Advertisement

International Journal of Automotive Technology

, Volume 19, Issue 6, pp 1081–1089 | Cite as

Optimization of Gear Ratio of In-Wheel Motor Vehicle Considering Probabilistic Driver Model

  • Kihan Kwon
  • Minsik Seo
  • Seungjae MinEmail author
Article
  • 19 Downloads

Abstract

A reduction gear of an in-wheel motor vehicle is mounted between a traction motor and wheel, to increase the wheel torque and decrease the rotational speed. To improve the energy efficiency of a vehicle, the determination of the optimal gear ratio is an important factor in the design of the reduction gear. This paper presents an optimization procedure to obtain the optimal gear ratio of an in-wheel motor vehicle that minimizes the electric energy consumption. Using a model-based design, a dynamic simulation model of a vehicle was developed for an analysis of the energy efficiency. Owing to a variation in energy efficiency across drivers, a probabilistic driver model that includes driver characteristics is employed. To reduce excessive calculations, a surrogate model was constructed for the optimization. The optimal gear ratio for saving energy was obtained, and the usefulness of the proposed optimization procedure was shown through a comparison of the results of the optimal gear ratio and the energy efficiency achieved between deterministic and probabilistic approaches.

Key Words

In-wheel motor vehicle Gear ratio Energy efficiency Probabilistic driver model Surrogate model 

