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JRM Vol.27 No.3 pp. 235-243
doi: 10.20965/jrm.2015.p0235
(2015)

Paper:

Simulated and Experimental Comparisons of Slip and Torque Control Strategies for Regenerative Braking in Instances of Parametric Uncertainties

Maxime Boisvert, Philippe Micheau, and Didier Mammosser

Centre des Technologies Avancées, Université de Sherbrooke
1277 Du Lierre, Sherbrooke, Québec J1E0K4, Canada

Received:
April 15, 2014
Accepted:
February 18, 2015
Published:
June 20, 2015
Keywords:
electric vehicles, regenerative braking, slip control, optimal strategies, mechatronics
Abstract

Slip efficiency map & control law

A three-wheel hybrid recreational vehicle was studied for the purpose of regenerative braking control. In order to optimize the amount of energy recovered from electrical braking, most of the existing literature presents optimal methods which consist in defining the optimal braking torque as a function of vehicle speed. The originality of the present study is to propose a new strategy based on the control of rear wheel slip. A simulator based on MATLAB/Simulink and validated with experimental measurements compared the two strategies and their sensitivities to variations in mass, slope and road conditions. Numerical simulations and experimental tests show that regenerative braking based on a slip controller was less affected by the majority of the parametric changes. Moreover, since the slip was limited, the longitudinal stability of the vehicle was thereby improved. It thus becomes possible to ensure optimal energy recovery and vehicle stability even in instances of parametric uncertainties.

Cite this article as:
M. Boisvert, P. Micheau, and D. Mammosser, “Simulated and Experimental Comparisons of Slip and Torque Control Strategies for Regenerative Braking in Instances of Parametric Uncertainties,” J. Robot. Mechatron., Vol.27, No.3, pp. 235-243, 2015.
Data files:
References
  1. [1] D. Mammosser, P. Micheau, and M. Boisvert, “Simulation of regenerative braking strategies on slippery roads for a 3-wheel hybrid vehicle,” EEVC Brussels, 2012.
  2. [2] M. Boisvert, D. Mammosser, and P. Micheau, “Comparison of two strategies for optimal regenerative braking, with their sensitivity to variations in mass, slope and road condition,” 7th IFAC Symposium on Advances in Automotive Control, 2013, Vol.7, Part 1, pp. 626-630, 2013.
  3. [3] A. A. Mukhitdinov, S. K. Ruzimov, and S. L. Eshkabilov, “Optimal Control Strategies for CVT of the HEV during regenerative process. Electric and Hybrid Vehicles,” ICEHV 06, IEEE Conf., 2006.
  4. [4] F. Wang and B. Zhuo, “Regenerative braking strategy for hybrid electric vehicles based on regenerative torque optimization control,” J. of Automobile Engineering, pp. 499-513, 2008.
  5. [5] H. Yeo and H. Kim, “Regenerative braking algorithm for a hybrid electric vehicle with CVT ratio control,” Institution of Mechanical Engineers, Part D: J. of Automobile Engineering, Vol.220, pp. 1589-1600, 2006.
  6. [6] G. Rill, “Vehicle Dynamics,” University of Applied Sciences in Regensburg, Germany, 2006.
  7. [7] M. Burckhardt, “Radschlupf Regelsysteme,” Vogelverlag, 1993.
  8. [8] D. Hu and H. Zong, “Research on Information Fusion Algorithm for Vehicle Speed information and Road Adhesion Property Estimation,” Int. Conf. on Mechatronics and Automation, pp. 3229-3234, 2009.
  9. [9] T. D. Gillespie, “Fundamentals of Vehicle Dynamics,” Society of Automotive Engineers, 1992.
  10. [10] U. Kiencke and L. Nielsen, “Automotive Control System,” Springer-Verlag, 2005.
  11. [11] N. Denis, M. Dubois, and K. A. Gil, “Model development and performance estimation of a three-wheel plug-in hybrid electric vehicle,” EV2012VÉ, 2012.
  12. [12] J.-J. E. Slotine and W. Li, “Applied Nonlinear Control,” Prendice Hall, 1991.
  13. [13] J.-P. Brossard, “Dynamique du Freinage,” Presse polytechniques et universitaires romandes, 2009.

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Last updated on Nov. 15, 2018