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 non-commercial 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.