State-of-health estimation of lithium-ion batteries based on probabilistic diffusion models and self-learning dynamic channel attention assisted extended long short-term memory

This publication has 41 references indexed in Scilit:

• A hybrid data-driven model for state of health estimation of Lithium-ion battery with capacity recovery
  Engineering Applications of Artificial Intelligence, 2025
• SOH early prediction of lithium-ion batteries based on voltage interval selection and features fusion
  Energy, 2024
• Convolutional autoencoder-based SOH estimation of lithium-ion batteries using electrochemical impedance spectroscopy
  Journal of Energy Storage, 2023
• Open-circuit voltage curve reconstruction for degrading lithium-ion batteries utilizing discrete curve fragments from an online dataset
  Journal of Energy Storage, 2022
• A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model
  Energy, 2022
• Data Augmentation by Guided Deep Interpolation
  Applied Soft Computing, 2021
• Practical state estimation using Kalman filter methods for large-scale battery systems
  Applied Energy, 2021
• Convolutional neural network based capacity estimation using random segments of the charging curves for lithium-ion batteries
  Energy, 2021
• An improved vehicle to the grid method with battery longevity management in a microgrid application
  Energy, 2020
• Random forest regression for online capacity estimation of lithium-ion batteries
  Applied Energy, 2018