Improved state of charge estimation for lithium-sulfur batteries

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

Research output: Contribution to journalJournal articleResearchpeer-review

1 Citation (Scopus)

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.
Original languageEnglish
JournalJournal of Energy Storage
Volume26
Number of pages13
ISSN2352-152X
DOIs
Publication statusPublished - Dec 2019

Fingerprint

Extended Kalman filters
Voltage measurement
Networks (circuits)
Electric current measurement
Open circuit voltage
Kalman filters
Equivalent circuits
Lithium
Current density
Sulfur
Health
Lithium sulfur batteries
Temperature

Keywords

  • Lithium-sulfur battery
  • State of charge estimation
  • Extended Kalman filter
  • Online parameterzation
  • Equivalent circuit network model

Cite this

Propp, Karsten ; Auger, Daniel J. ; Fotouhi, Abbas ; Marinescu, Monica ; Knap, Vaclav ; Longo, Stefano . / Improved state of charge estimation for lithium-sulfur batteries. In: Journal of Energy Storage. 2019 ; Vol. 26.
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title = "Improved state of charge estimation for lithium-sulfur batteries",
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.",
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author = "Karsten Propp and Auger, {Daniel J.} and Abbas Fotouhi and Monica Marinescu and Vaclav Knap and Stefano Longo",
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Improved state of charge estimation for lithium-sulfur batteries. / Propp, Karsten; Auger, Daniel J.; Fotouhi, Abbas; Marinescu, Monica; Knap, Vaclav; Longo, Stefano .

In: Journal of Energy Storage, Vol. 26, 12.2019.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Improved state of charge estimation for lithium-sulfur batteries

AU - Propp, Karsten

AU - Auger, Daniel J.

AU - Fotouhi, Abbas

AU - Marinescu, Monica

AU - Knap, Vaclav

AU - Longo, Stefano

PY - 2019/12

Y1 - 2019/12

N2 - 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.

AB - 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.

KW - Lithium-sulfur battery

KW - State of charge estimation

KW - Extended Kalman filter

KW - Online parameterzation

KW - Equivalent circuit network model

U2 - 10.1016/j.est.2019.100943

DO - 10.1016/j.est.2019.100943

M3 - Journal article

VL - 26

JO - Journal of Energy Storage

JF - Journal of Energy Storage

SN - 2352-152X

ER -