Accurate estimation of the state of health (SOH) of batteries is essential for maximizing the lifetime of the battery and improving the safety and economy of any energy storage system. Data-driven methods can use measurement data to effectively estimate the SOH, but the estimation performance depends on the relevance between the selected feature and SOH. In this article, fuzzy entropy (FE) of battery voltage is proposed as a new feature for SOH estimation and validated on Li-ion batteries. Compared with the traditional sample entropy, the FE can capture the variation of voltage during the battery degradation more efficiently in terms of the parameter selection, data noise, data size, and test condition. Moreover, the aging temperature variation is involved in the established SOH estimator as the temperature is a disturbance variable in the real applications. The FE-SOH is used as the input–output data pair of the support vector machine, and a single-temperature model, a full-temperature model, and a partial-temperature model are established. As a result, the FE-based method has better estimation accuracy under aging temperature variation. The FE-based method also decreases the dependence on the size of the required training data. Finally, the effectiveness of the proposed method is verified by experimental results.

中文翻译：

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基于模糊熵的锂离子电池健康状态估计

准确估计电池的健康状态 (SOH) 对于最大限度地延长电池寿命和提高任何储能系统的安全性和经济性至关重要。数据驱动的方法可以使用测量数据来有效地估计 SOH，但估计性能取决于所选特征与 SOH 之间的相关性。在本文中，电池电压的模糊熵 (FE) 被提出作为 SOH 估计的新特征，并在锂离子电池上进行了验证。与传统的样本熵相比，有限元可以在参数选择、数据噪声、数据大小和测试条件等方面更有效地捕捉电池退化过程中的电压变化。而且，由于温度是实际应用中的干扰变量，因此在建立的 SOH 估计器中涉及老化温度变化。FE-SOH作为支持向量机的输入输出数据对，建立了单温度模型、全温度模型和局部温度模型。因此，基于有限元的方法在老化温度变化下具有更好的估计精度。基于有限元的方法还减少了对所需训练数据大小的依赖。最后，通过实验结果验证了所提出方法的有效性。基于有限元的方法在老化温度变化下具有更好的估计精度。基于有限元的方法还减少了对所需训练数据大小的依赖。最后，通过实验结果验证了所提出方法的有效性。基于有限元的方法在老化温度变化下具有更好的估计精度。基于有限元的方法还减少了对所需训练数据大小的依赖。最后，通过实验结果验证了所提出方法的有效性。