Advertisement

International Journal of Automotive Technology

, Volume 19, Issue 4, pp 727–742 | Cite as

Design of Regenerative Anti-Lock Braking System Controller for 4 In-Wheel-Motor Drive Electric Vehicle with Road Surface Estimation

  • Andrei Aksjonov
  • Valery Vodovozov
  • Klaus Augsburg
  • Eduard Petlenkov
Article

Abstract

This paper presents a regenerative anti-lock braking system control method with road detection capability. The aim of the proposed methodology is to improve electric vehicle safety and energy economy during braking maneuvers. Vehicle body longitudinal deceleration is used to estimate a road surface. Based on the estimation results, the controller generates an appropriate braking torque to keep an optimal for various road surfaces wheel slip and to regenerate for a given motor the maximum possible amount of energy during vehicle deceleration. A fuzzy logic controller is applied to fulfill the task. The control method is tested on a four in-wheel-motor drive sport utility electric vehicle model. The model is constructed and parametrized according to the specifications provided by the vehicle manufacturer. The simulation results conducted on different road surfaces, including dry, wet and icy, are introduced.

Key Words

Fuzzy control Anti-lock braking system Electric vehicles Vehicle dynamics Vehicle safety 

Nomenclature

Nomenclature

ω

wheel angular speed, rad/s

avx

vehicle longitudinal acceleration, m/s2

pb

braking pressure, bar

r

wheel radius, m

m

mass of the quarter vehicle, g

g

gravitational acceleration, m/s2

Td

driving torque, Nm

Tt

tire torque, Nm

Tb

total braking torque, Nm

TRB

regenerative brake torque, Nm

TFB

friction brake torque, Nm

Iw

inertia about the wheel rotational axis, gm2

kb

braking coefficient

Tj

phase torque of motor, Nm

Ij

phase current of motor, A

θ

rotor aligned position of motor, °

L

phase bulk inductance of motor, H

N

number of phases of motor

vvx

vehicle longitudinal velocity, m/s

vwx

wheel longitudinal velocity, m/s

λ

wheel slip, %

μ

tire-road friction coefficient

μ*

estimated road surface

Fx

longitudinal force, N

Fz

vertical force, N

Ec

net energy consumption, kJ

Pd

power spent on driving, W

Pb

power recovered via regenerative braking area, W

ηm

electric motor efficiency, %

s

distance, m

aaverage

average deceleration, m/s2

ABSIP

ABS operation index of performance

λaverage

average wheel slip, %

λe

actual and optimal wheel slip difference absolute value, %

Preg

regenerated power comparing to the total power required for deceleration, %

Subscripts

i

subscript for each wheel; i ∈ [front left (FL), front right (FR), rear left (RL), rear right (RR)]

