تخمین لحظه‌ای نیروی عمودی تایرها با استفاده از مدل تلفیقی سخت‌افزاری- نرم‌افزاری

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکاه شهید باهنر کرمان

2 دانشگاه شهید باهنر کرمان

3 شهید باهنر کرمان*مهندسی مکانیک

چکیده

بار عمودی روی تایر از عوامل تأثیرگذار بر عملکرد خودرو است. پارامترهای زیادی بر بار عمودی تایر تأثیر می‌گذارند، ازجمله مهم‌ترین آن‌ها می‌توان به جرم خودرو، موقعیت مرکز جرم خودرو همچنین دینامیک و حالت‌های خودرو حین مانور اشاره کرد. در این مقاله الگوریتم جدیدی جهت تخمین در لحظه مقدار نیروی عمودی لحظه‌ای تایر توسعه داده‌شده است. در گام اول، داده‌های ماژول اندازه‌ده دینامیکی و شبکه عصبی مصنوعی به کار گرفته‌شده است. به منظور ایجاد داده مصنوعی و همچنین استفاده در گام‌های بعدی تخمین مدل تلفیقی نرم‌افزاری-سخت افزاری توسعه داده شد. در این مدل یک سخت‌افزار اندازه‌ده جایگزین مدل‌سازی تایر و جاده شده‌است. الگوریتم شامل بلوک مربوط به دینامیک رول و پیچ خودرو است که با ساختار چندلایه شبکه‌های عصبی مصنوعی آموزش داده‌ شده‌اند. خروجی بلوک‌های شبکه عصبی، توزیع بار استاتیک روی هر تایر خودرو است که توسط مدل توسعه داده شده، در هر لحظه پایش می‌شود. در گام دوم الگوریتم، به‌منظور تخمین مقدار انتقال بار حین مانور در خودرو، مدل تلفیقی سخت‌افزاری-نرم‌افزاری به‌کار‌گرفته شده‌است تا مقدار لحظه‌ای بار عمودی روی هر تایرها محاسبه گردد. مقایسه نتایج بدست آمده از الگوریتم پیشنهادی و مقدار خروجی مدل مرجع اعتباردهی‌ شده در نرم‌افزار کارسیم، نشان‌دهنده دقت قابل‌قبول و عملکرد مناسب این روش است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Online Estimation of Tire Normal Force with Applying Hardware-Software Couple Model

نویسندگان [English]

  • Ali Hosseini Salari 1
  • Hossein Mirzaeinejad 2
  • majied fooladi mahani 3
1 Department of Mechanical Engineering, Shahid Bahonar University of Kerman
2 Department of Mechanical Engineering, Shahid Bahonar university of Kerman
3 Department of Mechanical Engineering, Shahid Bahonar university of Kerman
چکیده [English]

Tire online normal force has effects on vehicle safety and performance and dynamic control systems. It is influenced by too many parameters such as vehicle mass and center of gravity position and vehicle instantaneous dynamics states. In this paper, a new estimation algorithm is developed to estimate tires’ online normal forces during a maneuver. The proposed algorithm uses a dynamic measure module to make a hardware-software coupled model which is validated by real test data. The algorithm uses artificial neural networks advantages to estimate the vehicle mass distributions. A combination of real and model-generated data is used to train, test, and validate the artificial neural network structure. By applying two roll and pitch artificial neural network blocks, it estimates tires’ static normal forces. In this respect, the validated vehicle model instantaneously monitors the estimated values. The results show that the proposed algorithm estimates the vehicle total mass with less than 5 percent. In addition, the coupled model uses the estimated static values to estimate the tire's online normal forces with considering the measured vehicle dynamics states by dynamic module. Comparing the obtained results from the proposed method with the outputs from Carsim indicates the acceptable accuracy of this method.

