Use of a driving simulator to develop a brake-by-wire system designed for electric vehicles and car stability controls

  • M. Montani
  • R. Capitani
  • M. Fainello
  • Claudio AnnicchiaricoEmail author
Conference paper
Part of the Proceedings book series (PROCEE)


Vehicles with electrical propulsion and energy recovery need specific brake systems able to guarantee the correct integration of electrical energy recovery and high braking capability. The use of advanced controls with brake by wire systems allows to integrate such functionalities leaving the correct feedback to brake pedal and the possibility to enhance vehicle dynamics. We present a study done with simulation and simulators with human in the loop to show the potentiality of specific controls to enhance vehicle dynamics and their integration with electric motor controls. In detail, we will show how a physical brake-by-wire unit can be installed on a simulator environment obtaining an enhancement in the driving realism and enabling the virtual development of advanced vehicle functions. The same testing architecture can be used also for fault-injection activities and precise pedal feeling calibration. The case-study test bench has been installed on a static driving simulator equipped with a concurrent real-time machine, a vigrade car real time model, the complete vehicle network and the full steering system.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1. van Zanten, A. T. & GmbH, R. B. Evolution of Electronic Control Systems for Improving the Vehicle Dynamic Behavior. 9Google Scholar
  2. 2. Novi, T., Capitani, R. & Annicchiarico, C. An integrated ANN-UKF vehicle sideslip angle estimation based on IMU measurements. 13Google Scholar
  3. 3. Du, X., Sun, H., Qian, K., Li, Y. & Lu, L. A prediction model for vehicle sideslip angle based on neural network. in 2010 2nd IEEE International Conference on Information and Financial Engineering 451–455 (IEEE, 2010).Google Scholar
  4. 4. Melzi, S. & Sabbioni, E. On the vehicle sideslip angle estimation through neural networks: Numerical and experimental results. Mechanical Systems and Signal Processing 25, 2005–2019 (2011).Google Scholar
  5. 5. Melzi, S., Sabbioni, E., Concas, A. & Pesce, M. Vehicle Sideslip Angle Estimation Through Neural Networks: Application to Experimental Data. in Volume 2: Automotive Systems, Bioengineering and Biomedical Technology, Fluids Engineering, Maintenance Engineering and Non-Destructive Evaluation, and Nanotechnology 2006, 219–224 (ASME, 2006).Google Scholar
  6. 6. Hodgson, G. & Best, M. C. A Parameter Identifying a Kalman Filter Observer for Vehicle Handling Dynamics. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 220, 1063–1072 (2006).Google Scholar
  7. 7. Vargas-Meléndez, L., Boada, B., Boada, M., Gauchía, A. & Díaz, V. A Sensor Fusion Method Based on an Integrated Neural Network and Kalman Filter for Vehicle Roll Angle Estimation. Sensors 16, 1400 (2016).Google Scholar
  8. 8. Li, L., Jia, G., Ran, X., Song, J. & Wu, K. A variable structure extended Kalman filter for vehicle sideslip angle estimation on a low friction road. Vehicle System Dynamics 52, 280–308 (2014).Google Scholar
  9. 9. Boada, B. L., Boada, M. J. L. & Diaz, V. Vehicle sideslip angle measurement based on sensor data fusion using an integrated ANFIS and an Unscented Kalman Filter algorithm. Mechanical Systems and Signal Processing 72–73, 832–845 (2016).Google Scholar
  10. 10. Jalali, M., Khosravani, S., Khajepour, A., Chen, S. & Litkouhi, B. Model predictive control of vehicle stability using coordinated active steering and differential brakes. Mechatronics 48, 30–41 (2017).Google Scholar
  11. 11. Falcone, P., Eric Tseng, H., Borrelli, F., Asgari, J. & Hrovat, D. MPC-based yaw and lateral stabilisation via active front steering and braking. Vehicle System Dynamics 46, 611–628 (2008).Google Scholar
