In the contemporary world with degrading natural resources, the urgency of
energy efficiency has become imperative due to the conservation and
environmental safeguarding. Therefore, it's crucial to look for advanced
technology to minimize energy consumption. This research focuses on the
optimization of battery-electric city style vehicles through the use of a
real-time in-car telemetry system that communicates between components through
the robust Controller Area Network (CAN) protocol. By harnessing real-time data
from various sensors embedded within vehicles, our driving assistance system
provides the driver with visual and haptic actionable feedback that guides the
driver on using the optimum driving style to minimize power consumed by the
vehicle. To develop the pace feedback mechanism for the driver, real-time data
is collected through a Shell Eco Marathon Urban Concept vehicle platform and
after pre-processing, it is analyzed using the novel machine learning algorithm
TEMSL, that outperforms the existing baseline approaches across various
performance metrics. This innovative method after numerous experimentation has
proven effective in enhancing energy efficiency, guiding the driver along the
track, and reducing human errors. The driving-assistance system offers a range
of utilities, from cost savings and extended vehicle lifespan to significant
contributions to environmental conservation and sustainable driving practices