Multi-temperature state-dependent equivalent circuit discharge model for lithium-sulfur batteries
Multi-temperature state-dependent equivalent circuit discharge model for lithium-sulfur batteries
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DOI：
10.1016/j.jpowsour.2016.07.090
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发表时间：
2016-10
影响因子：
9.2
通讯作者：
K. Propp;M. Marinescu;D. Auger;L. O'Neill;A. Fotouhi;Karthik Somasundaram;G. Offer;G. Minton;S. Longo;Mark Wild;V. Knap
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文献类型：
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作者：
K. Propp;M. Marinescu;D. Auger;L. O'Neill;A. Fotouhi;Karthik Somasundaram;G. Offer;G. Minton;S. Longo;Mark Wild;V. Knap
锂硫（Li-S）电池在文献中有广泛的描述，但现有的旨在科学理解的计算模型过于复杂，无法用于电池管理等应用。计算简单的模型对于开发至关重要。本文提出了一种非线性的充电状态相关的锂硫电池等效电路网络（ECN）模型在放电。锂硫电池与锂离子电池有着根本的不同，需要特定的化学模型。使用ECN模型的“行为”解释获得新的Li-S模型;由于Li-S在高荷电状态下表现出“陡峭”的开路电压（OCV）曲线，因此设计识别方法以考虑电流脉冲期间的OCV变化。使用预测误差最小化技术。该模型是从实验室实验中使用混合大小的电流脉冲分布在从10 °C到50 °C的四个温度下参数化的，给出了一系列充电状态、电流和温度的线性ECN参数。这些用于创建适合于在电池管理系统中使用的基于非线性多项式的电池模型。当该模型用于预测代表汽车NEDC驾驶循环的验证数据集的行为时，端子电压预测被判断为准确，均方根误差为32 mV。
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.