【摘要】：隨著我國經(jīng)濟(jì)的快速發(fā)展以及交通基礎(chǔ)設(shè)施的不斷完善,區(qū)域內(nèi)物資和人員的流通日益頻繁,物流業(yè)逐漸成為社會(huì)經(jīng)濟(jì)的重要組成部分。配送是物流業(yè)的終端,配送環(huán)節(jié)的車輛路徑優(yōu)化是提高運(yùn)輸速度、降低運(yùn)輸成本的重要手段。目前,普通燃料汽車是物流配送的主要工具,但隨著石油危機(jī)、能源安全和環(huán)境保護(hù)等問題的凸顯,電動(dòng)汽車作為新型經(jīng)濟(jì)環(huán)保的交通工具,有取代普通燃料汽車的趨勢(shì),進(jìn)而引出了電動(dòng)汽車行駛路徑優(yōu)化問題。電動(dòng)汽車使用電能,與普通燃料汽車在能量補(bǔ)給方式和使用特性上有明顯區(qū)別,使得電動(dòng)汽車行駛路徑優(yōu)化問題比傳統(tǒng)車輛路徑問題更加復(fù)雜。因此,對(duì)該問題的研究在理論和現(xiàn)實(shí)應(yīng)用方面都有重要意義。首先,構(gòu)建了固定電價(jià)下的單輛電動(dòng)汽車行駛路徑優(yōu)化模型。該模型同時(shí)考慮了電動(dòng)汽車在配送過程中的快速充電行為,載貨量對(duì)單位里程耗電量的影響以及快速充電對(duì)電池壽命的損耗,在滿足路徑約束和電池容量約束條件下實(shí)現(xiàn)配送成本最小。由電動(dòng)汽車在同一充電站多次快速充電所帶來的模型表述困難,通過引入充電站虛擬節(jié)點(diǎn)加以解決。模型采用遺傳算法求解,在種群初始化階段構(gòu)建染色體標(biāo)準(zhǔn)化操作,使得構(gòu)造的染色體適應(yīng)于遺傳算法的交叉操作。算法使用MATLAB編程,32節(jié)點(diǎn)配送系統(tǒng)的數(shù)值仿真顯示了電動(dòng)汽車在使用成本方面的優(yōu)勢(shì),驗(yàn)證了模型的有效性。其次,構(gòu)建了考慮分時(shí)充電成本的多輛電動(dòng)汽車行駛路徑優(yōu)化模型。該模型同時(shí)考慮了電動(dòng)汽車的快速充電行為,返回配送中心的常規(guī)充電優(yōu)化以及快速充電對(duì)電池壽命的損耗,在滿足路徑約束、時(shí)間約束、載貨量約束和電池容量約束條件下,實(shí)現(xiàn)配送成本最小。構(gòu)建學(xué)習(xí)型單親遺傳算法對(duì)模型進(jìn)行求解,該算法在種群初始化階段制定了初始化規(guī)則,克服了多約束條件下初始可行解生成困難的問題；在基因變異階段構(gòu)建充電站節(jié)點(diǎn)刪除算子和節(jié)點(diǎn)添加算子,保證算法的全局收斂性；構(gòu)建包含了精英個(gè)體知識(shí)和專家經(jīng)驗(yàn)知識(shí)的知識(shí)模型,通過個(gè)體對(duì)知識(shí)的學(xué)習(xí),提升算法求解效能�；�62節(jié)點(diǎn)配送系統(tǒng)和112節(jié)點(diǎn)配送系統(tǒng)進(jìn)行數(shù)值仿真,與遺傳算法和禁忌算法的優(yōu)化結(jié)果比較,顯示出學(xué)習(xí)型單親遺傳算法在求解速度、求解質(zhì)量和穩(wěn)定性方面的優(yōu)勢(shì)。基于62節(jié)點(diǎn)配送系統(tǒng),對(duì)比分析了電動(dòng)汽車的配送成本和行駛路徑對(duì)分時(shí)電價(jià)、固定電價(jià)和折扣電價(jià)等三種電價(jià)的響應(yīng)特性�；谝恍⌒团潆娋W(wǎng),研究了三種電價(jià)下較大規(guī)模電動(dòng)汽車的充電負(fù)荷對(duì)配電系統(tǒng)用電負(fù)荷的影響。
[Abstract]:With the rapid development of China's economy and the continuous improvement of transportation infrastructure, the circulation of goods and personnel in the region is becoming more and more frequent, and the logistics industry has gradually become an important part of the social economy. Distribution is the terminal of logistics industry. Vehicle routing optimization is an important means to improve transportation speed and reduce transportation cost. At present, ordinary fuel vehicles are the main tools of logistics and distribution. However, with the oil crisis, energy security and environmental protection, electric vehicles as a new type of economic and environmental protection vehicles, there is a trend to replace ordinary fuel vehicles. Furthermore, the optimization problem of electric vehicle driving path is introduced. The use of electric energy in electric vehicles is obviously different from that of ordinary fuel vehicles in the way of energy supply and performance, which makes the optimization problem of electric vehicles' driving path more complex than the traditional vehicle's path problem. Therefore, the study of this problem is of great significance both in theory and in practice. Firstly, a single electric vehicle (EV) driving path optimization model at fixed electricity price is constructed. The model also takes into account the fast charging behavior of electric vehicles during distribution, the influence of loading capacity on power consumption per mileage, and the loss of battery life caused by rapid charging. The delivery cost is minimized under the condition of satisfying path constraints and battery capacity constraints. It is difficult to describe the model caused by electric vehicle charging at the same charging station many times quickly, which is solved by introducing the virtual node of charging station. The model is solved by genetic algorithm, and the standardized operation of chromosome is constructed in the initial stage of population, which makes the constructed chromosome adapt to the crossover operation of genetic algorithm. The algorithm is programmed with MATLAB, and the numerical simulation of 32-node distribution system shows the advantages of the electric vehicle in the cost of use, and verifies the validity of the model. Secondly, a multi-vehicle driving path optimization model considering time-sharing charging cost is constructed. The model also takes into account the fast charging behavior of electric vehicles, the conventional charging optimization of return distribution center and the loss of battery life due to rapid charging, under the condition of satisfying path constraints, time constraints, load constraints and battery capacity constraints. The cost of distribution is minimized. A learning parthenogenetic genetic algorithm is constructed to solve the model. The algorithm formulates initialization rules in the initial stage of population initialization, which overcomes the difficulty of generating initial feasible solutions under the condition of multiple constraints. In the phase of gene mutation, the node deletion operator and node addition operator are constructed to ensure the global convergence of the algorithm. A knowledge model including elite individual knowledge and expert experiential knowledge is constructed to improve the efficiency of the algorithm through individual learning of knowledge. Compared with the optimization results of genetic algorithm and Tabu algorithm, the numerical simulation based on 62 node distribution system and 112 node distribution system shows the advantages of learning single parent genetic algorithm in solving speed, solution quality and stability. Based on the 62 node distribution system, the response characteristics of the distribution cost and the driving path of the electric vehicle to the time-sharing price, the fixed price and the discount price are compared and analyzed. Based on a small distribution network, the influence of charging load of large scale electric vehicle under three kinds of electricity price on the load of distribution system is studied.
【學(xué)位授予單位】：長沙理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：U492.22

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相關(guān)期刊論文 前1條

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本文編號(hào)：2408724