On the public citation graph

A Comparative Study on Hybrid Convolutional Neural Network-Recurrent Neural Network Architectures for Remaining Useful Life Prediction of Lithium-Ion Batteries

https://doi.org/10.2139/ssrn.5848172
0 signed · 28 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

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

inferredcites →https://doi.org/10.1007/s11581-024-06049-4openalex-snapshot
A decade of machine learning in lithium-ion battery state estimation: a systematic review
inferredcites →https://doi.org/10.1007/s11837-024-06674-4openalex-snapshot
Unraveling Magnesium Alloy Corrosion Patterns Through Unsupervised Machine Learning: Exploring Clustering Techniques for Enhanced Insight
inferredcites →https://doi.org/10.1016/j.egyr.2022.05.040openalex-snapshot
Performance of a hybrid battery energy storage system
inferredcites →https://doi.org/10.1016/j.est.2018.12.011openalex-snapshot
Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks
inferredcites →https://doi.org/10.1016/j.gce.2024.09.001openalex-snapshot
Advanced data-driven techniques in AI for predicting lithium-ion battery remaining useful life: a comprehensive review
inferredcites →https://doi.org/10.1016/j.mechmat.2024.105168openalex-snapshot
Accelerated intelligent prediction and analysis of mechanical properties of magnesium alloys based on scaled super learner machine-learning algorithms
inferredcites →https://doi.org/10.1016/j.nxmate.2025.101425openalex-snapshot
Accelerating age hardening behavior of Mg alloy using a data-driven screening strategy guided by machine learning
inferredcites →https://doi.org/10.1021/acsaem.0c01155openalex-snapshot
An Electrochemical Model of Lithium Plating and Stripping in Lithium Ion Batteries
inferredcites →https://doi.org/10.1109/access.2020.2968939openalex-snapshot
LSTM-Based Battery Remaining Useful Life Prediction With Multi-Channel Charging Profiles
inferredcites →https://doi.org/10.1109/access.2023.3304699openalex-snapshot
Optimizing Battery RUL Prediction of Lithium-Ion Batteries Based on Harris Hawk Optimization Approach Using Random Forest and LightGBM
inferredcites →https://doi.org/10.1109/access.2023.3329508openalex-snapshot
A Novel Approach for Predicting Remaining Useful Life and Capacity Fade in Lithium-Ion Batteries Using Hybrid Machine Learning
inferredcites →https://doi.org/10.1109/tia.2018.2877166openalex-snapshot
Efficiency Degradation Model of Lithium-Ion Batteries for Electric Vehicles
inferredcites →https://doi.org/10.1109/tii.2023.3266403openalex-snapshot
A Data-Model Interactive Remaining Useful Life Prediction Approach of Lithium-Ion Batteries Based on PF-BiGRU-TSAM
inferredcites →https://doi.org/10.1109/tim.2021.3111009openalex-snapshot
Remaining Useful Life Estimation for Prognostics of Lithium-Ion Batteries Based on Recurrent Neural Network
inferredcites →https://doi.org/10.1109/tim.2023.3260277openalex-snapshot
RUL Prediction of Lithium-Ion Battery in Early-Cycle Stage Based on Similar Sample Fusion Under Lebesgue Sampling Framework
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/10.1109/tvt.2020.3014932openalex-snapshot
State-of-Health Estimation and Remaining-Useful-Life Prediction for Lithium-Ion Battery Using a Hybrid Data-Driven Method
inferredcites →https://doi.org/10.1149/2.1131914jesopenalex-snapshot
Impacts of Current Rates on the Degradation Behaviors of Lithium-Ion Batteries under Over-Discharge Conditions
inferredcites →https://doi.org/10.1177/00202940221103622openalex-snapshot
A hybrid CNN-BiLSTM approach for remaining useful life prediction of EVs lithium-Ion battery
inferredcites →https://doi.org/10.24018/ejece.2023.7.1.485openalex-snapshot
Hybrid Deep Learning Mechanism for Charging Control and Management of Electric Vehicles
inferredcites →https://doi.org/10.3390/a18060303openalex-snapshot
Forecasting the Remaining Useful Life of Lithium-Ion Batteries Using Machine Learning Models—A Web-Based Application
inferredcites →https://doi.org/10.3390/batteries10070220openalex-snapshot
Exploring Lithium-Ion Battery Degradation: A Concise Review of Critical Factors, Impacts, Data-Driven Degradation Estimation Techniques, and Sustainable Directions for Energy Storage Systems
inferredcites →https://doi.org/10.3390/batteries10100356openalex-snapshot
Evaluation of Advances in Battery Health Prediction for Electric Vehicles from Traditional Linear Filters to Latest Machine Learning Approaches
inferredcites →https://doi.org/10.3390/batteries11010017openalex-snapshot
Advanced Machine Learning and Deep Learning Approaches for Estimating the Remaining Life of EV Batteries—A Review
inferredcites →https://doi.org/10.3390/computers12110219openalex-snapshot
A CNN-GRU Approach to the Accurate Prediction of Batteries’ Remaining Useful Life from Charging Profiles
inferredcites →https://doi.org/10.3390/s22239522openalex-snapshot
XGBoost-Based Remaining Useful Life Estimation Model with Extended Kalman Particle Filter for Lithium-Ion Batteries
inferredcites →https://doi.org/10.3390/sym16040436openalex-snapshot
Discharge Capacity Estimation for Li-Ion Batteries: A Comparative Study
inferredcites →https://doi.org/10.37934/araset.30.3.1631openalex-snapshot
LSTM Inefficiency in Long-Term Dependencies Regression Problems
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.