August & September  2019, 12(4&5): 1035-1052. doi: 10.3934/dcdss.2019071

A novel road dynamic simulation approach for vehicle driveline experiments

1. 

Key Laboratory of Advanced Manufacture Technology for Automobile Parts, Ministry of Education, Chongqing University of Technology, Chongqing 400054, China

2. 

Chongqing Tsingshan Industrial, Chongqing 402761, China

3. 

Chongqing Vocational Institute of Engineering, Chongqing 402260, China

4. 

Chongqing Tsingshan Industrial, Chongqing 402761, China

5. 

Chongqing Academy of Science and Technology, Chongqing 401123, China

* Corresponding author: Wen-Li Li

Received  July 2017 Revised  January 2018 Published  November 2018

A dynamic simulation approach for performing emulation experiments on vehicle driveline test bench is discussed in this paper. In order to reduce costs and shorten new vehicle development cycle time, vehicle simulation on the driveline test bench is an attractive alternative at the development phase to reduce the quantity of proto vehicles. This test method moves the test site from the road to the bench without the need for real chassis parts. Dynamic emulation of mechanical loads is a Hardware-in-the-loop (HIL) procedure, which can be used as a supplement of the conventional simulations in testing of the operation of algorithms without the need for the prototypes. The combustion engine is replaced by a electric drive motor, which replicates the torque and speed signature of an actual engine, The road load resistance of the vehicle on a real test road is accurately simulated on load dynamometer motor. On the basis of analyzing and comparing the advantages and disadvantages of the inverse dynamics model and the forward model based on speed closed loop control method, in view of the high order, nonlinear and multi variable characteristics of test bench system, a load simulation method based on speed adaptive predictive control is presented. It avoids the complex algorithm of closed loop speed compensation, and reduces the influence of inaccurate model parameters on the control precision of the simulation system. The vehicle start and dynamic shift process were simulated on the test bench.

Citation: Wen-Li Li, Jing-Jing Wang, Xiang-Kui Zhang, Peng Yi. A novel road dynamic simulation approach for vehicle driveline experiments. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1035-1052. doi: 10.3934/dcdss.2019071
References:
[1]

R. Ahlawat, S. Jiang and D. Medonza, et al., Engine torque pulse and wheel slip emulation for transmission-in-the-loop experiments, 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics Montréal, Canada, 2010, 688-695. Google Scholar

[2]

Z. H. AkpolatG. M. Asher and J. C. Clare, A practical approach to the design of robust speed controllers for machine drives, IEEE Trans. on Industrial Electronics, 47 (2000), 315-324.   Google Scholar

[3]

Z. H. AkpolatG. M. Asher and J. C. Clare, Dynamic emulation of mechanical loads using a vector controlled induction motor-generator set, IEEE Trans. on Industrial Electronics, 46 (1999), 370-379.   Google Scholar

[4]

J. Arellano-PadillaG. Asher and M. Sumner, Control of an ac-dynamometer for dynamic emulation of mechanical loads with stiff and flexible shafts, IEEE Transactions on Industrial Electronics, 53 (2006), 1250-1260.   Google Scholar

[5]

M. Corbett, P. Lamm and J. McNichols, et. al., Effects of transient power extraction on an integratated hardware-in-the-loop aircraft/propulsion/power system, Power Systems Conference. Washington, SAE Internatinal, 2008, Paper No. 2008-01-2926. Google Scholar

[6]

Z. Hakan AkpolatG. M. Ashen and J. C. Clare, Dynamic emulation of mechanical Loads using a vector-controlled induction motor-generator set, IEEE Transactions on Industrial Lectornics, 46 (1999), 370-379.   Google Scholar

[7]

Z. Hakan AkpolatG. M. Ashen and J. C. Clare, Experimental dynamometer emulation of nonlinear mechanical loads, Industry Applications Society Annual Meeting. St. Louis. IEEE transactions on industrial Applications, 35 (1999), 1367-1373.   Google Scholar

[8]

Z. Hakan Akpolat, G. Asher and J. Clare, Experimental dynamometer emulation of nonlinear mechanical loads, The 1998 IEEE Industry Applications Conference. St. Louis, 1998, 532-539. Google Scholar

[9]

C. Hewson, G. Asher and M. Sumner, Dynamometer control for emulation of mechanical loads, The 1998 IEEE Industry Applications Conference. St. Louis, 1998, 1511-1518. Google Scholar

[10]

S. Jiang, M. H. Smith and J. Kitchen, et al., Development of an engine-in-the-loop vehicle simulation system in engine dynamometer test cell, SAE 2009 World Congress & Amp, Exhibition, United States, SAE International 2009, Paper No 2009-01-1039. Google Scholar

[11]

