本文關(guān)鍵詞： Plug-In混合動力汽車 能量管理策略 模糊規(guī)則 再生制動 模擬退火粒子群算法 工況識別　出處：《山東大學》2017年碩士論文　論文類型：學位論文

【摘要】：面臨著全球能源短缺和環(huán)境污染的嚴峻現(xiàn)實,傳統(tǒng)汽車行業(yè)必須轉(zhuǎn)型以實現(xiàn)汽車行業(yè)的可持續(xù)發(fā)展,在這一轉(zhuǎn)型探索中,開發(fā)低油耗、低排放的新能源汽車已然成為當今汽車行業(yè)首要任務。在眾多的新能源汽車中,Plug-In混合動力汽車(Plug-In Hybrid Electric Vehicle,簡稱PHEV)以其節(jié)能環(huán)保、性能優(yōu)良的特點深受人們歡迎。整車能量管理作為PHEV 一大核心控制問題,直接影響著車輛的燃油經(jīng)濟性和動力性能,是PHEV實現(xiàn)低油耗和低排放的關(guān)鍵。能量管理的作用是通過協(xié)調(diào)控制發(fā)動機和動力電池間能量流動的方向和大小,從而滿足車輛動力需求,并提升PHEV的燃油經(jīng)濟性。本文針對PHEV的能量管理問題,以提高燃油經(jīng)濟性為目標,從驅(qū)動和制動兩個方面著手研究。動力系統(tǒng)的總成匹配是實現(xiàn)能量管理的基礎(chǔ)。本文針對并聯(lián)PHEV,在AVL CRUISE仿真軟件中建立了整車模型,結(jié)合車輛參數(shù)及動力性能要求對發(fā)動機、電動機、動力電池等部件進行參數(shù)匹配,并通過仿真實驗對動力性能進行了驗證。本文首先設計了基于模糊規(guī)則的PHEV能量管理策略,選擇電池SOC、車速及發(fā)動機工作效率作為策略的控制依據(jù)�？紤]到PHEV在行駛過程中因工作模式的不斷切換導致發(fā)動機頻繁啟停并引起動力系統(tǒng)沖擊,從而影響行車平順性和乘車舒適性。本文在能量管理策略中添加了發(fā)動機的啟�？刂撇呗�,顯著減少了發(fā)動機的啟停頻率。目前大多數(shù)能量管理策略側(cè)重于對驅(qū)動階段的能量流進行優(yōu)化。事實上,在啟停頻繁的市區(qū)工況存在著可觀的制動能量,充分回收這些制動能量對于改善PHEV的燃油經(jīng)濟性非常重要。但是,制動能量的回收常與制動性能相矛盾,在設計再生制動策略時還需要考慮制動性能。因此,本文對PHEV的再生制動系統(tǒng)進行了綜合分析,并設計了模糊再生制動策略。在三種典型循環(huán)工況下的仿真結(jié)果表明,該再生制動策略能夠顯著提高PHEV的燃油經(jīng)濟性,并且相對其他再生制動策略有著一定優(yōu)越性。針對模糊控制策略依然存在著依賴工程經(jīng)驗的缺陷,本文采用了模擬退火粒子群算法對文中設計的整車能量管理模糊控制器進行參數(shù)優(yōu)化。同時,為了提升優(yōu)化參數(shù)對不同工況的適應性,本文采用兩種方法進行改進:(1)采用綜合工況優(yōu)化方式,將 HWFET、LA92、Ja1015、NEDC、MANHATTAN 和 NYCC典型工況組成一個綜合工況,然后在綜合工況進行能量管理優(yōu)化。(2)采用工況識別技術(shù),將(1)中的綜合工況劃分成低速、中速、高速三種不同工況,并對每種工況進行能量管理優(yōu)化,最后通過在線工況識別技術(shù),選擇適當?shù)目刂茀?shù)。兩種策略還分別在其他工況下進行了仿真驗證。結(jié)果表明,綜合工況優(yōu)化方式和工況識別技術(shù)都能進一步提升PHEV的燃油經(jīng)濟性,且優(yōu)化結(jié)果同樣適用于其他工況。
[Abstract]:Facing the severe reality of global energy shortage and environmental pollution, the traditional automobile industry must be transformed to realize the sustainable development of the automobile industry. In this transformation exploration, the development of low fuel consumption. Low-emission new energy vehicles have become the top priority of the automotive industry today. Among the many new energy vehicles. Plug-In hybrid vehicle Plug-In Hybrid Electric vehicle (PHEV) is energy saving and environmental protection. As a core control problem of PHEV, vehicle energy management has a direct impact on vehicle fuel economy and power performance. The function of energy management is to control the direction and size of energy flow between engine and power battery in order to meet the vehicle power demand. And improve the fuel economy of PHEV. This paper aims at improving the fuel economy of PHEV. The power system assembly matching is the basis of energy management. The whole vehicle model is established in the AVL CRUISE simulation software for parallel pHEV. Combined with vehicle parameters and dynamic performance requirements of the engine, motor, power battery and other components for parameter matching. The dynamic performance is verified by simulation experiments. Firstly, the PHEV energy management strategy based on fuzzy rules is designed, and the battery SOC is selected. The speed and efficiency of the engine are considered as the control basis of the strategy. Considering the frequent start and stop of the engine and the impact of the power system due to the continuous switching of the working mode during the driving process of PHEV. In order to affect the ride comfort and ride comfort, this paper adds the engine start and stop control strategy to the energy management strategy. Most of the current energy management strategies focus on optimizing the energy flow in the drive phase. In fact, there is considerable braking energy in the urban areas where the engine starts and stops frequently. Fully recovering these braking energy is very important to improve the fuel economy of PHEV. However, the recovery of braking energy is often inconsistent with the braking performance. The braking performance should be considered when designing regenerative braking strategy. Therefore, the regenerative braking system of PHEV is comprehensively analyzed in this paper. The fuzzy regenerative braking strategy is designed. The simulation results under three typical cycle conditions show that the regenerative braking strategy can significantly improve the fuel economy of PHEV. Compared with other regenerative braking strategies, the fuzzy control strategy still has the defect of relying on engineering experience. In this paper, the simulated annealing particle swarm optimization algorithm is used to optimize the parameters of the vehicle energy management fuzzy controller designed in this paper. At the same time, in order to improve the adaptability of the optimized parameters to different working conditions. In this paper, two methods are used to improve the system. (1) the HWFETT LA92Ja1015NDC is optimized under the integrated working conditions. MANHATTAN and NYCC typical operating conditions constitute a comprehensive working condition, and then in the integrated conditions of energy management optimization. 2) the use of condition identification technology. The integrated working conditions are divided into three different working conditions: low speed, medium speed and high speed, and the energy management is optimized for each condition. Finally, the on-line working condition identification technology is adopted. The two strategies are also simulated under other operating conditions. The results show that the integrated mode of operation optimization and condition identification technology can further improve the fuel economy of PHEV. The optimization results are also applicable to other conditions.
【學位授予單位】：山東大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U469.7;TP273.4

