A New Method to Accelerate Road Test Simulation on Multi-Axial Test Rig

2017-01-0200

03/28/2017

Features
Event
WCX™ 17: SAE World Congress Experience
Authors Abstract
Content
Road test simulation on test rig is widely used in the automobile industry to shorten the development circles. However, there is still room for further improving the time cost of current road simulation test. This paper described a new method considering both the damage error and the runtime of the test on a multi-axial test rig. First, the fatigue editing technique is applied to cut the small load in road data to reduce the runtime initially. The edited road load data could be reproduced on a multi-axial test rig successfully. Second, the rainflow matrices of strains on different proving ground roads are established and transformed into damage matrices based on the S-N curve and Miner rules using a reduction method. A standard simulation test for vehicle reliability procedure is established according to the proving ground schedule as a target to be accelerated. Third, with the runtime and the damage matrix of each road, a multi-objective optimal model is built based on the principle of equivalent damage. In the objective function, there are two optimization objectives. One is minimizing the runtime on the test rig, and the other is minimizing the error of damage matrix between the target and the results. Finally, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is used to solve the problem. The Pareto curve of the two optimization objectives (errors vs. runtime) showed that a high accelerate ratio corresponds to a relatively high damage equivalent error.
Meta TagsDetails
DOI
https://doi.org/10.4271/2017-01-0200
Pages
9
Citation
Zhang, H., Gui, L., and Fan, Z., "A New Method to Accelerate Road Test Simulation on Multi-Axial Test Rig," SAE Technical Paper 2017-01-0200, 2017, https://doi.org/10.4271/2017-01-0200.
Additional Details
Publisher
Published
Mar 28, 2017
Product Code
2017-01-0200
Content Type
Technical Paper
Language
English