Browsing by Author "Marinescu, Monica"

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    Improved state of charge estimation for lithium-sulfur batteries
    (Elsevier, 2019-10-23) Propp, Karsten; Auger, Daniel J.; Fotouhi, Abbas; Marinescu, Monica; Knap, Vaclav; Longo, Stefano
    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 an ewbattery 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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    MATLAB and Simulink Models for 'Improved State of Charge Estimation for Lithium-Sulfur Batteries'
    (Cranfield University, 2022-05-01 01:10) Auger, Daniel; Propp, Karsten; Fotouhi, Abbas; Marinescu, Monica; Knap, Vaclav; Longo, Stefano
    This fileset consists of Simulink models of a state estimator for lithium-sulfur batteries, as described in a research paper that has been submitted for publication.
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    Multi-temperature state-dependent equivalent circuit discharge model for lithium-sulfur batteries
    (Elsevier, 2016-08-12) Propp, Karsten; Marinescu, Monica; Auger, Daniel J.; O'Neill, Laura; Fotouhi, Abbas; Somasundaram, Karthik; Offer, Gregory J.; Minton, Geraint; Longo, Stefano; Wild, Mark; Knap, Vaclav
    Lithium-sulfur (Li-S) batteries are described extensively in the literature, but existing computational models aimed at scientific understanding are too complex for use in applications such as battery management. Computationally simple models are vital for exploitation. This paper proposes a non-linear state-of-charge dependent Li-S equivalent circuit network (ECN) model for a Li-S cell under discharge. Li-S batteries are fundamentally different to Li-ion batteries, and require chemistry-specific models. A new Li-S model is obtained using a ‘behavioural’ interpretation of the ECN model; as Li-S exhibits a ‘steep’ open-circuit voltage (OCV) profile at high states-of-charge, identification methods are designed to take into account OCV changes during current pulses. The prediction-error minimization technique is used. The model is parameterized from laboratory experiments using a mixed-size current pulse profile at four temperatures from 10 °C to 50 °C, giving linearized ECN parameters for a range of states-of-charge, currents and temperatures. These are used to create a nonlinear polynomial-based battery model suitable for use in a battery management system. When the model is used to predict the behaviour of a validation data set representing an automotive NEDC driving cycle, the terminal voltage predictions are judged accurate with a root mean square error of 32 mV.