j

switched reluctance motor phase number

Abbreviations

4WD

4 in-Wheel-motor Drive

ABS

Antilock Braking System

ASM

Automotive Simulation Models™

DOF

Degree of Freedom

ESP

Electronic Stability Program

EV

Electric Vehicle

FLC

Fuzzy Logic Controller

ICE

Internal Combustion Engine

MF

Membership Function

MISO

Multiple Input, Single Output

PID

Proportional-Integral-Derivative

SRM

Switched Reluctance Motor

SUV

Sport Utility Vehicle

UOD

Universe of Discourse

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Acosta, M. and Kanarachos, S. (2017). Tire lateral force estimation and grip potential identification using neural networks, extended Kalman filter, and recursive least square. Neural Computing and Applications, 1–21Google Scholar
  2. Aksjonov, A., Augsburg, K. and Vodovozov, V. (2016). Design and simulation of the robust ABS and ESP fuzzy logic controller on the complex braking maneuvers. Applied Sciences 6, 12, 382–390CrossRefGoogle Scholar
  3. Aly, A. A. (2010). Intelligent fuzzy control for antilock brake system with road-surfaces identifier. Proc. IEEE Int. Conf. Mechatronics and Automation (ICMA), Xi'an, China.Google Scholar
  4. Bansal, R. C. (2005). Electric Vehicles, in: Emadi, A. (Ed.) Handbook of Automotive Power Electronics and Motor Drives. Taylor & Francis. Boca Raton, Florida, USA, 55–96Google Scholar
  5. Castillo, J. J., Cabrera, J. A., Guerra, A. J. and Simon, A. A. (2016). Novel electrohydraulic brake system with tire-road friction estimation and continuous brake pressure control. IEEE Trans. Industrial Electronics 63, 3, 1863–1875CrossRefGoogle Scholar
  6. Chen, H., Yang, J., Du, Z. and Wang, W. (2010). Adhesion control method based on fuzzy logic control for fourwheel driven electric vehicle. SAE Int. J. Passenger Cars-Mechanical Systems 3, 1, 217–225CrossRefGoogle Scholar
  7. Cikanek, S. R. (1994). Fuzzy Logic Electric Vehicle Regenerative Antiskid Braking and Traction Control System. U.S. Patent 5,358,317Google Scholar
  8. Dhameja, S. (2002). Electric Vehicle Battery Systems. Butterworth-Heinemann. Woburn, Massachusetts, USA, 1–42CrossRefGoogle Scholar
  9. Doumiati, M., Charara, A., Victorino, A. and Lechner, D. (2013). Vehicle Dynamics Estimation Using Kalman Filtering: Experimental Validation. 2nd edn. ISTE Ltd and John Wiley & Sons, Inc. Hoboken, New Jersey, USA, 37–61Google Scholar
  10. Ehret, T. (2014). Electronic Stability Program (ESP), in: Reif, K. (Ed.), Brakes, Brake Control and Driver Assistance Systems: Function, Regulation and Components. Springer, Friedrichshafen, Germany, 102–123Google Scholar
  11. Ehsani, M., Gao, Y., Gay, S. E. and Emadi, A. (2005). Modern Electric, Hybrid Electric, and Fuel Cell Vehicles. CRC Press. Boca Raton, Florida, USA, 99–116, 204–232, 277–298Google Scholar
  12. El-Garhy, A. M., El-Sheikh, G. A. and El-Saify, M. H. (2013). Fuzzy life-extending control of anti-lock braking system. Ain Shams Engineering Journal 4, 4, 735–751CrossRefGoogle Scholar
  13. Guo, J., Jian, X. and Lin, G. (2014). Performance evaluation of an anti-lock braking system for electric vehicles with a fuzzy sliding mode controller. Energies 7, 10, 6459–6476CrossRefGoogle Scholar
  14. Han, K., Hwang, Y., Lee, E. and Choi, S. (2015). Robust estimation of maximum tire-road friction coefficient considering road surface irregularity. Int. J. Automotive Technology 17, 3, 415–425CrossRefGoogle Scholar
  15. Ivanov, V. G., Algin, V. B. and Shyrokau, B. N. (2006). Intelligent control for ABS application with identification of road and environmental properties. Int. J. Vehicle Autonomous Systems 4, 1, 44–67CrossRefGoogle Scholar