کلیدواژه‌ها [English]

  • Mass estimation
  • Artificial neural network
  • Roll and pitch dynamics
  • Online tire force
[1] X. Ma, P.K. Wong, J. Zhao, Z. Xie, Cornering stability control for vehicles with active front steering system using T-S fuzzy based sliding mode control strategy, Mechanical Systems and Signal Processing, 125 (2019) 347-364.
[2] H. Mirzaeinejad, M. Mirzaei, R. Kazemi, Enhancement of vehicle braking performance on split-μ roads using optimal integrated control of steering and braking systems, Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics, 230(4) (2016) 401-415.
[3] M. Mirzaei, H. Mirzaeinejad, Fuzzy Scheduled Optimal Control of Integrated Vehicle Braking and Steering Systems, IEEE/ASME Transactions on Mechatronics, 22(5) (2017) 2369-2379.
[4] H. Mirzaeinejad, Robust predictive control of wheel slip in antilock braking systems based on radial basis function neural network, Applied Soft Computing, 70 (2018) 318-329.
[5] T. Shim, C. Ghike, Understanding the limitations of different vehicle models for roll dynamics studies, Vehicle System Dynamics, 45(3) (2007) 191-216.
[6] H. Mirzaeinejad, M. Mirzaei, S. Rafatnia, A novel technique for optimal integration of active steering and differential braking with estimation to improve vehicle directional stability, ISA Trans, 80 (2018) 513-527.
[7] H. Yang, P.-C. Shi, Q. Zhao, S.-S. Peng, Modeling and Simulation of Linear Two - DOF Vehicle Handling Stability, ITM Web of Conferences, 11 (2017).
[8] S. Guo, Y. Liu, L. Xu, X. Guo, L. Zuo, Performance evaluation and parameter sensitivity of energy-harvesting shock absorbers on different vehicles, Vehicle System Dynamics, 54(7) (2016) 918-942.
[9] T.A. Wenzel, K.J. Burnham, M.V. Blundell, R.A. Williams, Dual extended Kalman filter for vehicle state and parameter estimation, Vehicle System Dynamics, 44(2) (2006) 153-171.
[10] M. Doumiati, A. Victorino, A. Charara, D. Lechner, Lateral load transfer and normal forces estimation for vehicle safety: experimental test, Vehicle System Dynamics, 47(12) (2009) 1511-1533.
[11] J. Limroth, Real-Time Vehicle Parameter Estimation and Adaptive Stability Control, Clemson University, 2009.
[12] J. Zhu, Z. Wang, L. Zhang, W. Zhang, State and parameter estimation based on a modified particle filter for an in-wheel-motor-drive electric vehicle, Mechanism and Machine Theory, 133 (2019) 606-624.
[13] J. Park, M. Kwon, G. Du, J. Huh, S.-H. Hwang, Vehicle Dynamic Control for In-Wheel Electric Vehicles Via Temperature Consideration of Braking Systems, International Journal of Automotive Technology, 19(3) (2018) 559-569.
[14] B.L. Pence, J. Hays, H.K. Fathy, C. Sandu, J.L. Stein, Vehicle sprung mass estimation for rough terrain, International Journal of Vehicle Design, 61 (2013) 3-26.
[15] X. Gong, J. Suh, C. Lin, A novel method for identifying inertial parameters of electric vehicles based on the dual H infinity filter, Vehicle System Dynamics, 58(1) (2020) 28-48.
[16] M.N. Mahyuddin, J. Na, G. Herrmann, X. Ren, P. Barber, Adaptive Observer-Based Parameter Estimation With Application to Road Gradient and Vehicle Mass Estimation, IEEE Transactions on Industrial Electronics, 61(6) (2014) 2851-2863.
[17] K. Maalej, S. Kelouwani, K. Agbossou, Y. Dubé, Enhanced fuel cell hybrid electric vehicle power sharing method based on fuel cost and mass estimation, Journal of Power Sources, 248 (2014) 668-678.
[18] X. Huang, J. Wang, Real-Time Estimation of Center of Gravity Position for Lightweight Vehicles Using Combined AKF–EKF Method, IEEE Transactions on Vehicular Technology, 63(9) (2014) 4221-4231.
[19] G. Reina, M. Paiano, J.-L. Blanco-Claraco, Vehicle parameter estimation using a model-based estimator, Mechanical Systems and Signal Processing, 87 (2017) 227-241.
[20] D. Brian, Estimation of Uncertain Vehicle Center of Gravity using Polynomial Chaos Expansions, Virginia Polytechnic Institute and State University, ETDs, 2008.
