Direct-yaw-moment control of four-wheel-drive electrical vehicle based on lateral tyre–road forces and sideslip angle observer

Direct-yaw-moment control of four-wheel-drive electrical vehicle based on lateral tyre–road forces and sideslip angle observer

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Intelligent Transport Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Considering some technical and economic reasons, it is not easy to directly measure the vehicular moving parameters (such as tyre–road forces and vehicle sideslip angle) in electronic stability programme systems. This study proposes a method to estimate lateral tyre–road forces and vehicle sideslip angle by utilising real-time measurements, based on the unscented Kalman filter. Direct-yaw-moment control can effectively guarantee the stability of vehicle while steering at a high speed. This study proposed a hierarchical control strategy as the solution to the problem of the yaw-moment distribution. The overloop controller is designed to calculate the desired yaw moment based on the estimated lateral tyre–road forces and sideslip angle, using the sliding mode control. The servo-loop controller is designed to optimise the torque distribution using weighted-least-squares method based on the desired yaw moment obtained from the overloop controller. MATLAB/Simulink with Carsim is applied for the simulation experiment, the results demonstrate the effectiveness of the lateral tyre–road force and sideslip angle observer, and the optimal allocation controller could improve the handling stability and energy efficiency dramatically.


    1. 1)
      • 1. Huang, Y., Liang, W., Chen, Y.: ‘Estimation and analysis of vehicle lateral stability region’. Proc. American Control Conf., Seattle, WA, USA, May 2017, pp. 43034308.
    2. 2)
      • 2. Wang, F., Chen, Y.: ‘Vehicle safety enhancement through a novel active yaw stabilizer’. American Control Conf. (ACC), Seattle, WA, USA, May 2017, pp. 55565561.
    3. 3)
      • 3. Wang, R., Chen, Y., Feng, D., et al: ‘Development and performance characterization of an electric ground vehicle with independently actuated in-wheel motors’, J. Power Sources, 2011, 196, pp. 39623971.
    4. 4)
      • 4. De Novellis, L., Sorniotti, A., Gruber, P.: ‘Wheel torque distribution criteria for electric vehicles with torque-vectoring differentials’, IEEE Trans. Veh. Technol., 2014, 63, (4), pp. 15931602.
    5. 5)
      • 5. Saikia, A., Mahanta, C.: ‘Vehicle stability enhancement using sliding mode based active front steering and direct yaw moment control’ (Indian Institute of Technology, Guwahati, India, 2017), pp. 378384.
    6. 6)
      • 6. Ding, S., Liu, L., Zheng, W.X.: ‘Sliding mode direct yaw-moment control design for in-wheel electric vehicles’, IEEE Trans. Ind. Electron., 2017, 64, (8), pp. 67526762.
    7. 7)
      • 7. Zhang, J., Liao, W., Chen, L., et al: ‘Research on motor braking-based DYC strategy for distributed electric vehicle’, AIP Publishing Conf. Proc., 2017, 1864, (1), DOI: 10.1063/1.4992895.
    8. 8)
      • 8. Raksincharoensak, P., Lertsilpachalern, V., Lidberg, M., et al: ‘Robust vehicle handling dynamics of light-weight vehicles against variation in loading conditions’. IEEE Int. Conf. Vehicular Electronics and Safety (ICVES), Vienna, Austria, June 2017, pp. 202207.
    9. 9)
      • 9. Xiong, L., Teng, G.W., Yu, Z.P., et al: ‘Novel stability control strategy for distributed drive electric vehicle based on driver operation intention’, Int. J. Automot. Technol., 2016, 17, (4), pp. 651663.
    10. 10)
      • 10. Sun, J., Ding, S., Zhang, S., et al: ‘Nonsmooth stabilization for distributed electric vehicle based on direct yaw-moment control’. 35th Chinese Control Conf. (CCC), Chengdu, China, July 2016, pp. 88508855.
    11. 11)
      • 11. Chen, J., Song, J., Li, L., et al: ‘A novel pre-control method of vehicle dynamics stability based on critical stable velocity during transient steering maneuvering’, Chin. J. Mech. Eng., 2016, 29, (3), pp. 475485.
    12. 12)
      • 12. Zhang, S., Ding, S., Jiang, H.: ‘Direct yaw-moment control of in-wheel electric vehicle by sliding mode technique’. IEEE Int. Conf. Industrial Technology (ICIT), Taipei, Taiwan, March 2016, pp. 18441849.
    13. 13)
      • 13. Fu, C., Hu, M.: ‘Adaptive sliding mode-based direct yaw moment control for electric vehicles’. Int. Conf. Control, Automation and Information Sciences, Changshu, China, October 2015, pp. 470474.
    14. 14)
      • 14. Chen, Y., Wang, J.: ‘Design and experimental evaluations on energy efficient control allocation methods for over actuated electric vehicle: longitudinal motion case’, IEEE/ASME Trans. Mechatronics, 2014, 19, (2), pp. 538548.
    15. 15)
