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Electric vehicle battery parameter identification and SOC observability analysis: NiMH and Li-S case studies

机译：电动汽车电池参数识别和SOC可观察性分析：NiMH和Li-S案例研究

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摘要

In this study, battery model identification is performed to be applied in electric vehicle battery management systems. Two case studies are investigated: nickel-metal hydride (NiMH), which is a mature battery technology, and lithium-sulfur (Li-S), a promising next-generation technology. Equivalent circuit battery model parameterization is performed in both cases using the Prediction-Error Minimization algorithm applied to experimental data. Performance of the Li-S cell is also tested based on urban dynamometer driving schedule (UDDS). The identification results are then validated against the exact values of the battery parameters. The use of identified parameters for battery state-of-charge (SOC) estimation is also discussed. It is demonstrated that the set of parameters changes with a different battery chemistry. In the case of NiMH, the battery open circuit voltage (OCV) is adequate for SOC estimation whereas Li-S battery SOC estimation is more challenging due to its unique features such as flat OCV-SOC curve. An observability analysis shows that Li-S battery SOC is not fully observable and the existing methods might not be applicable for it. Finally, the effect of temperature on the identification results and the observability are discussed by repeating the UDDS test at 5, 10, 20, 30, 40 and 50 degree Celsius.
机译：在这项研究中，执行电池模型识别以应用于电动汽车电池管理系统。研究了两个案例研究：成熟的电池技术镍氢（NiMH）和有希望的下一代技术锂硫（Li-S）。在两种情况下，均使用应用于实验数据的预测误差最小化算法执行等效电路电池模型参数化。还根据城市测功机行驶时间表（UDDS）对Li-S电池的性能进行了测试。然后根据电池参数的准确值验证识别结果。还讨论了将识别出的参数用于电池电量状态（SOC）估计。结果表明，该组参数随不同的电池化学性质而变化。对于NiMH，电池开路电压（OCV）足以进行SOC估计，而Li-S电池SOC估计则更具挑战性，因为其独特的功能（例如平坦的OCV-SOC曲线）。可观察性分析表明，Li-S电池SOC不能完全观察到，现有方法可能不适用于它。最后，通过在5、10、20、30、40和50摄氏度下重复UDDS测试，讨论了温度对识别结果和可观察性的影响。

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