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Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction
Department of Automatic Test and Control, Harbin Institute of Technology, No.2, YiKuang Street, NanGang District, Harbin 150080, China
Department of Systems and Industrial Engineering, The University of Arizona, Tucson, AZ 85721, USA
* Author to whom correspondence should be addressed.
Received: 21 June 2013; in revised form: 11 July 2013 / Accepted: 12 July 2013 / Published: 25 July 2013
Abstract: Prognostics and remaining useful life (RUL) estimation for lithium-ion batteries play an important role in intelligent battery management systems (BMS). The capacity is often used as the fade indicator for estimating the remaining cycle life of a lithium-ion battery. For spacecraft requiring high reliability and long lifetime, in-orbit RUL estimation and reliability verification on ground should be carefully addressed. However, it is quite challenging to monitor and estimate the capacity of a lithium-ion battery on-line in satellite applications. In this work, a novel health indicator (HI) is extracted from the operating parameters of a lithium-ion battery to quantify battery degradation. Moreover, the Grey Correlation Analysis (GCA) is utilized to evaluate the similarities between the extracted HI and the battery’s capacity. The result illustrates the effectiveness of using this new HI for fading indication. Furthermore, we propose an optimized ensemble monotonic echo state networks (En_MONESN) algorithm, in which the monotonic constraint is introduced to improve the adaptivity of degradation trend estimation, and ensemble learning is integrated to achieve high stability and precision of RUL prediction. Experiments with actual testing data show the efficiency of our proposed method in RUL estimation and degradation modeling for the satellite lithium-ion battery application.
Keywords: satellite; lithium-ion battery; remaining useful life estimation; health indicator; echo state networks; ensemble learning
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Cite This Article
MDPI and ACS Style
Liu, D.; Wang, H.; Peng, Y.; Xie, W.; Liao, H. Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction. Energies 2013, 6, 3654-3668.
Liu D, Wang H, Peng Y, Xie W, Liao H. Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction. Energies. 2013; 6(8):3654-3668.
Liu, Datong; Wang, Hong; Peng, Yu; Xie, Wei; Liao, Haitao. 2013. "Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction." Energies 6, no. 8: 3654-3668.