Robust survival model for the prediction of Li-ion battery lifetime reliability and risk functions

This publication has 27 references indexed in Scilit:

• Subtle variation in sepsis-III definitions markedly influences predictive performance within and across methods
  Scientific Reports, 2024
• Early prediction of Lithium-ion cell degradation trajectories using signatures of voltage curves up to 4-minute sub-sampling rates
  Applied Energy, 2023
• Selecting the appropriate features in battery lifetime predictions
  Joule, 2023
• Capacity and Internal Resistance of lithium-ion batteries: Full degradation curve prediction from Voltage response at constant Current at discharge
  Journal of Power Sources, 2022
• Feature engineering for machine learning enabled early prediction of battery lifetime
  Journal of Power Sources, 2022
• Prediction of future capacity and internal resistance of Li-ion cells from one cycle of input data
  Energy and AI, 2021
• Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning
  Applied Energy, 2020
• Closed-loop optimization of fast-charging protocols for batteries with machine learning
  Nature, 2020
• Data-driven prediction of battery cycle life before capacity degradation
  Nature Energy, 2019
• Stochastic gradient boosting
  Computational Statistics & Data Analysis, 2002