Citation

Hao RR, Zhao XM, Ma J, Xu ZG. J. Traffic Transp. Eng. (Xi'an, Shaanxi) 2011; 11(5): 69-75.

Copyright

(Copyright © 2011, Chang'an Daxue)

DOI

unavailable

PMID

unavailable

Abstract

In order to improve the detection efficiency of automobile ABS, a novel detection system was developed based on bench. The inertia of moving automobile was simulated by using the rotational inertia of flywheel. Four torque controllers were adopted to load different torques on four rollers supporting four wheels of automobile, so that different road adhesion coefficients were simulated. CAN-bus-based distributed network control technology was used to complete the motion control of bench and the data acquisition of automotive speed. BP neural network was used to analyze the test data for its self-learning function. The summarized mapping relationship of ABS work states was stored in the network, and the automatic classification of test results was achieved by using the network. Comparative analysis of bench test result and road experiment result for ABS shows that the results are basically same, the errors of main parameters are less than 4%, so bench test can more accurately reflect the brake performances of automobile equipped with ABS in different road environments.