State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter
State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter
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DOI：
10.3390/en8065916
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发表时间：
2015-06
期刊：
影响因子：
3.2
通讯作者：
Bizhong Xia;Haiqing Wang;Yong-Liang Tian;Mingwang Wang;Wei Sun;Zhihui Xu
中科院分区：
文献类型：
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作者：
Bizhong Xia;Haiqing Wang;Yong-Liang Tian;Mingwang Wang;Wei Sun;Zhihui Xu
准确的荷电状态（SOC）估算对于锂离子电池确保其安全运行、防止过度充电或过度放电具有重要意义。然而，由于SOC是电池单元的内部状态，无法直接测量，因此很难获得准确的值。本文提出了一种基于自适应容积卡尔曼滤波器（ACKF）的电动汽车锂离子电池 SOC 估计算法。首先，采用二阶电阻电容（RC）等效电路对锂离子电池进行建模，并通过遗忘因子最小二乘法确定电池模型的参数。然后，介绍了用于电池SOC估计的自适应Cuature卡尔曼滤波器并给出了估计过程。最后，应用动态应力测试（DST）和新欧洲驾驶循环（NEDC）两个典型的驾驶循环来评估所提方法的性能，并与传统的扩展卡尔曼滤波器（EKF）和立方卡尔曼滤波器（CKF）算法进行比较。实验结果表明，与传统的EKF和CKF算法相比，ACKF算法在SOC估计精度、对不同初始SOC误差的收敛性以及对电压测量噪声的鲁棒性方面具有更好的性能。
Accurate state of charge (SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner sate of a battery cell, which cannot be directly measured. This paper presents an Adaptive Cubature Kalman filter (ACKF)-based SOC estimation algorithm for lithium-ion batteries in electric vehicles. Firstly, the lithium-ion battery is modeled using the second-order resistor-capacitor (RC) equivalent circuit and parameters of the battery model are determined by the forgetting factor least-squares method. Then, the Adaptive Cubature Kalman filter for battery SOC estimation is introduced and the estimated process is presented. Finally, two typical driving cycles, including the Dynamic Stress Test (DST) and New European Driving Cycle (NEDC) are applied to evaluate the performance of the proposed method by comparing with the traditional extended Kalman filter (EKF) and cubature Kalman filter (CKF) algorithms. Experimental results show that the ACKF algorithm has better performance in terms of SOC estimation accuracy, convergence to different initial SOC errors and robustness against voltage measurement noise as compared with the traditional EKF and CKF algorithms.