An Improved Adaptive Kalman Filter based on Auxiliary Model for State of Charge Estimation with Random Missing Outputs

被引:6
|
作者
Zhang, Zili [1 ]
Pu, Yan [1 ]
Xu, Fei [1 ]
Zhong, Hongxiu [1 ]
Chen, Jing [1 ]
机构
[1] Jiangnan Univ, Sch Sci, Wuxi 214122, Peoples R China
基金
中国国家自然科学基金;
关键词
Battery management system; State of charge; Recursive least squares algorithm; Second-order RC model; Kalman filter; Auxiliary model; GRADIENT ITERATIVE ALGORITHM; LITHIUM; BATTERY; HEALTH;
D O I
10.1149/1945-7111/acb84e
中图分类号
O646 [电化学、电解、磁化学];
学科分类号
081704 ;
摘要
In this study, an improved adaptive Kalman filter based on auxiliary model (IAKF-AM) is proposed for estimating the state of charge (SOC) with random missing outputs. Since the traditional auxiliary model (AM) method is inefficient for systems with scarce measurements, this paper provides an IAKF-AM method. Compared with the AM method, the proposed method uses the measurable data to adjust missing outputs in each interval, thus has higher estimation accuracy. In addition, a recursive least squares (RLS) algorithm is introduced, which can combine the IAKF-AM method to iteratively estimate the SOC and outputs. In the simulation part, the mean absolute errors (MAE) and the root mean squared error (RMSE) is used to evaluate the model performance under different cases. Simulation example verify the effectiveness of the proposed IAKF-AM algorithm.
引用
收藏
页数:9
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