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研究生: 周晟宇
Chen-Yu Chou
論文名稱: 複合動力車輛模式切換之模型預測控制
Model Predictive Control of Mode Switching in Hybrid Electric Vehicles
指導教授: 姜嘉瑞
Chia-Jui Chiang
口試委員: 林紀穎
Chi-Ying Lin
陳柏全
Bo-Chiuan Chen
學位類別: 碩士
Master
系所名稱: 工程學院 - 機械工程系
Department of Mechanical Engineering
論文出版年: 2018
畢業學年度: 107
語文別: 中文
論文頁數: 97
中文關鍵詞: 液壓離合器混合動力車輛模式切換模型預測控制
外文關鍵詞: hydraulic clutch, hybrid vehicle, mode switching, model predictive control
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近幾年溫室效應所帶來的衝擊遠大於大家所想像,若能藉由混合動力的方式驅動車輛,不但有助於溫室效應之改善,亦可提升車輛之動力。混合動力車輛藉由不同動力源之間的模式切換,達到最佳的能耗及排放。而在大多數混合動力車輛中,離合器正是達成模式切換最重要的原件。藉著離合器的接合或釋放,便可以達成多重模式切換的功能。不過離合器要順利接合有許多條件需要考慮,像是離合器兩端扭力及轉速、推動離合器推力、離合器接合時間及接合時碟片的瞬時速度等。若接合瞬間離合器兩端速度相差太大或推力太大,可能會導致離合器磨損、能量損耗或是使乘坐者察覺車輛的頓挫感;反之,推動離合器力道太小也可能會使接合時間變太長,進而延長模式切換所需時間。本論文建立液壓離合器物理模型,並以模型為基礎來進行最佳化控制器設計。控制目標分為三個階段:第一階段為離合器被推動但尚未接合前,能使離合器推動速度最大化,縮短模式切換所需時間;第二階段為離合器接近接合時,推力及碰撞速度能達到最小化,同時維持整體接合時間在可接受的範圍內;第三階段為離合器接合後,能依照車輛不同操作區間扭力傳遞需求提供不同程度的正向力,以避免離合器打滑及降低能耗。由於液壓離合器模型存在多個控制目標及輸入和輸出端的限制條件,故本論文選擇使用模型預測控制(Model Predictive Control) 來達成模式切換最佳化控制的目標。


The impact of the greenhouse effect in recent years is far greater than everyone’s imagination. If vehicles can be driven by hybrid power, it will not only contribute to the improvement of the greenhouse effect, but also increase the power of the vehicle. Hybrid vehicles achieve optimal energy consumption and emissions by switching modes between different power sources. In most hybrid vehicles, clutch is the most important element in achieving mode switching. With the clutch engaged or released, multiple mode switching functions can be achieved. However, there are many conditions to be considered when the clutch is to be smoothly engaged, such as torque and speed at both ends of the clutch, pushing clutch thrust, clutch engagement time, and the instantaneous speed of the disc when engaged. If the speed of the two ends of the clutch is too different or the thrust is too large, the clutch may wear out, the energy may be wasted, or the occupant may feel uncomfortable. Conversely, the thrust that pushing the clutch too small may also make the engagement time too long, which in turn extends the time required for mode switching. This paper establishes the physical model of hydraulic clutch, and design the optimal controller based on the model. The control target is divided into three phases: the first phase is to maximize the clutch pushing speed before the clutch is pushed but not yet engaged, and to shorten the time required for mode switching; the second phase is when the clutch is nearly engaged, the thrust and collision speed can be minimized, while maintaining the overall engagement time within an acceptable range; the third phase, after the clutch is engaged, can provide different degrees of normal force in accordance with the torque transmission requirements of the vehicle’s different operating ranges to avoid clutch slippage and reduce energy consumption. Because the hydraulic clutch model has multiple control targets and the input and output limit conditions, this paper chooses to use model predictive control to achieve the goal of mode switching optimal control.