S. Kaatz, T. Abe and W. Vanhaaften, et al., The ford motor company transmission NVH test cell, Noise & Vibration Conference and Exhibition. Michigan. SAE Internatinal, 2003, Paper No. 2003-01-1681. Google Scholar

[12]

Z.-J. Liu, J.-J. Hu and S. Wen, et al., Design of data acquisition and communication system for AMT comprehensive performance testbench, Journal of Chongqing University: Natural Science Edition, 32 (2009), 775-781. Google Scholar

[13]

N. Newberger, T. A. Nevius and P. Lasota, et al., Virtual engine dynamometer in service life testing of transmissions: A comparison between real engine and electric dynamometers as prime movers in validation test rigs, Extending Dynamometer Performance for Virtual Engine Simulation. International Congress and Exposition, SAE Internatinal, 2010, Paper No. 2010-01-0919. Google Scholar

[14]

R. W. NewtonR. E. Betz and H. B. Penfold, Emulating dynamics load characteristics using a dynamic dynamometer, Proc. Int. Conf. Power Electron. and Drive Syst., 1 (1995), 465-470.   Google Scholar

[15]

M. Rodic, K. Jezernik and M. Trlep, Use of dynamic emulation of mechanical loads in the design of adjustable speed applications, Advanced Motion Control, AMC, Kawasaki, 2004, 677-682. Google Scholar

[16]

M. Rodi$\check{c}$K. Jezernik and M. Trlep, Dynamic emulation of mechanical loads: And advanced approach, IEE Proc., Electr. Power Appl., 153 (2006), 159-166.   Google Scholar

[17]

M. Rodi$\check{c}$K. Jezernik and M. Trlep, A feedforward approach to the dynamic emulation of mechanical loads, Proceedings of the 35th Annual IEEE Power Electronics Specialists Conference(PESC'04), (2004), 4595-4601.   Google Scholar

[18]

W.-J. WangW.-G. Zhang and X. Li, Inertia electrical emulation and angular acceleration estimation for transmission test rig, Journal of Southeast University(Natural Science Edition), 42 (2012), 62-66.   Google Scholar

show all references

References:
[1]

R. Ahlawat, S. Jiang and D. Medonza, et al., Engine torque pulse and wheel slip emulation for transmission-in-the-loop experiments, 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics Montréal, Canada, 2010, 688-695. Google Scholar

[2]

Z. H. AkpolatG. M. Asher and J. C. Clare, A practical approach to the design of robust speed controllers for machine drives, IEEE Trans. on Industrial Electronics, 47 (2000), 315-324.   Google Scholar

[3]

Z. H. AkpolatG. M. Asher and J. C. Clare, Dynamic emulation of mechanical loads using a vector controlled induction motor-generator set, IEEE Trans. on Industrial Electronics, 46 (1999), 370-379.   Google Scholar

[4]

J. Arellano-PadillaG. Asher and M. Sumner, Control of an ac-dynamometer for dynamic emulation of mechanical loads with stiff and flexible shafts, IEEE Transactions on Industrial Electronics, 53 (2006), 1250-1260.   Google Scholar

[5]

M. Corbett, P. Lamm and J. McNichols, et. al., Effects of transient power extraction on an integratated hardware-in-the-loop aircraft/propulsion/power system, Power Systems Conference. Washington, SAE Internatinal, 2008, Paper No. 2008-01-2926. Google Scholar

[6]

Z. Hakan AkpolatG. M. Ashen and J. C. Clare, Dynamic emulation of mechanical Loads using a vector-controlled induction motor-generator set, IEEE Transactions on Industrial Lectornics, 46 (1999), 370-379.   Google Scholar

[7]

Z. Hakan AkpolatG. M. Ashen and J. C. Clare, Experimental dynamometer emulation of nonlinear mechanical loads, Industry Applications Society Annual Meeting. St. Louis. IEEE transactions on industrial Applications, 35 (1999), 1367-1373.   Google Scholar

[8]

Z. Hakan Akpolat, G. Asher and J. Clare, Experimental dynamometer emulation of nonlinear mechanical loads, The 1998 IEEE Industry Applications Conference. St. Louis, 1998, 532-539. Google Scholar

[9]

C. Hewson, G. Asher and M. Sumner, Dynamometer control for emulation of mechanical loads, The 1998 IEEE Industry Applications Conference. St. Louis, 1998, 1511-1518. Google Scholar

[10]

S. Jiang, M. H. Smith and J. Kitchen, et al., Development of an engine-in-the-loop vehicle simulation system in engine dynamometer test cell, SAE 2009 World Congress & Amp, Exhibition, United States, SAE International 2009, Paper No 2009-01-1039. Google Scholar

[11]

