中国机械工程

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[动力电池]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

刘征宇1,2;汤伟1;王雪松1;黎盼春1   

  1. 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
  • 出版日期:2018-08-10 发布日期:2018-08-06
  • 基金资助:
    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)

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

LIU Zhengyu1,2;TANG Wei1;WANG Xuesong1;LI  Panchun1   

  1. 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
  • Online:2018-08-10 Published:2018-08-06

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

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 battery. 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 estimation 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

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