Improved square root cubature Kalman filter for state of charge estimation with state vector outliers

被引：5
|
作者：
Zhang, Zili [1 ]
Chen, Jing [1 ]
Mao, Yawen [1 ]
Liao, Cuicui [1 ]
机构：
[1] Jiangnan Univ, Sch Sci, Wuxi 214122, Peoples R China
来源：
IONICS | 2023年 / 29卷 / 04期
基金：
中国国家自然科学基金;
关键词：
State of charge; Multi-innovation least square algorithm; Second-order RC model; Strong tracking filter; Square root cubature Kalman filter; LIKELIHOOD-ESTIMATION METHOD; LITHIUM-ION BATTERY; MODEL RECOVERY; OF-CHARGE; SYSTEMS; HEALTH;
D O I：
10.1007/s11581-022-04876-x
中图分类号：
O64 [物理化学（理论化学）、化学物理学];
学科分类号：
070304 ; 081704 ;
摘要：
In this paper, a square root cubature Kalman filter based multi-innovation least square (SRCKF-MILS) algorithm is proposed for state of charge (SOC) estimation. First, a second-order resistor-capacitor (RC) equivalent circuit model is constructed to approximate the dynamic performance of the battery. Then, the multi-innovation least square and the square root cubature Kalman filter are used to interactively estimate the battery model parameters and SOC. Since the state vector contains outliers, the strong tracking filtering (STF) theory is introduced to adaptively adjust the gain matrix. The STF theory combined with SRCKF-MILS algorithm can improve the robustness of the algorithm. Simulation examples show the effectiveness of the proposed algorithms.
引用
收藏
页码：1369 / 1379
页数：11
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