Prediction of Battery Remaining Capacity in Space Power System Using Extreme Learning Machine

被引：0
|
作者：
Zheng, Shenggen [1 ]
Huang, Wentao [2 ]
Fan, Qinqin [1 ]
机构：
[1] Shanghai Maritime Univ, Logist Res Ctr, Shanghai, Peoples R China
[2] Minist Educ China, Key Lab Control Power Transmiss & Convers, Shanghai, Peoples R China
来源：
2021 11TH INTERNATIONAL CONFERENCE ON POWER AND ENERGY SYSTEMS (ICPES 2021) | 2021年
关键词：
space power system; battery; battery remaining capacity; extreme learning machine; OF-CHARGE ESTIMATION; STATE; MODEL; REGRESSION;
D O I：
10.1109/ICPES53652.2021.9683837
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
State of a storage battery in the space power system is the most factor for the spacecraft safety. Therefore, how to estimate the remaining capacity of the battery is a vital task. To end this, a prediction model of battery remaining capacity is proposed in the current study. In the proposed model, the extreme learning machine (ELM) is used and a grid search is applied to find its optimal hyperparameters. Experimental results show that the prediction accuracy of the proposed model is better than that of other competitors. Moreover, the proposed algorithm uses less time.
引用
收藏
页码：178 / 182
页数：5
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