  In urban traffic environments, driver behaviors exhibit considerable
diversity in vehicle operation, encompassing a range of acceleration and
braking maneuvers as well as adherence to traffic regulations, such as speed
limits. It is well-established that these intrinsic driving behaviors
significantly influence vehicle energy consumption. Therefore, establishing a
quantitative relationship between driver behavior and energy usage is essential
for identifying energy-efficient driving practices and optimizing routes within
urban traffic. This study introduces a data-driven approach to predict the
equivalent fuel consumption of a plug-in hybrid electric vehicle (PHEV) based
on an integrated model of driver behavior and vehicle energy consumption.
Unlike traditional models that provide point predictions of fuel consumption,
this approach uses Conformalized Quantile Regression (CQR) to offer prediction
intervals that capture the variability and uncertainty in fuel consumption.
These intervals reflect changes in fuel consumption, as well as variations in
driver behavior, and vehicle and route conditions. To develop this model,
driver-specific data were collected through a driver-in-the-loop simulator,
which tested different human drivers responses. The CQR model was then trained
and validated using the experimental data from the driver-in-the-loop
simulator, augmented by the synthetic data generated from Monte Carlo
simulations conducted using a calibrated microscopic driver behavior and
vehicle energy model. The CQR model was evaluated by comparing its predictions
of equivalent fuel consumption intervals with those of baseline prediction
interval methods that rely solely on conformal prediction.
