Kalman-variant estimators for state of charge in lithium-sulfur batteries
| dc.contributor.author | Propp, Karsten | |
| dc.contributor.author | Auger, Daniel J. | |
| dc.contributor.author | Fotouhi, Abbas | |
| dc.contributor.author | Longo, Stefano | |
| dc.contributor.author | Knap, Vaclav | |
| dc.date.accessioned | 2017-02-13T17:35:18Z | |
| dc.date.available | 2017-02-13T17:35:18Z | |
| dc.date.issued | 2017-01-20 | |
| dc.description.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. | en_UK |
| dc.identifier.citation | Karsten Propp, Daniel J. Auger, Abbas Fotouhi, Stefano Longo, Vaclav Knap, Kalman-variant estimators for state of charge in lithium-sulfur batteries, Journal of Power Sources, Volume 343, 1 March 2017, Pages 254-267, ISSN 0378-7753, http://dx.doi.org/10.1016/j.jpowsour.2016.12.087. | en_UK |
| dc.identifier.cris | 16266062 | |
| dc.identifier.issn | 0378-7753 | |
| dc.identifier.uri | http://dx.doi.org/10.1016/j.jpowsour.2016.12.087 | |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/11442 | |
| dc.language.iso | en | en_UK |
| dc.publisher | Elsevier | en_UK |
| dc.rights | Attribution 4.0 International (CC BY 4.0) You are free to: Share — copy and redistribute the material in any medium or format, Adapt — remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. Information: No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. | |
| dc.subject | Lithium sulfur battery | en_UK |
| dc.subject | State of charge | en_UK |
| dc.subject | Extended Kalman filter | en_UK |
| dc.subject | Unscented Kalman filter | en_UK |
| dc.subject | Particle filter | en_UK |
| dc.title | Kalman-variant estimators for state of charge in lithium-sulfur batteries | en_UK |
| dc.type | Article | en_UK |