Kalman-variant estimators for state of charge in lithium-sulfur batteries

Karsten Propp, Daniel J. Auger, Abbas Fotouhi, Stefano Longo, Vaclav Knap

Research output: Contribution to journalJournal articleResearchpeer-review

44 Citations (Scopus)
307 Downloads (Pure)


Lithium-sulfur batteries are now commercially available, offering high specific energy density, low production costs and high safety. However, there is no commercially-available battery management system for them, and there are no published methods for determining state of charge in situ. This paper describes a study to address this gap. The properties and behaviours of lithium-sulfur are briefly introduced, and the applicability of ‘standard’ lithium-ion state-of-charge estimation methods is explored. Open-circuit voltage methods and ‘Coulomb counting’ are found to have a poor fit for lithium-sulfur, and model-based methods, particularly recursive Bayesian filters, are identified as showing strong promise. Three recursive Bayesian filters are implemented: an extended Kalman filter (EKF), an unscented Kalman filter (UKF) and a particle filter (PF). These estimators are tested through practical experimentation, considering both a pulse-discharge test and a test based on the New European Driving Cycle (NEDC). Experimentation is carried out at a constant temperature, mirroring the environment expected in the authors' target automotive application. It is shown that the estimators, which are based on a relatively simple equivalent-circuit–network model, can deliver useful results. If the three estimators implemented, the unscented Kalman filter gives the most robust and accurate performance, with an acceptable computational effort.
Original languageEnglish
JournalJournal of Power Sources
Pages (from-to)254-267
Number of pages14
Publication statusPublished - Mar 2017


  • Lithium sulfur battery
  • State of charge
  • Extended Kalman filter
  • Unscented Kalman filter
  • Particle filter


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