On the public citation graph

Cross-Domain State-of-Charge Estimation of Li-Ion Batteries Based on Deep Transfer Neural Network With Multiscale Distribution Adaptation

https://doi.org/10.1109/tte.2020.3041604
0 signed · 98 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/10.1016/j.apenergy.2013.07.008openalex-snapshot
State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures
inferredcites →https://doi.org/10.1016/j.apenergy.2016.09.010openalex-snapshot
Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.asoc.2017.05.031openalex-snapshot
Wind power prediction using deep neural network based meta regression and transfer learning
inferredcites →https://doi.org/10.1016/j.energy.2019.03.059openalex-snapshot
State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network
inferredcites →https://doi.org/10.1016/j.energy.2019.116538openalex-snapshot
Stacked bidirectional long short-term memory networks for state-of-charge estimation of lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.isatra.2019.08.012openalex-snapshot
Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application
inferredcites →https://doi.org/10.1016/j.jpowsour.2018.10.019openalex-snapshot
Towards a smarter battery management system: A critical review on battery state of health monitoring methods
inferredcites →https://doi.org/10.1016/j.measurement.2019.06.029openalex-snapshot
A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings
inferredcites →https://doi.org/10.1016/j.neunet.2020.01.017openalex-snapshot
EEG based multi-class seizure type classification using convolutional neural network and transfer learning
inferredcites →https://doi.org/10.1016/j.ress.2017.11.021openalex-snapshot
Remaining useful life estimation in prognostics using deep convolution neural networks
inferredcites →https://doi.org/10.1016/j.rser.2015.11.042openalex-snapshot
Critical review of state of health estimation methods of Li-ion batteries for real applications
inferredcites →https://doi.org/10.1016/j.rser.2017.05.001openalex-snapshot
A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations
inferredcites →https://doi.org/10.1109/access.2017.2780258openalex-snapshot
Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles
inferredcites →https://doi.org/10.1109/access.2019.2942213openalex-snapshot
State of Charge Estimation for Lithium-Ion Batteries Using Model-Based and Data-Driven Methods: A Review
inferredcites →https://doi.org/10.1109/ibcast.2019.8667172openalex-snapshot
Performance Comparison of NARX & RNN-LSTM Neural Networks for LiFePO4 Battery State of Charge Estimation
inferredcites →https://doi.org/10.1109/itec.2019.8790543openalex-snapshot
Li-ion Battery State of Charge Estimation Using Long Short-Term Memory Recurrent Neural Network with Transfer Learning
inferredcites →https://doi.org/10.1109/lsens.2019.2932099openalex-snapshot
Maximum Mean Discrepancy Minimization Based Transfer Learning for Indoor WLAN Personnel Intrusion Detection
inferredcites →https://doi.org/10.1109/mie.2013.2250351openalex-snapshot
Battery Management System: An Overview of Its Application in the Smart Grid and Electric Vehicles
inferredcites →https://doi.org/10.1109/tia.2018.2874588openalex-snapshot
A Compact Methodology Via a Recurrent Neural Network for Accurate Equivalent Circuit Type Modeling of Lithium-Ion Batteries
inferredcites →https://doi.org/10.1109/tie.2017.2787586openalex-snapshot
Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries
inferredcites →https://doi.org/10.1109/tmech.2017.2722479openalex-snapshot
Deep Learning for Infrared Thermal Image Based Machine Health Monitoring
inferredcites →https://doi.org/10.1109/tmi.2016.2528162openalex-snapshot
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
inferredcites →https://doi.org/10.1109/tr.2019.2896230openalex-snapshot
Nonlinear-Drifted Fractional Brownian Motion With Multiple Hidden State Variables for Remaining Useful Life Prediction of Lithium-Ion Batteries
inferredcites →https://doi.org/10.1109/tsmc.2017.2754287openalex-snapshot
A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis
inferredcites →https://doi.org/10.1109/tte.2015.2512237openalex-snapshot
Advanced Machine Learning Approach for Lithium-Ion Battery State Estimation in Electric Vehicles
inferredcites →https://doi.org/10.17632/wykht8y7tg.1openalex-snapshot
Panasonic 18650PF Li-ion Battery Data
inferredcites →https://doi.org/10.3390/en12091592openalex-snapshot
