發(fā)布時間：2018-03-17 14:26

  本文選題：城市交通　切入點：物流電動汽車　出處：《北京交通大學》2017年碩士論文　論文類型：學位論文

【摘要】：在當前全球汽車工業(yè)面臨能源短缺和環(huán)境污染兩大問題的雙重挑戰(zhàn)下,電動汽車憑借其零污染、低噪聲等優(yōu)勢成為人們所關(guān)注的焦點。物流業(yè)作為我國重要的服務(wù)產(chǎn)業(yè),成為電動汽車推廣的重要領(lǐng)域。在電動汽車及配套的充電設(shè)施產(chǎn)業(yè)發(fā)展的過程中,出現(xiàn)很多新的問題亟待解決。例如,如何在物流配送中心合理地規(guī)劃建設(shè)充電設(shè)施?物流企業(yè)如何最優(yōu)地進行車輛充電的時空分配,以避免產(chǎn)生車輛"充電排隊擁堵"的現(xiàn)象?如何規(guī)劃具有充電約束的物流電動汽車最優(yōu)的配送路徑?這些問題背后蘊含的道理都與城市配送物流電動汽車的充電行為緊密相關(guān),因此分析城市配送物流電動汽車的充電行為規(guī)律,建立充電行為模型具有十分重要的理論意義和應(yīng)用價值。目前國內(nèi)外對充電行為的研究主要是從電力網(wǎng)系統(tǒng)運行的角度來研究電動汽車的充電行為給電網(wǎng)帶來的影響,而從電動汽車出行角度分析電動汽車用戶的充電行為的研究成果相對較少。此外,上述研究基本上都是建立在模擬數(shù)據(jù)或者小樣本數(shù)據(jù)的基礎(chǔ)上。而本文基于城市配送物流電動汽車實際運行的海量數(shù)據(jù),挖掘城市配送物流電動汽車的充電行為規(guī)律,并在此基礎(chǔ)上建立了充電行為預測模型,更具有實用性和普適性。為研究城市配送中物流電動汽車用戶的充電行為規(guī)律,本文采集70輛物流電動車在2014年一年的充放電數(shù)據(jù),采用數(shù)據(jù)刪除、插值等一系列的數(shù)據(jù)處理方法對原始數(shù)據(jù)進行處理,并識別有效充電過程。在此基礎(chǔ)上,采用數(shù)據(jù)挖掘相關(guān)分析方法,建立考慮物流電動汽車充電電量狀態(tài)的充電行為模型。研究結(jié)果表明:用戶一般在剩余電量為30%-50%之間時為車輛進行充電,車輛開始充電時的剩余電量服從μ=0.48、σ=0.22的正態(tài)分布;車輛開始充電時刻主要集中在14:00-16:00之間。通過數(shù)據(jù)實驗表明,該模型具有較高的準確度。為了準確預測充電時長,本文對采集的數(shù)據(jù)之間的耦合關(guān)系進行了詳細的分析。分析表明,充電時長與已充電量SOCc之間存在著明顯的正線性相關(guān)關(guān)系。由此,本文建立了關(guān)于已充電量SOCc和充電時長的線性方程。但是在對回歸方程的誤差序列進行分析時發(fā)現(xiàn),誤差序列不符合正態(tài)分布。針對這一現(xiàn)象,本文引入時間序列的概念對回歸模型進行調(diào)整,最終建立了基于回歸和ARMA模型的組合預測模型。將兩個模型的預測結(jié)果進行對比表明,采用組合模型進行充電時長預測比單一的回歸模型具有更高的精度和準確度,其殘差均落在一條水平帶之間,服從正態(tài)分布。本文所建立的考慮物流電動汽車的充電電量狀態(tài)的充電行為模型及充電時長預測模型均具有較高的精確性,同時具有較好的實用性,為車輛的充電調(diào)度和用戶的出行安排提供科學的決策支持。
[Abstract]:Under the dual challenges of energy shortage and environmental pollution in the global automobile industry, electric vehicles have become the focus of attention because of their advantages of zero pollution and low noise. Logistics industry is an important service industry in China. It has become an important field of electric vehicle promotion. In the process of development of electric vehicle and its associated charging facilities, many new problems need to be solved. For example, how to plan and build charging facilities in logistics distribution center? How to optimize the space-time distribution of vehicle charging in order to avoid the phenomenon of "charging queue congestion"? How to plan the optimal distribution path for logistics electric vehicles with charging constraints? The reasons behind these problems are closely related to the charging behavior of urban distribution logistics electric vehicles, so the law of charging behavior of urban distribution logistics electric vehicles is analyzed. It is of great theoretical significance and practical value to establish a charging behavior model. At present, the research on charging behavior at home and abroad is mainly to study the influence of charging behavior of electric vehicles on the power grid from the point of view of power grid system operation. However, there is relatively little research on the charging behavior of electric vehicle users from the perspective of electric vehicle trip. The above research is basically based on the simulation data or small sample data. However, based on the mass data of the actual operation of the urban distribution logistics electric vehicle, this paper excavates the charging behavior of the urban distribution logistics electric vehicle. On this basis, a charging behavior prediction model is established, which is more practical and universal. In order to study the charging behavior of users of logistics electric vehicles in urban distribution, this paper collects the charging and discharging data of 70 logistics electric vehicles in 2014. A series of data processing methods, such as data deletion, interpolation and so on, are used to process the original data and identify the effective charging process. A charging behavior model considering the charging state of the logistics electric vehicle is established. The results show that the user generally charges the vehicle when the remaining charge is between 30% and 50%, and the residual charge is normally distributed from 渭 0.48 to 蟽 0.22 when the vehicle starts charging. The starting charging time of vehicle is mainly between 14: 00-16: 00. The data experiment shows that the model has high accuracy. In this paper, the coupling relationship between the collected data is analyzed in detail. The analysis shows that there is an obvious positive linear correlation between the length of charging time and the amount of SOCc charged. In this paper, the linear equations of charge amount SOCc and charge time length are established. However, when the error series of regression equation is analyzed, it is found that the error series does not conform to the normal distribution. In this paper, the concept of time series is introduced to adjust the regression model. Finally, the combined prediction model based on regression and ARMA model is established. The combined model has higher precision and accuracy than the single regression model, and the residual error of the model falls between a horizontal band. In this paper, the charging behavior model and the prediction model of charging time of logistics electric vehicle have high accuracy and good practicability. It provides scientific decision support for vehicle charging scheduling and user travel arrangement.
【學位授予單位】：北京交通大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U491.8

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