A Thermal Image based Fault Detection in Electric Vehicle Battery Cells Utilizing CNN U-Net Model

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Published: Nov 2, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i10.8502
Keywords:
Convolutional Neural Network (CNN) U-Net model, Thermal camera, Electric vehicle systems, Battery cells, Root Mean Square Error (RMSE)

Main Article Content

Senthilraj, N. R. Shanker

Abstract

It entails the formation of thermal images from battery cells under different conditions, capturing crucial thermal patterns such as hotspots, insulation degradation, and overheating. For robust model training, data preprocessing and augmentation techniques are applied. The U-Net model, known for its expertise in semantic segmentation tasks, is applied to evaluate thermal images and to detect fault-related features. The results demonstrate the U-Net's unique precision, sensitivity, and specificity in detecting thermal anomalies. This research adds to the improvement of the safety and dependability of EV battery systems, with applications in the electric mobility and automotive industries.

Article Details

How to Cite
Senthilraj, et al. (2023). A Thermal Image based Fault Detection in Electric Vehicle Battery Cells Utilizing CNN U-Net Model. International Journal on Recent and Innovation Trends in Computing and Communication, 11(10), 381–389. https://doi.org/10.17762/ijritcc.v11i10.8502
Issue
Vol. 11 No. 10 (2023)
Section
Articles
Author Biography

Senthilraj, N. R. Shanker

Senthilraj1, N. R. Shanker2

1 Research Scholar, Electronics and Communication Engineering,

PRIST University,

Thanjavur, Tamil Nadu, India

senthilrajvlsi@gmail.com

2 Professor / Supervisor, Computer Science and Engineering,

Aalim Muhammed Salegh College of Engineering,

Chennai, Tamil Nadu, India

nr_phd@yahoo.in

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