Optimizing Electric Vehicle Motor Testing with Fine Gaussian SVM and Bilayered Neural Network BS Abdur Rahman Crescent University

Format:

Book

Conference/Event

Author/Creator:

S, Ravikumar, author.

Contributor:

D, Pradeep Kumar

Sharik, N.

Syed, Shaul

V, Muralidharan

Conference Name:

SAENIS TTTMS Thermal Management Systems Conference (2025-11-06 : Guwahati, India)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

Electric vehicles are shaping the future of the automotive industry, with the drive motor being a crucial component in their operation. Ensuring motor reliability requires rigorous testing using specialized test benches to validate key performance parameters. However, inefficiencies in the helical gear configuration within these test systems have led to frequent malfunctions, affecting production flow. This study focuses on optimizing the motor test bench by refining critical design parameters through vibration signal analysis and machine learning techniques. Vibrational data is collected under different gear configurations, utilizing an accelerometer integrated with a Data Acquisition (DAQ) system and MATLAB-based directives for seamless data collection. Machine learning classifiers, including Fine Gaussian SVM and Bilayered Neural Network, are applied to categorize signals into normal and faulty conditions, both with and without a 0.25 KW load. The analysis reveals that SVM achieves an accuracy of approximately 85%, while the neural network attains 92%, demonstrating superior fault detection capabilities. This approach enhances the reliability and efficiency of motor testing, ultimately contributing to improved production processes in electric vehicle manufacturing

Notes:

Vendor supplied data

Publisher Number:

2025-28-0397

Access Restriction:

Restricted for use by site license