【摘要】：隨著汽車產(chǎn)業(yè)的快速成長(zhǎng),傳統(tǒng)的燃油發(fā)動(dòng)機(jī)汽車帶來的環(huán)境污染與能源危機(jī)問題日漸凸顯,新能源汽車成為未來汽車工業(yè)的重要發(fā)展方向之一。以電動(dòng)汽車為代表的新能源汽車要代替?zhèn)鹘y(tǒng)內(nèi)燃機(jī)汽車,尚存在電池續(xù)航能力等問題有待處理�，F(xiàn)有電動(dòng)汽車充電時(shí)間長(zhǎng),而公共充電樁資源有限,長(zhǎng)時(shí)間在外運(yùn)行的電動(dòng)汽車會(huì)出現(xiàn)何時(shí)前往何地充電的路徑規(guī)劃問題。為此,本文針對(duì)“充電焦慮”問題,主要研究在有限的充電設(shè)施網(wǎng)絡(luò)之上,如何為電動(dòng)汽車提供智能充電路徑規(guī)劃,以期提高充電效率,降低排除時(shí)間,促進(jìn)新能源汽車的發(fā)展。在現(xiàn)有研究成果基礎(chǔ)之上,本文首先針對(duì)電動(dòng)汽車剩余電量(SOC)的估算及其與可行駛距離的關(guān)系進(jìn)行了分析,建立起了支持向量機(jī)(SVM)預(yù)測(cè)模型,同時(shí)基于信息�；�,將實(shí)際駕駛過程中人為因素與交通狀況等影響SOC的外在因子也一并考慮,從而得出更為精準(zhǔn)可預(yù)測(cè)SOC波動(dòng)范圍及其與可行駛距離的關(guān)系模型。在此基礎(chǔ)上,對(duì)實(shí)時(shí)工況下電動(dòng)汽車的充電路徑規(guī)劃問題抽象為實(shí)時(shí)路況下的旅行商問題(TSP),通過對(duì)時(shí)間的離散化,將動(dòng)態(tài)路況簡(jiǎn)化為階段性靜態(tài)路徑規(guī)劃,給出了基于“Dijkstra+模擬退火”的路徑規(guī)劃算法,保證在當(dāng)前時(shí)間段內(nèi)路徑規(guī)劃最短,同時(shí)采用“模糊+精確”兩段式計(jì)算,以緩解充電樁的排隊(duì)問題。最后,建立起仿真實(shí)驗(yàn)平臺(tái),通過對(duì)相關(guān)算法的對(duì)比實(shí)驗(yàn),對(duì)相關(guān)算法的有效性進(jìn)行了驗(yàn)證。
[Abstract]:With the rapid growth of automobile industry, the problems of environmental pollution and energy crisis caused by traditional fuel engine vehicles are becoming more and more prominent. New energy vehicles have become one of the important development directions of the future automobile industry. In order to replace the traditional internal combustion engine vehicle, the new energy vehicle represented by electric vehicle still has some problems such as battery life ability and so on. The existing electric vehicle has a long charging time, but the public charging pile resource is limited, and the long-running electric vehicle will have the problem of when to go and where to charge the electric vehicle. Therefore, aiming at the problem of "charging anxiety", this paper mainly studies how to provide intelligent charging path planning for electric vehicles on the limited charging facility network, in order to improve the charging efficiency and reduce the elimination time. Promote the development of new energy vehicles. On the basis of the existing research results, this paper firstly analyzes the estimation of the residual power (SOC) of electric vehicles and its relationship with the driving distance, and establishes a (SVM) prediction model based on support vector machine, at the same time, based on the information granulation. The human factors in the driving process and the external factors that affect the SOC, such as traffic conditions, are also considered, so as to obtain a more accurate and predictable fluctuation range of the SOC and the relationship model between the fluctuation range and the driving distance. On this basis, the charging path planning problem for electric vehicles under real-time working conditions is abstracted as the traveling salesman problem under real-time conditions. By discretization of the time, the dynamic road condition is simplified to the stage static path planning by (TSP), and the dynamic road condition is simplified to the stage static path planning. This paper presents a path planning algorithm based on "Dijkstra simulated annealing", which ensures the shortest path planning in the current time period and adopts the "fuzzy and accurate" two-stage calculation to alleviate the queuing problem of charging piles. Finally, the simulation platform is set up, and the validity of the related algorithm is verified by the comparison experiment of the related algorithms.
【學(xué)位授予單位】：山東理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U491.8

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本文編號(hào)：2453375