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Multivariate gated recurrent unit for battery remaining useful life prediction: A deep learning approach

https://doi.org/10.1002/er.6910
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/er.5413openalex-snapshot
Online state of health prediction method for lithium‐ion batteries, based on gated recurrent unit neural networks
inferredcites →https://doi.org/10.1002/er.5464openalex-snapshot
A novel remaining useful life prediction framework for lithium‐ion battery using grey model and particle filtering
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Li‐ion battery state of health estimation through Gaussian process regression with Thevenin model
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A Learning-Rate Schedule for Stochastic Gradient Methods to Matrix Factorization
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inferredcites →https://doi.org/10.1016/j.aej.2020.10.037openalex-snapshot
Fuzzy clustering to classify several time series models with fractional Brownian motion errors
inferredcites →https://doi.org/10.1016/j.apenergy.2017.09.106openalex-snapshot
A new hybrid method for the prediction of the remaining useful life of a lithium-ion battery
inferredcites →https://doi.org/10.1016/j.apenergy.2020.116410openalex-snapshot
Lithium-ion battery capacity estimation — A pruned convolutional neural network approach assisted with transfer learning
inferredcites →https://doi.org/10.1016/j.energy.2019.116467openalex-snapshot
State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression
inferredcites →https://doi.org/10.1016/j.energy.2020.119490openalex-snapshot
A novel online method for predicting the remaining useful life of lithium-ion batteries considering random variable discharge current
inferredcites →https://doi.org/10.1016/j.engfailanal.2016.04.014openalex-snapshot
Remaining useful lifetime prediction based on the damage-marker bivariate degradation model: A case study on lithium-ion batteries used in electric vehicles
inferredcites →https://doi.org/10.1016/j.jbi.2020.103627openalex-snapshot
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inferredcites →https://doi.org/10.1016/j.jisa.2020.102582openalex-snapshot
Computational intelligence intrusion detection techniques in mobile cloud computing environments: Review, taxonomy, and open research issues
inferredcites →https://doi.org/10.1016/j.jpowsour.2019.03.008openalex-snapshot
Prognostic health condition for lithium battery using the partial incremental capacity and Gaussian process regression
inferredcites →https://doi.org/10.1016/j.jpowsour.2020.228863openalex-snapshot
Online capacity estimation of lithium-ion batteries with deep long short-term memory networks
inferredcites →https://doi.org/10.1016/j.measurement.2019.107162openalex-snapshot
Railway turnout system RUL prediction based on feature fusion and genetic programming
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A New Online Learned Interval Type-3 Fuzzy Control System for Solar Energy Management Systems
inferredcites →https://doi.org/10.1109/icrms.2018.00067openalex-snapshot
Lithium-Ion Battery Remaining Useful Life Prediction Based on GRU-RNN
inferredcites →https://doi.org/10.1109/ieses45645.2020.9210642openalex-snapshot
Machine Learning Approaches in Battery Management Systems: State of the Art: Remaining useful life and fault detection
inferredcites →https://doi.org/10.1109/tie.2018.2889623openalex-snapshot
On-Board Short-Circuit Detection of Li-ion Batteries Undergoing Fixed Charging Profile as in Smartphone Applications
inferredcites →https://doi.org/10.1109/tii.2019.2941747openalex-snapshot
Gaussian Process Regression With Automatic Relevance Determination Kernel for Calendar Aging Prediction of Lithium-Ion Batteries
inferredcites →https://doi.org/10.1109/tim.2019.2917735openalex-snapshot
Predicting Remaining Useful Life of Rolling Bearings Based on Deep Feature Representation and Transfer Learning
inferredcites →https://doi.org/10.1109/tmech.2020.3049046openalex-snapshot
Feature Analyses and Modeling of Lithium-Ion Battery Manufacturing Based on Random Forest Classification
inferredcites →https://doi.org/10.1109/tpel.2018.2873247openalex-snapshot
Lithium-Ion Batteries Health Prognosis Considering Aging Conditions
inferredcites →https://doi.org/10.1109/tr.2014.2299152openalex-snapshot
Review of Hybrid Prognostics Approaches for Remaining Useful Life Prediction of Engineered Systems, and an Application to Battery Life Prediction
inferredcites →https://doi.org/10.1109/tte.2019.2944802openalex-snapshot
Modified Gaussian Process Regression Models for Cyclic Capacity Prediction of Lithium-Ion Batteries
inferredcites →https://doi.org/10.1109/tte.2020.3017090openalex-snapshot
Battery Health Prediction Using Fusion-Based Feature Selection and Machine Learning
inferredcites →https://doi.org/10.1109/tvt.2018.2805189openalex-snapshot
Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries
inferredcites →https://doi.org/10.1177/0144598719881223openalex-snapshot
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Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction
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State-of-Health Estimation of Li-ion Batteries in Electric Vehicle Using IndRNN under Variable Load Condition

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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