  16. Ivanov, V. (2015). A review of fuzzy methods in automotive engineering applications. European Transport Research Review 7, 29, 19–29Google Scholar
  17. Jianyao, H., Huawei, X., Zhiyuan, H., Linyi, H. and Qunxing, L. (2015). Study on braking force distribution based on fuzzy control algorithm. Proc. IEEE Advanced Information Technology, Electronic and Automation Control Conf. (IAEAC), Chongqing, China.Google Scholar
  18. Khatun, P., Bingham, C. M., Schofield, N. and Mellor, P. H. (2003). Application of fuzzy control algorithm for electric vehicle antilock braking/traction control systems. IEEE Trans. Vehicular Technology 52, 5, 1356–1364CrossRefGoogle Scholar
  19. Kiencke, U. and Nielsen, L. (2005). Automotive Control Systems: For Engine, Driveline, and Vehicle. 2nd edn. Springer-Verlag Berlin Heidelberg, Berlin, Germany, 301–350CrossRefGoogle Scholar
  20. Kim, D., Hwang, S. and Kim, H. (2008). Vehicle stability enhancement of four-wheel-drive hybrid electric vehicle using rear motor control. IEEE Trans. Vehicular Technology 57, 2, 727–735MathSciNetCrossRefGoogle Scholar
  21. Kim, D.-H., Kim, J.-M., Hwang, S.-H. and Kim, H.-S. (2007). Optimal brake torque distribution for a fourwheel-drive hybrid electric vehicle stability enhancement. Proc. Institution of Mechanical Engineers, Part D: J. Automobile Engineering 221, 11, 1357–1366Google Scholar
  22. Koch-Dücker, H.-J. and Papert, U. (2014). Antilock Braking System (ABS), in: Reif, K. (Ed.), Brakes, Brake Control and Driver Assistance Systems: Function, Regulation and Components. Springer, Friedrichshafen, Germany, 74–93Google Scholar
  23. Layne, J. R., Passino, K. M. and Yurkovich, S. (1993). Fuzzy learning control for antiskid braking systems. IEEE Trans. Control Systems Technology 1, 2, 122–129CrossRefGoogle Scholar
  24. Li, X., Xu, L., Hua, J., Li, J. and Ouyang, M. (2008). Regenerative braking control strategy for fuel cell hybrid vehicle using fuzzy logic. Proc. IEEE Int. Conf. Electrical Machines and Systems, Wuhan, China.Google Scholar
  25. Long, B., Lim, S. T., Ryu, J. H. and Chong, K. T. (2014). Energy-regenerative braking control of electric vehicles using three-phase brushless direct-current motors. Energies 7, 1, 99–114CrossRefGoogle Scholar
  26. Miller, J. M. (2005). Hybrid Electric Vehicles, in: Emadi, A. (Ed.), Handbook of Automotive Power Electronics and Motor Drives. Taylor & Francis. Boca Raton, Florida, USA, 21–36Google Scholar
  27. Negnevitsky, M. (2005). Artificial Intelligence: A Guide to Intelligent Systems. 2nd edn. Addison-Wesley. Harlow, UK, 87–131Google Scholar
  28. Nian, X., Peng, F. and Zhang, H. (2014). Regenerative braking system of electric vehicle driven by brushless DC motor. IEEE Trans. Industrial Electronics 61, 10, 5798–5808CrossRefGoogle Scholar
  29. Pacejka, H. B. (2006). Tyre and Vehicle Dynamics. 2nd edn. Butterworth-Heinemann. Oxford, UK, 156–215MATHGoogle Scholar
  30. Passino, K. M. and Yurkovich, S. (1998). Fuzzy Control. Addison-Wesley. Menlo Park, California, USA, 1–22, 23–118, 187–232Google Scholar
  31. Paterson, J. and Ramsay, M. (1993). Electric vehicle braking by fuzzy logic control. Proc. Conf. Record of the IEEE Industry Applications Society Annual Meeting, Toronto, Canada.Google Scholar
  32. Paul, D., Velenis, E., Cao, D. and Dobo, T. (2016). Optimal μ-estimation based regenerative braking strategy for an AWD HEV. IEEE Trans. Transportation Electrification 3, 1, 249–258CrossRefGoogle Scholar
  33. Peng, D., Zhang, J. and Yin, C. (2006). Regenerative braking control system improvement for parallel hybrid electric vehicle. Proc. IEEE Int. Technology and Innovation Conf., Hangzhou, China.Google Scholar
