A two-stage method with twin autoencoders for the degradation trajectories prediction of lithium-ion batteries

This publication has 39 references indexed in Scilit:

• A transferable long-term lithium-ion battery aging trajectory prediction model considering internal resistance and capacity regeneration phenomenon
  Applied Energy, 2024
• Research on the remaining useful life prediction method for lithium-ion batteries by fusion of feature engineering and deep learning
  Applied Energy, 2024
• Data-driven capacity estimation for lithium-ion batteries with feature matching based transfer learning method
  Applied Energy, 2023
• SOH prediction for Lithium-Ion batteries by using historical state and future load information with an AM-seq2seq model
  Applied Energy, 2023
• Transfer learning for battery smarter state estimation and ageing prognostics: Recent progress, challenges, and prospects
  Advances in Applied Energy, 2022
• Two-stage deep learning for online prediction of knee-point in Li-ion battery capacity degradation
  Applied Energy, 2022
• A physics-informed dynamic deep autoencoder for accurate state-of-health prediction of lithium-ion battery
  Neural Computing and Applications, 2022
• Identification and machine learning prediction of knee-point and knee-onset in capacity degradation curves of lithium-ion cells
  Energy and AI, 2020
• A survey on semi-supervised learning
  Machine Learning, 2019
• A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation
  Applied Energy, 2015