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首页> 外文期刊>Electric power systems research >A novel model-based state of charge estimation for lithium-ion battery using adaptive robust iterative cubature Kalman filter
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A novel model-based state of charge estimation for lithium-ion battery using adaptive robust iterative cubature Kalman filter

机译：自适应鲁棒迭代培养卡尔曼滤波器的锂离子电池基于模型的荷电状态估计

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摘要

Accurate and robust state of charge (SOC) estimation is the key evaluation index for battery management system (BMS) in electric vehicles (EVs). To improve the SOC estimation precision and reliability, a novel model-based estimation approach has been proposed. Firstly, the dynamic property of lithium-ion battery (LIB) is approximated by the auto-regressive and moving average (ARMA) model which compensates the measurement errors of terminal voltage and discharge current. Secondly, a variant of the Kalman filter (KF), namely improved cubature Kalman filter (CKF) based on the combination of singular value decomposition (SVD) and Gauss Newton iterative technology is employed to develop a reliable estimator for SOC. Furthermore, an adaptive robust strategy is used to improve anti-interference performance by accounting for bidirectional adjustment of observation covariance and gain matrix. Finally, the Dynamic Stress Test (DST) and Federal Urban Driving Schedule (FUDS) are loaded on LIB to test the validity of the improved approach. The experiment results demonstrate the effectiveness of the combination of ARMA model and filtering method in terms of SOC estimation. Besides, simulated measurement noise is added to the test data to prove the robustness of the proposed method.
机译：准确，可靠的充电状态（SOC）估计是电动汽车（EV）电池管理系统（BMS）的关键评估指标。为了提高SOC估计的准确性和可靠性，提出了一种基于模型的新颖估计方法。首先，锂离子电池（LIB）的动态特性通过自动回归和移动平均值（ARMA）模型进行近似，该模型可补偿端子电压和放电电流的测量误差。其次，采用卡尔曼滤波器（KF）的一种变体，即基于奇异值分解（SVD）和高斯牛顿迭代技术相结合的改进的库尔曼卡尔曼滤波器（CKF），来开发可靠的SOC估计器。此外，通过考虑观测协方差和增益矩阵的双向调整，采用了一种自适应鲁棒策略来提高抗干扰性能。最后，将动态压力测试（DST）和联邦城市驾驶计划（FUDS）加载到LIB上，以测试改进方法的有效性。实验结果证明了ARMA模型和滤波方法相结合在SOC估计方面的有效性。此外，将模拟测量噪声添加到测试数据中以证明所提出方法的鲁棒性。

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