An Approach to State of Charge Estimation of Lithium-Ion Batteries Based on Recurrent Neural Networks with Gated Recurrent Unit
inferredcites →https://doi.org/10.4230/oasics.dx.2026.15openalex-snapshot
AutoResearch Lite: a reproducible paper-inspired experiment runner (Adam mechanism reproduction and convergence-speed comparison)
inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot
Adam: A Method for Stochastic Optimization

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.4874801openalex-extracted
inferred← citeshttps://doi.org/10.1002/adts.202300302openalex-snapshot
An Initial Value Problem for State of Charge Estimation of Lithium‐Ion Batteries with an Adaptive Fractional‐Order Unscented Particle Filter
inferred← citeshttps://doi.org/10.1002/cta.3788openalex-snapshot
High‐precision state of charge estimation of lithium‐ion batteries based on improved particle swarm optimization‐backpropagation neural network‐dual extended Kalman filtering
inferred← citeshttps://doi.org/10.1002/er.8067openalex-snapshot
A Fletcher‐Reeves conjugate gradient optimized multi‐reservoir echo state network for state of charge estimation in vehicle battery
inferred← citeshttps://doi.org/10.1002/er.8373openalex-snapshot
Battery hysteresis compensation modeling and state‐of‐charge estimation adaptive to time‐varying ambient temperature conditions
inferred← citeshttps://doi.org/10.1002/jctb.7823openalex-snapshot
A comprehensive review on progression and innovations in microwave assisted extraction technology for essential oils
inferred← citeshttps://doi.org/10.1007/978-3-031-43520-1_5openalex-snapshot
Advancing Lithium-Ion Battery Management with Deep Learning: A Comprehensive Review
inferred← citeshttps://doi.org/10.1007/978-3-031-54674-7_7openalex-snapshot
Prediction of Lithium-Ion Batteries Output Voltage in Electric Vehicles
inferred← citeshttps://doi.org/10.1007/s10462-024-10858-4openalex-snapshot
A concise review towards a novel target specific multi-source unsupervised transfer learning technique for GDP estimation using CO2 emission data
inferred← citeshttps://doi.org/10.1007/s11581-026-07240-5openalex-snapshot
The state-of-the-art in state of charge estimation methods for lithium-ion batteries: Ongoing evolution and future perspective
inferred← citeshttps://doi.org/10.1007/s11581-026-07346-wopenalex-snapshot
Collaborative estimation of lithium-ion battery state based on multi-innovation identification and improved dual adaptive filtering
inferred← citeshttps://doi.org/10.1016/j.adapen.2022.100117openalex-snapshot
Transfer learning for battery smarter state estimation and ageing prognostics: Recent progress, challenges, and prospects
inferred← citeshttps://doi.org/10.1016/j.apenergy.2022.118726openalex-snapshot
A controllable deep transfer learning network with multiple domain adaptation for battery state-of-charge estimation
inferred← citeshttps://doi.org/10.1016/j.cles.2024.100131openalex-snapshot
Advancing battery state of charge estimation in electric vehicles through deep learning: A comprehensive study using real-world driving data
inferred← citeshttps://doi.org/10.1016/j.energy.2023.128094openalex-snapshot
Using the evolutionary mating algorithm for optimizing deep learning parameters for battery state of charge estimation of electric vehicle
inferred← citeshttps://doi.org/10.1016/j.energy.2024.133032openalex-snapshot
Multi-output fusion SOC and SOE estimation algorithm based on deep network migration
inferred← citeshttps://doi.org/10.1016/j.energy.2025.136988openalex-snapshot
Enhancing cross-temperature state-of-charge estimation accuracy for lithium-ion batteries using multi-physics features and physical guidance
inferred← citeshttps://doi.org/10.1016/j.ensm.2023.102883openalex-snapshot
Deep Learning Framework for Lithium-ion Battery State of Charge Estimation: Recent Advances and Future Perspectives
inferred← citeshttps://doi.org/10.1016/j.est.2023.106812openalex-snapshot
Deep domain adaptation network for transfer learning of state of charge estimation among batteries
inferred← citeshttps://doi.org/10.1016/j.est.2025.116636openalex-snapshot
Multi-source transfer network for cross-domain state of charge estimation of lithium-ion batteries
inferred← citeshttps://doi.org/10.1016/j.est.2025.117334openalex-snapshot
Accurate state of charge estimation for lithium-ion batteries based on cross-domain transfer learning with limited data
inferred← citeshttps://doi.org/10.1016/j.est.2025.120307openalex-snapshot
