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研究生:林苑婷
研究生(外文):Yuan-Ting Lin
論文名稱:變比例機車連動煞車系統之設計與分析
論文名稱(外文):Design and Analysis on the Variable Ratio Combined Brake System for Motorcycles
指導教授:光灼華黃永茂
指導教授(外文):Kuang,Jao-HwaHwang,Yeong-Maw
學位類別:博士
校院名稱:國立中山大學
系所名稱:機械與機電工程學系研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:中文
論文頁數:162
中文關鍵詞:機車煞車系統連動煞車系統變比例連動煞車系統適應性控制凸輪外型設計機器學習
外文關鍵詞:MotorcycleBrake systemCombined brake systemVariable ratio combined brake systemAdaptive controlCam Profile DesignMachine Learning
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兩輪機車傳統上以配備前後獨立的煞車系統為主,左煞車把手產生後輪煞車,右煞車煞車把手控制前輪煞車。緊急煞車時,若正確地先煞後輪再加入前輪煞車,可以獲得足夠的煞車力並使車身穩定煞停。然而,在緊急狀態下,駕駛可反應時間不到2秒,常見的動作是單獨使用後輪煞車而未及時在碰撞前使用前煞車,使得煞車力不足而發生事故,或者單獨使用前輪煞車而摔倒。連動煞車系統(Combined brake system, CBS)是一種整合前後煞車系統的機構,讓駕駛者使用單一煞車把手即可同時產生適當配比的前後輪煞車力,因此在大多數行駛條件下,駕駛者僅操作單一煞車把手即可產生足夠高的煞車力以應付緊急狀況。
CBS模組本身是一個系統,在成本與機車狹窄的配置空間之限制下,必須滿足煞車性能、安全性、操作舒適性、操控性、模組的耐候性與對邊界條件變異的強健性等需求,因此CBS的設計開發與生產是一項艱鉅的工程。文中首先研究機車煞車動力學以及CBS的設計原理,然後從探討機車消費者的安全與舒適性需求開始,探討CBS的設計與分析方法,並建立CBS模組品質監測技術,研究工作包括下列三大部分:
首先解析市場上通用的簡式CBS產品之特性、性能極限、設計準則,然後建立性能預測方法,以及系統性的參數設計方法,然後實車驗證優化設計之效果。實測結果顯示,利用分析方法預測的煞車性能與實車測試結果非常吻合,預測誤差< 1%。在整車前、後輪煞車系統均正常條件下,實測整車減速度性能:對應把手平均入力173.86 N之最大減速度5.24 m/s2 (0.53g)。受限於簡式CBS的可調整參數不足,雖然經過參數優化後,可讓該款車輛煞車性能高於法規認證要求:當前後輪煞車系統均正常時,左煞車把手(意即連動側煞車把手)的操作力≤ 200 N時,整車減速度須達5.1 m/s2以上。但前輪煞車力的分配比例難以提高(對應後輪鎖死點之前煞車力:後煞車力= 35%:65%),緊急煞車性能與低速煞車的舒適性都無法令人滿意。
為了解決現有CBS產品之機構性能瓶頸,文中針對一種高效能的變比例CBS產品(Variable ratio combined brake system, VRCBS)探討設計方法。VRCBS是一種適用於機車的機械式CBS系統,目的為突破傳統式CBS(又稱為簡式CBS)的性能極限,能夠同時滿足安全性、高煞車性能和駕駛舒適性的要求。文中推導了VRCBS機制的數學模型,提出一種基於自適應控制理論的參數匹配設計方法來完成其核心元件之設計。利用本文所提出的設計方法所開發的VRCBS原型,於實車道路動態煞車測試結果為:把手平均入力154.29 N時之最大減速度6.37 m/s2 (0.65 g)與後輪鎖死點之前、後煞車力分配比例(50%:50%)的表現均明顯高於簡式CBS,且VRCBS的煞車性能與體感舒適度均優於簡式CBS設計,此一結果驗證了所提出設計方法的可行性。
論文中最後提出VRCBS之自動化量產檢測技術,並且探討應用機器學習於生產線上檢測VRCBS性能的關鍵技術。研究中首先發展快速檢測系統與軟體,於取得足夠的數據後,分析快速檢測系統與實車測試數據的相關性,然後選擇與實車性能要求規範相關的特徵參數作為檢測分析的依據。將此參數經過主成分分析使其降維成為二個主成分後,再利用核函數支持向量機(Kernel support vector machine, KSVM)進行分類。驗證結果顯示,KSVM分類器對NG產品的召回率可達100%、正確率為90%、F1分數則為72.72%。
Two-wheeled motorcycles are traditionally equipped with independent front and rear brake systems. The left-hand brake lever produces the rear wheel brake, while the right handbrake lever controls the front-wheel brake. When braking, if the driver brakes first the rear wheel and then brakes the front wheel, sufficient braking force can be generated to stop the motorcycle stably. However, in an emergency, the driver often uses the rear brake or the front brake alone to cause rollover or collision accidents. The combined brake system (CBS) is a mechanism that integrates the front and rear brake systems, allowing the driver to use a single brake lever to generate a proper ratio of front and rear wheel braking forces. Therefore, in most driving conditions, the driver only operating a single brake lever can generate high enough braking force to cope with all emergencies.