  34. Pusca, R., Ait-Amirat, Y., Berthon, A. and Kauffmann, J. M. (2004). Fuzzy-logic-based control applied to a hybrid electric vehicle with four separate wheel drives. IEE Proc.-Control Theory and Applications 151, 1, 73–81CrossRefGoogle Scholar
  35. Rajamani, R. (2012). Vehicle Dynamics and Control. 2nd edn. Springer. New York, USA, 87–112CrossRefMATHGoogle Scholar
  36. Rath, J. J., Veluvolu, K. C. and Defoort, M. (2015). Simultaneous estimation of road profile and tire road friction for automotive vehicle. IEEE Trans. Vehicular Technology 64, 10, 4461–4471CrossRefGoogle Scholar
  37. Reznik, L. (1997). Fuzzy Controllers. Butterworth-Heinemann. Oxford, UK, 1–18Google Scholar
  38. Savitski, D., Augsburg, K. and Ivanov, V. (2014). Enhancement of energy efficiency, vehicle safety and ride comfort for all-wheel drive full electric vehicles. Proc. Eurobrake, Lille, France.Google Scholar
  39. Savitski, D., Ivanov, V., Shyrokau, B., Pütz, T., De Smet, J. and Theunissen, J. (2016). Experimental investigations on continuous regenerative anti-lock braking system of full electric vehicle. Int. J. Automotive Technology 17, 2, 327–338CrossRefGoogle Scholar
  40. Sharkawy, A. B. (2006). Genetic fuzzy self-tuning PID controllers for antilock braking systems. Alexandria Engineering Journal 45, 6, 657–673Google Scholar
  41. Tahami, F., Kazemi, R. and Farhanghi, S. (2003). A novel driver assist stability system for all-wheel-drive electric vehicles. IEEE Trans. Vehicular Technology 52, 3, 683–692CrossRefGoogle Scholar
  42. Xia, X., Xiong, L., Sun, K. and Yu, Z. P. (2016). Estimation of maximum road friction coefficient based on Lyapunov method. Int. J. Automotive Technology 17, 6, 991–1002CrossRefGoogle Scholar
  43. Xiong, L. and Yu, Z. (2011). Vehicle Dynamics Control of 4 In-Wheel-Motor Drived Electric Vehicle, in: Soylu, S., (Ed.) Electric Vehicles–Modelling and Simulation. InTech. Rijeka, Croatia, 67–106CrossRefGoogle Scholar
  44. Xu, G., Li, W., Xu, K. and Song, Z. (2011). An intelligent regenerative braking strategy for electric vehicles. Energies 4, 9, 1461–1477CrossRefGoogle Scholar
  45. Ye, M., Jiao, S. and Cao, B. (2010). Energy recovery for the main and auxiliary sources of electric vehicles. Energies 3, 10, 1673–1690CrossRefGoogle Scholar
  46. Zabler, E. (2014). Sensors for Brake Control, in: Reif, K. (Ed.), Brakes, Brake Control and Driver Assistance Systems: Function, Regulation and Components. Springer. Friedrichshafen, Germany, 142–153Google Scholar
  47. Zhang, J., Song, B., Cui, S. and Ren, D. (2009). Fuzzy logic approach to regenerative braking system. Proc. IEEE Int. Conf. Intelligent Human-Machine Systems and Cybernetics, Hangzhou, Zhejiang, China.Google Scholar
  48. Zhang, X., Wang, Y., Liu, G. and Yuan, X. (2016). Robust regenerative charging control based on T-S fuzzy sliding-mode approach for advanced electric vehicle. IEEE Trans. Transportation Electrification 2, 1, 52–65CrossRefGoogle Scholar

Copyright information

© The Korean Society of Automotive Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Andrei Aksjonov
    • 1
  • Valery Vodovozov
    • 2
  • Klaus Augsburg
    • 3
  • Eduard Petlenkov
    • 4
  1. 1.SKODA AUTO a.s.Mlada BoleslavCzech Republic
  2. 2.Department of Electrical Power Engineering and Mechatronics, School of EngineeringTallinn University of TechnologyTallinnEstonia
  3. 3.Automotive Engineering Group, Department of Mechanical EngineeringTechnische Universität IlmenauIlmenauGermany
  4. 4.Department of Computer Systems, School of Information TechnologiesTallinn University of TechnologyTallinnEstonia

Personalised recommendations