Optimization of Antenna Coupling through Machine Learning for “Smart”
TPMS Readers 2021-01-0154
Tire pressure monitoring system (TPMS) is becoming ubiquitous in modern day
vehicles with advanced safety and driver assist systems and plays a key role in
predictive maintenance. One of the key challenges to realize an efficient TPMS
system is to ensure good antenna coupling between the reader antenna in the
cabin or on the roof of the vehicle and the antennas in the tires. Understanding
the different external factors that affect the antenna coupling is vital to
realize an efficient design. Computer aided simulations on antenna coupling is a
cost-effective method to reduce the chances of failure before a TPMS is deployed
in an actual vehicle. In this work, a computational approach is presented to
optimize the antenna coupling and hence the link budget between the reader
antennas and the TPMS antennas at 915 MHz. This is achieved by employing machine
learning based optimization using commercially available tools, Altair’s
HyperStudy and Altair’s Feko. A powerful combination of machine learning
technique (regression-based mathematical modelling) to develop a surrogate
mathematical model coupled with Global Response Search Method (GRSM)
optimization is demonstrated for achieving the goals with very few design
iterations A case study is presented that demonstrates the workflow process of
optimizing the TPMS antenna coupling using body in white of an automobile. A
comparison is also shown between traditional GRSM reader antenna position
optimization and optimization coupled with machine learning showcasing
significant reduction in computational time and memory.
Citation: Karuppuswami, S. and Reddy, C., "Optimization of Antenna Coupling through Machine Learning for “Smart” TPMS Readers," SAE Int. J. Adv. & Curr. Prac. in Mobility 3(5):2611-2616, 2021, https://doi.org/10.4271/2021-01-0154. Download Citation
Author(s):
Saranraj Karuppuswami, C. J. Reddy
Affiliated:
Altair Engineering
Pages: 6
Event:
SAE WCX Digital Summit
e-ISSN:
2641-9645
Also in:
SAE International Journal of Advances and Current Practices in Mobility-V130-99EJ
Related Topics:
Tire pressure monitoring systems
Tires
Design processes
Driver assistance systems
Machine learning
Mathematical models
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