The design of CBS is a challenging task. Because under the constraints of cost and limited layout space, the brake system equipped with CBS must meet lots of requirements such as high braking performance, safety, comfort, maneuverability, durability, and robustness to the variation of boundary conditions. This dissertation is intended to develop the design method to solve these difficulties. Firstly, the characteristics, performance limits, and design criteria of common conventional CBS products on the market are analyzed, and then braking performance prediction methods and systematic parameter design methods are established. A prototype is made to verify the effects of optimized design in an actual vehicle. The test results show that the braking performance predicted by the analysis method is very consistent with the actual vehicle test results, and the prediction error is less than 1%. The measured maximum deceleration is 5.24 m/s2 (0.53 g) at an average hand brake lever input force of 173.86 N. Although its braking performance is higher than the certification requirements after parameter optimization, the performance of emergency braking and the comfort of low-speed braking are still unsatisfactory because the maximum attainable front-wheel braking force distribution ratio is 35% when the rear wheel locked. This is due to the limitation of the number of tunable parameters of the conventional CBS.
To solve the performance limitations of the existing CBS products, this dissertation develops a design method for a high-performance CBS design called the Variable Ratio Combined brake system (VRCBS), which is purposed to simultaneously meet the requirements of safety, high braking performance, and driving comfort. The mathematical model of the VRCBS mechanism is first developed, and a parameter matching design method based on adaptive control theory is proposed. A prototype developed by using the proposed method is tested on an actual vehicle. The maximum deceleration is obtained with 6.37 m/s2 (0.65g) at the average input force of 154.29 N. The front-wheel braking force distribution ratio when rear wheel locked is obtained as 50%, which is significantly higher than that of the conventional CBS.
For the third part of this thesis, a quality screening system for the VRCBS mass production line by applying machine learning algorithms is proposed. Firstly, a testing system is developed. The correlation between the test system and the actual vehicle test data is studied. Then the characteristic parameters related to the actual vehicle performance are used as the basis for the analysis. Finally, the principal component analysis (PCA) and the kernel support vector machine (KSVM) are applied for quality classification. For the test samples, the KSVM classifier obtains a recall rate of 100% for NG products, a correct rate of 90%, and an F1 score of 72.72%.
論文審定書 i
謝 辭 ii
摘 要 iii
Abstract v
目 錄 vii
圖 次 x
表 次 xiv
符號說明 xv
第一章 緒論 1
1.1 兩輪機車之煞車動態特性 1
1.2 配置獨立式煞車系統之機車駕駛者的操作行為 7
1.3 機車煞車系統之改良 8
1.4 機車安裝CBS之法規規範 11
1.5 機車CBS煞車系統之性能指標 13
1.6 CBS的設計原則 14
1.7 研究目的與論文結構 16
第二章 簡式CBS 設計方法之探討 18
2.1 簡式CBS系統 18
2.2 煞車性能之預測 23
2.3 CBS之設計參數 27
2.3.1 設計左煞車把手槓桿比GLH 27
2.3.2 設計CBS的槓桿比Rf 28
2.3.3 設計延遲力Fd 30
2.4 駕駛者乘坐座位和質量變化對重心影響 33
2.5 實車測試與驗證 35
2.5.1 繪製BFD圖 37
2.5.2 評估指標的計算 37
2.6 小結 39
第三章 VRCBS關鍵幾何參數之匹配設計 40
3.1 目標煞車力分配比例 41
3.2 VRCBS機構 43
3.3 數學模型推導 45
3.4 參數匹配設計 48
3.5 設計之實現與實車測試驗證 58
3.6 VRCBS與簡式CBS之綜合比較 63
3.6.1 客觀性能評價(Objective Evaluation) 63
3.6.2 主觀性能評價(Subjective Evaluation) 64
3.7 小結 68
第四章 VRCBS之設計探討 69
4.1 擺臂與滾輪材料之選擇 69
4.2 參數變動影響分析 78
4.2.1 彈簧參數變異之影響 79
4.2.2 擺臂幾何參數變異之影響 81
4.2.3 滾輪參數變異之影響 87
4.2.4 連桿之參數變異的影響 87
4.3 小結 91
第五章 VRCBS模組性能自動篩選系統之設計 92
5.1 研究方法 93
5.1.1 模組安裝於實車之測試資料分析 95
5.1.2 VRCBS性能指標及規範制定 96
5.2 VRCBS模組檢測平台 98
5.2.1 檢測平台測試資料分析 99
5.2.2 實車與平台測試數據之關聯性 100
5.3 檢測平台測試資料之特徵分析 103
5.3.1 主成分分析 103
5.3.2 PCA結果與分析 107
5.4 測試平台Rr曲線特徵與PCA座標之關係探討 109
5.5 檢測數據之自動分類 122
5.5.1 分類器之性能指標 123
5.5.2 模型訓練 125
5.5.3 模型之訓練結果與驗證 126
5.6 小結 129
第六章 結 論 130
6.1 論文之具體貢獻 130
6.2 建議與未來努力方向 132
參考文獻 133
附 錄 140
1.Cossalter V. Motorcycle dynamics. 2nd ed. Morrisville, NC, US: LULU, 2006, pp.96–97.
