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Remaining Useful Life Prediction of Ev Lithium-Ion Batteries Based on Vmd Decomposition and Bo-Gru

https://doi.org/10.2139/ssrn.4753828
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Multivariate gated recurrent unit for battery remaining useful life prediction: A deep learning approach
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An Improved Algorithm of SOC Testing Based on Open-Circuit Voltage-Ampere Hour Method
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Exploring Bayesian model averaging with multiple ANNs for meteorological drought forecasts
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Prognostics of battery capacity based on charging data and data-driven methods for on-road vehicles
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Comparison of Bayesian and particle swarm algorithms for hyperparameter optimisation in machine learning applications in high energy physics
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A comparison of piecewise cubic Hermite interpolating polynomials, cubic splines and piecewise linear functions for the approximation of projectile aerodynamics
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Remaining useful life prediction of lithium battery based on capacity regeneration point detection
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A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM
inferredcites →https://doi.org/10.1016/j.energy.2022.123622openalex-extracted
Remaining Useful Life Prediction of Lithium-Ion Batteries Using a Hybrid Model
inferredcites →https://doi.org/10.1016/j.energy.2022.125278openalex-extracted
A hybrid method for prognostics of lithium-ion batteries capacity considering regeneration phenomena
inferredcites →https://doi.org/10.1016/j.energy.2023.126880openalex-extracted
Prediction of Remaining Useful Life and State of Health of Lithium Batteries Based on Time Series Feature and Savitzky-Golay Filter Combined with Gated Recurrent Unit Neural Network
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Battery degradation prediction against uncertain future conditions with recurrent neural network enabled deep learning
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Estimating battery state of health using electrochemical impedance spectroscopy and the relaxation effect
inferredcites →https://doi.org/10.1016/j.est.2022.105046openalex-extracted
State-of-health estimation of lithium-ion batteries based on improved long short-term memory algorithm
inferredcites →https://doi.org/10.1016/j.est.2022.105752openalex-extracted
Deep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: Methods, implementations, issues and prospects
inferredcites →https://doi.org/10.1016/j.est.2022.106050openalex-extracted
Particle swarm optimized data-driven model for remaining useful life prediction of lithium-ion batteries by systematic sampling
inferredcites →https://doi.org/10.1016/j.microrel.2016.07.151openalex-extracted
Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model
inferredcites →https://doi.org/10.1016/j.microrel.2017.02.002openalex-extracted
Prognostics of Li(NiMnCo)O2-based lithium-ion batteries using a novel battery degradation model
inferredcites →https://doi.org/10.1016/j.ress.2015.07.013openalex-extracted
An integrated unscented kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction
inferredcites →https://doi.org/10.1016/j.ymssp.2022.109347openalex-extracted
Battery health management using physics-informed machine learning: Online degradation modeling and remaining useful life prediction
inferredcites →https://doi.org/10.1038/s41467-020-15235-7openalex-extracted
Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning
inferredcites →https://doi.org/10.1038/s41586-020-1994-5openalex-extracted
Closed-loop optimization of fast-charging protocols for batteries with machine learning
inferredcites →https://doi.org/10.1038/s42256-020-0156-7openalex-extracted
Predicting the state of charge and health of batteries using data-driven machine learning
inferredcites →https://doi.org/10.1109/access.2018.2816684openalex-extracted
Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Exponential Model and Particle Filter
inferredcites →https://doi.org/10.1109/dsit55514.2022.9943913openalex-extracted
SOC estimation of lithium-ion batteries based on the condition of vessels
inferredcites →https://doi.org/10.1109/icaiis49377.2020.9194944openalex-extracted
Hilbert-Huang Transform and the Application
inferredcites →https://doi.org/10.1109/tim.2022.3142757openalex-extracted
Transfer Learning-Based Hybrid Remaining Useful Life Prediction for Lithium-Ion Batteries Under Different Stresses
inferredcites →https://doi.org/10.1109/tmech.2022.3202642openalex-extracted
Remaining Useful Life Prediction of Lithium-Ion Battery With Adaptive Noise Estimation and Capacity Regeneration Detection
inferredcites →https://doi.org/10.1109/tpel.2022.3182135openalex-extracted
Prognostics of Remaining Useful Life for Lithium-Ion Batteries Based on Hybrid Approach of Linear Pattern Extraction and Nonlinear Relationship Mining
inferredcites →https://doi.org/10.1109/tsp.2013.2288675openalex-extracted
Variational Mode Decomposition
inferredcites →https://doi.org/10.1162/neco.1997.9.8.1735openalex-extracted
Long Short-Term Memory
inferredcites →https://doi.org/10.3115/v1/d14-1179openalex-extracted
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
inferredcites →https://doi.org/10.3390/batteries9090448openalex-extracted
Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Iterative Transfer Learning and Mogrifier LSTM
inferredcites →https://doi.org/10.3390/en12091685openalex-extracted
Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Wiener Processes with Considering the Relaxation Effect
inferredcites →https://doi.org/10.3390/en8042889openalex-extracted
Regression Models Using Fully Discharged Voltage and Internal Resistance for State of Health Estimation of Lithium-Ion Batteries
inferredcites →https://doi.org/10.3390/su132313333openalex-extracted
Data-Driven Remaining Useful Life Prediction for Lithium-Ion Batteries Using Multi-Charging Profile Framework: A Recurrent Neural Network Approach
inferredcites →https://doi.org/10.48550/arxiv.2007.07588openalex-extracted
Importance of Tuning Hyperparameters of Machine Learning Algorithms
inferredcites →https://doi.org/10.6113/jpe.2017.17.5.1288openalex-extracted
Prediction of Remaining Useful Life of Lithium-ion Battery based on Multi-kernel Support Vector Machine with Particle Swarm Optimization
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