【摘要】：鋰離子電池(簡稱鋰電池)是一種綠色的高能充電電池,它具有放電電壓穩(wěn)定性高、工作時對外界溫度限制寬、循環(huán)使用壽命長、所占空間體積小、電池質(zhì)量輕、對環(huán)境無危害等優(yōu)點(diǎn),因此鋰電池被廣泛應(yīng)用于電子產(chǎn)品、電動汽車、航空航天等領(lǐng)域。但是鋰電池在工作過程中性能會逐漸衰減,有時候還可能發(fā)生意外的故障會導(dǎo)致鋰電池失效而產(chǎn)生嚴(yán)重后果。因此對鋰電池的健康狀態(tài)監(jiān)測和剩余循環(huán)壽命預(yù)測是至關(guān)重要的,這方面的研究對進(jìn)一步指導(dǎo)鋰電池的運(yùn)行和維護(hù),對于系統(tǒng)的安全具有重要意義。本文的主要工作有:(1)選擇基于數(shù)據(jù)驅(qū)動的方法建立高斯過程回歸模型,對鋰電池的電壓、電池容量的數(shù)據(jù)進(jìn)行處理。對鋰電池的健康狀態(tài)和剩余使用壽命進(jìn)行了預(yù)測和分析。同時,把預(yù)測結(jié)果和人工神經(jīng)網(wǎng)絡(luò)的方法進(jìn)行對比,分析選擇高斯過程回歸的優(yōu)越性。(2)研究了建立高斯過程回歸模型中核函數(shù)選擇的問題。對不同的核函數(shù)進(jìn)行分析、比較和組合,將核函數(shù)分為局部核函數(shù)和全局核函數(shù),針對電池?cái)?shù)據(jù)的特點(diǎn)選擇了最佳的核函數(shù)或組合核函數(shù),提高了預(yù)測結(jié)果的精確度。(3)把稀疏高斯過程回歸運(yùn)用到鋰電池的數(shù)據(jù)處理中。選取了稀疏偽輸入法進(jìn)行建模,在保證精確度的前提下,有效地減少了建模的計(jì)算量,提高了用高斯過程回歸模型處理電池?cái)?shù)據(jù)的實(shí)時性。
[Abstract]:Lithium-ion battery (Li-ion battery) is a kind of green high-energy rechargeable battery. It has high stability of discharge voltage, wide limit to external temperature, long cycle life, small volume of space and light quality of battery. Lithium battery is widely used in electronic products, electric vehicles, aerospace and other fields. However, the performance of lithium batteries will gradually decline in the process of operation, and sometimes unexpected failures may lead to the failure of lithium batteries, resulting in serious consequences. Therefore, it is very important to monitor the health status and predict the residual cycle life of lithium batteries. The research on this aspect is of great significance for further guiding the operation and maintenance of lithium batteries and for the safety of the system. The main work of this paper is as follows: (1) the Gao Si process regression model is established based on the data-driven method, and the data of lithium battery voltage and battery capacity are processed. The health status and residual service life of lithium battery were predicted and analyzed. At the same time, comparing the prediction results with the method of artificial neural network, the superiority of selecting Gao Si process regression is analyzed. (2) the problem of kernel function selection in establishing Gao Si process regression model is studied. Different kernel functions are analyzed, compared and combined. The kernel function is divided into local kernel function and global kernel function, and the best kernel function or combined kernel function is selected according to the characteristics of battery data. The accuracy of the prediction results is improved. (3) the sparse Gao Si process regression is applied to the data processing of lithium batteries. The sparse pseudo-input method is selected to model, which can effectively reduce the calculation amount of modeling and improve the real-time processing of battery data with Gao Si process regression model under the premise of ensuring the accuracy of modeling.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM912

【參考文獻(xiàn)】

相關(guān)期刊論文 前9條

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

2 魏克新;陳峭巖;;基于自適應(yīng)無跡卡爾曼濾波算法的鋰離子動力電池狀態(tài)估計(jì)[J];中國電機(jī)工程學(xué)報(bào);2014年03期

3 龐景月;馬云彤;劉大同;彭宇;;鋰離子電池剩余壽命間接預(yù)測方法[J];中國科技論文;2014年01期

4 何志昆;劉光斌;趙曦晶;王明昊;;高斯過程回歸方法綜述[J];控制與決策;2013年08期

5 羅廣求;段艷麗;羅萍;;衛(wèi)星用鋰離子蓄電池性能分析與在軌管理方法[J];電源技術(shù);2012年07期

6 申倩倩;孫宗海;;基于自適應(yīng)自然梯度法的在線高斯過程建模[J];計(jì)算機(jī)應(yīng)用研究;2011年01期

7 蘇國韶;張研;燕柳斌;;隧道圍巖變形預(yù)報(bào)的高斯過程機(jī)器學(xué)習(xí)模型[J];桂林理工大學(xué)學(xué)報(bào);2010年04期

8 劉開云;劉保國;徐沖;;基于遺傳 組合核函數(shù)高斯過程回歸算法的邊坡非線性變形時序分析智能模型[J];巖石力學(xué)與工程學(xué)報(bào);2009年10期

9 蘇國韶;燕柳斌;張小飛;江權(quán);;基坑位移時間序列預(yù)測的高斯過程方法[J];廣西大學(xué)學(xué)報(bào)(自然科學(xué)版);2007年02期

相關(guān)博士學(xué)位論文 前1條

1 徐沖;分岔隧道設(shè)計(jì)施工優(yōu)化與穩(wěn)定性評價[D];北京交通大學(xué);2011年

，

本文編號：2462681