Improved state of charge estimation for lithium-sulfur batteries. (December 2019)

• Record Type: Journal Article
• Authors: Propp, Karsten; Auger, Daniel J.; Fotouhi, Abbas; Marinescu, Monica; Knap, Vaclav; Longo, Stefano
• Is Part Of: Journal of energy storage. Volume 26(2019)
• Publication Date: 2019-12
• Subjects: Lithium-sulfur battery -- State of charge estimation -- Extended Kalman filter -- Online parameterzation -- Equivalent circuit network model; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2019.100943
• Languages: English
• ISSNs: 2352-152X

Abstract

Highlights: Li-S batteries differ to Li-ion batteries, and require specific state of charge estimation. The 'dual extended Kalman filter' architecture for the state estimator is applied to Li-S for the first time. The new method combines online parameter estimation, a new model of the dependency between current and internal resistance, and a separate state of charge estimator. The model is tested against automotive driving cycles. Compared to pre-existing methods of state estimation for Li-S, the new method is more accurate, particularly when the initial state of charge is unknown. Abstract: Good state of charge estimation in lithium-sulfur batteries (Li-S) is vital, as the simplest convention methods commonly used in lithium-ion batteries – open-circuit voltage measurement and 'coulomb counting' – are often ineffective for Li-S. Since Li-S is a new battery chemistry, there are few published techniques. Existing techniques based on the extended Kalman filter and the unscented Kalman filter have shown some promise, existing work has explored only one of many possible estimator architectures: a single filter based on a pre-calibrated behavioural reparameterization of an equivalent circuit network whose parameters vary as a function of state of charge and temperature. Such filters have been shown to be reasonably effective in practical cases, but they can converge slowly if initial conditions are unknown, and they can become inaccurate with changes in current density. It is desirable to understand whether other possible estimator architectures offer improved performance. One such alternative architecture is the 'dual extended Kalman filter', which uses voltage and current measurements to estimate into a short-term dynamic circuit parameters then uses the outputs of this in a slower-acting state-of-charge estimator. This paper develops a 'behavioural' form of the dual extended Kalman filter, and applies this to a lithium-sulfur battery. The estimator is adapted with a term to model circuit current dependence, and demonstrated using pulse-discharge tests and scaled automotive driving cycles including some with initially partially discharged batteries. Compared to the published state-of-the-art, the new estimators were are found to be between 16.4% and 28.2% more accurate for batteries that are initially partially discharged to a 60% SoC level; the new estimators also converge faster. The resulting estimators have the potential to be extended to state-of-health measures, and the 'behavioural' circuit reparameterization is likely to be of use for other battery chemistries beside lithium-sulfur.