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.
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Abbreviations
- J eq :
-
equivalent inertia of vehicle at wheel, kgm2
- ω whl :
-
rotational speed of wheel, rad/s
- T mot :
-
motor torque, Nm
- T res :
-
resistance torque, Nm
- r :
-
gear ratio
- m body :
-
mass of body, kg
- J whl :
-
inertia of wheels, kgm2
- J mot :
-
inertia of motors, kgm2
- R tire :
-
radius of tire, m
- V veh :
-
velocity of vehicle, m/s
- μ r :
-
coefficient of rolling resistance
- g :
-
gravity acceleration, m/s2
- C d :
-
coefficient of air resistance
- A fr :
-
frontal area, m2
- ρ air :
-
air density, kg/m3
- T max :
-
maximum motor torque, Nm
- ω mot :
-
motor speed, rad/s
- T regen :
-
maximum regenerative braking torque, Nm
- C brk :
-
capacity of braking torque, Nm
- V bat :
-
terminal voltage of battery, V
- V OCV :
-
open circuit voltage, V
- R in :
-
internal resistance, ohm
- I bat :
-
load current, A
- SOC ini :
-
initial SOC, %
- C nom :
-
nominal capacity of battery, As
- η :
-
motor efficiency
- K :
-
proportional gain
- τ r :
-
reaction time delay, s
- τ n :
-
neuromuscular lag, s V
References
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.
Day, T. D. and Metz, L. D. (2000). The simulation of driver inputs using a vehicle driver model. SAE Paper No. 2000–01-1313.
De Vlieger, I. (1997). On-board emission and fuel consumption measurement campaign on petrol-driven passenger cars. Atmospheric Environment 31, 22, 3753–3761.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Lixin, S. (2009). Electric vehicle development: The past, present & future. Proc. IEEE 3rd Int. Conf. Power Electronics Systems and Applications (PESA), Hong Kong, China.
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.
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.
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.
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.
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.
Reif, K. (2014). Fundamentals of Automotive and Engine Technology. Springer. Wiesbaden, Germany.
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.
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.
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.
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.
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.
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Kwon, K., Seo, M. & Min, S. Optimization of Gear Ratio of In-Wheel Motor Vehicle Considering Probabilistic Driver Model. Int.J Automot. Technol. 19, 1081–1089 (2018). https://doi.org/10.1007/s12239-018-0106-0
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DOI: https://doi.org/10.1007/s12239-018-0106-0