2.Burckhardt M. Fahrwerktechnik: Radschlupf-Regelsysteme. 1st ed. Wurzburg, Germany: Vogel Verlag, 1993.
3.Ružinskas A and Sivilevičius H. Magic Formula Tyre Model Application for a Tyre-Ice Interaction, Procedia Eng. 2017; 187, 335–341.
4.Hurt HH, Ouellet JV and Thom DR. Motorcycle accident cause factors and identification of countermeasures. NHTSA Report DOT-HS-5-01160, 1981.
5.Prem H. The emergency straight-path braking behaviour of skilled versus less-skilled motorcycle riders. SAE Paper 871228, 1987.
6.Ecker H, Wassermann J, Hauer G, Ruspekhofer R and Grill M. Braking deceleration of motorcycle riders. In: International Motorcycle Safety Conference, Orlando, Florida, 1-4 March 2001.
7.Carter T. Motorscooter Braking Control Response Study. SAE Paper 2000-01-0180, 2000.
8.Lenkeit JF, Hagoski BK and Bakker AI. A Study of Motorcycle Rider Braking Control Behavior. NHTSA Technical Report DOT-HS-811-448, 1999.
9.Tani K and Nakanura H. Development of advanced brake system for small motorcycles. SAE paper 2015-01-2680, 2015.
10.Tetsuo T, Kanau I, Yoshiaki S, Yukimasa N and Hiroyuki N. Further research on new combined brake system (Dual CBS) for motorcycles. HONDA R&D Tech Rev 1994; 6: 194–201.
11.Jon Lawes. Car Brakes: A Guide to Upgrading, Repair and Maintenance. Illustrated ed. Ramsbury Marlborough UK: Crowood, 2014, pp.71–73.
12.Burton DM, Delaney AK, Newstead SV, Logan D and Fildes BN. Effectiveness of ABS and Vehicle Stability Control Systems. 04/01 ed. Melbourne Vic Australia: Royal Automobile Club of Victoria Ltd, 2004.
13.Forkenbrock G, Flick M and Garrott W. NHTSA light vehicle antilock brake system research program task 4: a test track study of light vehicle ABS performance over a broad range of surfaces and maneuvers. NHTSA Report DOT-HS-808-875, 1999.
14.Ebberg J. Bosch’s new motorcycle ABS 10 goes into production with Kawasaki and Suzuki, Stuttgart, Germany: Robert Bosch, 2016.
15.Anderson B, Baxter A and Robal N. Comparison of Motorcycle Braking System Effectiveness, SAE paper 2010-01-0072, 2010.
16.Cornoa M, Savaresia SM, Tanellia M and Fabbrib L. On optimal motorcycle braking. Control Eng. Pract. 2008; 16, 644–657.
17.UN GTR No.3, ECE/TRAN/180/Add.3, 2006. Motorcycle Brake Systems.
18.UN Regulation No. 78, Rev 2, 2012.
19.Meng D, Zhang L and Yu Z. A dynamic model for brake pedal feel analysis in passenger cars. Proc IMechE Part D: J Automobile Engineering 2016; 230(7): 955–968.
20.Lee S and Kim S. Characterization and development of the ideal pedal force, pedal travel, and response time in the brake system for the translation of the voice of the customer to engineering specifications. Proc IMechE Part D: J Automobile Engineering 2010; 224(11): 1433–1450.
