本文選題：地下礦車 + 電傳動控制策略　； 參考：《北京科技大學》2015年博士論文

【摘要】：隨著交通運輸業(yè)的發(fā)展,汽車保有量快速增加,世界正面臨能源緊張和排放污染的緊迫局勢,各國紛紛致力于“節(jié)能減排”的研究。目前的研究已經(jīng)從減小乘用車、輕型車的燃油消耗開始,擴大到非公路重型車輛燃油經(jīng)濟性的提高上。電傳動礦用自卸車是礦山運輸中重要的運輸工具,其運載能力強單車燃油消耗量大,在礦石開采成本中燃油成本占很大比重,因此針對礦用汽車燃油經(jīng)濟性的研究具有重要意義。 電傳動系統(tǒng)是電傳動礦用自卸車運行的核心機構,在牽引工況控制能量從柴油機、發(fā)電機傳遞至輪邊牽引電機,在制動工況利用制動電阻將輪邊牽引電機再生發(fā)電的制動能量以熱的形式消耗掉。因此電傳動系統(tǒng)性能極大地影響了地下礦車的動力性、平順性和燃油經(jīng)濟性。本文針對地下礦用自卸車的電傳動系統(tǒng)控制策略進行研究以提高地下礦車的燃油經(jīng)濟性。 首先從人-車-路閉環(huán)的角度對地下礦車傳統(tǒng)恒功率控制算法深入分析,探究影響車輛燃油經(jīng)濟性的因素。在此基礎上提出一種電傳動控制策略,對駕駛意圖和車輛行駛工況進行識別,綜合駕駛員需求和行駛工況對電傳動系統(tǒng)功率工作點進行決策,以達到在保障動力性前提下提高燃油經(jīng)濟性的目的。 運用模糊識別方法對駕駛員加速意圖、制動意圖及平穩(wěn)行駛意圖進行識別,并應用實際工況統(tǒng)計數(shù)據(jù)指導識別參數(shù)隸屬度函數(shù)的確定,提高模糊識別的準確性。 基于目前尚無地下礦車行駛工況研究的情況,對地下礦車行駛工況分類進行了研究,依托實際工況數(shù)據(jù),運用統(tǒng)計學K-means聚類方法進行工況分析,得到帶有車輛驅動功率特征信息的工況分類。應用LVQ神經(jīng)網(wǎng)絡技術,對所建立的4種行駛工況進行識別。 最后在恒功率控制策略架構基礎之上,提出了基于駕駛意圖和行駛工況的地下礦車電傳動控制策略。在Maplesim/Simulink環(huán)境下研發(fā)了電傳動地下礦車前向仿真平臺,進行多工況仿真實驗。結果表明,相較于恒功率控制策略,本文提出的控制策略提高了地下礦車的工況適應性和燃油經(jīng)濟性。
[Abstract]:With the development of transportation and the rapid increase of vehicle ownership, the world is facing the urgent situation of energy stress and emission pollution. Many countries are devoting themselves to the research of "energy saving and emission reduction". The current research has begun with reducing the fuel consumption of passenger vehicles and light vehicles, and has been expanded to improve the fuel economy of off-road heavy vehicles. Electric drive mine dump truck is an important transport vehicle in mine transportation, and its transportation capacity is strong, the fuel consumption of single vehicle is large, and the fuel cost accounts for a large proportion in the mining cost of ore. Therefore, it is of great significance to study the fuel economy of mining vehicles. The electric drive system is the core mechanism of the electric drive mine dump truck. In the traction condition, the control energy is transferred from the diesel engine to the generator to the wheel-side traction motor. The braking energy of regenerative generation of wheel-side traction motor is consumed in the form of heat by using brake resistance in braking condition. Therefore, the performance of electric transmission system greatly affects the power performance, ride comfort and fuel economy of underground mining vehicle. In this paper, the electric drive control strategy of underground dump truck is studied to improve the fuel economy of underground dump truck. Firstly, the traditional constant power control algorithm of underground mining vehicle is deeply analyzed from the angle of man-vehicle-road closed loop, and the factors that affect vehicle fuel economy are explored. On this basis, an electric drive control strategy is proposed to identify the driving intention and vehicle driving conditions, and to make decision on the power working point of the electric drive system by synthesizing the driver's requirements and driving conditions. In order to achieve the purpose of improving fuel economy under the premise of ensuring power performance. The fuzzy identification method is used to identify the driver's acceleration intention, braking intention and steady driving intention, and the statistical data of actual working conditions are used to guide the determination of membership function of the identification parameters to improve the accuracy of fuzzy recognition. Based on the fact that there is no research on the driving conditions of underground mining vehicles, the classification of driving conditions of underground mining vehicles is studied. Based on the actual working condition data, the working conditions are analyzed by K-means clustering method of statistics. The working condition classification with the characteristic information of vehicle driving power is obtained. LVQ neural network technology is used to identify the four driving conditions. Finally, based on the structure of constant power control strategy, the electric drive control strategy of underground mining vehicle based on driving intention and driving condition is proposed. In the environment of Maplesimr / Simulink, the forward simulation platform of electric drive underground mining vehicle is developed, and the simulation experiment is carried out under multiple working conditions. The results show that compared with the constant power control strategy, the proposed control strategy improves the operating adaptability and fuel economy of underground mining vehicles.
【學位授予單位】：北京科技大學
【學位級別】：博士
【學位授予年份】：2015
【分類號】：TD634

【參考文獻】

相關期刊論文 前10條

1 顏南明,馬曉軍,臧克茂;履帶裝甲車輛電傳動技術初探[J];兵工學報;2004年05期

2 姚志良,王岐東,胡燕霞;成都市實際道路汽車行駛工況的研究[J];北京工商大學學報(自然科學版);2004年01期

3 周德佳;莊亞平;冀路明;徐定海;李江;;具有電流前饋的中頻無刷勵磁控制系統(tǒng)及其實現(xiàn)[J];電工技術學報;2011年06期

4 嚴糾;;電傳動車輛的研究與發(fā)展(上)[J];國外坦克;2005年12期

5 嚴糾;;電傳動車輛的研究與發(fā)展(下)[J];國外坦克;2006年02期

6 王連震;裴玉龍;;基于貝葉斯網(wǎng)絡的駕駛疲勞程度識別模型[J];城市交通;2014年03期

7 馬志雄,李孟良,朱西產(chǎn),張富興;乘用車實際行駛工況開發(fā)方法的研究[J];武漢理工大學學報(信息與管理工程版);2004年03期

8 張永剛 ,申群太;基于DSP交流感應電機直接轉矩模糊控制系統(tǒng)[J];微計算機信息;2003年08期

9 吳宇光;;廈門市機動車道路行駛工況研究[J];中國西部科技;2006年10期

10 吳夙慧;成穎;鄭彥寧;潘云濤;;K-means算法研究綜述[J];現(xiàn)代圖書情報技術;2011年05期

，

本文編號：2033524