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一种基于SD-ICA的卫星电池健康状态估计方法

陈景龙 王日新 李玉庆 徐敏强 黄文虎

陈景龙, 王日新, 李玉庆, 等 . 一种基于SD-ICA的卫星电池健康状态估计方法[J]. 北京航空航天大学学报, 2021, 47(10): 2058-2067. doi: 10.13700/j.bh.1001-5965.2020.0376
引用本文: 陈景龙, 王日新, 李玉庆, 等 . 一种基于SD-ICA的卫星电池健康状态估计方法[J]. 北京航空航天大学学报, 2021, 47(10): 2058-2067. doi: 10.13700/j.bh.1001-5965.2020.0376
CHEN Jinglong, WANG Rixin, LI Yuqing, et al. A state of health estimation method for satellite battery based on smooth and discharge applicative increment capacity analysis[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(10): 2058-2067. doi: 10.13700/j.bh.1001-5965.2020.0376(in Chinese)
Citation: CHEN Jinglong, WANG Rixin, LI Yuqing, et al. A state of health estimation method for satellite battery based on smooth and discharge applicative increment capacity analysis[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(10): 2058-2067. doi: 10.13700/j.bh.1001-5965.2020.0376(in Chinese)

一种基于SD-ICA的卫星电池健康状态估计方法

doi: 10.13700/j.bh.1001-5965.2020.0376
基金项目: 

哈尔滨工业大学关键实验室开放基金 HIT.KLOF.2018.076

哈尔滨工业大学关键实验室开放基金 HIT.KLOF.2018.074

详细信息
    通讯作者:

    李玉庆, E-mail: bradley@hit.edu.cn

  • 中图分类号: V19

A state of health estimation method for satellite battery based on smooth and discharge applicative increment capacity analysis

Funds: 

Key Laboratory Opening Funding of Harbin Institute of Technology HIT.KLOF.2018.076

Key Laboratory Opening Funding of Harbin Institute of Technology HIT.KLOF.2018.074

More Information
  • 摘要:

    针对容量增量分析(ICA)法应用在卫星电池的健康状态(SOH)估计中存在较大误差的问题,提出了基于带平滑处理并使用放电数据的容量增量分析(SD-ICA)的电池健康状态估计方法。首先,利用光滑样条函数的拟合结果具有二阶导连续的特性,对低分辨率的遥测数据进行平滑处理,从而提高了计算结果的准确性。其次,针对ICA必须使用微小电流放电数据的限制,推导出有负载条件下的容量增量(IC)计算方法,降低了对卫星电池放电工况的要求。最后,利用IC曲线的第一特征点(FOI1)与电池容量的关系,对卫星电池的健康状态进行估计。经验证,所提方法具有对数据分辨率要求低、不需要增加电池工况、计算简便等优势,可以准确地从卫星遥测数据中估计电池健康状态。研究成果在卫星电池的健康管理和任务规划中具有重要的应用价值。

     

  • 图 1  GEO卫星电池组放电电流和电池组端电压

    Figure 1.  GEO satellite battery discharge current and battery pack terminal voltage

    图 2  LEO卫星电池组放电电流和电池组端电压

    Figure 2.  LEO satellite battery discharge current and battery pack terminal voltage

    图 3  IC曲线及FOI1和FOI2

    Figure 3.  IC curve and FOI1 & FOI2

    图 4  电池内阻退化趋势

    Figure 4.  Aging trend of battery resistance

    图 5  电池测试系统

    Figure 5.  Battery test system

    图 6  实测电压值与重采样模拟遥测值

    Figure 6.  Voltage from measurement and resampling

    图 7  测量数据的IC计算结果

    Figure 7.  IC calculation results from measurement data

    图 8  重采样后ICA法和Gau-ICA法计算结果

    Figure 8.  Results of ICA and Gau-ICA from resampling data

    图 9  重采样后MA-ICA法、SD-ICA法和Model-ICA法计算结果

    Figure 9.  Results of MA-ICA, SD-ICA, and model-ICA from resampling data

    图 10  重采样数据增加采样间隔后计算结果

    Figure 10.  Calculation results from large-interval resampling data

    图 11  电池衰退对IC曲线的影响

    Figure 11.  Impact of battery aging on IC curves

    图 12  FOI1的变化趋势与电池容量的退化趋势

    Figure 12.  Variation of FOI1 and degradation of battery capacity

    图 13  FOI1与电池容量的关系

    Figure 13.  Relationship between FOI1 and battery capacity

    图 14  负载对IC值的影响

    Figure 14.  Impact of load on IC value

    图 15  电池衰退对仿遥测数据的IC曲线的影响

    Figure 15.  Impact of battery aging on IC curves

    图 16  平衡态FOI1与带负载FOI1的关系

    Figure 16.  FOI1 relationship between equilibrium state and loaded state

    图 17  基于SD-ICA法的估计结果

    Figure 17.  Estimation results based on SD-ICA method

    图 18  基于SD-ICA法的SOH估计误差

    Figure 18.  SOH estimation error based on SD-ICA method

    表  1  电池测试仪主要参数

    Table  1.   Main parameters of battery tester

    参数 数值
    通道数 8
    充放电电流/A 0~5
    充放电电压/V 0~5
    电流分辨率/mA 0.1
    电压分辨率/mA 0.1
    温度分辨率/℃ 0.1
    最小记录间隔/s 0.1
    下载: 导出CSV

    表  2  IC值计算误差

    Table  2.   Calculation error of IC

    误差 SD-ICA法 ICA法 MA-ICA法 Gau-ICA法 Model-ICA法
    MAE 0.009 7 0.150 9 0.071 1 0.032 1 0.129 5
    RMSE 0.017 7 0.191 2 0.111 7 0.052 1 0.194 5
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-07-31
  • 录用日期:  2020-08-14
  • 网络出版日期:  2021-10-20

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