[21] H. Sar, P. Fundowicz, Empirical equations for determining the height of the center of mass of a passenger car, in:  2018 XI International Science-Technical Conference Automotive Safety, 2018, pp. 1-4.
[22] Z. Deng, D. Chu, F. Tian, Y. He, C. Wu, Z. Hu, X. Pei, Online estimation for vehicle center of gravity height based on unscented Kalman filter, in:  2017 4th International Conference on Transportation Information and Safety (ICTIS), 2017, pp. 33-36.
[23] C. Lin, X. Gong, R. Xiong, X. Cheng, A novel H∞ and EKF joint estimation method for determining the center of gravity position of electric vehicles, Applied Energy, 194(C) (2017) 609-616.
[24] C. Lin, X. Cheng, H. Zhang, X. Gong, Estimation of Center of Gravity Position for Distributed Driving Electric Vehicles Based on Combined H∞-EKF Method, Energy Procedia, 88 (2016) 970-977.
[25] M. Chen, G. Yin, N. Zhang, J. Chen, Joint estimation of center of gravity position and mass for the front and rear independently driven electric vehicle with payload in the start stage, in:  2016 35th Chinese Control Conference (CCC), 2016, pp. 1932-1937.
[26] M.L. McIntyre, T.J. Ghotikar, A. Vahidi, X. Song, D.M. Dawson, A Two-Stage Lyapunov-Based Estimator for Estimation of Vehicle Mass and Road Grade, IEEE Transactions on Vehicular Technology, 58(7) (2009) 3177-3185.
[27] A. Vahidi, M. Druzhinina, A. Stefanopoulou, P. Huei, Simultaneous mass and time-varying grade estimation for heavy-duty vehicles, in:  Proceedings of the 2003 American Control Conference, 2003., 2003, pp. 4951-4956 vol.4956.
[28] D. Mikulski, Neural Network Approach For Estimating Mass Moments of Inertia and Center of Gravity in Military Vehicles, 2006.
[29] Integrated Vehicle Dynamics Control, in:  Integrated Vehicle Dynamics and Control, 2016, pp. 201-282.
[30] U. Peckelsen, Objective Tyre Development : Definition and Analysis of Tyre Characteristics and Quantification of their Conflicts, Ph.D. thesis, Karlsruher Institut für Technologie (KIT), 2017.
[31] M.H. Haider, T. Islam, M. Islam, M. Shajid-Ul-Mahmud, Comparison of Complementary and Kalman Filter Based Data Fusion for Attitude Heading Reference System, 2017.
[32] P. Gui, L. Tang, S. Mukhopadhyay, MEMS based IMU for tilting measurement: Comparison of complementary and kalman filter based data fusion, in:  2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA), 2015, pp. 2004-2009.
[33] W. Zhou, J. Hou, L. Liu, T. Sun, J. Liu, Design and Simulation of the Integrated Navigation System based on Extended Kalman Filter, Open Physics, 15(1) (2017) 182-187.
[34] W. Liu, H. He, F. Sun, J. Lv, Integrated chassis control for a three-axle electric bus with distributed driving motors and active rear steering system, Vehicle System Dynamics, 55(5) (2017) 601-625.
[35] M. Aftatah, A. Lahrech, A. Abdelouahed, A. Soulhi, GPS/INS/Odometer Data Fusion for Land Vehicle Localization in GPS Denied Environment, Modern Applied Science, 11 (2016) 62.
[36] T. Van, T. Van, D. Nguyen, T. Chu Duc, D.-T. Tran, 15-State Extended Kalman Filter Design for INS/GPS Navigation System, Journal of Automation and Control Engineering, 3 (2015) 109-114.
[37] F. Girrbach, J.D. Hol, G. Bellusci, M. Diehl, Optimization-Based Sensor Fusion of GNSS and IMU Using a Moving Horizon Approach, Sensors, 17(5) (2017) 1159.
[38] ISO, Passenger cars — Test track for a severe lane-change manoeuvre — Part 2: Obstacle avoidance, in, 2006.
[39] H.B. Pacejka, Chapter 8 - Applications of Transient Tire Models, in: H.B. Pacejka (Ed.) Tire and Vehicle Dynamics (Third Edition), Butterworth-Heinemann, Oxford, 2012, pp. 355-401.
[40] F.S. Göküzüm, L.T.K. Nguyen, M.-A. Keip, An Artificial Neural Network Based Solution Scheme for Periodic Computational Homogenization of Electrostatic Problems, Mathematical and Computational Applications, 24(2) (2019) 40.
[41] G. Bologna, A Simple Convolutional Neural Network with Rule Extraction, Applied Sciences, 9(12) (2019) 2411.
[42] N. Kriegeskorte, T. Golan, Neural network models and deep learning, Current Biology, 29(7) (2019) R231-R236.