21.Ebert DG and Kaatz RA. Objective characterization of vehicle brake feel. SAE paper 940331, 1994.
22.Johnston M, Leonard E, Monsere P and Riefe M. Vehicle brake performance assessment using subsystem testing and modeling. SAE paper 2005-01-0791, 2005.
23.Dairou V, Priez A, Sieffermann J and Danzart M. An original method to predict brake feel: a combination of design of experiments and sensory science. SAE paper 2003-01- 0598, 2003.
24.Basch R, Sanders P, Hartsock D and Evans C. Correlation of lining properties with brake pedal feel. SAE paper 2002-01-2602, 2002.
25.Day AJ, Ho HP, Hussain K and Johnstone A. Brake system simulation to predict brake pedal feel in a passenger car. SAE paper 2009-01-3043, 2009.
26.Antanaitis D, Riefe M and Sanford J. Automotive brake hose fluid consumption characteristics and its effects on brake system pedal feel. SAE paper 2010-01-0082, 2010.
27.De Arruda Pereira JA. New Fiesta: brake pedal feeling development to improve customer satisfaction. SAE paper 2003-01-3598, 2003.
28.Goto Y, Yasuda A and Ishida S. Brake master cylinder for secure brake feel and improved system failure performance. SAE paper 2003-01-3304, 2003.
29.Keerthi M, Shete S, Jadhav N, Nandkeolyar K and Sonar S. Optimization of brake pedal feel and performance for dual air over hydraulic system on light commercial vehicles. SAE paper 2010-01-1888, 2010.
30.Rajesh AT, Bisen B and Sharad P. Co-relating subjective and objective brake performance: a case study. SAE paper 2006-01-3204, 2006.
31.Jolliffe IT. Principal Component Analysis. 2nd ed. Medford MA: Springer-Verlag, 2002: pp.10–59.
32.Han K, Choi SB, Lee J, Hyun D and Lee J. Accurate brake torque estimation with adaptive uncertainty compensation using a brake force distribution characteristic. IEEE Trans. Veh. Technol. 2017; 66(12): 10830–10840.
33.Yukimasa N, Kanau I, Tetsuo T and Yoshiaki S. Research on dual combined brake system (Dual-CBS) for motorcycle. HONDA R&D Technical Review 1993; 5: 100–108.
34.Ghosh S, Samy BA, Balwada RS and Kaushik R. Mechanical combined braking system design and validation for Scooter. SAE paper 2014-01-2485, 2014.
35.Soni L, Domada D and Venkateswaran S. Combi brake system (CBS) design and tuning on an electric two wheeler for cornering maneuver. SAE paper 2019-28-2399, 2019.
36.Keerthi M, Shete S, Jadhav N, Nandkeolyar K and Sonar S. Optimization of brake pedal feel and performance for dual air over hydraulic system on light commercial vehicles. SAE paper 2010-01-1888, 2010.
37.Limpert R. Brake design and safety. 3rd ed. Warrendale, PA, US: SAE International, 2012, pp.27–63.
38.Cossalter V. Motorcycle dynamics. 2nd ed. Morrisville, NC, US: LULU, 2006, pp.97–104.
39.Limpert R. Brake Design and Safety, 3rd ed. Warrendale, PA, US: SAE International, 2012, pp.213–228.
40.Racelogic support centre. 18-VBOX Tools Test Configurations, https://racelogic. support/01VBOX_Automotive/03Software_applications/VBOX_Tools/VBOX_Tools_User_Manual/18_-_VBOX_Tools_Test_Configurations (2018, accessed on 30 August 2021).
41.Tseng C, Chiu Y, Lin Y and Teng C. High Degree of variation ratio range of brake interlocking braking system. Patent 1593593, Taiwan, 1 August 2017.
42.Müller M, Hüsing M, Beckermann A and Corves B. Linkage and cam design with MechDev based on non-uniform rational B-splines. Machines 2020; 8(1), 5.
43.Cardona A and Géradin M. Kinematic and dynamic analysis of mechanisms with cams. Comput. Methods Appl. Mech. Eng. 1993; 103: 115–134.
44.Fisette P, Péterkenne JM, Vaneghem B and Samin JC. A multibody loop constraints approach for modelling cam/follower devices. Nonlinear Dyn. 2000; 22: 335–359.
45.Qiu D, Paredes M and Seguy S. Variable pitch spring for nonlinear energy sink: Application to passive vibration control. J. Mech. Eng. Sci. 2019; 233: 611–622.