Nomenclature

Jeq

equivalent inertia of vehicle at wheel, kgm2

ωwhl

rotational speed of wheel, rad/s

Tmot

motor torque, Nm

Tres

resistance torque, Nm

r

gear ratio

mbody

mass of body, kg

Jwhl

inertia of wheels, kgm2

Jmot

inertia of motors, kgm2

Rtire

radius of tire, m

Vveh

velocity of vehicle, m/s

μr

coefficient of rolling resistance

g

gravity acceleration, m/s2

Cd

coefficient of air resistance

Afr

frontal area, m2

ρair

air density, kg/m3

Tmax

maximum motor torque, Nm

ωmot

motor speed, rad/s

Tregen

maximum regenerative braking torque, Nm

Cbrk

capacity of braking torque, Nm

Vbat

terminal voltage of battery, V

VOCV

open circuit voltage, V

Rin

internal resistance, ohm

Ibat

load current, A

SOCini

initial SOC, %

Cnom

nominal capacity of battery, As

η

motor efficiency

K

proportional gain

τr

reaction time delay, s

τn

neuromuscular lag, s V

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Changyu, S., Lixia, W. and Qian, L. (2007). Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method. J. Materials Processing Technology 183, 2–3, 412−418.Google Scholar
  2. Day, T. D. and Metz, L. D. (2000). The simulation of driver inputs using a vehicle driver model. SAE Paper No. 2000–01-1313.Google Scholar
  3. De Vlieger, I. (1997). On-board emission and fuel consumption measurement campaign on petrol-driven passenger cars. Atmospheric Environment 31, 22, 3753–3761.CrossRefGoogle Scholar
  4. Di Nicola, F., Sorniotti, A., Holdstock, T., Viotto, F. and Bertolotto, S. (2012). Optimization of a multiple-speed transmission for downsizing the motor of a fully electric vehicle. SAE Int. J. Alternative Powertrains 1, 1, 134–143.CrossRefGoogle Scholar
  5. Gao, B., Liang, Q., Xiang, Y., Guo, L. and Chen, H. (2015). Gear ratio optimization and shift control of 2-speed IAMT in electric vehicle. Mechanical Systems and Signal Processing, 50–51, 615−631.Google Scholar
  6. Gorissen, D., Couckuty, I., Demeester, P., Dhaene, T. and Crombecq, K. (2010). A surrogate modeling and adaptive sampling toolbox for computer based design. J. Machine Learning Research, 11, 2051–2055.Google Scholar
  7. He, H., Xiong, R. and Fan, J. (2011). Evaluation of lithiumion battery equivalent circuit models for state of charge estimation by an experimental approach. Energies 4, 4, 582–598.CrossRefGoogle Scholar
  8. Kannan, G. R., Balasubramanian, K. R. and Anand, R. (2013). Artificial neural network approach to study the effect of injection pressure and timing on diesel engine performance fueled with biodiesel. Int. J. Automotive Technology 14, 4, 507–519.CrossRefGoogle Scholar
  9. Kim, D., Shin, K., Kim, Y. and Cheon, J. (2010). Integrated design of in-wheel motor system on rear wheels for small electric vehicle. World Electric Vehicle Journal 4, 3, 597–602.CrossRefGoogle Scholar
  10. Kim, S. C., Kim, W. and Kim, M. S. (2013). Cooling performance of 25 kw in-wheel motor for electric vehicles. Int. J. Automotive Technology 14, 4, 559–567.CrossRefGoogle Scholar
  11. Ko, S., Song, C. and Kim, H. (2016). Cooperative control of the motor and the electric booster brake to improve the stability of an in-wheel electric vehicle. Int. J. Automotive Technology 17, 3, 447–456.CrossRefGoogle Scholar
  12. LeBlanc, D. J., Sivak, M. and Bogard, S. (2010). Using Naturalistic Driving Data to Assess Variations in Fuel Efficiency among Individual Drivers. The University of Michigan Transportation Research Institute. UMTRI-2010-34.Google Scholar
  13. Lerspalungsanti, S., Albers, A., Ott, S. and Duser, T. (2015). Human ride comfort prediction of drive train using modeling method based on artificial neural networks. Int. J. Automotive Technology 16, 1, 153–166.CrossRefGoogle Scholar
  14. Lixin, S. (2009). Electric vehicle development: The past, present & future. Proc. IEEE 3rd Int. Conf. Power Electronics Systems and Applications (PESA), Hong Kong, China.Google Scholar
  15. Mahapatra, S., Egel, T., Hassan, R., Shenoy, R. and Carone, M. (2008). Model-based design for hybrid electric vehicle system. SAE Paper No. 2008–01-0085.Google Scholar
  16. McGordon, A., Poxon, J. E., Cheng, C., Jones, R. P. and Jennings, P. A. (2011). Development of a driver model to study the effects of real-world driver behaviour on the fuel consumption. Proc. Institution of Mechanical Engineers, Part D: J. Automobile Engineering 225, 11, 1518–1530.Google Scholar
  17. Noh, K. H., Rah, C. K., Yoon, Y. S. and Yi, K. S. (2014). Experimental approach to developing human driver models considering driver’s human factors. Int. J. Automotive Technology 15, 4, 655–666.CrossRefGoogle Scholar
  18. Pi, J. M., Bak, Y. S., You, Y. K., Park, D. H. and Kim, S. H. (2016). Development of route information based driving control algorithm for a range-extended electric vehicle. Int. J. Automotive Technology 17, 6, 1101–1111.CrossRefGoogle Scholar
  19. Ren, Q., Crolla, D. A. and Morris, A. (2009). Effect of transmission design on electric vehicle performance. Proc. IEEE Vehicle Power and Propulsion Conf., Dearborn, Michigan, USA.Google Scholar
  20. Reif, K. (2014). Fundamentals of Automotive and Engine Technology. Springer. Wiesbaden, Germany.Google Scholar
  21. Son, J., Park, M., Won, K., Kim, Y., Son, S., McGordon, A., Jennings, P. and Birrell, S. (2016). Comparative study between Korea and UK: Relationship between driving style and real-world fuel consumption. Int. J. Automotive Technology 17, 1, 175–181.CrossRefGoogle Scholar
  22. Sorniotti, A., Subramanyan, S., Turner, A., Cavallono, C., Viotto, F. and Bertolotto, S. (2011). Selection of the optimal gearbox layout for an electric vehicle. SAE Int. J. Engines 4, 1, 1267–1280.CrossRefGoogle Scholar
  23. Wang, B., Choi, J. H., Song, H. W., Chol, H. K. and Hwang, S. H. (2014). Development of the performance simulator for electric scooters with an in-wheel motor. Int. J. Automotive Technology 15, 5, 835–841.CrossRefGoogle Scholar
  24. Wang, J., Wang, Q. N., Wang, P. Y., Wang, J. N. and Zou, N. W. (2015). Hybrid electric vehicle modeling accuracy verification and global optimal control algorithm research. Int. J. Automotive Technology 16, 3, 513–524.CrossRefGoogle Scholar
  25. Zhou, X., Walker, P. and Zhang, N. (2013). Performance improvement of a two speed EV through combined gear ratio and shift schedule optimization. SAE Paper No. 2013–01-1477.Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Automotive EngineeringHanyang UniversitySeoulKorea

Personalised recommendations