本文選題：輪轂電驅(qū)動(dòng)汽車 + 制動(dòng)能量回收��； 參考：《吉林大學(xué)》2017年碩士論文

【摘要】：伴隨著節(jié)能減排政策的推廣,綠色出行理念深入人心,高效零污染的電動(dòng)汽車的普及勢(shì)在必行。雖然電動(dòng)汽車的充電設(shè)施正在逐步完善,但其電池成本、續(xù)駛里程的弊端尚不能很好解決。制動(dòng)能量回收技術(shù)屬于電動(dòng)汽車節(jié)能環(huán)保的關(guān)鍵技術(shù),不但能夠回收制動(dòng)能量提升續(xù)駛里程,還能提供一定的制動(dòng)力矩,減少傳統(tǒng)制動(dòng)系統(tǒng)的磨損和熱衰退,提高制動(dòng)效能及安全性。而合理完善的控制技術(shù)是電動(dòng)汽車安全制動(dòng)條件下實(shí)現(xiàn)能量回收最大化的保障,制動(dòng)能量回收控制系統(tǒng)的研究具有重要的理論意義和實(shí)際的工程應(yīng)用價(jià)值,無(wú)論是車企還是相關(guān)科研機(jī)構(gòu)都加大了研發(fā)力度。然而制動(dòng)能量回收系統(tǒng)中的控制問(wèn)題是很復(fù)雜的,屬于多目標(biāo)多約束優(yōu)化問(wèn)題,工程中實(shí)際應(yīng)用的控制方法并沒(méi)有達(dá)到理想的效果。本文圍繞提高能量回收效果、合理分配整車制動(dòng)力、保證制動(dòng)安全穩(wěn)定等核心問(wèn)題進(jìn)行研究,針對(duì)這些復(fù)雜的控制問(wèn)題,設(shè)計(jì)了基于模型預(yù)測(cè)控制的輪轂電驅(qū)動(dòng)汽車制動(dòng)能量回收控制系統(tǒng)。模型預(yù)測(cè)控制算法在處理多目標(biāo)多約束問(wèn)題方面擁有很大的優(yōu)勢(shì)。制動(dòng)能量回收系統(tǒng)的核心控制問(wèn)題是分配汽車前后軸制動(dòng)力,以及協(xié)調(diào)控制電機(jī)和液壓制動(dòng)力,達(dá)到安全穩(wěn)定制動(dòng)的前提下回收盡可能多能量的目的。為了保證整車制動(dòng)的制動(dòng)效能和穩(wěn)定性,文中引入汽車制動(dòng)力理想分配曲線和ECE法規(guī)限制;車輛制動(dòng)執(zhí)行機(jī)構(gòu)存在物理限制,加入了電機(jī)和液壓制動(dòng)轉(zhuǎn)矩最大值約束;電機(jī)在轉(zhuǎn)速很低時(shí)發(fā)電能力有限,因此考慮了電機(jī)再生制動(dòng)的轉(zhuǎn)速下限值約束。此外,電池充電SOC上限值約束作為外部閥值約束在模型預(yù)測(cè)控制算法外部實(shí)現(xiàn)。電機(jī)和液壓制動(dòng)系統(tǒng)在合理選取的目標(biāo)函數(shù)的作用下協(xié)同工作,使得整車能夠滿足駕駛員制動(dòng)需求,在制動(dòng)安全穩(wěn)定的情況下提高能量回收能力。最后在AMESim環(huán)境中搭建的四輪輪轂電驅(qū)動(dòng)汽車高精度整車模型上,驗(yàn)證了所設(shè)計(jì)控制系統(tǒng)的有效性和優(yōu)勢(shì)。本文的主要內(nèi)容:1.本文首先針對(duì)四輪輪轂電驅(qū)動(dòng)汽車的結(jié)構(gòu)特點(diǎn)和動(dòng)力學(xué)方程,在AMESim環(huán)境中建立了整車動(dòng)力學(xué)模型。著重對(duì)模型中的關(guān)鍵部件進(jìn)行了參數(shù)匹配和動(dòng)態(tài)性能分析,通過(guò)仿真標(biāo)定選定的電機(jī)效率map圖,應(yīng)用在模型預(yù)測(cè)控制系統(tǒng)中,用于實(shí)時(shí)得到電機(jī)當(dāng)前發(fā)電效率進(jìn)而優(yōu)化電機(jī)的制動(dòng)轉(zhuǎn)矩;利用真實(shí)實(shí)驗(yàn)數(shù)據(jù)證實(shí)所選用電池模型的充放電特性符合實(shí)際情況;分析了液壓制動(dòng)系統(tǒng)的動(dòng)態(tài)響應(yīng)效果;最后進(jìn)行了整車模型功能及動(dòng)力學(xué)合理性驗(yàn)證,模型中考慮了電機(jī)制動(dòng)和液壓制動(dòng)轉(zhuǎn)矩的輸出時(shí)延的影響,更好的模擬工程實(shí)際情況。2.針對(duì)制動(dòng)能量回收系統(tǒng)的特殊性,綜合考慮電機(jī)發(fā)電特性、蓄電池安全充電、制動(dòng)安全性等因素,引入模型預(yù)測(cè)算法滾動(dòng)優(yōu)化控制的思想,設(shè)計(jì)基于模型預(yù)測(cè)控制的制動(dòng)能量回收控制系統(tǒng)。建立了控制系統(tǒng)的動(dòng)力學(xué)模型,對(duì)制動(dòng)轉(zhuǎn)矩進(jìn)行集成控制;選定的目標(biāo)函數(shù)包括需求制動(dòng)轉(zhuǎn)矩的跟蹤、能量回收效率及制動(dòng)轉(zhuǎn)矩波動(dòng),分別用于滿足駕駛員制動(dòng)需求、回收能量最大化及良好的制動(dòng)舒適性;考慮了電機(jī)最大制動(dòng)轉(zhuǎn)矩的時(shí)變約束和液壓最大制動(dòng)轉(zhuǎn)矩約束,同時(shí)加入了ECE制動(dòng)法規(guī)和電機(jī)發(fā)電最低轉(zhuǎn)速的限制,并在模型預(yù)測(cè)控制算法外部加入電池充電最高SOC約束。3.針對(duì)控制系統(tǒng)對(duì)仿真平臺(tái)的需求,提出AMESim和Matlab/Simulink聯(lián)合仿真解決方案。所設(shè)計(jì)的控制系統(tǒng)在Simulink中實(shí)現(xiàn),結(jié)合二者各自的優(yōu)點(diǎn)建立仿真工況,對(duì)所設(shè)計(jì)模型預(yù)測(cè)控制系統(tǒng)的制動(dòng)安全性、穩(wěn)定性、舒適性、能量回收效果進(jìn)行仿真測(cè)試,驗(yàn)證控制系統(tǒng)有效性,最后通過(guò)與制動(dòng)能量回收模糊控制系統(tǒng)的仿真對(duì)比實(shí)驗(yàn),證實(shí)模型預(yù)測(cè)控制的應(yīng)用能夠大幅度提升制動(dòng)能量回收率。
[Abstract]:With the promotion of energy saving and emission reduction policies, the concept of green travel is deeply rooted in the hearts of the people. The popularization of high efficiency and zero pollution electric vehicles is imperative. Although the charging facilities of electric vehicles are being improved gradually, the cost of battery and the disadvantages of the driving range are not well solved. The key of braking energy recovery is the key to the energy saving and environmental protection of electric vehicles. Technology can not only recover the braking energy and drive mileage, but also provide a certain braking torque, reduce the wear and heat decline of the traditional brake system, improve the braking efficiency and safety. The research of the system has important theoretical significance and practical value of engineering application. Both the car enterprise and the related scientific research institutions have increased the research and development efforts. However, the control problem in the braking energy recovery system is very complex, which belongs to the multi-objective and multi constraint optimization problem. The control method of the actual application in the engineering has not reached the ideal effect. In this paper, the core problems such as improving the energy recovery effect, distributing the vehicle braking force reasonably and ensuring the safety and stability of the brake are studied. In view of these complex control problems, the brake energy recovery control system based on model predictive control is designed. The model predictive control algorithm is