S. Kaatz, T. Abe and W. Vanhaaften, et al., The ford motor company transmission NVH test cell, Noise & Vibration Conference and Exhibition. Michigan. SAE Internatinal, 2003, Paper No. 2003-01-1681. Google Scholar

[12]

Z.-J. Liu, J.-J. Hu and S. Wen, et al., Design of data acquisition and communication system for AMT comprehensive performance testbench, Journal of Chongqing University: Natural Science Edition, 32 (2009), 775-781. Google Scholar

[13]

N. Newberger, T. A. Nevius and P. Lasota, et al., Virtual engine dynamometer in service life testing of transmissions: A comparison between real engine and electric dynamometers as prime movers in validation test rigs, Extending Dynamometer Performance for Virtual Engine Simulation. International Congress and Exposition, SAE Internatinal, 2010, Paper No. 2010-01-0919. Google Scholar

[14]

R. W. NewtonR. E. Betz and H. B. Penfold, Emulating dynamics load characteristics using a dynamic dynamometer, Proc. Int. Conf. Power Electron. and Drive Syst., 1 (1995), 465-470.   Google Scholar

[15]

M. Rodic, K. Jezernik and M. Trlep, Use of dynamic emulation of mechanical loads in the design of adjustable speed applications, Advanced Motion Control, AMC, Kawasaki, 2004, 677-682. Google Scholar

[16]

M. Rodi$\check{c}$K. Jezernik and M. Trlep, Dynamic emulation of mechanical loads: And advanced approach, IEE Proc., Electr. Power Appl., 153 (2006), 159-166.   Google Scholar

[17]

M. Rodi$\check{c}$K. Jezernik and M. Trlep, A feedforward approach to the dynamic emulation of mechanical loads, Proceedings of the 35th Annual IEEE Power Electronics Specialists Conference(PESC'04), (2004), 4595-4601.   Google Scholar

[18]

W.-J. WangW.-G. Zhang and X. Li, Inertia electrical emulation and angular acceleration estimation for transmission test rig, Journal of Southeast University(Natural Science Edition), 42 (2012), 62-66.   Google Scholar

Figure 1.  Mechanical Load Dynamic Emulation Control System
Figure 2.  Inverse Dynamic Model
Figure 3.  Speed Closed Loop Control Algorithm
Figure 4.  Speed closed loop control with feed-forward compensation
Figure 5.  Speed Closed-loop Control with Feed-forward Compensation
Figure 6.  Speed adaptive predictive control
Figure 7.  Schematic diagram of vehicle acceleration resistance
Figure 8.  Control model of speed adaptive predictive control
Figure 9.  The characteristic curves of drive motor and engine
Figure 10.  The characteristic curves of load motor
Figure 11.  Simulation range of electrical inertia on the platform system
Figure 12.  Acceleration inertia simulation curve
Figure 13.  The setup of vehicle driveline test bench
Figure 14.  The clutch control unit of the test bench
Figure 15.  The starting characteristics curves of simulated vehicle
Figure 16.  The shift control unit of the test bench
Figure 17.  Dynamic simulation curves of upshift
Figure 18.  Dynamic simulation curves of downshift
Figure 19.  Dynamic simulation curves of continuous shifting process
Table 1.  The technical data of drive motor
Power
(Kw)
Frequency
(Hz)
Torque
$N\cdot m$
Speed
(r/min)
Moment of inertia
(kg$\cdot$ m$^2$)
235.6 250 360 5000 0.042
Power
(Kw)
Frequency
(Hz)
Torque
$N\cdot m$
Speed
(r/min)
Moment of inertia
(kg$\cdot$ m$^2$)
235.6 250 360 5000 0.042
Table 2.  The technical data of load motor
Power
(Kw)
Frequency
(Hz)
Torque
($N\cdot m$)
Speed
(r/min)
Moment of inertia
(kg$\cdot$ m$^2$)
310 27.2 3701 800 6.3
Power
(Kw)
Frequency
(Hz)
Torque
($N\cdot m$)
Speed
(r/min)
Moment of inertia
(kg$\cdot$ m$^2$)
310 27.2 3701 800 6.3
[1]

Christina Surulescu, Nicolae Surulescu. Modeling and simulation of some cell dispersion problems by a nonparametric method. Mathematical Biosciences & Engineering, 2011, 8 (2) : 263-277. doi: 10.3934/mbe.2011.8.263

[2]

Hailing Xuan, Xiaoliang Cheng. Numerical analysis and simulation of an adhesive contact problem with damage and long memory. Discrete & Continuous Dynamical Systems - B, 2021, 26 (5) : 2781-2804. doi: 10.3934/dcdsb.2020205

[3]

Yila Bai, Haiqing Zhao, Xu Zhang, Enmin Feng, Zhijun Li. The model of heat transfer of the arctic snow-ice layer in summer and numerical simulation. Journal of Industrial & Management Optimization, 2005, 1 (3) : 405-414. doi: 10.3934/jimo.2005.1.405

