Research on Electric Vehicle Road Identification Method Based on RBF Neural Network

Zhi Yu Huang; Jia Li

doi:10.4028/www.scientific.net/AMM.543-547.1413

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 543-547Research on Electric Vehicle Road Identification...

Research on Electric Vehicle Road Identification Method Based on RBF Neural Network

Abstract:

Accurately identifying road condition can send relevant information to the motor control system, so that control system of the motor can adjust the control strategy timely, eventually, the intelligent and optimal control of electric vehicles is realized. In this paper, according to these mathematical model, the permanent magnet synchronous motors simulation model and vehicles simulation model are proposed. Then, output torque of motor and speed of motor are served as the input of RBF neural network, which helps road condition to be identified. The simulation result shows that the road condition is well identified by proposed method based on RBF neural network.

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Periodical:

Applied Mechanics and Materials (Volumes 543-547)

Pages:

1413-1416

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.543-547.1413

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Online since:

March 2014

Authors:

Zhi Yu Huang*, Jia Li

Keywords:

PMSM, RBF Neural Network, Road Identification, Simulation Model

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* - Corresponding Author

References

[1] Wang Xiuhe. Permanent magnet motor, Beijing: Electric Power Press, (2007) 252-256.

Google Scholar

[2] WeiJian, XieHui. The vehicle driving model and simulation based on Simulink, Journal of tianjin normal university of engineering, 2007, 17(2): 38-39, 42.

Google Scholar

[3] Ma Yichao, Li Bin, Zhang Tiening. Permanent magnet synchronous motor vector control system design, Guigu, 2010, (18): 42-43.

Google Scholar

[4] Fredrik Gustafson. Slip-based tire-road friction estimation, Elsevier Science Ltd. 1997, 33 (6): 1087-1099.

Google Scholar

[5] Hideo Sado, Shin-ichiro Sakai, Yoichi Hori. Road Condition Estimation for Traction Control in Electric Vehicle, IEEE International Symposium on Industrial Electronics, 1999: 973-978.

DOI: 10.1109/isie.1999.798747

Google Scholar