A state of health estimation method for satellite battery based on smooth and discharge applicative increment capacity analysis
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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。
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关键词:
- 容量增量分析(ICA) /
- 卫星 /
- 锂电池 /
- 健康状态(SOH)估计 /
- 低分辨率遥测数据
Abstract:Aimed at the large error when using Increment Capacity Analysis (ICA) to estimate the State of Health (SOH) of satellites battery, this paper proposes an advanced SOH estimation method based on Smooth and Discharge applicative Increment Capacity Analysis (SD-ICA). First, the proposed method does the smoothing processing to the low-resolution telemetry data by using the fitting results of the smooth spline functions which have the continuous second-order derivative. Thus the calculation accuracy is improved. Then, in consideration of the limitation that ICA must use the micro current discharge data, the IC calculation method under load conditions is deduced, which reduces the requirements for the satellite battery discharge conditions. Finally, a linear regression relationship between the battery capacity and Features of Interest 1 (FOI 1) of IC curve is found and used to estimate the SOH of the satellite battery. The results show that the proposed SOH estimation method can accurately obtain the battery SOH from satellite telemetry data. In addition, this method is easy to calculate, has low requirements for sampling resolution, and does not need to add battery working conditions. Therefore, it is valuable for battery health management and mission planning of satellite.
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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 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 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 16 平衡态FOI1与带负载FOI1的关系
Figure 16. FOI1 relationship between equilibrium state and loaded state
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 
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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
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