Combined estimation of State-of-Charge and State-of-Health of Li-ion battery cells using SMO on electrochemical model

Satadru Dey, Beshah Ayalew, Pierluigi Pisu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

25 Scopus citations

Abstract

Advanced battery management systems require accurate information of battery State-of-Charge (SOC) and State-of-Health (SOH) for diagnostics and prognostics as well as for efficient capacity utilization. In this paper, an integrated SOC and SOH estimation scheme is presented that applies sliding modes on an electrochemical model for Li-ion battery cell. The electrochemical model is selected and progressively reduced to sufficiently describe the relevant temporal and spatial evolution of Li-ion concentration in each electrode. The proposed estimation scheme is comprised of three sub-estimators which work jointly: two separate adaptive sliding mode observers (SMO) for estimation of Li-ion concentration and film resistance, and a separate parameter estimator for the solid state diffusion coefficient of negative electrode. Convergence of the observers has been proven using Lyapunov's stability theory. Simulation results are included to demonstrate the effectiveness of the overall scheme.

Original languageEnglish (US)
Title of host publicationVSS 2014 - Proceedings of 13th International Workshop on Variable Structure Systems
PublisherIEEE Computer Society
ISBN (Print)9781479955664
DOIs
StatePublished - 2014
Event13th International Workshop on Variable Structure Systems, VSS 2014 - Nantes, France
Duration: Jun 29 2014Jul 2 2014

Publication series

NameProceedings of IEEE Workshop on Applications of Computer Vision
ISSN (Print)2158-3978
ISSN (Electronic)2158-3986

Conference

Conference13th International Workshop on Variable Structure Systems, VSS 2014
CountryFrance
CityNantes
Period6/29/147/2/14

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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