Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction
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 re...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2013-07-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1996-1073/6/8/3654 |
| _version_ | 1811186145803894784 |
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| author | Haitao Liao Wei Xie Yu Peng Datong Liu Hong Wang |
| author_facet | Haitao Liao Wei Xie Yu Peng Datong Liu Hong Wang |
| author_sort | Haitao Liao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T13:40:37Z |
| format | Article |
| id | doaj.art-663d0f9859714040a8f3edb0f38cbc46 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T13:40:37Z |
| publishDate | 2013-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-663d0f9859714040a8f3edb0f38cbc462022-12-22T04:21:14ZengMDPI AGEnergies1996-10732013-07-01683654366810.3390/en6083654Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator ExtractionHaitao LiaoWei XieYu PengDatong LiuHong WangPrognostics 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.http://www.mdpi.com/1996-1073/6/8/3654satellitelithium-ion batteryremaining useful life estimationhealth indicatorecho state networksensemble learning |
| spellingShingle | Haitao Liao Wei Xie Yu Peng Datong Liu Hong Wang Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction Energies satellite lithium-ion battery remaining useful life estimation health indicator echo state networks ensemble learning |
| title | Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction |
| title_full | Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction |
| title_fullStr | Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction |
| title_full_unstemmed | Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction |
| title_short | Satellite Lithium-Ion Battery Remaining Cycle Life Prediction with Novel Indirect Health Indicator Extraction |
| title_sort | satellite lithium ion battery remaining cycle life prediction with novel indirect health indicator extraction |
| topic | satellite lithium-ion battery remaining useful life estimation health indicator echo state networks ensemble learning |
| url | http://www.mdpi.com/1996-1073/6/8/3654 |
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