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Journal of Electrical and Computer Engineering
Volume 2017, Article ID 5301602, 9 pages
https://doi.org/10.1155/2017/5301602
Research Article

Estimation of Sideslip Angle Based on Extended Kalman Filter

1School of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng, Shandong 252000, China
2Department of Automotive Engineering, Tsinghua University, Haidian District, Beijing 100084, China

Correspondence should be addressed to Chunjiang Bao; nc.ude.ucl@gnaijnuhcoab

Received 14 October 2016; Accepted 5 March 2017; Published 28 March 2017

Academic Editor: Jit S. Mandeep

Copyright © 2017 Yupeng Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The sideslip angle plays an extremely important role in vehicle stability control, but the sideslip angle in production car cannot be obtained from sensor directly in consideration of the cost of the sensor; it is essential to estimate the sideslip angle indirectly by means of other vehicle motion parameters; therefore, an estimation algorithm with real-time performance and accuracy is critical. Traditional estimation method based on Kalman filter algorithm is correct in vehicle linear control area; however, on low adhesion road, vehicles have obvious nonlinear characteristics. In this paper, extended Kalman filtering algorithm had been put forward in consideration of the nonlinear characteristic of the tire and was verified by the Carsim and Simulink joint simulation, such as the simulation on the wet cement road and the ice and snow road with double lane change. To test and verify the effect of extended Kalman filtering estimation algorithm, the real vehicle test was carried out on the limit test field. The experimental results show that the accuracy of vehicle sideslip angle acquired by extended Kalman filtering algorithm is obviously higher than that acquired by Kalman filtering in the area of the nonlinearity.