46.Ioannou P and Fidan B. Adaptive control for discrete-time systems. In Adaptive Control Tutorial; Smith RC, Antoulas AC, Eds.; Philadelphia, PA, US: Society for Industrial and Applied Mathematics, 2006, pp.255–258.
47.Slotine J and Li W. Adaptive control. In Applied Nonlinear Control; International ed. Upper Saddle River, NJ, US: Prentice Hall, 1991, pp.326–335.
48.Zhao L, Choi K and Lee I. A metamodeling method using dynamic Kriging and sequential sampling. AIAA J. 2010; 49: 2034–2046.
49.Simpson TW, Toropov V, Balabanov V and Viana FAC. Design and analysis of computer experiments in multidisciplinary design optimization: A review of how far we have come—or not. In: the 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Victoria, BC, Canada, 10–12 September 2008, paper no. AIAA 2008-2508. Reston, VA: AIAA.
50.Mercorelli P. Parameters identification in a permanent magnet three-phase synchronous motor of a city-bus for an intelligent drive assistant. Int. J. Model. Identif. Control 2014; 21: 352–361.
51.Chen L, Mercorelli P and Liu S. A Kalman estimator for detecting repetitive disturbances. In: the 2005 American Control Conference, Portland, OR, US, 8–10 June 2005; 3, pp.1631–1636, paper no. 8589095. New York: IEEE.
52.Kim C, Wang S and Choi KK. Efficient response surface modeling by using moving least-squares method and sensitivity. AIAA J. 2005; 43: 2404–2411.
53.Lancaster P and Salkauskas K. Surfaces generated by moving least squares methods. Math. Comput. 1981; 37: 141–158.
54.Johansson R. System Modeling and Identification; International ed. Englewood Cliffs, NJ, US: Prentice-Hall, 1993, pp.266–267.
55.Budynas RG and Nisbett JK. Load and Stress Analysis. In Shigley’s Mechanical Engineering Design. 9th ed. NY, US: McGraw-Hill, 2011, pp.122–126.
56.Budynas RG and Nisbett JK. Failures Resulting from Static Loading. In Shigley’s Mechanical Engineering Design. 9th ed. NY, US: McGraw-Hill, 2011, pp.213–227.
57.American Gear Manufacturers Association, ANSI/AGMA 2001-C95, 2004. American National standard, Fundamental rating factors and calculation methods for involute spur and helical gear teeth.
58.Pearson K. On lines and planes of closest fit to systems of points in space. Philos. Mag. 1901, 2(6): 559–572.
59.Burnham KP and Anderson DR. Model Selection and Multimodel Inference. 2nd ed. Medford MA: Springer-Verlag, 2002.
60.Abdi H and Williams JL. Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat. 2010, 2(4): 433–459.
61.Shaw PJA. Multivariate statistics for the Environmental Sciences, London: Arnold, 2003.
62.Jongerling J and Hoijtink H. Explained Variance and Intraclass Correlation in a Two-Level AR(1) Model. Multivar. Behav. Res. 2017; 52(4), 403–415.
63.Tague NR. Pareto Chart. In The Quality Toolbox. Milwaukee, Wisconsin: American Society for Quality 2005; pp.376–380.
64.Platt JC. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Smola AJ, Bartlett P, Schölkopf B and Schuurmans D (eds) Advances in Large-Margin Classifiers. 1st ed. Cambridge, MA: MIT Press, 1999, pp.1–11.
65.Smola AJ and Schölkopf B. A tutorial on support vector regression, Stat. Comput. 2004; 14: 199–222.
66.Vert J, Tsuda K and Schölkopf B. A primer on kernel methods. In Kernel Methods in Computational Biology. Cambridge, MA, US: MIT, 2004, pp.35–70.
67.Confusion matrix, https://en.wikipedia.org/wiki/Confusion_matrix#cite_note-:1-11. (2007, accessed on 30 August 2021).
68.Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 2011; 12: 2825–2830.
69.McLachlan GJ, Do K and Ambroise C. Discriminant Analysis. In Analyzing microarray gene expression data. 1st ed. Hoboken, NJ, US: Wiley, 2004, pp.185–220.
70.Hastie T, Tibshirani R and Friedman J. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. NY US: Springer, 2009, pp.241–248.
71.Vanwinckelen G. On Estimating Model Accuracy with Repeated Cross-Validation. Leuven, Belgium: lirias.ku leuven, 2019, pp.39–44.
72.Arlot S and Celisse A. A survey of cross-validation procedures for model selection. Stat. Surv. 2010; 4: 40–79.
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