研究生: |
張軒墉 Chang, Hsuan-Yung |
---|---|
論文名稱: |
混合動力平台機電整合測試驗證與智慧型轉速控制 Experimental Verification for a Hybrid Powertrain Platform with the Intelligent Speed Control |
指導教授: |
洪翊軒
Hung, Yi-Hsuan 吳建勳 Wu, Chien-Hsun |
學位類別: |
碩士 Master |
系所名稱: |
工業教育學系 Department of Industrial Education |
論文出版年: | 2019 |
畢業學年度: | 107 |
語文別: | 中文 |
論文頁數: | 78 |
中文關鍵詞: | 細菌覓食演算法 、雙自由度PID 、空氣引擎 、混合動力 |
英文關鍵詞: | BFA, 2 degree of freedom PID, compressed air engine, hybrid powertrain |
DOI URL: | http://doi.org/10.6345/NTNU201900118 |
論文種類: | 學術論文 |
相關次數: | 點閱:143 下載:0 |
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在此論文研究中,總共分為三大部分。第一部分為混合動力平台設計,第二部分為三動力源混合動力平台實驗,第三部分為基於細菌覓食演算法進行雙自由度PID參數控制。
第一部分三動力源混合動力平台設計方面,是由汽油引擎、電動馬達、及空氣馬達三種動力來源組成之混合動力系統,利用電磁離合器使得動力源可以獨立運作或進行混合動力模式。搭配相關能源輸入或是輸出感知器收集各項參數。
第二部分是建立各動力源性能測試,針對三種動力源:引擎、電動馬達及空氣馬達進行全面性的性能測試後,進行三種動力源複合動力模式測試,在此測試中觀察三種動力源在電磁離合器接合瞬間,動力輸出反應情形及瞬間能源消耗量,並利用Matlab/simulink軟體進行記錄。
第三部分為了讓平台控制更加穩定,在此利用雙自由度PID控制器進行控制。雖然雙自由度PID控制器能夠更精準控制,但因為控制參數較多(共五參數),如採用試誤法,需要相當長的時間才能將參數調整至最佳化。因此在此部份採用細菌覓食演算法透過模擬細菌的運動方式,在連續的進行趨化、複製、驅散 三個過程。持續的修正雙自由度PID的控制參數,使平台控制更為精準。
In this study, the platform consist of three parts, including the design of three-power-source mechatronics platform, performance of three-power-source mechatronics platform experiments and seeking the best parameter of 2 degree of freedom PID by Bacterial Foraging Algorithm (BFA).
First, Engine, Air motor and Hub motor are combined into three-power-source mechatronics platform. With the help of e-clutch, different power sources can be combined into hybrid powertrain or separated from it. Furthermore, sensors are used to collect data from three-power-source mechatronics platform.Second, the performance of power sources on this platform are derived. The experiments of three-power-source focus on the performance of engine, air motor and hub motor. Transient experiments of three-power-source can be conducted with the e-clutch. During the experiments, transient output power and energy consumption were recorded when e-clutch jointed. The efficiency can be calculated by former data, rotation speed and torque afterward the power sources.Third, in order to increase the controlling stabilization and decrease the rotation speed error, the 2 degree of freedom PID was derived. The 2 degree of freedom control performed better accuracy. However, to reach the optimization, more parameters are considered which needs longer time dealing with adjusting parameters. Therefore, the BFA was conducted in this step. Through the simulation of bacteria moving under continuous spaces, including chemotaxis, reproduction and elimination-dispersal. Considering the different driving situation, 2 degree of freedom PID will adjust controlling parameters continuously to minimize the error and reach the best cruise control result.
[1] B. Dudley, “Statistical Review of World Energy”, UK: British Petroleum, 2017.
[2] K. Ç. Bayindir and M A Gözüküçük, “A comprehensive overview of hybrid electric vehicle Powertrain configurations, powertrain control techniques and electronic control units,” Energy Convers Manage, vol. 52, pp. 1305–1311, 2011.
[3] G. Hiermann, R. F. Hartl and T Vidal, “Routing a mix of conventional, plug-in hybrid, and electric vehicles,” European Journal of Operational Research, 2008.
