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Energies 2013, 6(8), 3654-3668; doi:10.3390/en6083654
Article

Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction

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Received: 21 June 2013; in revised form: 11 July 2013 / Accepted: 12 July 2013 / Published: 25 July 2013
(This article belongs to the Special Issue Li-ion Batteries and Energy Storage Devices)
Download PDF [661 KB, updated 30 July 2013; original version uploaded 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 satellite; lithium-ion battery; remaining useful life estimation; health indicator; echo state networks; ensemble learning
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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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.

AMA 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(8):3654-3668.

Chicago/Turabian Style

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.


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