Online estimation of lithium-ion battery capacity using sparse Bayesian learning
- 1 September 2015
- journal article
- Published by Elsevier BV in Journal of Power Sources
- Vol. 289, 105-113
- https://doi.org/10.1016/j.jpowsour.2015.04.166
Abstract
No abstract availableKeywords
This publication has 17 references indexed in Scilit:
- A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion polymer battery in electric vehiclesApplied Energy, 2014
- Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determinationApplied Energy, 2013
- State of charge estimation for electric vehicle batteries using unscented kalman filteringMicroelectronics Reliability, 2013
- Behavior and state-of-health monitoring of Li-ion batteries using impedance spectroscopy and recurrent neural networksInternational Journal of Electrical Power & Energy Systems, 2012
- A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimationApplied Energy, 2012
- Intelligent prognostics for battery health monitoring based on sample entropyExpert Systems with Applications, 2011
- Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteriesApplied Energy, 2009
- State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-chargeJournal of Power Sources, 2008
- Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2: Simultaneous state and parameter estimationJournal of Power Sources, 2006
- Extended Kalman filtering for battery management systems of LiPB-based HEV battery packsJournal of Power Sources, 2004