Paper Info
Title
A Low-Cost Embedded Data Collection System for Traction Control Systems in Electric Vehicles
Abstract
The transition from conventional vehicles to electric vehicles (EVs) has increased interest in research in the area of autonomy to prevent traffic accidents. Despite the relevance of the related research to the well-being of the society, commercial vehicles offered by automotive industries often do not provide the openness required for research and realistic experiments. In this paper, we propose the use of a noncommercial electric vehicle, and a novel low-cost embedded (LCE) data collection system for research and education in advanced driver-assistance systems (ADAS). This LCE data system for EV can collect vehicle-dynamics related data and environmental context via a low-cost platform. These inputs are mainly the wheel motor current indicating the torque demand, steering wheel angle, angular wheel velocity, global positioning, 3 axis acceleration, 3 axis rotation and 3 axis magnetics measurements. Using these inputs, we propose the design of a prospective traction control system that would allow for different levels of autonomy. In this work, for traction control of the EV, the maximum transmissible torque estimation method (MTTE) is used. Our experimental results demonstrate a 10% improvement in the maximum slip rate of EV.
Year
DOI
Venue
2019
10.1109/ICMECH.2019.8722837
2019 IEEE International Conference on Mechatronics (ICM)
Keywords
Field
DocType
Data collection,Global Positioning System,Electric vehicles,Wheels,Torque,Safety
Torque,Electric vehicle,Steering wheel,Slip (materials science),Control engineering,Acceleration,Global Positioning System,Engineering,Traction control system,Automotive industry
Conference
Volume
ISBN
Citations 
1
978-1-5386-6959-4
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Daghan Dogan111.03
S. Bogosyan212510.53
Pinar Boyraz Baykas300.34