本文選題：退化數(shù)據(jù)　切入點：鋰離子電池　出處：《西安理工大學》2017年碩士論文

【摘要】：隨著衛(wèi)星技術的發(fā)展,衛(wèi)星載體中的許多關鍵部件變得越來越復雜。在設備運行的環(huán)境中,由于復雜性和多種不確定因素的作用,其功能與性能都將不可避免的發(fā)生退化,進而造成設備的最終失效。假如,能夠在部件開始出現(xiàn)性能退化的時候?qū)ζ溥M行剩余使用壽命(Remaining Useful Life, RUL)估計,并在此基礎上確定對設備的最佳維修時機,可以有效地避免可能會發(fā)生的重大故障,從而達到降低設備運行的風險的目的,這對于提高設備運行的安全性和可靠性具有重要意義。本課題是來自XXXX測控中心的委托項目,是該項目的子課題,以衛(wèi)星的關鍵部件——鋰離子電池為對象研究其剩余使用壽命預測方法。首先,本文在對鋰離子電池(Lithium Ion Battery,LIB)工作原理和失效機理清楚認識的基礎上,針對衛(wèi)星鋰離子電池在軌實際運行失效數(shù)據(jù)難以獲得的問題,采用了美國航空航天實驗室(National Aeronautics and Space Administration, NASA)對外公布的鋰離子試驗退化數(shù)據(jù)替代失效數(shù)據(jù),并分析了試驗數(shù)據(jù)的統(tǒng)計特性,從而采用相應的算法進行剩余使用壽命估計。其次,針對單一算法存在一些不可避免的缺陷,如,擴展卡爾曼濾波、粒子濾波強烈地依賴于鋰離子電池的模型,而自回歸方法只與樣本數(shù)據(jù)有關。不管是依賴模型還是只依賴數(shù)據(jù)的統(tǒng)計特性,都會對估計的結果產(chǎn)生影響,因此提出了一種混合模型算法,該模型既依賴于數(shù)據(jù)的統(tǒng)計特性,又依賴于研究對象的模型,取其各自算法的優(yōu)勢,使混合模型算法的預測性能得到改善。最后,借助一些算法的性能評估指標,量化地分析了本文中使用的RUL預測算法的性能。通過量化指標,更進一步地說明了,提出的混合模型的預測的算法性能相對于單一算法的預測性能有所提高。鋰離子電池剩余使用壽命預測問題對許多領域的應用都至關重要,準確實現(xiàn)RUL估計對視情維修和提高系統(tǒng)可靠性具有重要意義。本文中的預測方法都已用MATLAB仿真實現(xiàn),驗證了算法的有效性。
[Abstract]:With the development of satellite technology, many key components in satellite carrier become more and more complex. If it is possible to estimate the remaining service life of the components at the beginning of performance degradation, and on this basis determine the optimal repair time for the equipment, It can effectively avoid the major failures that may occur, thus reducing the risk of equipment operation, which is of great significance for improving the safety and reliability of the equipment operation. This project is a commissioned project from the XXXX Measurement and Control Center. Lithium Ion battery is a sub-project of the project. The residual service life prediction method of Lithium Ion battery is studied with the key component of satellite as the object. Firstly, based on a clear understanding of the working principle and failure mechanism of Lithium Ion Battery Lib, a key component of the satellite, Lithium Ion BatteryLib is studied in this paper. Aiming at the problem that it is difficult to obtain the failure data of the satellite lithium ion battery in orbit, the degradation data of the lithium ion test published by the National Aeronautics and Space Administration (NASAA) of the American Aeronautics and Astronautics Laboratory are used to replace the failure data. The statistical characteristics of the experimental data are analyzed, and the corresponding algorithm is used to estimate the remaining service life. Secondly, there are some unavoidable defects in the single algorithm, such as extended Kalman filter. Particle filter strongly depends on the model of lithium-ion battery, and autoregressive method is only related to sample data. Whether it depends on the model or only on the statistical characteristics of the data, it will have an effect on the estimated results. Therefore, a hybrid model algorithm is proposed, which depends on both the statistical properties of the data and the model of the research object. The prediction performance of the hybrid model algorithm is improved by taking advantage of their respective algorithms. The performance of the RUL prediction algorithm used in this paper is quantitatively analyzed with the help of some performance evaluation indexes of the algorithm. The predictive performance of the proposed hybrid model is better than that of the single algorithm. The residual life prediction of lithium ion batteries is of great importance in many applications. Accurate realization of RUL estimation is of great significance to maintenance of visual condition and improvement of system reliability. All the prediction methods in this paper have been realized by MATLAB simulation, which verifies the validity of the algorithm.
【學位授予單位】：西安理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM912

【參考文獻】

相關期刊論文 前10條

1 彭宇;劉大同;;數(shù)據(jù)驅(qū)動故障預測和健康管理綜述[J];儀器儀表學報;2014年03期

2 王竣德;孫權;潘正強;劉天宇;;基于性能退化的控制力矩陀螺壽命預測[J];科學技術與工程;2013年33期

3 房紅征;史慧;韓立明;羅凱;李蕊;;基于粒子群優(yōu)化神經(jīng)網(wǎng)絡的衛(wèi)星故障預測方法[J];計算機測量與控制;2013年07期

4 羅偉林;張立強;呂超;王立欣;;鋰離子電池壽命預測國外研究現(xiàn)狀綜述[J];電源學報;2013年01期

5 朱增鋒;張鵬杰;王培勝;魏向輝;;AR模型的整體最小二乘解算及其應用[J];測繪科學;2013年02期

6 晁代宏;馬靜;陳淑英;張春熹;;基于性能退化的衛(wèi)星用光纖陀螺可靠性評估[J];紅外與激光工程;2011年09期

7 邱衛(wèi)寧;齊公玉;田豐瑞;;整體最小二乘求解線性模型的改進算法[J];武漢大學學報(信息科學版);2010年06期

8 馬進;周國標;李元祥;;ARMA模型參數(shù)估計的GW-LS兩段算法[J];科學技術與工程;2010年03期

9 賈治宇;康銳;;基于ARIMA模型的備件消耗預測方法[J];兵工自動化;2009年06期

10 石章松,王樹宗,劉忠;直角坐標系下的水下被動目標跟蹤自適應卡爾曼濾波算法[J];聲學技術;2004年03期

相關博士學位論文 前1條

1 范玉波;敏感問題RRT模型下（分層）三階段抽樣調(diào)查的統(tǒng)計方法及其應用[D];蘇州大學;2013年

相關碩士學位論文 前1條

1 王紅;衛(wèi)星鋰離子電池剩余壽命預測方法及應用研究[D];哈爾濱工業(yè)大學;2013年

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