本文選題：不確定度　切入點：高斯過程　出處：《哈爾濱工業(yè)大學》2017年碩士論文　論文類型：學位論文

【摘要】：電池管理系統(tǒng)作為電動汽車的核心部件之一,它的性能直接關系到電動汽車的使用安全與使用壽命。隨著電動汽車的普及,電池管理系統(tǒng)的檢測成為當前電動汽車行業(yè)發(fā)展需要解決的問題。動力電池組荷電狀態(tài)SOC(state of charge)的估算精度成為電池管理系統(tǒng)檢測的核心內(nèi)容,本文主要針對電池管理系統(tǒng)SOC的檢測,提出了評定動力電池SOC測量不確定度的方法。本文通過分析鋰電池工作原理及充放電數(shù)據(jù)特點,提出了基于高斯過程的鋰電池充放電數(shù)據(jù)處理方法,與最小二乘擬合和高斯擬合相比,充放電曲線的預測精度更高。針對標準QC/T897-2011《電動汽車用電池管理系統(tǒng)技術條件》中SOC檢測項目規(guī)程,設計了電池電量測量不確定度評定的方案,根據(jù)電量測量的特點,建立基于高斯過程的電池充放電模型,分析影響電量測量模型的不確定因素,確定電量測量模型中輸入量的概率密度函數(shù),利用蒙特卡洛法評定出不同充放電模式下電量測量的不確定度。分析電池老化對放電電量產(chǎn)生的影響,提出了利用高斯過程回歸算法估算電池健康狀態(tài)(SOH)的方法,采用不同的核函數(shù)進行SOH估算,并根據(jù)SOH的估算方法提出容量估算的不確定性,進行可用容量的測試,評定出可用容量不確定度。設計了基于虛擬儀器測量技術的SOC測試系統(tǒng),搭建了硬件測試平臺,同時選用NI公司的Labwindows/CVI設計其軟件平臺。用該測試系統(tǒng)來完成對電池管理系統(tǒng)SOC估算精度的檢測實驗,SOC測試系統(tǒng)搭建完成后,進行電池充放電數(shù)據(jù)的采樣,以及不同工況下的SOC測試,最后根據(jù)實驗數(shù)據(jù)評定出SOC測量不確定度。
[Abstract]:As one of the core components of electric vehicles, battery management system has a direct bearing on the safety and service life of electric vehicles. The detection of battery management system has become a problem that needs to be solved in the development of electric vehicle industry. The estimation accuracy of SOC(state of charge status of power battery pack becomes the core content of battery management system detection. This paper presents a method to evaluate the uncertainty of power battery SOC measurement based on the detection of battery management system (SOC). The principle of lithium battery and the characteristics of charging and discharging data are analyzed in this paper. A charging and discharging data processing method for lithium battery based on Gao Si process is proposed, which is compared with least square fitting and Gao Si fitting. The prediction accuracy of charge-discharge curve is higher. According to the SOC test item specification in the standard QC/T897-2011 "Technical condition of Battery Management system for Electric vehicles", a scheme to evaluate the uncertainty of battery quantity measurement is designed, and according to the characteristics of electric quantity measurement, a method is designed to evaluate the uncertainty of battery quantity measurement. The battery charge and discharge model based on Gao Si process is established. The uncertain factors affecting the electric quantity measurement model are analyzed, and the probability density function of the input quantity in the electric quantity measurement model is determined. The uncertainty of electric quantity measurement under different charging and discharging modes was evaluated by Monte Carlo method. The effect of battery aging on discharge quantity was analyzed, and a method of estimating battery healthy state by Gao Si process regression algorithm was put forward. Different kernel functions are used to estimate SOH, and uncertainty of capacity estimation is proposed according to the estimation method of SOH. The uncertainty of available capacity is evaluated by testing the available capacity. A SOC test system based on virtual instrument measurement technology is designed. The hardware test platform is built, and the software platform is designed by Labwindows/CVI of NI Company. The battery charge and discharge data are sampled after the test system is built to test the accuracy of SOC estimation of battery management system. Finally, the uncertainty of SOC measurement is evaluated according to the experimental data.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM912

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本文編號：1593148