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Physics-Informed Transfer Learning for Cross-Scale SOC/SOE Co-Estimation in Battery Energy Storage

https://doi.org/10.2139/ssrn.6793812
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/er.7339openalex-snapshot
Modeling, state of charge estimation, and charging of lithium‐ion battery in electric vehicle: A review
inferredcites →https://doi.org/10.1007/s42835-021-00954-8openalex-snapshot
State-of-Charge Estimation of Lithium-ion Batteries Using LSTM Deep Learning Method
inferredcites →https://doi.org/10.1016/j.apm.2013.01.024openalex-snapshot
Battery state-of-charge estimator using the SVM technique
inferredcites →https://doi.org/10.1016/j.egyr.2022.02.116openalex-snapshot
An extended Kalman filter based SOC estimation method for Li-ion battery
inferredcites →https://doi.org/10.1016/j.energy.2022.125375openalex-snapshot
SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output
inferredcites →https://doi.org/10.1016/j.energy.2024.130779openalex-snapshot
Deep transfer learning enables battery state of charge and state of health estimation
inferredcites →https://doi.org/10.1016/j.est.2016.07.002openalex-snapshot
Battery state of charge estimation using a load-classifying neural network
inferredcites →https://doi.org/10.1016/j.geits.2024.100177openalex-snapshot
State of charge estimation for electric vehicles using random forest
inferredcites →https://doi.org/10.1016/j.jechem.2025.01.019openalex-snapshot
Enhanced electrode-level diagnostics for lithium-ion battery degradation using physics-informed neural networks
inferredcites →https://doi.org/10.1109/access.2019.2957698openalex-snapshot
Battery Energy Storage Models for Optimal Control
inferredcites →https://doi.org/10.1109/access.2023.3337215openalex-snapshot
Transfer Learning Techniques for the Lithium-Ion Battery State of Charge Estimation
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/tste.2016.2615638openalex-snapshot
Battery ESS Planning for Wind Smoothing via Variable-Interval Reference Modulation and Self-Adaptive SOC Control Strategy
inferredcites →https://doi.org/10.1109/tte.2020.3041604openalex-snapshot
Cross-Domain State-of-Charge Estimation of Li-Ion Batteries Based on Deep Transfer Neural Network With Multiscale Distribution Adaptation
inferredcites →https://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
inferredcites →https://doi.org/10.1109/tte.2023.3324760openalex-snapshot
Multitimescale Feature Extraction From Multisensor Data Using Deep Neural Network for Battery State-of-Charge and State-of-Health Co-Estimation
inferredcites →https://doi.org/10.1109/tte.2025.3533540openalex-snapshot
Transfer Learning-Based Lithium-Ion Battery State of Health Estimation With Electrochemical Impedance Spectroscopy
inferredcites →https://doi.org/10.1109/tvt.2023.3267500openalex-snapshot
An RNN With Small Sequence Trained by Multi-Level Optimization for SOC Estimation in Li-Ion Battery Applications
inferredcites →https://doi.org/10.1109/tvt.2024.3350663openalex-snapshot
TTSNet: State-of-Charge Estimation of Li-Ion Battery in Electrical Vehicles With Temporal Transformer-Based Sequence Network
inferredcites →https://doi.org/10.1149/2.0551509jesopenalex-snapshot
Parameterization of a Physico-Chemical Model of a Lithium-Ion Battery
inferredcites →https://doi.org/10.3390/batteries11020049openalex-snapshot
A Novel Method for Estimating the State of Health of Lithium-Ion Batteries Based on Physics-Informed Neural Network
inferredcites →https://doi.org/10.3390/batteries9060301openalex-snapshot
Hybrid Modeling of Lithium-Ion Battery: Physics-Informed Neural Network for Battery State Estimation
inferredcites →https://doi.org/10.3390/wevj11010023openalex-snapshot
Review on the State of Charge Estimation Methods for Electric Vehicle Battery
inferredcites →https://doi.org/10.3390/wevj15120562openalex-snapshot
An RNN-CNN-Based Parallel Hybrid Approach for Battery State of Charge (SoC) Estimation Under Various Temperatures and Discharging Cycle Considering Noisy Conditions
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