate the Battery State of Charge
inferredcites →https://doi.org/10.1109/tpel.2015.2439578openalex-extracted
Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine
inferredcites →https://doi.org/10.1109/tte.2015.2512237openalex-extracted
Advanced Machine Learning Approach for Lithium-Ion Battery State Estimation in Electric Vehicles
inferredcites →https://doi.org/10.1109/tte.2022.3180077openalex-extracted
State of Charge Estimation for Lithium-Ion Battery Based on Hybrid Compensation Modeling and Adaptive H-Infinity Filter
inferredcites →https://doi.org/10.1109/tvt.2012.2235474openalex-extracted
State of Charge Estimation of Lithium-Ion Batteries in Electric Drive Vehicles Using Extended Kalman Filtering
inferredcites →https://doi.org/10.1109/tvt.2017.2715333openalex-extracted
State of Charge and State of Health Estimation for Lithium Batteries Using Recurrent Neural Networks
inferredcites →https://doi.org/10.1109/vppc.2011.6042988openalex-extracted
Kalman filter based state-of-charge estimation for lithium-ion batteries in hybrid electric vehicles using pulse charging
inferredcites →https://doi.org/10.3390/en11040995openalex-extracted
A Novel Intelligent Method for the State of Charge Estimation of Lithium-Ion Batteries Using a Discrete Wavelet Transform-Based Wavelet Neural Network
inferredcites →https://doi.org/10.3390/s23010467openalex-extracted
Stable and Accurate Estimation of SOC Using eXogenous Kalman Filter for Lithium-Ion Batteries
inferredcites →https://doi.org/10.48550/arxiv.1302.4922openalex-extracted
Structure Discovery in Nonparametric Regression through Compositional Kernel Search
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