Multi-method fusion estimation of state of charge for Li-ion batteries based on model-data dual-driven approach
inferred← citeshttps://doi.org/10.1016/j.est.2026.124510openalex-snapshot
Fourier neural operators combined with the U-Net network for state of charge estimation of Li-ion batteries under dynamic temperatures and different operating conditions
inferred← citeshttps://doi.org/10.1016/j.etran.2026.100639openalex-snapshot
Ultrasonic sensing-based cross-domain transfer learning framework for lithium-ion battery
inferred← citeshttps://doi.org/10.1016/j.jechem.2023.02.019openalex-snapshot
Robust state of charge estimation of lithium-ion battery via mixture kernel mean p-power error loss LSTM with heap-based-optimizer
inferred← citeshttps://doi.org/10.1016/j.jechem.2023.05.034openalex-snapshot
Boosting battery state of health estimation based on self-supervised learning
inferred← citeshttps://doi.org/10.1016/j.jmsy.2024.05.011openalex-snapshot
Feature structured domain adaptation for quality prediction of cross working conditions in industrial processes
inferred← citeshttps://doi.org/10.1016/j.jpowsour.2024.234879openalex-snapshot
A state-dependent quasi-linear parameter-varying model of lithium-ion batteries for state of charge estimation
inferred← citeshttps://doi.org/10.1016/j.jpowsour.2024.235760openalex-snapshot
State-of-charge estimation across battery chemistries: A novel regression-based method and insights from unsupervised domain adaptation
inferred← citeshttps://doi.org/10.1016/j.measurement.2025.116705openalex-snapshot
State of charge estimation of lithium-ion batteries with unknown parameters using an adaptive fractional-order center difference Kalman filter
inferred← citeshttps://doi.org/10.1016/j.ress.2021.108012openalex-snapshot
Deep residual LSTM with domain-invariance for remaining useful life prediction across domains
inferred← citeshttps://doi.org/10.1016/j.ress.2024.110613openalex-snapshot
Health management of power batteries in low temperatures based on Adaptive Transfer Enformer framework
inferred← citeshttps://doi.org/10.1038/s41598-024-72286-2openalex-snapshot
Artificial intelligence for cybersecurity monitoring of cyber-physical power electronic converters: a DC/DC power converter case study
inferred← citeshttps://doi.org/10.1109/icaibd69640.2026.11637198openalex-snapshot
FTiformer: A Dual-Domain Inverted Transformer for Accurate Battery State-of-Charge Estimation
inferred← citeshttps://doi.org/10.1109/iceccme62383.2024.10797117openalex-snapshot
Efficient Neural Network-Based State-of-Charge Estimation for Electric Vehicles Through Transfer Learning
inferred← citeshttps://doi.org/10.1109/intelec60315.2024.10679009openalex-snapshot
A Long Horizon Data Augmentation Technique to Produce Reliable Synthetic Battery Parameters
inferred← citeshttps://doi.org/10.1109/powercon66300.2025.11295853openalex-snapshot
Lithium-Ion Battery Soc Estimation Based on the Joint Algorithm of Mils Online Parameter Identification and Fuzzy Control-Improved Extended Kalman Filter
inferred← citeshttps://doi.org/10.1109/tec.2023.3285405openalex-snapshot
Lithium-Ion Battery State of Charge Estimation With Adaptability to Changing Conditions
inferred← citeshttps://doi.org/10.1109/tim.2024.3497053openalex-snapshot
A Bayesian Inferred Health Prognosis and State of Charge Estimation for Power Batteries
inferred← citeshttps://doi.org/10.1109/tim.2024.3522658openalex-snapshot
Unsupervised Diagnosis of Interturn Short Circuit of Permanent Magnet Synchronous Motor Guided by Density-Based Clustering and Deep Learning
inferred← citeshttps://doi.org/10.1109/tim.2025.3612551openalex-snapshot
A Multiscale Fusion DNN With Adaptive Gradient Optimization for Lithium-Ion Battery Pack Fault Diagnosis and SoC Prediction
inferred← citeshttps://doi.org/10.1109/tim.2026.3659627openalex-snapshot
State-of-Charge Estimation Considering Cell-to-Cell Differences Using Two-Stage Transfer Estimation
inferred← citeshttps://doi.org/10.1109/tiv.2024.3386819openalex-snapshot
A Data-Driven-Based SOC Estimation Method Using Newly Constructed Features for Different Application Environments
inferred← citeshttps://doi.org/10.1109/tpel.2022.3220760openalex-snapshot
Temperature Adaptive Transfer Network for Cross-Domain State-of-Charge Estimation of Li-Ion Batteries
inferred← citeshttps://doi.org/10.1109/tpel.2023.3251568openalex-snapshot
Source-Free Cross-Domain State of Charge Estimation of Lithium-Ion Batteries at Different Ambient Temperatures
inferred← citeshttps://doi.org/10.1109/tpel.2025.3595437openalex-snapshot
