TY - GEN
T1 - Lithium-ion Battery Face Imaging with Contactless Walabot and Machine Learning
AU - Wang, Yanan
AU - Chen, Yangquan
AU - Liao, Xiaozhong
AU - Dong, Lei
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - By using a three-dimensional (3D) radio-frequency based sensor, which is called Walabot, and machine learning (ML) algorithm, this paper presents a contactless way to generate lithium-ion battery face images for battery voltage classification. First, Walabot was applied to sampling images, which can reflect inside physic structure of lithium-ion batteries (LIBs). Second, these images were preprocessed by data enhancement or wavelet transform. Finally, these preprocessed images were set as inputs of a convolutional neural network (CNN). After images network training, the CNN can be applied to validating test images in different voltage values. Experiment results of five LIBs illustrate that the proposed contactless battery face imaging method provides a totally new way to conduct voltage classification for LIBs.
AB - By using a three-dimensional (3D) radio-frequency based sensor, which is called Walabot, and machine learning (ML) algorithm, this paper presents a contactless way to generate lithium-ion battery face images for battery voltage classification. First, Walabot was applied to sampling images, which can reflect inside physic structure of lithium-ion batteries (LIBs). Second, these images were preprocessed by data enhancement or wavelet transform. Finally, these preprocessed images were set as inputs of a convolutional neural network (CNN). After images network training, the CNN can be applied to validating test images in different voltage values. Experiment results of five LIBs illustrate that the proposed contactless battery face imaging method provides a totally new way to conduct voltage classification for LIBs.
KW - Battery face imaging
KW - convolutional neural network
KW - linear discriminant analysis
KW - voltage classification
KW - wavelet transform
UR - http://www.scopus.com/inward/record.url?scp=85072390345&partnerID=8YFLogxK
U2 - 10.1109/ICMA.2019.8816512
DO - 10.1109/ICMA.2019.8816512
M3 - Conference contribution
AN - SCOPUS:85072390345
T3 - Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019
SP - 1067
EP - 1072
BT - Proceedings of 2019 IEEE International Conference on Mechatronics and Automation, ICMA 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th IEEE International Conference on Mechatronics and Automation, ICMA 2019
Y2 - 4 August 2019 through 7 August 2019
ER -