[4]

Lars Grüne, Luca Mechelli, Simon Pirkelmann, Stefan Volkwein. Performance estimates for economic model predictive control and their application in proper orthogonal decomposition-based implementations. Mathematical Control & Related Fields, 2021  doi: 10.3934/mcrf.2021013

[5]

Qiang Guo, Dong Liang. An adaptive wavelet method and its analysis for parabolic equations. Numerical Algebra, Control & Optimization, 2013, 3 (2) : 327-345. doi: 10.3934/naco.2013.3.327

[6]

Zhihua Zhang, Naoki Saito. PHLST with adaptive tiling and its application to antarctic remote sensing image approximation. Inverse Problems & Imaging, 2014, 8 (1) : 321-337. doi: 10.3934/ipi.2014.8.321

[7]

Murat Uzunca, Ayşe Sarıaydın-Filibelioǧlu. Adaptive discontinuous galerkin finite elements for advective Allen-Cahn equation. Numerical Algebra, Control & Optimization, 2021, 11 (2) : 269-281. doi: 10.3934/naco.2020025

[8]

Yves Dumont, Frederic Chiroleu. Vector control for the Chikungunya disease. Mathematical Biosciences & Engineering, 2010, 7 (2) : 313-345. doi: 10.3934/mbe.2010.7.313

[9]

Rabiaa Ouahabi, Nasr-Eddine Hamri. Design of new scheme adaptive generalized hybrid projective synchronization for two different chaotic systems with uncertain parameters. Discrete & Continuous Dynamical Systems - B, 2021, 26 (5) : 2361-2370. doi: 10.3934/dcdsb.2020182

[10]

Tobias Geiger, Daniel Wachsmuth, Gerd Wachsmuth. Optimal control of ODEs with state suprema. Mathematical Control & Related Fields, 2021  doi: 10.3934/mcrf.2021012

[11]

J. Frédéric Bonnans, Justina Gianatti, Francisco J. Silva. On the convergence of the Sakawa-Shindo algorithm in stochastic control. Mathematical Control & Related Fields, 2016, 6 (3) : 391-406. doi: 10.3934/mcrf.2016008

[12]

Diana Keller. Optimal control of a linear stochastic Schrödinger equation. Conference Publications, 2013, 2013 (special) : 437-446. doi: 10.3934/proc.2013.2013.437

[13]

Alberto Bressan, Ke Han, Franco Rampazzo. On the control of non holonomic systems by active constraints. Discrete & Continuous Dynamical Systems - A, 2013, 33 (8) : 3329-3353. doi: 10.3934/dcds.2013.33.3329

[14]

Lorenzo Freddi. Optimal control of the transmission rate in compartmental epidemics. Mathematical Control & Related Fields, 2021  doi: 10.3934/mcrf.2021007

[15]

Paula A. González-Parra, Sunmi Lee, Leticia Velázquez, Carlos Castillo-Chavez. A note on the use of optimal control on a discrete time model of influenza dynamics. Mathematical Biosciences & Engineering, 2011, 8 (1) : 183-197. doi: 10.3934/mbe.2011.8.183

[16]

Guirong Jiang, Qishao Lu. The dynamics of a Prey-Predator model with impulsive state feedback control. Discrete & Continuous Dynamical Systems - B, 2006, 6 (6) : 1301-1320. doi: 10.3934/dcdsb.2006.6.1301

[17]

A. K. Misra, Anupama Sharma, Jia Li. A mathematical model for control of vector borne diseases through media campaigns. Discrete & Continuous Dynamical Systems - B, 2013, 18 (7) : 1909-1927. doi: 10.3934/dcdsb.2013.18.1909

[18]

Luke Finlay, Vladimir Gaitsgory, Ivan Lebedev. Linear programming solutions of periodic optimization problems: approximation of the optimal control. Journal of Industrial & Management Optimization, 2007, 3 (2) : 399-413. doi: 10.3934/jimo.2007.3.399

[19]

Xiaohong Li, Mingxin Sun, Zhaohua Gong, Enmin Feng. Multistage optimal control for microbial fed-batch fermentation process. Journal of Industrial & Management Optimization, 2021  doi: 10.3934/jimo.2021040

[20]

John T. Betts, Stephen Campbell, Claire Digirolamo. Examination of solving optimal control problems with delays using GPOPS-Ⅱ. Numerical Algebra, Control & Optimization, 2021, 11 (2) : 283-305. doi: 10.3934/naco.2020026

2019 Impact Factor: 1.233

Metrics

  • PDF downloads (91)
  • HTML views (545)
  • Cited by (1)

Other articles
by authors

[Back to Top]