Multi-temperature state-dependent equivalent circuit discharge model for lithium-sulfur batteries

Propp, Karsten; Marinescu, Monica; Auger, Daniel J.; O’Neill, Laura; Fotouhi, Abbas; Somasundaram, Karthik; Offer, Gregory J.; Minton, Geraint; Longo, Stefano; Wild, Mark; Knap, Václav

doi:10.1016/j.jpowsour.2016.07.090

Cited by 75 publications

(85 citation statements)

References 41 publications

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“…Nevertheless, most of the models available in the literature are focused on understanding the polysulphide shuttle process, which governs and limits the performance of this battery technology [6]. To some extent, the availability of reliable dynamic models for Li-S batteries is limited [7], [8].…”

Section: Introductionmentioning

confidence: 99%

“…To some extent, the availability of reliable dynamic models for Li-S batteries is limited [7], [8]. Furthermore, in all of these works, the electrical circuit model (ECM), which estimates the dynamics of the battery, is parametrized based on the DC pulse technique (i.e., a current pulse of a certain amplitude and duration is applied to the battery and the battery voltage response is recorded) [6], [7] [8].…”

Section: Introductionmentioning

confidence: 99%

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Electric circuit modeling of lithium-sulfur batteries during discharging state

Stroe

Knap

Świerczyński

et al. 2017

2017 IEEE Energy Conversion Congress and Exposition (ECCE)

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Electric circuit modeling of lithium-sulfur batteries during discharging state

Stroe

Knap

Świerczyński

et al. 2017

2017 IEEE Energy Conversion Congress and Exposition (ECCE)

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“…There are few studies on Li-S cell ECN modelling in the literature. The original application of ECN models to lithium sulfur was carried out by in works such as [10,11,47,48].…”

Section: State-of-the-art Li-s Cell Modelling and State Estimation Tementioning

confidence: 99%

Lithium-Sulfur Battery Technology Readiness and Applications—A Review

et al. 2017

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Lithium Sulfur (Li-S) battery is generally considered as a promising technology where high energy density is required at different applications. Over the past decade, there has been an ever increasing volume of Li-S academic research spanning materials development, fundamental understanding and modelling, and application-based control algorithm development. In this study, the Li-S battery technology, its advantages and limitations from the fundamental perspective are firstly discussed. In the second part of this study, state-of-the-art Li-S cell modelling and state estimation techniques are reviewed with a focus on practical applications. The existing studies on Li-S cell equivalent-circuit-network modelling and state estimation techniques are then discussed. A number of challenges in control of Li-S battery are also explained such as the flat open-circuit-voltage curve and high sensitivity of Li-S cell's behavior to temperature variation. In the last part of this study, current and future applications of Li-S battery are mentioned.

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“…8 By applying state transformation, the self-discharge model state-space representation becomes as follows.…”

Section: Li-s Cell Self-discharge Modellingmentioning

confidence: 99%

“…The Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) have been demonstrated for use on real lithium-sulfur cells subject to automotive drive cycles: the UKF was found to cope better with unknown initial states of charge. 8 more detail in ECN modelling approach section, but it is worth noting that their original application to lithium sulfur was carried out in works such as. [7][8][9][10] These were then used for state estimation in Ref.…”

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confidence: 99%

Self-Discharge Effects in Lithium-Sulfur Equivalent Circuit Networks for State Estimation

Yousif

Fotouhi

Auger

et al. 2017

J. Electrochem. Soc.

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This study considers application-oriented models of lithium-sulfur (Li-S) cells. Existing ECN models often neglect self-discharge, but this can be important in applications. After describing the context in which control-oriented models and estimators are based, the self-discharge phenomenon is investigated for a new 21 Ah Li-S cell. As a contribution of this study, an equivalent-circuit-network (ECN) model was extended to account for cells' self-discharge. Formal system identification techniques were used to parameterize a model from experimental data. The original model was then extended by adding terms to represent a self-discharge resistance. To obtain the self-discharge resistance, a particular new series of experiments were designed and performed on the Li-S cell at various temperature and initial state-of-charge (SoC) levels. The results demonstrate the dependency of self-discharge rate on the SoC and temperature. The self-discharge rate is much higher at high SoC levels and it increases as temperature decreases. Much of today's research into lithium-sulfur (Li-S) batteries concerns the development and understanding of materials, construction and the fundamental scientific understanding of cell behavior. Many will recognize the importance of this, but lithium-sulfur is beginning to reach maturity, and there is a need to develop the engineering science and techniques necessary for deployment in practical applications. In particular, there is a need to devise algorithms that can be used to estimate state-of-charge and state-of-health measures in operando. Electrochemistry is of course key here, but it is equally vital to draw from other disciplines: in particular, control theory has much to offer, particularly in respect of state estimation.When electrochemists create models, they usually do so 'as scientists': the aim of a scientific model is to enhance understanding. Of course, no model is perfect, and the pure scientist uses model imperfections to identify gaps in present knowledge and as the inspiration for further research. The aim is to improve understanding and get a 'better model'. However, at some point, cells may be put to practical use, and at this point, the application engineer will often have to make do with the best models available at that time, despite the model's imperfections.Control systems engineers are well accustomed to dealing with model errors and 'uncertainty': there are many excellent text books on control, and any of them will give a short overview of the key principles of control; Aström and Murray's work 1 is a good example. Typically, a dynamic system is modelled as a set of dynamic equations: x, u, w) [1]where u and y represent observed system input and outputs (voltages, currents and temperatures, for example), x comprehensively accounts for past history by representing the system's non-or partlymeasurable dynamic 'states' (perhaps including state of charge in a simple model, or concentrations of chemical species in a complex one), h(·) and f (·) are known functions describ...

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Multi-temperature state-dependent equivalent circuit discharge model for lithium-sulfur batteries

Cited by 75 publications

References 41 publications

Electric circuit modeling of lithium-sulfur batteries during discharging state

Electric circuit modeling of lithium-sulfur batteries during discharging state

Lithium-Sulfur Battery Technology Readiness and Applications—A Review

Self-Discharge Effects in Lithium-Sulfur Equivalent Circuit Networks for State Estimation

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