Lithium-ion batteries remaining useful life prediction based on BLS-RVM

This publication has 30 references indexed in Scilit:

• A Rest Time-Based Prognostic Framework for State of Health Estimation of Lithium-Ion Batteries with Regeneration Phenomena
  Energies, 2016
• Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model
  Microelectronics Reliability, 2016
• Remaining useful life prediction based on noisy condition monitoring signals using constrained Kalman filter
  Reliability Engineering & System Safety, 2016
• Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Spherical Cubature Particle Filter
  IEEE Transactions on Instrumentation and Measurement, 2016
• Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article]
  IEEE Computational Intelligence Magazine, 2016
• Information-Theoretic Measures and Sequential Monte Carlo Methods for Detection of Regeneration Phenomena in the Degradation of Lithium-Ion Battery Cells
  IEEE Transactions on Reliability, 2015
• Ensemble Support Vector Machine Algorithm for Reliability Estimation of a Mining Machine
  Quality and Reliability Engineering International, 2014
• Prognostic modelling options for remaining useful life estimation by industry
  Mechanical Systems and Signal Processing, 2011
• The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
  Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 1998
• Learning and generalization characteristics of the random vector functional-link net
  Neurocomputing, 1994