摘要............................................................................. i 英文摘要........................................................................ ii 致謝............................................................................ iv 目錄........................................................................... vii 圖目錄........................................................................... x 第一章 導論...................................................................... 1 1.1 研究背景..................................................................... 1 1.2 文獻回顧..................................................................... 4 1.2.1 液壓離合器文獻回顧......................................................... 4 1.2.2 模型預測控制(Model Predictive Control)文獻回顧............................. 6 1.3 研究目的..................................................................... 8 1.4 研究方法..................................................................... 9 1.4.1 Matlab .................................................................... 9 1.4.2 Simulink................................................................... 9 1.5 論文架構.................................................................... 10 第二章 液壓離合器物理模型....................................................... 11 2.1 液壓系統物理模型理論與推導.................................................. 11 2.1.1 液壓系統物理模型理論...................................................... 11 2.1.2 液壓系統物理模型推導...................................................... 14 2.1.3 PWM 訊號原理介紹.......................................................... 17 2.2 離合器物理模型理論與推導.................................................... 18 2.3 液壓離合器系統模型.......................................................... 20 第三章 模型預測控制(MPC)........................................................ 22 3.1 基本概念與原理.............................................................. 22 3.1.1 預測區間及控制區間........................................................ 24 3.1.2 成本函數.................................................................. 24 3.1.3 二次規劃(Quadratic Programming)........................................... 25 3.2 限制條件.................................................................... 26 3.2.1 輸入限制.................................................................. 26 3.2.2 輸出限制.................................................................. 28 3.3 模型預測控制器設計.......................................................... 30 3.3.1 模型預測控制器建立........................................................ 30 3.3.2 系統狀態估測器設計........................................................ 36 3.3.3 模型預測控制於液壓離合器應用控制架構...................................... 41 第四章 模擬結果................................................................. 46 4.1 非線性及線性化模型之模擬結果................................................ 47 4.2 單ㄧMPC控制液壓系統......................................................... 52 4.2.1 MPC應用於液壓系統之模擬結果............................................... 52 4.2.2 MPC加入卡爾曼濾波器之模擬結果............................................. 57 4.3 單ㄧMPC控制液壓離合器系統................................................... 62 4.3.1 MPC應用於液壓離合器系統之模擬結果........................................ 62 4.3.2 MPC加入卡爾曼濾波器之模擬結果............................................. 68 4.4 多個MPC控制液壓離合器系統................................................... 73 4.4.1 ps 為固定之多個MPC應用於液壓離合器系統之模擬結果........................... 73 4.4.2 ps 不為固定之多個MPC應用於液壓離合器系統之模擬結果......................... 76 第五章 結論與未來展望........................................................... 79 5.1 結論........................................................................ 79 5.2 未來展望.................................................................... 79 附錄............................................................................ 80 參考文獻........................................................................ 84

[1] L. Guzzella and C. H. Onder. Introduction to modeling and control of internal combustion engine systems. Springer, Berlin; New York, NY, 2004.
[2] Y. Gao M. Ehsani and A. Emadi. Modern electric, hybrid electric, and fuel cell vehicles: Fundamentals, theory, and design. In CRC Press, Energy Conversion and Management, page 1000–1009, 2014.
[3] W. Lhomme, R. Trigui, P. Delarue, B. Jeanneret, A. Bouscayrol, and F. Badin. Switched causal modeling of transmission with clutch in hybrid electric vehicles. In 2006 IEEE Vehicle Power and Propulsion Conference, pages 1–6.
[4] T. D. Burton. Introduction to dynamic systems analysis. McGraw-Hill, New York,1994.
[5] E. Camacho and C. Bordons. Model Predictive Control. Springer, 2003.
[6] Katsuhiko Ogata. Discrete-time Control Systems. Prentice-Hall, Inc., Upper Saddle River, NJ, USA, 1987.
[7] Takeo Nakagawa, Kazubiko Nakamura, and Hiroyuki Amino. Various applications of hydraulic counter-pressure deep drawing. Journal of Materials Processing Technology, 71(1):160–167, 1997.
[8] T. Letrouve, A. Bouscayrol, and W. Lhomme. Influence of the clutch model in a simulation of a parallel hybrid electric vehicle. In 2009 IEEE Vehicle Power and Propulsion Conference, pages 1330–1337.
[9] K. van Berkel, F. Veldpaus, T. Hofman, B. Vroemen, and M. Steinbuch. Fast and smooth clutch engagement control for a mechanical hybrid powertrain. IEEE Transactions on Control Systems Technology, 22(4):1241–1254, 2014.
[10] V. T. Minh and A. A. Rashid. Automatic control of clutches and simulations for parallel hybrid vehicles. International Journal of Automotive Technology, 13(4):645–651, 2012.
