Development of a duty cycle with K-means clustering technique for hydraulic steering in an instrumented TIBA vehicle

Nowadays, developing a driving cycle has become argumentative with the local country traffic infrastructure considerations to make emission standard drafts, control vehicles emissions, and also develop related parts. Aside from that, constructing a duty cycle would be a better option for vehicle parts development. In this article, the output is a hydraulic pressure-time plot of the TIBA vehicle for the best similarity with all acquired data. The main ideas and also algorithms are the same as developing the driving cycle. However, the difference is new definitions of inputs such as the average hydraulic pressure, the average loaded hydraulic pressure, the average steering angle, the angular velocity, and the unloaded pressure ratio. The K-means clustering technique is conducted in this research with the principal component analysis (PCA) and the effects of the number of clustering are checked to make results more efficient. The outputs illustrate that the root mean square (RMS) and Chi-square errors for the methodology are 0.166 and 0.068, respectively, which has a significant drop of 86.4% and 95.4%, in comparison to the conventional method.

Abstract licence: CC BY-NC

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