【參考文獻】

相關(guān)期刊論文 前10條

1 詹森;秦大同;曾育平;;基于多循環(huán)工況的混合動力汽車參數(shù)優(yōu)化研究[J];汽車工程;2016年08期

2 張志強;張曉莉;熊禹;韋杰宏;伍健;;插電式混合動力汽車技術(shù)特點綜述[J];汽車實用技術(shù);2016年06期

3 楊東徽;李軍;;淺析混合動力汽車控制策略[J];汽車工業(yè)研究;2015年08期

4 蔣科軍;何仁;束馳;王群山;;混合動力汽車動力耦合技術(shù)綜述[J];機械傳動;2015年04期

5 張明凱;;AVL Cruise和AVL Driver聯(lián)合虛擬仿真在整車性能開發(fā)和評價方面的應用[J];工業(yè)技術(shù)創(chuàng)新;2014年05期

6 邱利宏;錢立軍;程偉;王振國;;插電式串聯(lián)混合動力汽車參數(shù)匹配及控制策略研究[J];汽車工程學報;2014年04期

7 錢人一;;雪佛蘭Volt和歐寶Ampera帶增程器的Voltec動力系統(tǒng)(二)[J];汽車與配件;2012年15期

8 劉輝;王偉達;何嬌;項昌樂;;基于模糊控制的混合動力電動車再生制動系統(tǒng)的建模與仿真[J];汽車工程;2012年01期

9 王強;;利用FMI適配部件擴展IPG CarMaker[J];河南機電高等�？茖W校學報;2012年01期

10 崔納新;步剛;吳劍;符曉玲;張承慧;;Plug-In并聯(lián)式混合動力汽車實時優(yōu)化能量管理策略[J];電工技術(shù)學報;2011年11期

相關(guān)博士學位論文 前5條

1 王光平;并聯(lián)插電式混合動力汽車控制技術(shù)研究[D];吉林大學;2016年

2 楊官龍;基于駕駛意圖與工況識別的插電式混合動力汽車能量管理策略研究[D];重慶大學;2014年

3 顧瑞蘭;促進我國新能源汽車產(chǎn)業(yè)發(fā)展的財稅政策研究[D];財政部財政科學研究所;2013年

4 趙國柱;電動汽車再生制動若干關(guān)鍵問題研究[D];南京航空航天大學;2012年

5 吳劍;并聯(lián)式混合動力汽車能量管理策略優(yōu)化研究[D];山東大學;2008年

相關(guān)碩士學位論文 前7條

1 解慶波;基于駕駛意圖與行駛工況的混合動力電動汽車能量管理策略[D];重慶大學;2016年

2 張潔麗;基于模型預測控制的插電式混合動力客車能量管理策略研究[D];北京理工大學;2016年

3 賴昊翔;并聯(lián)型混合動力汽車再生制動控制策略研究[D];吉林大學;2014年

4 趙新慶;基于CVT的插電式混合動力汽車能量管理策略研究[D];重慶大學;2014年

5 龐巖;混合動力汽車再生制動與防抱死系統(tǒng)協(xié)調(diào)控制研究[D];山東大學;2013年

6 步剛;Plug-In混合動力汽車能量管理策略研究[D];山東大學;2011年

7 劉雪梅;基于CVT的插電式混合動力電動汽車系統(tǒng)研究[D];湖南大學;2009年

，

本文編號：1443304