Unsupervised feature extraction for lithium-ion battery electrochemical impedance spectroscopy and capacity estimation using deep learning method

This publication has 35 references indexed in Scilit:

• Adaptive state of health estimation for lithium-ion batteries using impedance-based timescale information and ensemble learning
  Energy, 2023
• Comparative analysis for commercial li-ion batteries degradation using the distribution of relaxation time method based on electrochemical impedance spectroscopy
  Energy, 2022
• State of health estimation for lithium-ion batteries using geometric impedance spectrum features and recurrent Gaussian process regression
  Energy, 2022
• The Critical Role of Carbon Nanotubes in Bridging Academic Research to Commercialization of Lithium Batteries
  The Chemical Record, 2022
• Identification and quantification of ageing mechanisms in Li-ion batteries by Electrochemical impedance spectroscopy.
  Electrochimica Acta, 2022
• A comparative study of different features extracted from electrochemical impedance spectroscopy in state of health estimation for lithium-ion batteries
  Applied Energy, 2022
• Alternating Current Impedance Probing Capacity of Lithium‐Ion Battery by Gaussian Process Regression
  Energy Technology, 2022
• Chaining Zscore and feature scaling methods to improve neural networks for classification
  Applied Soft Computing, 2022
• Impedance-based capacity estimation for lithium-ion batteries using generative adversarial network
  Applied Energy, 2022
• Cycle aging studies of lithium nickel manganese cobalt oxide-based batteries using electrochemical impedance spectroscopy
  Electrochimica Acta, 2018