      • 15. Guo, J., Wang, J.: ‘Lateral stability control of distributed drive electric vehicle based on fuzzy sliding mode control’. Information Technology and Mechatronics Engineering Conf. (ITOEC), Chongqing, China, July 2017, pp. 675680.
    16. 16)
      • 16. Chen, Y., Wang, J.: ‘Fast and global optimal energy-efficient control allocation with applications to over-actuated electric ground vehicles’, IEEE Trans. Control Syst. Technol., 2012, 20, (5), pp. 12021211.
    17. 17)
      • 17. Wang, R., Jing, H., Yan, F., et al: ‘Optimization and finite-frequency H∞ control of active suspensions in in-wheel motor driven electric ground vehicles’, J. Franklin Inst., 2015, 352, (2), pp. 468484.
    18. 18)
      • 18. Ivanov, V., Savitski, D., Augsburg, K., et al: ‘Electric vehicles with individually controlled on-board motors: revisiting the ABS design’. 15th IEEE Int. Conf. Data Mining (ICDM 2015), Atlantic City, NJ, USA, November 2015, pp. 323328.
    19. 19)
      • 19. Ivanov, V., Savitski, D., Augsburg, K., et al: ‘Wheel slip control for all-wheel drive electric vehicle with compensation of road disturbances’, J. Terra Mech., 2015, 61, pp. 110.
    20. 20)
      • 20. Jafari, M., Mirzaei, M., Mirzaeinejad, H.: ‘Optimal nonlinear control of vehicle braking torques to generate practical stabilizing yaw moments’, Int. J. Automot. Mech. Eng., 2015, 11, pp. 26392653.
    21. 21)
      • 21. Lian, Y.F., Wang, X.Y., Zhao, Y., et al: ‘Direct yaw-moment robust control for electric vehicles based on simplified lateral tire dynamic models and vehicle model’, IFAC-PapersOnLine, 2015, 48, (28), pp. 3338.
    22. 22)
      • 22. Shi, S., Lupker, H., Bremmer, P., et al: ‘Estimation of vehicle side slip angle based on fuzzy logic’, Automot. Eng., 2005, 27, (4), pp. 426430.
    23. 23)
      • 23. Rajamani, R.: ‘Vehicle dynamics and control’ (Springer-Verlag, New York, 2012, 2nd edn.).
    24. 24)
      • 24. Jin, X., Yin, G.: ‘Estimation of lateral tyre–road forces and sideslip angle for electric vehicles using interacting multiple model filter approach’, J. Franklin Inst., 2015, 352, (2), pp. 686707.
    25. 25)
      • 25. Liu Y, H., Li, T., Yang, Y.Y., et al: ‘Estimation of tire-road friction coefficient based on combined APF-IEKF and iteration algorithm’, Mech. Syst. Signal Process., 2017, 88, pp. 2535.
    26. 26)
      • 26. Jung, H., Choi, S.B.: ‘Real-time individual tire force estimation for an all-wheel drive vehicle’, IEEE Trans. Veh. Technol., 2017, 67, (99), pp. 11.
    27. 27)
      • 27. Yoon, J.H., Li, S.E., Ahn, C.: ‘Estimation of vehicle sideslip angle and tire-road friction coefficient based on magnetometer with GPS’, Int. J. Automot. Technol., 2016, 17, (3), pp. 427435.
    28. 28)
      • 28. Zhang, H., Huang, X., Wang, J., et al: ‘Robust energy-to-peak sideslip angle estimation with applications to ground vehicles’, Mechatronics, 2015, 30, pp. 338347.
    29. 29)
      • 29. Jin, X., Yin, G.: ‘Estimation of lateral tire–road forces and sideslip angle for electric vehicles using interacting multiple model filter approach’, J. Franklin Inst., 2015, 352, (2), pp. 686707.
    30. 30)
      • 30. Lian, Y.F., Zhao, Y., Hu, L.L., et al: ‘Cornering stiffness and sideslip angle estimation based on simplified lateral dynamic models for four-in-wheel-motor-driven electric vehicles with lateral tire force information’, Int. J. Automot. Technol., 2015, 16, (4), pp. 669683.
    31. 31)
      • 31. Jin, X., Yin, G., Li, Y., et al: ‘Stabilizing vehicle lateral dynamics with considerations of state delay of AFS for electric vehicles via robust gain-scheduling control’, Asian J. Control, 2016, 18, (1), pp. 8997.
    32. 32)
      • 32. Pacejka, H.B.: ‘Tyre and vehicle dynamics’ (Butterworth-Heinemann, Oxford, UK/Waltham, MA, 2012, 3rd edn.).
    33. 33)
      • 33. Yang, B., Ji, H.: ‘Multi-passive-sensor fusion tracking based on unscented Kalman filter’, Control Decis., 2008, 23, (4), pp. 460463.
    34. 34)
      • 34. Simon, D.: ‘Optimal state estimation-Kalman, H∞ and nonlinear approaches’ (John Wiley & Sons, Inc., Cleveland, USA, 2006, 1st edn.).
    35. 35)
      • 35. Kang, J., Heo, H.: ‘Control allocation based optimal torque vectoring for 4WD electric vehicle’, SAE Technical Paper, 2012, doi: 10.4271/2012-01-0246.
    36. 36)
      • 36. Schofield, B., Hagglund, T.: ‘Optimal control allocation in vehicle dynamics control for rollover mitigation’. American Control Conf., Seattle, Washington, USA, June 2008, pp. 32313236.
    37. 37)
      • 37. Harkegard, O.: ‘Efficient active set algorithms for solving constrained least squares problems in aircraft control allocation’. 41st IEEE Conf. Decision and Control, Las Vegas, NV, December 2002, vol. 2, no. 2, pp. 12951300.

Related content

This is a required field
Please enter a valid email address