中国电机工程学报2017，Vol.37Issue(15)：4514-4520,7.DOI:10.13334/j.0258-8013.pcsee.161687
基于平方根无迹卡尔曼滤波的锂电池状态估计
State-of-Charge Estimation Based on Square Root Unscented Kalman Filter Algorithm for Li-ion Batteries
摘要
Abstract
To estimate the state of charge (SOC) of batteries accurately and reliably is of great importance for plug-in hybrid electric vehicles (PHEV) and electric vehicles (EVs).However,the traditional methods have drawbacks of heavy computation and inaccurate estimation.In this study,a square root unscented Kalman filter (SRUKF) algorithm was proposed for estimating the SOC of Lithium-Ion batteries.An optimization algorithm was used to update the model's state vector during a charge/discharge period.When the estimation of the means and covariance of the state vector was used,the unscented transformation (UT) takes advantages of deterministic sampling.The square root algorithm of the filter improves the numerical stability by ensuring the state covariance,which is always semi-positive definite.The proposed method has been validated experimentally and the results are compared with the unscented Kalman filter.Experimental results have shown that the proposed method has better performance in terms of lower error and shorter convergence time,and can meet the actual requirements.关键词
锂电池/荷电状态/平方根无迹卡尔曼滤波/无迹变换/平方根算法Key words
Lithium-ion battery/state of charge (SOC)/square root unscented Kalman filter (SRUKF)/unscented transformation (UT)/square root algorithm分类
信息技术与安全科学引用本文复制引用
费亚龙,谢长君,汤泽波,曾春年,全书海..基于平方根无迹卡尔曼滤波的锂电池状态估计[J].中国电机工程学报,2017,37(15):4514-4520,7.基金项目
国家自然科学基金项目(51477125) （51477125）
国家重点基础研究发展计划项目(973项目)(2013CB632505) （973项目）
湖北省科技支撑计划项目(2014BEC074) （2014BEC074）
武汉市青年科技晨光计划项目(2016070204010155).Project Supported by National Natural Science Foundation of China (51477125) （2016070204010155）
The National Basic Research Program of China (973 Program) (2013CB632505) （973 Program）
Hubei Provincial Science and Technology Support Project of China (2014BEC074) （2014BEC074）
Wuhan Youth Morning Project of China (2016070204010155). （2016070204010155）
