  This paper presents a vehicle-independent, non-intrusive, and light
monitoring system for accurately measuring fuel consumption in road vehicles
from longitudinal speed and acceleration derived continuously in time from GNSS
and IMU sensors mounted inside the vehicle. In parallel to boosting the
transition to zero-carbon cars, there is an increasing interest in low-cost
instruments for precise measurement of the environmental impact of the many
internal combustion engine vehicles still in circulation. The main contribution
of this work is the design and comparison of two innovative black-box
algorithms, one based on a reduced complexity physics modeling while the other
relying on a feedforward neural network for black-box fuel consumption
estimation using only velocity and acceleration measurements. Based on suitable
metrics, the developed algorithms outperform the state of the art best
approach, both in the instantaneous and in the integral fuel consumption
estimation, with errors smaller than 1\% with respect to the fuel flow ground
truth. The data used for model identification, testing, and experimental
validation is composed of GNSS velocity and IMU acceleration measurements
collected during several trips using a diesel fuel vehicle on different roads,
in different seasons, and with varying numbers of passengers. Compared to
built-in vehicle monitoring systems, this methodology is not customized, uses
off-the-shelf sensors, and is based on two simple algorithms that have been
validated offline and could be easily implemented in a real-time environment.
