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Health monitoring algorithms for space application batteries
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1017/cbo9780511801389openalex-snapshot
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
inferredcites →https://doi.org/10.1049/cp:20010633openalex-snapshot
Results of discrete frequency immittance spectroscopy (DFIS) measurements of lead acid batteries
inferredcites →https://doi.org/10.1108/k.2001.30.1.103.6openalex-snapshot
An Introduction to Support Vector Machines and Other Kernel‐based Learning Methods
inferredcites →https://doi.org/10.1109/aero.2000.877904openalex-snapshot
Shipboard machinery diagnostics and prognostics/condition based maintenance: a progress report
inferredcites →https://doi.org/10.1109/intlec.2002.1048716openalex-snapshot
Fuzzy logic-based state-of-health determination of lead acid batteries
inferredcites →https://doi.org/10.1109/pesc.2007.4342463openalex-snapshot
State Estimation of a Lithium-Ion Battery Through Kalman Filter
inferredcites →https://doi.org/10.1109/tvt.2004.842461openalex-snapshot
Nonlinear Observers for Predicting State-of-Charge and State-of-Health of Lead-Acid Batteries for Hybrid-Electric Vehicles
inferredcites →https://doi.org/10.1117/12.434227openalex-snapshot
Intelligent approach to prognostic enhancements of diagnostic systems
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4654130openalex-extracted
A Meta-Learning Lifetime Prediction Approach for Lithium-Ion Batteries Considering Label Noise
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Predicting Lithium-Ion Battery State of Health with Hybrid Ensemble Modeling
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Satellite lithium-ion battery remaining useful life estimation with an iterative updated RVM fused with the KF algorithm
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A critical review on self-adaptive Li-ion battery ageing models
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Lithium-ion battery remaining useful life estimation with an optimized Relevance Vector Machine algorithm with incremental learning
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A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries
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An ensemble model for predicting the remaining useful performance of lithium-ion batteries
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A Bayesian framework for on-line degradation assessment and residual life prediction of secondary batteries inspacecraft
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Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications
inferred← citeshttps://doi.org/10.1080/17517575.2019.1670361openalex-snapshot
Health condition estimation of spacecraft key components using belief rule base
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Dynamic battery remaining useful life estimation: An on-line data-driven approach
inferred← citeshttps://doi.org/10.1109/i2mtc.2013.6555479openalex-snapshot
An optimized Relevance Vector Machine with incremental learning strategy for lithium-ion battery remaining useful life estimation
inferred← citeshttps://doi.org/10.1109/icecet61485.2024.10698741openalex-snapshot
Lithium-Ion Battery Prognostic and Health Prediction Using Machine Learning Models
inferred← citeshttps://doi.org/10.1109/icept-hdp.2012.6474786openalex-snapshot
Influence of parameter initialization on battery life prediction for online applications
inferred← citeshttps://doi.org/10.1109/icmla.2018.00137openalex-snapshot
Battery Degradation Temporal Modeling Using LSTM Networks
inferred← citeshttps://doi.org/10.1109/icphm.2013.6621441openalex-snapshot
Uncertainty quantification of fusion prognostics for lithium-ion battery remaining useful life estimation
inferred← citeshttps://doi.org/10.1109/icphm.2016.7542828openalex-snapshot
SOH estimation for lithium-ion batteries: A cointegration and error correction approach
inferred← citeshttps://doi.org/10.1109/icrms.2009.5269967openalex-snapshot
Health assessment and prognostics of electronic products
inferred← citeshttps://doi.org/10.1109/icsrs68021.2025.11422134openalex-snapshot
Data-Driven Reliability Model for Lithium-Ion Battery Cells using the Eyring-Weibull Approach
inferred← citeshttps://doi.org/10.1109/iecon.2010.5675138openalex-snapshot
Prognosis of electrical faults in permanent magnet AC machines using the hidden Markov model
