On the public citation graph
Stgcn-Transformer Deep Learning Network-Based Estimation of the SOC for Shipboard Lithium Batteries
0 signed · 39 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.
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known work and none has been retracted — and get a dated CiteStamped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.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/er.5374openalex-extracted
Lithium‐ion battery SOC / SOH adaptive estimation via simplified single particle model
inferredcites →https://doi.org/10.1016/j.apenergy.2020.116360openalex-extracted
Modelling of modular battery systems under cell capacity variation and degradation
inferredcites →https://doi.org/10.1016/j.applthermaleng.2024.123120openalex-extracted
Intelligent control for rapidity and security of all-electric ships gas turbine under complex mutation load using optimized neural network
inferredcites →https://doi.org/10.1016/j.combustflame.2023.113274openalex-extracted
An a priori analysis on principal component analysis based conditional source-term estimation model for Sandia jet flames
inferredcites →https://doi.org/10.1016/j.conengprac.2022.105176openalex-extracted
Electrochemical modeling and parameterization towards control-oriented management of lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.egypro.2017.12.388openalex-extracted
Open circuit voltage and state of charge online estimation for lithium ion batteries
inferredcites →https://doi.org/10.1016/j.energy.2021.120266openalex-extracted
Decarbonising inland ship power system: Alternative solution and assessment method
inferredcites →https://doi.org/10.1016/j.energy.2022.123178openalex-extracted
Lithium-ion battery state-of-charge estimation for small target sample sets using the improved GRU-based transfer learning
inferredcites →https://doi.org/10.1016/j.energy.2023.126724openalex-extracted
Time-efficient identification of lithium-ion battery temperature-dependent OCV-SOC curve using multi-output Gaussian process
inferredcites →https://doi.org/10.1016/j.energy.2023.129462openalex-extracted
An intelligent fusion estimation method for state of charge estimation of lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.energy.2023.129826openalex-extracted
Differential current in constant-voltage charging mode: A novel tool for state-of-health and state-of-charge estimation of lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.eng.2020.10.022openalex-extracted
Co-Estimation of State of Charge and Capacity for Lithium-Ion Batteries with Multi-Stage Model Fusion Method
inferredcites →https://doi.org/10.1016/j.engappai.2023.106717openalex-extracted
MVMA-GCN: Multi-view multi-layer attention graph convolutional networks
inferredcites →https://doi.org/10.1016/j.ensm.2023.102883openalex-extracted
Deep Learning Framework for Lithium-ion Battery State of Charge Estimation: Recent Advances and Future Perspectives
inferredcites →https://doi.org/10.1016/j.est.2020.101250openalex-extracted
An online SOC and capacity estimation method for aged lithium-ion battery pack considering cell inconsistency
inferredcites →https://doi.org/10.1016/j.est.2021.102896openalex-extracted
Influence of the connection topology on the performance of lithium-ion battery pack under cell-to-cell parameters variations
inferredcites →https://doi.org/10.1016/j.est.2022.105392openalex-extracted
A probabilistic risk-averse approach for energy storage sizing in all-electric ship
inferredcites →https://doi.org/10.1016/j.est.2023.107110openalex-extracted
Electric vehicle battery state of health estimation using Incremental Capacity Analysis
inferredcites →https://doi.org/10.1016/j.est.2023.107161openalex-extracted
State of health and remaining useful life prediction of lithium-ion batteries based on a disturbance-free incremental capacity and differential voltage analysis method
inferredcites →https://doi.org/10.1016/j.est.2023.107430openalex-extracted
Experimental analysis of lithium-ion cell procurement: Quality differences, correlations, and importance of cell characterization
inferredcites →https://doi.org/10.1016/j.est.2023.108481openalex-extracted
State of charge estimation method based on linearization of voltage hysteresis curve
inferredcites →https://doi.org/10.1016/j.est.2023.109244openalex-extracted
State-space modeling for electrochemical performance of Li-ion batteries with physics-informed deep operator networks
inferredcites →https://doi.org/10.1016/j.est.2023.109318openalex-extracted
Development of equivalent circuit model for thermal runaway in lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.etran.2023.100251openalex-extracted
All-electric ship operations and management: Overview and future research directions
inferredcites →https://doi.org/10.1016/j.etran.2023.100295openalex-extracted
System identification and state estimation of a reduced-order electrochemical model for lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.ins.2024.120398openalex-extracted
Dynamic multiobjective optimization with varying number of objectives assisted by dynamic principal component analysis
inferredcites →https://doi.org/10.1016/j.jclepro.2019.06.273openalex-extracted
State-of-charge estimation of lithium-ion battery using an improved neural network model and extended Kalman filter
inferredcites →https://doi.org/10.1016/j.jmsy.2021.12.003openalex-extracted
End to end multi-task learning with attention for multi-objective fault diagnosis under small sample
inferredcites →https://doi.org/10.1016/j.jpowsour.2022.231483openalex-extracted
New method for acquisition of impedance spectra from charge/discharge curves of lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.measurement.2023.114026openalex-extracted
Efficient estimation of state of charge of lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.neucom.2021.05.004openalex-extracted
Rethinking the ST-GCNs for 3D skeleton-based human action recognition
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.2021.3092743openalex-extracted
Lithium-Ion Battery Ageing Behavior Pattern Characterization and State-of-Health Estimation Using Data-Driven Method
inferredcites →https://doi.org/10.1109/tpel.2009.2014868openalex-extracted
State-of-Charge (SOC)-Balancing Control of a Battery Energy Storage System Based on a Cascade PWM Converter
inferredcites →https://doi.org/10.48550/arxiv.1502.04623openalex-extracted
DRAW: A Recurrent Neural Network For Image Generation
inferredcites →https://doi.org/10.48550/arxiv.1609.02907openalex-extracted
Semi-Supervised Classification with Graph Convolutional Networks
inferredcites →https://doi.org/10.4230/lipics.ecoop.2025.6openalex-snapshot
Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories
inferredcites →https://doi.org/10.4230/lipics.itp.2023.19openalex-snapshot
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
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.