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Temperature-Gated Causal Multi-Scale Physics-Informed Network for Lithium-Ion Battery State-of-Charge Estimation
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inferredcites →https://doi.org/10.1016/j.apenergy.2008.11.021openalex-snapshot
Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.apenergy.2020.114789openalex-snapshot
A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter
inferredcites →https://doi.org/10.1016/j.apenergy.2023.121578openalex-snapshot
A novel lithium-ion battery state of charge estimation method based on the fusion of neural network and equivalent circuit models
inferredcites →https://doi.org/10.1016/j.apenergy.2024.122669openalex-snapshot
Enhancing state of charge and state of energy estimation in Lithium-ion batteries based on a TimesNet model with Gaussian data augmentation and error correction
inferredcites →https://doi.org/10.1016/j.electacta.2022.140241openalex-snapshot
A novel fuzzy adaptive cubature Kalman filtering method for the state of charge and state of energy co-estimation of lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.energy.2022.123233openalex-snapshot
A deep learning approach to state of charge estimation of lithium-ion batteries based on dual-stage attention mechanism
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.2024.130584openalex-snapshot
A novel hybrid deep learning model for accurate state of charge estimation of Li-Ion batteries for electric vehicles under high and low temperature
inferredcites →https://doi.org/10.1016/j.energy.2024.130760openalex-snapshot
Robust state-of-charge estimation for LiFePO4 batteries under wide varying temperature environments
inferredcites →https://doi.org/10.1016/j.energy.2024.132730openalex-snapshot
Battery asynchronous fractional-order thermoelectric coupling modeling and state of charge estimation based on frequency characteristic separation at low temperatures
inferredcites →https://doi.org/10.1016/j.energy.2025.134722openalex-snapshot
Enhanced state of charge estimation in lithium-ion batteries based on Time-Frequency-Net with time-domain and frequency-domain features
inferredcites →https://doi.org/10.1016/j.ensm.2022.06.007openalex-snapshot
Battery state-of-charge estimation amid dynamic usage with physics-informed deep learning
inferredcites →https://doi.org/10.1016/j.est.2022.105396openalex-snapshot
State of charge estimation for lithium-ion batteries using gated recurrent unit recurrent neural network and adaptive Kalman filter
inferredcites →https://doi.org/10.1016/j.est.2023.108777openalex-snapshot
A comprehensive review of state of charge estimation in lithium-ion batteries used in electric vehicles
inferredcites →https://doi.org/10.1016/j.est.2024.110906openalex-snapshot
An LSTM-SA model for SOC estimation of lithium-ion batteries under various temperatures and aging levels
inferredcites →https://doi.org/10.1016/j.est.2024.111435openalex-snapshot
Review of battery state estimation methods for electric vehicles - Part I: SOC estimation
inferredcites →https://doi.org/10.1016/j.est.2024.111524openalex-snapshot
A self-attention-based CNN-Bi-LSTM model for accurate state-of-charge estimation of lithium-ion batteries
inferredcites →https://doi.org/10.1016/j.est.2025.118307openalex-snapshot
Real-time discharge curve and state of charge estimation of lithium-ion batteries via a physics-informed full homogenized macroscale model
inferredcites →https://doi.org/10.1016/j.est.2025.119451openalex-snapshot
State of charge estimation of lithium-ion batteries using transfer dual-stream physics-informed network
inferredcites →https://doi.org/10.1016/j.jpowsour.2025.238456openalex-snapshot
Robust state of charge estimation for lithium-ion batteries using a fully entangled temporal convolutional network with particle swarm optimization
inferredcites →https://doi.org/10.1016/j.renene.2022.08.123openalex-snapshot
A hybrid neural network model with improved input for state of charge estimation of lithium-ion battery at low temperatures
inferredcites →https://doi.org/10.1016/j.rser.2020.110015openalex-snapshot
A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems
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.2021.3057371openalex-snapshot
State of Charge Estimation of Lithium-Ion Batteries Based on Temporal Convolutional Network and Transfer Learning
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/10.1214/aoms/1177703732openalex-snapshot
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