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State of Health Estimation for Battery Modules with Parallel-Connected Cells Under Cell-to-Cell Variations
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Bayesian Learning for Neural Networks
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Bayesian Inference: An Introduction to Principles and Practice in Machine Learning
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The significance probability of the smirnov two-sample test
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A review of feature selection methods based on mutual information
inferredcites →https://doi.org/10.1016/j.apenergy.2016.01.125openalex-extracted
On-board state of health estimation of LiFePO4 battery pack through differential voltage analysis
inferredcites →https://doi.org/10.1016/j.apenergy.2016.07.126openalex-extracted
State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking
inferredcites →https://doi.org/10.1016/j.electacta.2017.10.025openalex-extracted
State of health estimation of battery modules via differential voltage analysis with local data symmetry method
inferredcites →https://doi.org/10.1016/j.est.2016.04.007openalex-extracted
Cell sorting for parallel lithium-ion battery systems: Evaluation based on an electric circuit model
inferredcites →https://doi.org/10.1016/j.est.2018.04.031openalex-extracted
Parameter variations within Li-Ion battery packs – Theoretical investigations and experimental quantification
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Robust State of Health estimation of lithium-ion batteries using convolutional neural network and random forest
inferredcites →https://doi.org/10.1016/j.est.2022.104560openalex-extracted
State of health estimation for fast-charging lithium-ion battery based on incremental capacity analysis
inferredcites →https://doi.org/10.1016/j.est.2023.109248openalex-extracted
State of health estimation of lithium-ion batteries based on Mixers-bidirectional temporal convolutional neural network
inferredcites →https://doi.org/10.1016/j.isci.2021.103103openalex-extracted
Reconstruction of the incremental capacity trajectories from current-varying profiles for lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.jpowsour.2004.02.033openalex-extracted
Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs
inferredcites →https://doi.org/10.1016/j.jpowsour.2004.07.021openalex-extracted
Differential voltage analyses of high-power, lithium-ion cells
inferredcites →https://doi.org/10.1016/j.jpowsour.2006.06.004openalex-extracted
Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs
inferredcites →https://doi.org/10.1016/j.jpowsour.2010.09.048openalex-extracted
Recursive approximate weighted total least squares estimation of battery cell total capacity
inferredcites →https://doi.org/10.1016/j.jpowsour.2012.10.058openalex-extracted
Particle filter for state of charge and state of health estimation for lithium–iron phosphate batteries
inferredcites →https://doi.org/10.1016/j.jpowsour.2013.02.012openalex-extracted
On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression
inferredcites →https://doi.org/10.1016/j.jpowsour.2013.05.040openalex-extracted
A review on lithium-ion battery ageing mechanisms and estimations for automotive applications
inferredcites →https://doi.org/10.1016/j.jpowsour.2014.02.026openalex-extracted
A unified open-circuit-voltage model of lithium-ion batteries for state-of-charge estimation and state-of-health monitoring
inferredcites →https://doi.org/10.1016/j.jpowsour.2016.07.121openalex-extracted
A Study of Cell-to-Cell Interactions and Degradation in Parallel Strings: Implications for the Battery Management System
inferredcites →https://doi.org/10.1016/j.rser.2015.11.042openalex-extracted
Critical review of state of health estimation methods of Li-ion batteries for real applications
inferredcites →https://doi.org/10.1103/physreve.69.066138openalex-extracted
Estimating mutual information
inferredcites →https://doi.org/10.1109/ccta54093.2023.10252701openalex-extracted
Sensitivity Analysis of Support Vector Regression-Based Incremental Capacity Analysis for Battery State of Health Estimations
inferredcites →https://doi.org/10.1109/tia.2014.2345951openalex-extracted
Study of the Characteristics of Battery Packs in Electric Vehicles With Parallel-Connected Lithium-Ion Battery Cells
inferredcites →https://doi.org/10.1109/tia.2019.2891213openalex-extracted
Lithium-Ion Battery Degradation Indicators Via Incremental Capacity Analysis
inferredcites →https://doi.org/10.1109/tit.2020.3024886openalex-extracted
Conditional Mutual Information Estimation for Mixed, Discrete and Continuous Data
inferredcites →https://doi.org/10.1109/tnn.2008.2005601openalex-extracted
Normalized Mutual Information Feature Selection
inferredcites →https://doi.org/10.1109/tpel.2009.2034966openalex-extracted
A Technique for Estimating the State of Health of Lithium Batteries Through a Dual-Sliding-Mode Observer
inferredcites →https://doi.org/10.1109/tvt.2014.2364554openalex-extracted
Model Parametrization and Adaptation Based on the Invariance of Support Vectors With Applications to Battery State-of-Health Monitoring
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/10.1149/1.2221767openalex-extracted
Incremental Capacity Analysis and Close-to-Equilibrium OCV Measurements to Quantify Capacity Fade in Commercial Rechargeable Lithium Batteries
inferredcites →https://doi.org/10.1186/1752-0509-1-51openalex-extracted
Experimental design for efficient identification of gene regulatory networks using sparse Bayesian models
inferredcites →https://doi.org/10.1214/aoms/1177704477openalex-extracted
On the Distribution of the Two-Sample Cramer-von Mises Criterion
inferredcites →https://doi.org/10.3390/batteries7010002openalex-extracted
Incremental Capacity Analysis as a State of Health Estimation Method for Lithium-Ion Battery Modules with Series-Connected Cells
inferredcites →https://doi.org/10.3390/e19110631openalex-extracted
On Normalized Mutual Information: Measure Derivations and Properties
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A Review of Battery State of Health Estimation Methods: Hybrid Electric Vehicle Challenges
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A Review of Lithium-Ion Battery State of Health Estimation and Prediction Methods
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Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information
inferredcites →https://doi.org/10.48550/arxiv.1709.06212openalex-extracted
Estimating Mutual Information for Discrete-Continuous Mixtures
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Diagnosis and prognosis of degradation in lithium-ion batteries
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