Abstract
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 language | English |
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Journal | Journal of Power Sources |
Volume | 343 |
Pages (from-to) | 254-267 |
Number of pages | 14 |
ISSN | 0378-7753 |
DOIs | |
Publication status | Published - Mar 2017 |
Keywords
- Lithium sulfur battery
- State of charge
- Extended Kalman filter
- Unscented Kalman filter
- Particle filter
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Kalman-variant estimators for state of charge in lithium-sulfur batteries
Auger, D. J. (Creator), Knap, V. (Creator), Propp, K. (Creator), Longo, S. (Creator) & Fotouhi, A. (Creator), Cranfield Online Research Data (CORD), 27 Aug 2021
DOI: 10.17862/cranfield.rd.3834057, https://cord.cranfield.ac.uk/articles/dataset/Kalman-variant_estimators_for_state_of_charge_in_lithium-sulfur_batteries/3834057
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