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Prognostics of Lithium ion battery using functional principal component analysis

https://doi.org/10.1109/icphm.2017.7998299
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What this work says

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

inferredcites →https://doi.org/10.1002/0471667196.ess3138openalex-snapshot
Functional Data Analysis
inferredcites →https://doi.org/10.1007/978-1-4757-7107-7openalex-snapshot
Functional Data Analysis
inferredcites →https://doi.org/10.1007/b97702openalex-snapshot
Nonlinear Time Series: Nonparametric and Parametric Methods
inferredcites →https://doi.org/10.1007/s12239-008-0090-xopenalex-snapshot
Estimation of battery state-of-charge using ν-support vector regression algorithm
inferredcites →https://doi.org/10.1016/j.apenergy.2014.04.077openalex-snapshot
Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery
inferredcites →https://doi.org/10.1016/j.electacta.2008.05.032openalex-snapshot
Phenomenologically modeling the formation and evolution of the solid electrolyte interface on the graphite electrode for lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.enconman.2009.05.001openalex-snapshot
Auxiliary diagnosis method for lead–acid battery health based on sample entropy
inferredcites →https://doi.org/10.1016/j.eswa.2011.03.063openalex-snapshot
Intelligent prognostics for battery health monitoring based on sample entropy
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.2004.09.020openalex-snapshot
Support vector based battery state of charge estimator
inferredcites →https://doi.org/10.1016/j.jpowsour.2006.06.004openalex-snapshot
Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs
inferredcites →https://doi.org/10.1016/j.jpowsour.2012.11.146openalex-snapshot
Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods
inferredcites →https://doi.org/10.1016/j.jpowsour.2013.02.012openalex-snapshot
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.2015.01.164openalex-snapshot
A Bayesian approach for Li-Ion battery capacity fade modeling and cycles to failure prognostics
inferredcites →https://doi.org/10.1016/j.jpowsour.2015.04.166openalex-snapshot
Online estimation of lithium-ion battery capacity using sparse Bayesian learning
inferredcites →https://doi.org/10.1016/j.microrel.2012.11.010openalex-snapshot
State of charge estimation for electric vehicle batteries using unscented kalman filtering
inferredcites →https://doi.org/10.1016/j.microrel.2013.03.010openalex-snapshot
Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression
inferredcites →https://doi.org/10.1016/s0196-8904(01)00078-4openalex-snapshot
A new battery available capacity indicator for electric vehicles using neural network
inferredcites →https://doi.org/10.1037/cp2007014openalex-snapshot
Introduction to functional data analysis.
inferredcites →https://doi.org/10.1109/tim.2008.2005965openalex-snapshot
Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework
inferredcites →https://doi.org/10.1109/tsmcc.2010.2089979openalex-snapshot
System Identification and Estimation Framework for Pivotal Automotive Battery Management System Characteristics
inferredcites →https://doi.org/10.1149/2.044302jesopenalex-snapshot
Theory of SEI Formation in Rechargeable Batteries: Capacity Fade, Accelerated Aging and Lifetime Prediction
inferredcites →https://doi.org/10.1149/2.049210jesopenalex-snapshot
Battery Cycle Life Prediction with Coupled Chemical Degradation and Fatigue Mechanics
inferredcites →https://doi.org/10.1198/016214504000001745openalex-snapshot
Functional Data Analysis for Sparse Longitudinal Data
inferredcites →https://doi.org/10.1198/jcgs.2009.08011openalex-snapshot
A Geometric Approach to Maximum Likelihood Estimation of the Functional Principal Components From Sparse Longitudinal Data
inferredcites →https://doi.org/10.1214/11-ejs662openalex-snapshot
Principal components analysis for sparsely observed correlated functional data using a kernel smoothing approach
inferredcites →https://doi.org/10.48550/arxiv.1507.05135openalex-snapshot
Review of Functional Data Analysis

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/10.1016/j.energy.2022.123263openalex-snapshot
Power dynamic allocation strategy for urban rail hybrid energy storage system based on iterative learning control
inferred← citeshttps://doi.org/10.1016/j.eswa.2023.120711openalex-snapshot
Toward Automated Instructor Pilots in Legacy Air Force Systems: Physiology-Based Flight Difficulty Classification Via Machine Learning
inferred← citeshttps://doi.org/10.1038/s41524-022-00819-2openalex-snapshot
Functional data-driven framework for fast forecasting of electrode slurry rheology simulated by molecular dynamics
inferred← citeshttps://doi.org/10.1109/icphm.2018.8448947openalex-snapshot
An Online State of Health Estimation Method for Lithium-ion Batteries Based on Integrated Voltage
inferred← citeshttps://doi.org/10.1109/icphm51084.2021.9486674openalex-snapshot
A Multiscale Entropy-Based Long Short Term Memory Model for Lithium-Ion Battery Prognostics
inferred← citeshttps://doi.org/10.1109/icphm57936.2023.10194194openalex-snapshot
Reliable Thermal Monitoring of Electric Machines through Machine Learning
inferred← citeshttps://doi.org/10.1109/tase.2021.3070532openalex-snapshot
Individualized Degradation Modeling and Prognostics in a Heterogeneous Group via Incorporating Intrinsic Covariate Information
inferred← citeshttps://doi.org/10.1109/tr.2019.2957965openalex-snapshot
A Review on Prognostics Methods for Engineering Systems
inferred← citeshttps://doi.org/10.23940/ijpe.18.12.p28.31843194openalex-snapshot
Lithium-Ion Battery Management System for Electric Vehicles
inferred← citeshttps://doi.org/10.32469/10355/83928openalex-snapshot
Prediction of lithium-ion battery capacity by functional monitoring data using functional principal component analysis
inferred← citeshttps://doi.org/10.3390/app12094296openalex-snapshot
Prediction of Lithium-Ion Battery Capacity by Functional Principal Component Analysis of Monitoring Data
inferred← citeshttps://doi.org/10.70675/9aec8757zfa3cz4c3cza7b0zc600262689b0openalex-snapshot
Artificial Intelligence to accelerate battery cell manufacturing and design
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