  12. 12. Falcone, P., Borrelli, F., Asgari, J., Tseng, H. E. & Hrovat, D. Predictive Active Steering Control for Autonomous Vehicle Systems. IEEE Transactions on Control Systems Technology 15, 566–580 (2007).Google Scholar
  13. 13. Barbarisi, O., Palmieri, G., Scala, S. & Glielmo, L. LTV-MPC for Yaw Rate Control and Side Slip Control with Dynamically Constrained Differential Braking. European Journal of Control 15, 468–479 (2009).Google Scholar
  14. 14. Ataei, M., Khajepour, A. & Jeon, S. Model Predictive Control for integrated lateral stability, traction/braking control, and rollover prevention of electric vehicles. Vehicle System Dynamics 1–25 (2019).Google Scholar
  15. 15. Zhu, B., Piao, Q., Zhao, J. & Guo, L. Integrated chassis control for vehicle rollover prevention with neural network time-to-rollover warning metrics. Advances in Mechanical Engineering 8, 168781401663267 (2016).Google Scholar
  16. 16. Ohno, H., Suzuki, T., Aoki, K., Takahasi, A. & Sugimoto, G. Neural network control for automatic braking control system. Neural Networks 7, 1303–1312 (1994).Google Scholar
  17. 17. Ćirović, V., Aleksendrić, D. & Smiljanić, D. Longitudinal wheel slip control using dynamic neural networks. Mechatronics 23, 135–146 (2013).Google Scholar
  18. 18. Velenis, E., Katzourakis, D., Frazzoli, E., Tsiotras, P. & Happee, R. Steady-state drifting stabilization of RWD vehicles. Control Engineering Practice 19, 1363–1376 (2011).Google Scholar
  19. 19. Dal Poggetto, V. F. & Serpa, A. L. Vehicle rollover avoidance by application of gain-scheduled LQR controllers using state observers. Vehicle System Dynamics 54, 191–209 (2016).Google Scholar
  20. 20. Li, L. et al. A novel vehicle dynamics stability control algorithm based on the hierarchical strategy with constrain of nonlinear tyre forces. Vehicle System Dynamics 53, 1093–1116 (2015).Google Scholar
  21. 21. Jagga, D., Lv, M. & Baldi, S. Hybrid Adaptive Chassis Control for Vehicle Lateral Stability in the Presence of Uncertainty. in 2018 26th Mediterranean Conference on Control and Automation (MED) 1–6 (IEEE, 2018).Google Scholar
  22. 22. Alcala, E. et al. Comparison of two non-linear model-based control strategies for autonomous vehicles. in 2016 24th Mediterranean Conference on Control and Automation (MED) 846–851 (IEEE, 2016).Google Scholar
  23. 23. Heinzl, P., Lugner, P. & Plöchl, M. Stability Control of a Passenger Car by Combined Additional Steering and Unilateral Braking. Vehicle System Dynamics 37, 221–233 (2002).Google Scholar
  24. 24. You, S.-S. & Jeong, S.-K. Controller design and analysis for automatic steering of passenger cars. Mechatronics 12, 427–446 (2002).Google Scholar
  25. 25. Weiss, Y., Allerhand, L. I. & Arogeti, S. Yaw stability control for a rear doubledriven electric vehicle using LPV-H∞ methods. Science China Information Sciences 61, (2018).Google Scholar
  26. 26. Adaptive Vehicle Stability Control by means of Tire Slip-angle.pdf.Google Scholar
  27. 27. Zhou, H. & Liu, Z. Vehicle Yaw Stability-Control System Design Based on Sliding Mode and Backstepping Control Approach. IEEE Transactions on Vehicular Technology 59, 3674–3678 (2010).Google Scholar
  28. 28. Mirzaei, M. A new strategy for minimum usage of external yaw moment in vehicle dynamic control system. Transportation Research Part C: Emerging Technologies 18, 213–224 (2010).Google Scholar
  29. 29. Regulation No 13-H of the Economic Commission for Europe of the United Nations (UN/ECE) — Uniform provisions concerning the approval of passenger cars with regard to braking [2015/].Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2020

Authors and Affiliations

  • M. Montani
    • 1
  • R. Capitani
    • 1
  • M. Fainello
    • 2
  • Claudio Annicchiarico
    • 1
    Email author
  1. 1.Meccanica 42 SrlSesto Fiorentino (FI)Italy
  2. 2.Danisi EngineeringModenaItaly

Personalised recommendations