[11] ES Bettis and ER Mann. A servo employing the magnetic fluid clutch. Review of Scientific Instruments, 20(2):97–101, 1949.
[12] A. E. Balau and C. Lazar. State-space model of an electro-hydraulic actuated wet clutch. IFAC Proceedings Volumes, 43(7):506–511, 2010.
[13] Shengdun Zhao, Ji Wang, Jun Wang, and Yupeng He. Expansion-chamber muffler for impulse noise of pneumatic frictional clutch and brake in mechanical presses. Applied Acoustics, 67(6):580–594, 2006.
[14] R. Gasper, M. G. Chávez, and D. Abel. Adaptive flatness based control of a hydraulic clutch actuator *. IFAC Proceedings Volumes, 43(14):707–712, 2010.
[15] Abhishek Dutta, Clara M. Ionescu, Bart Wyns, Robin De Keyser, Julian Stoev, Gregory Pinte, and Wim Symens. Switched nonlinear predictive control with adaptive references for engagement of wet clutches. IFAC Proceedings Volumes, 45(17):460–465, 2012.
[16] A. Grancharova and T. A. Johansen. Design and comparison of explicit model predictive controllers for an electropneumatic clutch actuator using on/off valves. IEEE/ASME Transactions on Mechatronics, 16(4):665–673, 2011.
[17] Vijay A. Neelakantan, Gregory N. Washington, and Norman K. Bucknor. Model predictive control of a two stage actuation system using piezoelectric actuators for controllable industrial and automotive brakes and clutches. Journal of Intelligent Material Systems and Structures, 19(7):845–857, 2007.
[18] Jacques Richalet, A Rault, JL Testud, and J Papon. Model predictive heuristic control. Automatica (Journal of IFAC), 14(5):413–428, 1978.
[19] Ramine Rouhani and Raman K. Mehra. Model algorithmic control (mac); basic theoretical properties. Automatica, 18(4):401–414, 1982.
[20] Charles R Cutler and Brian L Ramaker. Dynamic matrix control- a computer control algorithm. In joint automatic control conference, page 72.
[21] D. W. Clarke, C. Mohtadi, and P. S. Tuffs. Generalized predictive control—part i. the basic algorithm. Automatica, 23(2):137–148, 1987.
[22] D. W. Clarke, C. Mohtadi, and P. S. Tuffs. Generalized predictive control—part ii extensions and interpretations. Automatica, 23(2):149–160, 1987.
[23] M. A. LeliĆ and M. B. Zarrop. Generalized pole-placement self-tuning controller part 1, basic algorithm. International Journal of Control, 46(2):547–568, 1987.
[24] W. Kwon and A. Pearson. A modified quadratic cost problem and feedback stabilization of a linear system. IEEE Transactions on Automatic Control, 22(5):838–842, 1977.
[25] W. Kwon and A. Pearson. On feedback stabilization of time-varying discrete linear systems. IEEE Transactions on Automatic Control, 23(3):479–481, 1978.
[26] Wook Hyun Kwon and Dae Gyu Byun. Receding horizon tracking control as a predictive control and its stability properties. International Journal of Control, 50(5):1807–1824, 1989.
[27] 張智星. MATLAB 程式設計與應用. 清蔚科技股份有限公司.
[28] 李宜達. 控制系統設計與模擬. 全華科技圖書股份有限公司.
[29] Katsuhiko Ogata. System dynamics. Fourth edition. Upper Saddle River, NJ : Pearson/Prentice Hall, [2004] ©2004, 2004.
[30] Per Nobrant. Driveline modelling using mathmodelica. Universität von Linköping, Institute of Techology, Linköping (Schweden), 2001.
[31] Stéphane Guilain. Contribution à la réalisation d’un simulateur du comportement dynamique des moteurs à allumage commandé équipant des véhicules automobiles. 1994.
[32] Liuping Wang. Model Predictive Control System Design and Implementation Using MATLAB. Springer Publishing Company, Incorporated, 2009.
[33] R. E. Kalman. A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1):35–45, 1960.
[34] Abhishek Dutta, Clara M. Ionescu, Bart Wyns, Robin De Keyser, Julian Stoev, Gregory Pinte, and Wim Symens. Switched nonlinear predictive control with adaptive references for engagement of wet clutches. IFAC Proceedings Volumes, 45(17):460–465, 2012.

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