Online parameters identification and state of charge estimation for lithium-ion capacitor based on improved Cubature Kalman filter
Online parameters identification and state of charge estimation for lithium-ion capacitor based on improved Cubature Kalman filter
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DOI：
10.1016/j.est.2019.100810
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发表时间：
2019-08
影响因子：
9.4
通讯作者：
Hao Yang;Xianzhong Sun;Yabin An;Xiong Zhang;Tongzhen Wei;Yanwei Ma
中科院分区：
文献类型：
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作者：
Hao Yang;Xianzhong Sun;Yabin An;Xiong Zhang;Tongzhen Wei;Yanwei Ma
锂离子电容器是一种混合电化学储能装置，它结合了锂离子电池和双电层电容器的优点。准确的充电状态估计是锂离子电容器高效利用的关键。本文对锂离子电容器的电学特性进行了研究，建立了相应的等效电路模型。在此基础上，提出了一种变遗忘因子递推最小二乘自适应平方根立方卡尔曼滤波算法，实现了每一采样步长的电荷状态估计。该方法能够实时识别模型参数和噪声统计量，即使在参数不准确的情况下也能获得准确的SOC估计。实验结果表明，该方法具有较好的估计性能，相应的最大均方根误差控制在1%以内。此外，稳健性验证表明，在考虑外部干扰和初始SOC不准确的情况下，该方法具有较高的稳定性和可接受的精度。
Lithium-ion capacitor is a hybrid electrochemical energy storage device which combines the merits of lithium-ion battery and electric double-layer capacitor. The precise state of charge estimation is essential to utilize lithium-ion capacitor efficiently. In this paper, the electrical characteristics of lithium-ion capacitor have been studied and the corresponding equivalent circuit model is established. Then, an adaptive square root cubature Kalman filter with variable forgetting factor recursive least square has been proposed to achieve state of charge estimation at each sampling step. The proposed method identifies the model parameters and noise statistics in real time, leading to obtain accurate SOC estimation even if the method is initialized with inaccurate parameters. Estimations under four working conditions are quantitatively studied, the experimental results show that the proposed method has better performance and the corresponding maximum root mean square error is controlled within 1%. Furthermore, the verification of robustness illustrates that the proposed method remains stability and acceptable accuracy when external interference and inaccurate initial SOC are included.