used to deal with multi-objective and multi constraint problems. The core control problem of the braking energy recovery system is to allocate the driving force of the front and rear axle of the car and coordinate the control of the motor and hydraulic power to achieve the purpose of recovering as much energy as possible under the premise of safe and stable braking. In order to ensure the braking efficiency and stability of the whole vehicle brake, the automobile brake is introduced in this paper. The force ideal distribution curve and the ECE regulation limit; the vehicle brake actuator has physical restriction, adding the maximum value constraint of the motor and hydraulic braking torque; the generator has limited power generation ability when the speed is very low, so the lower limit limit of the motor regenerative braking is considered. In addition, the limit limit of the battery charge SOC is used as the external threshold constraint. The external realization of the model predictive control algorithm. The motor and hydraulic brake system work together under the function of the reasonable target function, making the whole vehicle meet the driver's braking demand and improve the energy recovery ability under the condition of safe and stable braking. Finally, the high precision whole four wheel hub electric drive car built in the AMESim environment is high precision. On the vehicle model, the effectiveness and advantages of the designed control system are verified. The main contents of this paper are as follows: 1. firstly, the structure characteristics and dynamic equations of the four wheel hub electric drive vehicle are first set up in the AMESim environment, and the key parts in the model are analyzed and the parameters matching and dynamic performance analysis are carried out, through which the key parts of the model are analyzed. The map diagram of the selected motor efficiency is simulated and calibrated. It is applied to the model predictive control system to obtain the current power efficiency of the motor and optimize the braking torque of the motor. The real experimental data is used to verify the charge discharge characteristics of the selected battery model, and the dynamic response of the hydraulic brake system is analyzed. Finally, the dynamic response of the hydraulic brake system is analyzed. The function and dynamics of the vehicle model are verified, and the effect of the output delay of the motor braking and the hydraulic braking torque is considered in the model, and a better simulation of the engineering actual situation.2. is given to the particularity of the braking energy recovery system, and the factors such as the electric generator characteristics, the battery safety charging, the braking safety and so on are introduced, and the model is introduced into the model. The idea of rolling optimization control is predicted and the braking energy recovery control system based on model predictive control is designed. The dynamic model of the control system is established, and the braking torque is integrated. The selected target functions include the tracking of the braking torque, the efficiency of energy recovery and the fluctuation of braking torque, which are used to satisfy driving respectively. At the same time, the maximum braking torque of the motor and the maximum braking torque are taken into consideration. At the same time, the ECE braking regulation and the minimum motor power generation speed limit are added, and the maximum SOC constraint.3. for battery charging is added to the model predictive control algorithm for control. In order to meet the requirements of the simulation platform, the AMESim and Matlab/Simulink joint simulation solutions are proposed. The designed control system is implemented in Simulink, and the simulation conditions are established by combining the advantages of the two parties. The simulation test is made for the braking safety, stability, comfort and energy recovery effect of the designed model predictive control system, and the verification control is carried out. The effectiveness of the system is made. Finally, through the simulation comparison experiment with the fuzzy control system of braking energy recovery, it is proved that the application of model predictive control can greatly improve the braking energy recovery rate.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：U469.72;TP273

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