Lithium Sulfur (Li-S) batteries are one of the most promising next generation battery chemistries with potential to achieve 500-600 Wh/kg in the next few years. Yet understanding the underlying mechanisms of operation remains a major obstacle to their continued improvement. From a review of a range of analytical studies and physical models, it is clear that experimental understanding is well ahead of state-of-the-art models. Yet this understanding is still hindered by the limitations of available techniques and the implications of experiment and cell design on the mechanism. The mechanisms at the cor e of physical models for Li-S cells are overly simplistic compared to the latest thinking based upon experimental results, but creating more complicated models will be difficult, due to the lack of and inability to easily measure the necessary parameters. Despite this, there are significant opportunities to improve models with the latest experimentally derived mechanisms. Such models can inform materials research and lead to improved high fidelity models for controls and application engineers.

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

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