Lithium–sulfur (Li–S) batteries offer potential for higher gravimetric energy density in comparison to lithium–ion batteries. Since they behave quite different from lithium–ion batteries, distinctive approaches to state estimation and battery management are required to be developed specifically for them. This article describes an experimental work to model and perform real-time estimation of the progression of use-induced aging in prototype Li–S cells. To do that, state-of-the-art 19-Ah Li–S pouch cells were subject to cycling tests in order to determine progressive changes in parameters of a nonlinear equivalent-circuit-network (ECN) model due to aging. A state-of-health (SoH) estimation algorithm was then designed to work based on identifying ECN parameters using forgetting-factor recursive least squares (FFRLS). Two techniques, nonlinear curve fitting and support vector machine (SVM) classification, were used to generate SoH values according to the identified parameters. The results demonstrate that Li–S cell’s SoH can be estimated with an acceptable level of accuracy of 96.7% using the proposed method under realistic driving conditions. Another important outcome was that the “power fade” in Li–S cells happens at a much slower rate than the “capacity fade” which is a useful feature for applications where consistency of power delivery is important.

中文翻译：

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用于老化分析和健康状况估计的原型锂硫电池的实验研究

与锂离子电池相比，锂硫 (Li-S) 电池具有更高的重量能量密度潜力。由于它们的行为与锂离子电池完全不同，因此需要专门为它们开发独特的状态估计和电池管理方法。本文描述了对原型 Li-S 电池中使用引起的老化进程进行建模和实时估计的实验工作。为此，对最先进的 19-Ah Li-S 软包电池进行循环测试，以确定非线性等效电路网络 (ECN) 模型参数因老化而发生的渐进变化。然后设计了一种基于使用遗忘因子递归最小二乘法 (FFRLS) 识别 ECN 参数的健康状态 (SoH) 估计算法。两种技术，非线性曲线拟合和支持向量机 (SVM) 分类用于根据识别的参数生成 SoH 值。结果表明，在实际驾驶条件下，使用所提出的方法可以以可接受的 96.7% 的准确度估计 Li-S 电池的 SoH。另一个重要的结果是，Li-S 电池中的“功率衰减”发生的速度比“容量衰减”慢得多，这对于电力输送的一致性很重要的应用来说是一个有用的特征。