中国空间科学技术 ›› 2021, Vol. 41 ›› Issue (6): 79-84.doi: 10.16708/j.cnki.1000-758X.2021.0084

• 论文 • 上一篇    下一篇

基于多特征融合的航天器锂电池健康评估技术

杨同智,党建成,钟靓,刘洋,刘廷玉   

  1. 上海卫星工程研究所,上海201109
  • 出版日期:2021-12-25 发布日期:2021-12-15

Health assessment technology of lithium-ion battery for spacecraft based on multi-feature fusion

YANG Tongzhi,DANG Jiancheng,ZHONG Liang,LIU Yang,LIU Tingyu   

  1. Shanghai Institute of Satellite Engineering,Shanghai 201109,China
  • Published:2021-12-25 Online:2021-12-15

摘要: 传统的航天器蓄电池可靠性试验按照最大放电深度进行定额充放电,所构建的失效模型用于支撑航天器总体可靠性设计,不能用于在轨锂离子蓄电池健康评估任务;航天器在蓄电池遥测的采样率、精度、样本量方面无法与民用领域相比,基于高采样、大样本的民用蓄电池健康估计方法也不适用于在轨锂离子蓄电池健康评估。针对该问题,从在轨航天器蓄电池数据特性出发,挖掘在轨状态下所能提取的退化特征,并采用多特征综合评价方法,设计了一种基于多特征融合的在轨锂离子蓄电池健康评估方法,实现了在轨蓄电池放电内阻、同放电深度下的终端电压、恒压充电时间3项退化特征融合的健康量化评估,应用于某型号卫星的在轨监测与健康评估,具有良好的工程实用性,可作为国内航天器健康评估技术的参考。

关键词: 放电内阻, 放电深度, 恒压充电时间, 特征提取, 多特征融合

Abstract: The battery is charged and discharged according to the maximum discharge depth in traditional spacecraft battery reliability test. The failure model is used to support the overall reliability design of spacecraft, and cannot be used for the onorbit battery health assessment task. The sample rate, sampling precision and sample size of onorbit spacecraft telemetry are not comparable with those in civil field. The civil battery health assessment method based on high-speed sampling and large sample is not suitable for on-orbit battery health assessment. In order to solve this problem, based on the data characteristics of the on-orbit battery, the degradation features that can be extracted in the on-orbit state were excavated, and a multifeature comprehensive evaluation method was designed to realize the quantitative assessment of on-orbit battery health based on the fusion of degradation features including battery internal resistance, terminal voltage at the same depth of discharge and constant voltage charging time, which can be used as a reference for spacecraft health assessment technology.

Key words: discharge internal resistance, depth of discharge, constant voltage charging time, feature extraction, multi-feature fusion