[4] C. T. Chung and Y. H. Hung, “Energy improvement and performance evaluation of a novel full hybrid electric motorcycle with power split e-CVT,” Energy Convers Manage, vol. 86, no. 7, pp. 216–225, 2014.
[5] S. G. Osborn, A. Vengosh, N. R. Warner and R. B. Jackson. “Methane Contamination of Drinking Water Accompanying Gas-Well Drilling and Hydraulic Fracturing,” in Proceedings of the National Academy of Sciences USA, vol. 108, no. 20, pp. 8172–8176, May 17, 2010.
[6] T. Katrašnik, “Energy conversion phenomena in plug-in hybrid-electric vehicles,” Energy Convers Manage, vol. 52, no. 7, pp. 2637–2650, , 2017.
[7] T. W. Wakui, N. Wada, “Energy-saving effect of a residential polymer electrolyte fuel cell cogeneration system combined with a plug-in hybrid electric vehicle,” Energy Convers Manage, vol. 44, pp. 40-51, 2014.
[8] T. E. Harpster, O.M. Harpster and P.J. Savagian, “The Electrification of the automobile: from conventional hybrid, to plug-in hybrids to extended-range electric vehicles,” in Proceedings of the National Academy of Sciences USA, vol. 1, no. 1, pp. 156–166, 2009.
[9] M. Pischinger, et al. “A Low NVH Range-Extender Application with a Small V-2 Engine - Based on a New Vibration Compensation System,” SAE paper, vol. 1, no. 15, pp. 2012–2032, 2012.
[10] 田政弘,“空氣電動混合動力機車之系統設計與控制發展”,國立虎尾科技大學 ,碩士論文,2016年.
[11] Y. H. Hung, Y. M. Tung and C. H. Chang, “Optimal control of integrated energy management/mode switch timing in a three-power-source hybrid powertrain,” Applied Energy, vol. 173, pp. 184–196, 2016.
[12] K. D. Huang and S. C. Tzeng. “Development of a hybrid pneumatic-power vehicle,” Applied Energy, vol. 108, no. 20, pp. 8172–8176, May 17, 2010.
[13] M. Poursamad and A. Poursamad, “Application of genetic algorithm for optimization of control strategy in parallel hybrid electric vehicles,” Journal of the Franklin Institute, vol. 343, pp. 420–435, 2006.
[14] Y. T. Shen and Y. R. Hwang, “Design and implementation of an air-powered motorcycles,” Applied Energy, vol. 86, pp. 1105–1110, 2009
[15] K. B. Sheu, “Conceptual design of hybrid scooter transmissions with planetary gear-trains,” Applied Energy, vol. 84(5), pp. 526-541, 2007.
[16] Rajkumar Ba al, “Design of PID Controller for Plant Control and Comparison with Z-N PID Controller”, International Journal of Emerging Technology and Advanced Engineering (IJETAE), Vol. 2, Issue 4 of April 2012
[17] X. D. Hongze, “Two degree of freedom PID regulator design using an improved genetic algorithm,” Journal of System Simulation, vol. 11, no. 2 pp. 59-64, 1999.
[18] Z. L. Gaing, “A particle swarm optimization approach for optimum design of PID controller in AVR system.,” IEEE Transactionon Energy Conversion, vol. 19, no. 2 pp.384-391.
[19] A. Bagis, “ABC Algorithm Based PID Controller Design for Higher Order Oscillatory Systems,” Department of Electrical & Electronics Engineering, Faculty of Engineering, Erciyes University, 2017.
[20] J, Keedy and R. Eberhart, “Particle Swarm Optimization,” Proc. IEEE International conference on Neural Networks, pp.1942-1948, 1995.
[21] K. M. Passino, “Biomimicry of bacterial foraging for distributed optimization and control,” IEEE Journals, vol. 22, pp. 52-67. 2002.
[22] A. Mituhiko and T. Hidefumi, “Two-Degree-of-Freedom PID Controllers,” International Journal of Control, Automation, and Systems, vol. 1, pp. 91-96. 2000.