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首页> 外文会议>IET International Conference on Power Electronics, Machines and Drives >Electric vehicle battery parameter identification and SOC observability analysis: NiMH and Li-S case studies
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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, a framework is proposed for battery model identification to be applied in electric vehicle energy storage systems. The main advantage of the proposed approach is having capability to handle different battery chemistries. Two case studies are investigated: nickel-metal hydride (NiMH), which is a mature battery technology, and Lithium-Sulphur (Li-S), a promising next-generation technology. Equivalent circuit battery model parametrisation is performed in both cases using the Prediction-Error Minimization (PEM) algorithm applied to experimental data. The use of identified parameters for battery state-of-charge (SOC) estimation is then discussed. It is demonstrated that the set of parameters needed can change with a different battery chemistry. In the case of NiMH, the battery's open circuit voltage (OCV) is adequate for SOC estimation. However, Li-S battery SOC estimation can be challenging due to the chemistry's unique features and the SOC cannot be estimated from the OCVSOC curve alone because of its flat gradient. An observability analysis demonstrates that Li-S battery SOC is not observable using the common state-space representations in the literature. Finally, the problem's solution is discussed using the proposed framework.
机译：在这项研究中，提出了一种用于电池模型识别的框架，该框架将应用于电动汽车储能系统。所提出的方法的主要优点是具有处理不同电池化学物质的能力。研究了两个案例研究：成熟的电池技术镍氢（NiMH）和有前途的下一代技术锂硫（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（PEM）算法执行等效电路电池模型参数化。然后讨论将识别出的参数用于电池电量状态（SOC）估计。事实证明，所需的参数集会随电池化学性质的不同而变化。对于NiMH，电池的开路电压（OCV）足以进行SOC估算。但是，由于化学性质的独特性，Li-S电池SOC的估算可能具有挑战性，并且由于其平坦的梯度，无法仅从OCVSOC曲线估算SOC。可观察性分析表明，使用文献中常见的状态空间表示法无法观察到Li-S电池SOC。最后，使用提出的框架讨论了问题的解决方案。

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