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题名: 双卡尔曼滤波算法在锂电池SOC估算中的应用
其他题名: Application of dual extended Kalman filtering algorithm in the state-of-charge estimation of lithium-ion battery
作者: 王笑天 ; 杨志家 ; 王英男 ; 王忠锋
作者部门: 工业控制网络与系统研究室
关键词: 双卡尔曼滤波 ; 荷电状态 ; 锂离子电池 ; 电池模型
刊名: 仪器仪表学报
ISSN号: 0254-3087
出版日期: 2013
卷号: 34, 期号:8, 页码:1732-1738
收录类别: EI ; CSCD
产权排序: 1
摘要: 以在线估计锂离子电池组的荷电状态(SOC)为目的,建立了双卡尔曼滤波(DEKF)算法。以Thevenin电池模型和卡尔曼滤波算法为基础,对电池模型建立了状态空间表达式。分别采用最小二乘法和DEKF算法对该模型参数进行辨识,提高了该模型的精度,使电池模型能够较好地反映电池内部的真实状态。介绍了双卡尔曼滤波算法在线估算荷电状态的原理,并设计了相关的电池测试实验。实验结果表明在不同的工况环境下,该算法在线估计SOC具有较高的精度和对环境的适应度,最大误差小于4.5%。最后,验证了DEKF算法具有较好的收敛性和鲁棒性,可以有效解决初值估算不准和累积误差的问题。
英文摘要: This paper proposes a dual extended Kalman filtering ( DEKF) algorithm for estimating the State-of-Charge ( SOC) of lithium-ion batteries on line. First of all, the state-space representation of the battery model is established based on Thevenin battery model and Kalman filtering algorithm. The least squares method and the DEKF algorithm are used to identify the battery model parameters,which improves the model accuracy and facilitates the battery model to well reflect the actual internal state of the battery, Moreover, the principle of using DEKF algorithm to estimate the inner SOC of the battery on line is introduced,and corresponding battery test experiments are designed. Experiment results demonstrate that under various operating conditions, the algorithm has relatively high accuracy and good environment adaptability when applied to evaluate SOC on line; and the maximum error is less than 4.5%. The DEKF algorithm is proved to have good convergence and robustness,and can efficiently solve the problems of inaccurate initial- value estimation and error accumulation.
语种: 中文
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内容类型: 期刊论文
URI标识: http://ir.sia.cn/handle/173321/14007
Appears in Collections:工业控制网络与系统研究室_期刊论文

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