基于容积卡尔曼滤波算法估计动力锂电池荷电状态
基于容积卡尔曼滤波算法估计动力锂电池荷电状态
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广东省科技计划项目(2014B090901052);国家自然科学基金青年资金项目(51707191);深圳市科技计划项目(JCYJ20160531185555169、 JCYJ20170818164527303)

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Power Lithium Battery State of Charge Estimation Cubature Kalman Filtering
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    摘要:

    动力锂电池荷电状态的准确估计是电池管理系统的关键功能之一。该文结合二阶电阻-电容等效电路模型,通过建立状态空间表达式,利用最小二乘法对等效电路模型各参数进行辨识,并通过多项式拟合方法获得了开路电压与剩余电荷的关系曲线,进而基于容积卡尔曼滤波方法对锂电池荷电状态进行建模,建立了基于数字信号处理器的充放电实验平台,实现了锂电池放电时荷电状态的实时估算。实验结果表明,该方法能够实现实时在线估算,且最大误差小于 2%,具有良好的估算精度。

    Abstract:

    Accurate estimation of charging state of the power lithium battery is an important function in the battery management system of electric vehicle. In this paper, based on the second-order resistor-capacitance equivalent circuit model, an accurate charging state estimation of power lithium battery was investigated. State space expression was established firstly, and the parameters of equivalent circuit model were identified by the least square method. The relationship between open circuit voltage and residual charge was fitted by polynomial fitting method. By the usage of cubature Kalman filter, the state of charge of lithium battery was estimated at the same time. In the experiment, a digital signal processor-based charge and discharge platform was constructed. And the experimental results show that, the cubature Kalman filtering algorithm can achieve real-time online estimation, and the maximum error is less than 2%, which has high estimation accuracy.

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引文格式
梁嘉宁,谭霁宬,孙天夫,王 峥.基于容积卡尔曼滤波算法估计动力锂电池荷电状态 [J].集成技术,2018,7(6):31-38

Citing format
LIANG Jianing, TAN Jicheng, SUN Tianfu, WANG Zheng. Power Lithium Battery State of Charge Estimation Cubature Kalman Filtering[J]. Journal of Integration Technology,2018,7(6):31-38

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  • 在线发布日期: 2018-11-20
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