Lithium-Sulfur (Li-S) batteries are a promising next-generation technology providing high gravimetric energy density compared to existing lithium-ion (Li-ion) technologies in the market. The literature shows that in Li-S, estimation of state of charge (SoC) is a demanding task, in particular due to a large flat section in the voltage-SoC curve. This study proposes a new SoC estimator using an online parameter identification method in conjunction with a classification technique. This study investigates a new prototype Li-S cell. Experimental characterization tests are conducted under various conditions; the duty cycle – intended to represent a real-world application – is based on an electric city bus. The characterization results are then used to parameterize an equivalent-circuit-network (ECN) model, which is then used to relate real-time parameter estimates derived using a Recursive Least Squares (RLS) algorithm to state of charge using a Support Vector Machine (SVM) classifier to estimate an approximate SoC range. The estimate is used together with a conventional coulomb-counting technique to achieve continuous SoC estimation in real-time. It is shown that this method can provide an acceptable level of accuracy with less than 3% error under realistic driving conditions.

中文翻译：

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使用分类技术估算锂硫电池的荷电状态

与市场上现有的锂离子（Li-ion）技术相比，锂-硫（Li-S）电池是一种有前途的下一代技术，可提供较高的重量能量密度。文献表明，在Li-S中，充电状态（SoC）的估计是一项艰巨的任务，特别是由于电压-SoC曲线中的平坦部分较大。这项研究提出了一种使用在线参数识别方法和分类技术的新型SoC估计器。这项研究调查了一个新的原型锂硫电池。实验表征测试在各种条件下进行；占空比（旨在代表实际应用）基于电动城市公交车。然后将表征结果用于参数化等效电路网络（ECN）模型，然后使用支持向量机（SVM）分类器将使用递归最小二乘（RLS）算法导出的实时参数估计与充电状态相关联，以估计近似SoC范围。该估计与常规的库仑计数技术一起使用可实时实现连续SoC估计。结果表明，该方法可以在实际驾驶条件下提供可接受的精度水平，且误差小于3％。