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In this research, the interior noise of a passenger car was measured, and the sound quality metrics including sound pressure level, loudness, sharpness, and roughness were calculated. An artificial neural network was designed to successfully apply on automotive interior noise as well as numerous different fields of technology which aim to overcome difficulties of experimentations and save cost, time and workforce. Sound pressure level, loudness, sharpness, and roughness were estimated by using the artificial neural network designed by using the experiment values. The predicted values and experiment results are compared. The comparison results show that the realized artificial intelligence model is an appropriate model to estimate the sound quality of the automotive interior noise. The reliability value is calculated as 0.9995 by using statistical analysis.

A vehicle vibration issue emerged for a hybrid prototype during low speed driving in EV mode. This work is focused on the effort to identify the root cause and resolve the issue. The endeavor begins by performing a motor test in moderate acceleration with an imposed constant torque load. All relevant information is simultaneously recorded, including vehicle speed, vibration of motor structure and seat track, motor rpm, voltage and current signals, etc. Then analyses are carried out to strive for a better understanding of the vibration characteristics and identify its mechanism. It is found that the torque ripple from the driving motor is the root cause of the low speed vehicle vibration in EV mode, and the torque ripple is found to be induced by the current distortion resulted from the current sensor drift and electromagnetic interference due to high current signals.