基于道路工況分析的HEV控制策略方法研究.pdf

1、 大連理工大學(xué)學(xué)位論文獨創(chuàng)性聲明 作者鄭重聲明：所呈交的學(xué)位論文，是本人在導(dǎo)師的指導(dǎo)下進行研究工作所取得的成果。盡我所知，除文中已經(jīng)注明引用內(nèi)容和致謝的地方外，本論文不包含其他個人或集體已經(jīng)發(fā)表的研究成果，也不包含其他已申請學(xué)位或其他用途使用過的成果。與我一同工作的同志對本研究所做的貢獻均已在論文中做了明確的說明并表示了謝意。 若有不實之處，本人愿意承擔(dān)相關(guān)法律責(zé)任。 學(xué)位論文題目： 基于道路工況分析的 HEV 控制策略方法研究

2、 作 者 簽 名 ： 日期： 年 月 日 并聯(lián)混合動力客車能量管理優(yōu)化控制策略研究 -II- Control Strategy Optimization Method Based on Driving Cycle Recognition for HEV Abstract During rece

3、ntly years, the development of hybrid electric vehicle industry has received widespread attention from social, because of possess with both combustion and new type of pure electric vehicles’ characteristics, its seen as

4、one of the effective way to solve the energy and environmental problems. The improvement of control strategy has been the best key point in the study of hybrid electric vehicle, a good control strategy can help with the

5、reasonable distribution power supply of the vehicle engine and electric motor to ensure vehicle’ good driving performance. In this article, we did research on a biaxial parallel hybrid bus, explores the control strategy

6、optimization process, the main content is: Firstly, we built a research platform in simulation software ADVISOR based on the hybrid electric vehicle energy saving mechanism and currently key technical problems exists in

7、it. After the analysis for a series of research purpose and application characteristics of the ADVISOR software itself, we built the vehicle dynamics model, motor, engine, battery and other several major system component

8、s model, finished the necessary parameters matching. Secondly, this paper puts forward the factors which affecting the operation condition of vehicle could be divided into two parts: the road condition and the driving ac

9、quisition. With the use of Google Earth and its extension application Goody GIS software, we completed the vehicle traffic parameter extraction with ensuring accuracy of the data reliability; with the use of hybrid data

10、acquisition and remote monitoring system which built by our team, we have completed the condition of parameter acquisition, construction of typical working condition and get identification of driving cycle by fuzzy class

11、ification algorithm. Thirdly, this paper presents the optimization of charge\discharge equivalent factor under the different driving cycle condition in minimal equivalent fuel consumption control strategy. Using the algo

12、rithm to calculate four groups of optimal equivalent factors under the four typical driving cycles of Surburban, Rural, Stopngo and Urban. Proposed the correlation between battery SOC control parameters and the change of

13、 vehicle driving cycles. With the standard of minimal fuel consumption, we probed out the empirical formula of describing its function quantitatively. Established the adaptive optimal control model under the environment

14、of MATLAB/Simulink, to deal with the power system real time optimization of control parameters. Finally, in the platform of ADVISOR, we established the vehicle model based on a bus real data, built the driving cycle of 4