本文選題：輪轂電驅動汽車 + 制動能量回收�。� 參考：《吉林大學》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,高效零污染的電動汽車的普及勢在必行。雖然電動汽車的充電設施正在逐步完善,但其電池成本、續(xù)駛里程的弊端尚不能很好解決。制動能量回收技術屬于電動汽車節(jié)能環(huán)保的關鍵技術,不但能夠回收制動能量提升續(xù)駛里程,還能提供一定的制動力矩,減少傳統(tǒng)制動系統(tǒng)的磨損和熱衰退,提高制動效能及安全性。而合理完善的控制技術是電動汽車安全制動條件下實現(xiàn)能量回收最大化的保障,制動能量回收控制系統(tǒng)的研究具有重要的理論意義和實際的工程應用價值,無論是車企還是相關科研機構都加大了研發(fā)力度。然而制動能量回收系統(tǒng)中的控制問題是很復雜的,屬于多目標多約束優(yōu)化問題,工程中實際應用的控制方法并沒有達到理想的效果。本文圍繞提高能量回收效果、合理分配整車制動力、保證制動安全穩(wěn)定等核心問題進行研究,針對這些復雜的控制問題,設計了基于模型預測控制的輪轂電驅動汽車制動能量回收控制系統(tǒng)。模型預測控制算法在處理多目標多約束問題方面擁有很大的優(yōu)勢。制動能量回收系統(tǒng)的核心控制問題是分配汽車前后軸制動力,以及協(xié)調控制電機和液壓制動力,達到安全穩(wěn)定制動的前提下回收盡可能多能量的目的。為了保證整車制動的制動效能和穩(wěn)定性,文中引入汽車制動力理想分配曲線和ECE法規(guī)限制;車輛制動執(zhí)行機構存在物理限制,加入了電機和液壓制動轉矩最大值約束;電機在轉速很低時發(fā)電能力有限,因此考慮了電機再生制動的轉速下限值約束。此外,電池充電SOC上限值約束作為外部閥值約束在模型預測控制算法外部實現(xiàn)。電機和液壓制動系統(tǒng)在合理選取的目標函數(shù)的作用下協(xié)同工作,使得整車能夠滿足駕駛員制動需求,在制動安全穩(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動汽車高精度整車模型上,驗證了所設計控制系統(tǒng)的有效性和優(yōu)勢。本文的主要內容:1.本文首先針對四輪輪轂電驅動汽車的結構特點和動力學方程,在AMESim環(huán)境中建立了整車動力學模型。著重對模型中的關鍵部件進行了參數(shù)匹配和動態(tài)性能分析,通過仿真標定選定的電機效率map圖,應用在模型預測控制系統(tǒng)中,用于實時得到電機當前發(fā)電效率進而優(yōu)化電機的制動轉矩;利用真實實驗數(shù)據(jù)證實所選用電池模型的充放電特性符合實際情況;分析了液壓制動系統(tǒng)的動態(tài)響應效果;最后進行了整車模型功能及動力學合理性驗證,模型中考慮了電機制動和液壓制動轉矩的輸出時延的影響,更好的模擬工程實際情況。2.針對制動能量回收系統(tǒng)的特殊性,綜合考慮電機發(fā)電特性、蓄電池安全充電、制動安全性等因素,引入模型預測算法滾動優(yōu)化控制的思想,設計基于模型預測控制的制動能量回收控制系統(tǒng)。建立了控制系統(tǒng)的動力學模型,對制動轉矩進行集成控制;選定的目標函數(shù)包括需求制動轉矩的跟蹤、能量回收效率及制動轉矩波動,分別用于滿足駕駛員制動需求、回收能量最大化及良好的制動舒適性;考慮了電機最大制動轉矩的時變約束和液壓最大制動轉矩約束,同時加入了ECE制動法規(guī)和電機發(fā)電最低轉速的限制,并在模型預測控制算法外部加入電池充電最高SOC約束。3.針對控制系統(tǒng)對仿真平臺的需求,提出AMESim和Matlab/Simulink聯(lián)合仿真解決方案。所設計的控制系統(tǒng)在Simulink中實現(xiàn),結合二者各自的優(yōu)點建立仿真工況,對所設計模型預測控制系統(tǒng)的制動安全性、穩(wěn)定性、舒適性、能量回收效果進行仿真測試,驗證控制系統(tǒng)有效性,最后通過與制動能量回收模糊控制系統(tǒng)的仿真對比實驗,證實模型預測控制的應用能夠大幅度提升制動能量回收率。
[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.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：U469.72;TP273

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