锂离子电池作为电动汽车的动力源,其荷电状态(SOC)的准确估算可以有效提高系统的工作效率,防止过充过放带来的安全隐患。首先建立了电池的二阶RC等效电路模型,采用递推最小二乘法对其进行参数辨识,然后在UKF的基础上引入自适应迭代,对SOC估计值重新进行UT变换,并再次利用观测值来改善状态估计,最后采用改进的Sage-Husa估计器对过程和量测噪声进行自适应修正。仿真结果表明,所提方法具有良好的估算精度及适用性。
Abstract
Lithium-ion battery is the power source of electric vehicles. The accurate estimation of the state of charge (SOC) can effectively improve the system efficiency and prevent the safety hazard caused by overcharge and over discharge. In this paper, the second-order RC model of the battery was established, and the recursive least squares method was used to identify the parameters. Then the adaptive iteration was introduced on the basis of UKF, the state estimation value was re-transformed with unscented transformation (UT), and the observation value was used again to improve the state estimation. Finally, the improved Sage-Husa estimator was used to adaptively correct the process and measurement noise. The simulation results show that the proposed method has good estimation accuracy and applicability.
关键词
锂电池 /
SOC /
AIUKF /
BMS
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Key words
lithium batteries /
SOC /
AIUKF /
BMS
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中图分类号：
TM912.9
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参考文献
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脚注
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基金
国家重点研发计划“新能源汽车”重点专项(2018YFB0104400)
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