基于双时间尺度扩展卡尔曼粒子滤波算法的 电池组单体荷电状态估计

China Mechanical Engineering - - 中国机械工程 -

Cell SOC Estimation of Battery Packs Based on Dual Time-Scale EKPF

LIU Zhengyu1,2 TANG Wei1 WANG Xuesong1 LI Panchun1

1.School of Mechanical Engineering,Hefei University of Technology,Hefei,230009 2.Engineering Research Center of Safety Critical Industry Measurement and Control Technology,

Ministry of Education,Hefei,230009

Abstract: In order to accurately estimate the SOC of the battery packs,a enhance self correcting ( ESC) model was established for the lithium battery packs,and then an average battery model and a SOC difference model for each battery were established according to the ESC model of the lithium bat⁃ tery. The dual ⁃ time ⁃ scale EKPF algorithm was used to estimate the average SOC of the batteries and the differential SOC of each cell,so as to obtain the SOC of each cell in the batteries. The SOC estima⁃ tion experiments of 12 lithium battery series were carried out . The results show that the SOC estimation method based on the dual time ⁃ scale EKPF algorithm may achieve accurate estimate of the cell SOC. And it is proved that the dual⁃time⁃scale EKPE algorithm has higher estimation accuracy than that of the dual time⁃scale EKF algorithm and EKF algorithm.

Key words: extended Kalman particle filter(EKPF);cell state ⁃ of ⁃ charge(SOC)estimation;dual time⁃scale;battery pack

0 引言

电池荷电状态( state⁃of⁃charge,SOC)估算对于电动汽车意义重大。目前常用的SOC估计算法主要有电流积分法 、放电试验法、开路电压法、负

[] 1载电压法、电化学阻抗谱法、内阻法、神经网络法和基于电池模型的卡尔曼滤波算法 以及状态观

[] 2⁃3

测器方法 等。近年来,出现了自适应无迹卡尔曼

[] 4

滤波 ( adaptive unscented Kalman filter,

[] 5

收稿日期: 2017-06-12

基金项目:国家国际科技合作专项(2012DFB10060);安徽省自然科学基金资助项目( 1808085MF200) AUKF)、鲁棒卡尔曼滤波 ( robust extended Kal⁃

[] 6 man filter,REKF)等改进的卡尔曼滤波算法,以及改进的观测器算法,包括H∞观测器算法 、自适应

[] 7 Luenberger 观测器算法 及 PI观测器算法 等。

[] 8 [] 9但以上方法主要研究如何对单个电池进行精确的SOC估计,而实际应用中电池组中电池单体由于制造工艺的不一致和使用环境的不一致,或多或少会导致单体间SOC的不一致 。在电池组单体

[] 10

SOC不一致时,如果使用以上方法则只能对电池组单体SOC进行逐个估计,从而导致计算复杂度太高。

1,2 1 1 1

刘征宇 汤 伟 王雪松 黎盼春

1.合肥工业大学机械工程学院,合肥, 230009 2.安全关键工业测控技术教育部工程研究中心,合肥, 230009

摘要:为实现对电池组单体荷电状态( SOC)的精确估算,首先对锂电池组单体建立增强自校正( ESC)模型,然后根据锂电池ESC模型建立电池组平均模型和各单体SOC差异模型,再对其用双时间尺度的扩展卡尔曼粒子滤波( EKPF)算法来估算电池组平均SOC值和各单体差异SOC值,从而得到电池组中各单体SOC值。对12节锂电池串联电池组进行SOC估算实验,结果表明,基于双时间尺度EKPF算法的电池组单体SOC估计方法可实现对单体SOC的精确估计,且该方法比双时间尺度扩展卡尔曼滤波算法和扩展卡尔曼滤波( EKF)算法具有更高的估算精度。

关键词:扩展卡尔曼粒子滤波;单体荷电状态估计;双时间尺度;电池组

中图分类号: U463

DOI:10.3969/j.issn.1004⁃132X.2018.15.010 开放科学(资源服务)标识码(OSID) :

Newspapers in Chinese (Simplified)

Newspapers from China

© PressReader. All rights reserved.