inferred← citeshttps://doi.org/10.1109/imccc.2012.113openalex-snapshot
Combined Sparse Bayesian Learning Strategy for Remaining Useful Life Forecasting of Lithium-ion Battery
inferred← citeshttps://doi.org/10.1109/ipemc.2012.6259285openalex-snapshot
The state-of-health diagnosis of Li-Co batteries with fuzzy identification
inferred← citeshttps://doi.org/10.1109/isgt.2016.7781028openalex-snapshot
A novel algorithm to integrate battery Cyclic and Calendar agings within a single framework
inferred← citeshttps://doi.org/10.1109/phm.2016.7819919openalex-snapshot
Hybrid approach of iterative updating for lithium-ion battery remaining useful life estimation
inferred← citeshttps://doi.org/10.1109/rcae59706.2023.10398837openalex-snapshot
A SA-PSO Based LS-SVM Method for Lithium-Ion Battery Lifetime Prediction
inferred← citeshttps://doi.org/10.1109/sasg64132.2024.11535823openalex-snapshot
Predicting Lithium-Ion Battery State of Health with Hybrid Ensemble Modeling
inferred← citeshttps://doi.org/10.1109/tcst.2021.3091108openalex-snapshot
Thermal-Enhanced Adaptive Interval Estimation in Battery Packs With Heterogeneous Cells
inferred← citeshttps://doi.org/10.1109/tie.2010.2052540openalex-snapshot
Prognosis of Gear Failures in DC Starter Motors Using Hidden Markov Models
inferred← citeshttps://doi.org/10.1109/tie.2010.2076414openalex-snapshot
Energy Storage Systems for Transport and Grid Applications
inferred← citeshttps://doi.org/10.1109/tim.2012.2215142openalex-snapshot
Particle-Filtering-Based Prognosis Framework for Energy Storage Devices With a Statistical Characterization of State-of-Health Regeneration Phenomena
inferred← citeshttps://doi.org/10.1109/tim.2014.2348613openalex-snapshot
An Integrated Probabilistic Approach to Lithium-Ion Battery Remaining Useful Life Estimation
inferred← citeshttps://doi.org/10.1109/tim.2016.2622838openalex-snapshot
Particle Learning Framework for Estimating the Remaining Useful Life of Lithium-Ion Batteries
inferred← citeshttps://doi.org/10.1115/1.4042987openalex-snapshot
A Review of State of Health Estimation of Energy Storage Systems: Challenges and Possible Solutions for Futuristic Applications of Li-Ion Battery Packs in Electric Vehicles
inferred← citeshttps://doi.org/10.1155/2014/564894openalex-snapshot
Artificial Fish Swarm Algorithm‐Based Particle Filter for Li‐Ion Battery Life Prediction
inferred← citeshttps://doi.org/10.1177/1748006xjrr342openalex-snapshot
Lithium-ion battery life prognostic health management system using particle filtering framework
inferred← citeshttps://doi.org/10.17764/jiet.53.1.18763271g23n61x0openalex-snapshot
Prognostics and Health Assessment Implementation for Electronic Products
inferred← citeshttps://doi.org/10.23919/acc45564.2020.9147468openalex-snapshot
Interval Observer for SOC Estimation in Parallel-Connected Lithium-ion Batteries
inferred← citeshttps://doi.org/10.28948/ngumuh.1112985openalex-snapshot
Batarya sağlık durumunun makine öğrenmesi ile kestirimi
inferred← citeshttps://doi.org/10.3390/batteries10060181openalex-snapshot
Review of Various Machine Learning Approaches for Predicting Parameters of Lithium-Ion Batteries in Electric Vehicles
inferred← citeshttps://doi.org/10.3390/batteries3040032openalex-snapshot
Statistical Characterization of the State-of-Health of Lithium-Ion Batteries with Weibull Distribution Function—A Consideration of Random Effect Model in Charge Capacity Decay Estimation
inferred← citeshttps://doi.org/10.3390/en6083654openalex-snapshot
Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction
inferred← citeshttps://doi.org/10.3390/en81112322openalex-snapshot
Prediction of State-of-Health for Nickel-Metal Hydride Batteries by a Curve Model Based on Charge-Discharge Tests
inferred← citeshttps://doi.org/10.36001/phmconf.2012.v4i1.2165openalex-snapshot
Lithium-ion Battery Remaining Useful Life Estimation Based on Nonlinear AR Model Combined with Degradation Feature
inferred← citeshttps://doi.org/10.4271/2012-01-0125openalex-snapshot
A Review Study of Methods for Lithium-ion Battery Health Monitoring and Remaining Life Estimation in Hybrid Electric Vehicles
inferred← citeshttps://doi.org/10.70675/21d6a90fz8de3z44efz9c85zd72eb246becbopenalex-snapshot
Contribution à la maintenance proactive par la formalisation du processus de pronostic des performances de systèmes industriels
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