Particle-filtering-based estimation of maximum available power state in Lithium-Ion batteries
Authors:
- Claudio Burgos-MelladoClaudio Burgos-Mellado
- Marcos E. OrchardMOMarcos E. Orchard
- Mehrdad KazeraniMKMehrdad Kazerani
- Roberto CárdenasRoberto Cárdenas
- Doris SáezDoris Sáez
- 1 January 2016
- journal article
- Published by Elsevier in Applied Energy
- Vol. 161, 349-363
Abstract
No abstract availableKeywords
Funding Information
- Fondecyt Chile (1140774, 1140775)
- Advanced Center for Electrical and Electronic Engineering (FB0008, EQM130058)
- FONDEQUIP
This publication has 35 references indexed in Scilit:
- Energy management strategy for a renewable-based residential microgrid with generation and demand forecastingApplied Energy, 2015
- Microgrids: Experiences, barriers and success factorsRenewable and Sustainable Energy Reviews, 2014
- Multi-objective optimal design of lithium-ion battery packs based on evolutionary algorithmsJournal of Power Sources, 2014
- Smart Microgrids as a Solution for Rural Electrification: Ensuring Long-Term Sustainability Through Cadastre and Business ModelsIEEE Transactions on Sustainable Energy, 2014
- Adaptive on-line prediction of the available power of lithium-ion batteriesJournal of Power Sources, 2013
- Model-based state of charge and peak power capability joint estimation of lithium-ion battery in plug-in hybrid electric vehiclesJournal of Power Sources, 2013
- Self-learning state-of-available-power prediction for lithium-ion batteries in electrical vehicles2012
- Requirements for charging of an electric vehicle system based on state of power (SoP) and state of energy (SoE)2012
- Recent developments in microgrids and example cases around the world—A reviewRenewable and Sustainable Energy Reviews, 2011
- Comparison of NiMH and Li-ion batteries in automotive applications2008