Abstract

The main objective of the study is to analyze Tire Pressure Monitoring System (TPMS) data that contributes significantly towards the enhancement of the intelligent vehicle performance evaluation. TPMS pressure and temperature data were collected from the prototype model of the MEMS Tire Pressure Module (TPM) that was fitted on to an intelligent tire rim through its receiver. In this study, we are focusing only analytical data analysis of TPMS. In the analytical study, a novel method for data classification, goodness of fit and hypothesis testing was proposed. A classification scheme was employed to classify the temperature and pressure data based on ID at the quadrant basis operating zone of the Front Right (FR), Front Left (FL), Rear Left (RL) and Rear Right (RR) tires. Principle Component Analysis (PCA) with polynomial fitting for exploring goodness of fit of tire data was also applied. Finally, hypothesis testing using Satterthwaite statistic was carried out. Results obtained are in agreement with the null hypothesis and as such validate the usefulness of the TPMS system in maintaining and enhancing vehicle performance.