Improved Battery SOC Estimation Accuracy Using a Modified UKF With an Adaptive Cell Model Under Real EV Operating Conditions
Improved Battery SOC Estimation Accuracy Using a Modified UKF With an Adaptive Cell Model Under Real EV Operating Conditions
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DOI：
10.1109/tte.2018.2802043
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发表时间：
2018-06-01
影响因子：
7
通讯作者：
Abdel-Hafez, Mamoun F.
中科院分区：
文献类型：
--
作者：
El Din, Menatalla Shehab;Hussein, Ala A.;Abdel-Hafez, Mamoun F.
电动汽车（ev）需要可靠且非常准确的电池荷电状态（SOC）估计，以最大限度地提高其性能。一种常用的估计技术，扩展卡尔曼滤波（EKF），提供了一个准确的估计SOC。然而，EKF也有一定的局限性，例如它假设过程噪声和测量噪声的统计知识是可用的，这实际上是无法保证的。本文提出了一种自适应等效电路模型，并将其用于SOC估计。该模型基于具有自适应参数跟踪特征的通用单元模型，该模型使用嵌入在模型中的人工神经网络控制器实现。EKF的一种变体，即无气味卡尔曼滤波器（UKF），用于以相对较快的收敛速度实现更准确的SOC估计。UKF使用无气味变换来获得过程噪声协方差的统计量。此外，利用自协方差最小二乘技术对测量噪声协方差进行估计，考虑了测量创新中可能存在的相关性，提高了估计的准确性。本文给出了该方法的推导，并进行了实验验证。
Electric vehicles (EVs) require reliable and very accurate battery state-of-charge (SOC) estimation to maximize their performance. A commonly used estimation technique, the extended Kalman filter (EKF), provides an accurate estimate of the SOC. However, EKF has some limitations, such as it assumes the knowledge of the statistics of the process noise and measurement noise is available, which practically cannot be guaranteed. In this paper, an adaptive equivalent-circuit model is proposed and used for SOC estimation. The proposed model is based on a common cell model with adaptive parameters tracking feature implemented using an artificial neural network controller embedded within the model. A variant of the EKF, namely the unscented Kalman filter (UKF), is used to achieve more accurate estimates of the SOC with a relatively fast convergence speed. The UKF uses the unscented transform to obtain the statistics of the process noise covariance. Furthermore, the autocovariance least-squares technique is used to estimate the measurement noise covariance by accounting for possible correlation in the measurement innovations, which enhances the accuracy of the estimate. Derivation of the proposed method followed by experimental verification is presented in this paper.