Deep Transfer Learning Technology-Based Condition Monitoring and Fault Diagnosis of Electric Vehicle Electric Powertrain Systems: A Review
inferred← citeshttps://doi.org/10.1109/tte.2021.3138230openalex-snapshot
2021 Index IEEE Transactions on Transportation Electrification Vol. 7
inferred← citeshttps://doi.org/10.1109/tte.2023.3274548openalex-snapshot
Online Adaptive Model Identification and State of Charge Estimation for Vehicle-Level Battery Packs
inferred← citeshttps://doi.org/10.1109/tte.2023.3291501openalex-snapshot
Joint Estimation of State of Charge and State of Energy of Lithium-Ion Batteries Based on Optimized Bidirectional Gated Recurrent Neural Network
inferred← citeshttps://doi.org/10.1109/tte.2023.3293551openalex-snapshot
Transfer Learning-Based State of Charge and State of Health Estimation for Li-Ion Batteries: A Review
inferred← citeshttps://doi.org/10.1109/tte.2023.3310159openalex-snapshot
Predicting State-of-Charge Using Gradient-Boosted SVR Ensemble Technique for Lithium Ion Battery Used in EVs
inferred← citeshttps://doi.org/10.1109/tte.2023.3324822openalex-snapshot
An Online Transfer Learning Framework for Cell SOC Online Estimation of Battery Pack in Complex Application Conditions
inferred← citeshttps://doi.org/10.1109/tte.2024.3391938openalex-snapshot
Hybrid Prompt-Driven Large Language Model for Robust State-of-Charge Estimation of Multitype Li-ion Batteries
inferred← citeshttps://doi.org/10.1109/tte.2024.3483973openalex-snapshot
An Unsupervised Domain Adaptation Framework for Cross-Conditions State of Charge Estimation of Lithium-Ion Batteries
inferred← citeshttps://doi.org/10.1109/tte.2025.3538624openalex-snapshot
Transfer Adaptive Digital Twin for Cross-Domain State-of-Health Estimation of Li-Ion Batteries
inferred← citeshttps://doi.org/10.1145/3495018.3495038openalex-snapshot
A Novel Method of SOC Estimation based on Neural Network
inferred← citeshttps://doi.org/10.1149/1945-7111/ace65bopenalex-snapshot
Improved Feature Decoupling Transfer Network Modeling based on Singular Value Decomposition for SOC Estimation in Energy-Storage Lithium-ion Batteries
inferred← citeshttps://doi.org/10.1155/2024/8837186openalex-snapshot
Cross‐Domain State‐of‐Charge Estimation of Li‐Ion Batteries Based on Source Domain Selection and Attention Mechanism
inferred← citeshttps://doi.org/10.11648/j.jeee.20241202.11openalex-snapshot
Comparative Study of Joint Estimation of State of Charge (SOC) and State of Health (SOH) of Lithium-ion Batteries Based on Different Tree Models
inferred← citeshttps://doi.org/10.1177/09544070261436531openalex-snapshot
State-of-charge estimation of two-wheeler electric vehicle battery pack by long-short term memory model trained using cell-level data
inferred← citeshttps://doi.org/10.1360/ssi-2025-0356openalex-snapshot
Wiener-LSTM fusion framework for lithium-ion battery end-of-life interval estimation using multi-degradation features
inferred← citeshttps://doi.org/10.2139/ssrn.6793812openalex-snapshot
Physics-Informed Transfer Learning for Cross-Scale SOC/SOE Co-Estimation in Battery Energy Storage
inferred← citeshttps://doi.org/10.3390/batteries11110414openalex-snapshot
Review of Fault Detection Approaches for Large-Scale Lithium-Ion Battery Systems: A Spatio-Temporal Perspective
inferred← citeshttps://doi.org/10.3390/en15103768openalex-snapshot
A Robust Kalman Filter-Based Approach for SoC Estimation of Lithium-Ion Batteries in Smart Homes
inferred← citeshttps://doi.org/10.3390/en17163884openalex-snapshot
Dynamic K-Decay Learning Rate Optimization for Deep Convolutional Neural Network to Estimate the State of Charge for Electric Vehicle Batteries
inferred← citeshttps://doi.org/10.3390/en17205029openalex-snapshot
Multi-Temperature State-of-Charge Estimation of Lithium-Ion Batteries Based on Spatial Transformer Network
inferred← citeshttps://doi.org/10.3390/en18164364openalex-snapshot
SOC Estimation for Lithium-Ion Batteries Based on Weighted Multi-Innovation Sage–Husa Adaptive EKF
inferred← citeshttps://doi.org/10.3390/machines10100912openalex-snapshot
Deep Learning in the State of Charge Estimation for Li-Ion Batteries of Electric Vehicles: A Review
inferred← citeshttps://doi.org/10.3390/su142315912openalex-snapshot
Overview of Model- and Non-Model-Based Online Battery Management Systems for Electric Vehicle Applications: A Comprehensive Review of Experimental and Simulation Studies
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.