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A Joint-Loss Transformer Model Based Semi-Supervised Learning for Predicting Electrochemical Impedance Spectroscopy of Lithium-Ion Batteries

https://doi.org/10.2139/ssrn.7411640
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Fractional order battery modelling methodologies for electric vehicle applications: Recent advances and perspectives
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A comparison and accuracy analysis of impedance-based temperature estimation methods for Li-ion batteries
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Exploring Differences between Charge and Discharge of LiMn2O4/Li Half-cell with Dynamic Electrochemical Impedance Spectroscopy
inferredcites →https://doi.org/10.1016/j.electacta.2015.07.017openalex-snapshot
Dynamic Electrochemical Impedance Spectroscopy of a Three-Electrode Lithium-Ion Battery during Pulse Charge and Discharge
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State of charge prediction of EV Li-ion batteries using EIS: A machine learning approach
inferredcites →https://doi.org/10.1016/j.ensm.2020.11.026openalex-snapshot
Mechanism, modeling, detection, and prevention of the internal short circuit in lithium-ion batteries: Recent advances and perspectives
inferredcites →https://doi.org/10.1016/j.ensm.2021.05.047openalex-snapshot
Deep neural network battery impedance spectra prediction by only using constant-current curve
inferredcites →https://doi.org/10.1016/j.est.2022.104608openalex-snapshot
Electrochemical impedance correlation analysis for the estimation of Li-ion battery state of charge, state of health and internal temperature
inferredcites →https://doi.org/10.1016/j.ijhydene.2021.05.012openalex-snapshot
Online impedance spectrum measurement of fuel cells based on Morlet wavelet transform
inferredcites →https://doi.org/10.1016/j.jechem.2023.01.004openalex-snapshot
Battery impedance spectrum prediction from partial charging voltage curve by machine learning
inferredcites →https://doi.org/10.1016/j.jpowsour.2010.12.102openalex-snapshot
Characterization of high-power lithium-ion batteries by electrochemical impedance spectroscopy. I. Experimental investigation
inferredcites →https://doi.org/10.1016/j.jpowsour.2015.11.044openalex-snapshot
Voltage relaxation and impedance spectroscopy as in-operando methods for the detection of lithium plating on graphitic anodes in commercial lithium-ion cells
inferredcites →https://doi.org/10.1016/j.jpowsour.2019.04.040openalex-snapshot
Impedance characterization of lithium-ion batteries aging under high-temperature cycling: Importance of electrolyte-phase diffusion
inferredcites →https://doi.org/10.1016/j.jpowsour.2022.231955openalex-snapshot
Optimization and application of the distribution of relaxation times based on characteristic frequency resolution and hyperparameters
inferredcites →https://doi.org/10.1016/j.jpowsour.2023.232737openalex-snapshot
High-efficient prediction of state of health for lithium-ion battery based on AC impedance feature tuned with Gaussian process regression
inferredcites →https://doi.org/10.1109/tie.2017.2686324openalex-snapshot
Single-Perturbation-Cycle Online Battery Impedance Spectrum Measurement Method With Closed-Loop Control of Power Converter
inferredcites →https://doi.org/10.1109/tim.2022.3196439openalex-snapshot
Binary Sequences for Online Electrochemical Impedance Spectroscopy of Battery Cells
inferredcites →https://doi.org/10.1109/tpel.2019.2939269openalex-snapshot
Online Impedance Measurement of Batteries Using the Cross-Correlation Technique
inferredcites →https://doi.org/10.1109/tvt.2013.2293597openalex-snapshot
Online Measurement of Battery Impedance Using Motor Controller Excitation
inferredcites →https://doi.org/10.1149/1945-7111/abd452openalex-snapshot
Probing of Internal Short Circuit in Lithium-Ion Pouch Cells by Electrochemical Impedance Spectroscopy under Mechanical Abusive Conditions
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