Energy Consumption Uncertainty Model For Battery-Electric Buses in Transit

This study develops a Deep Learning Neural Network (DLNN) model to predict the consumed energy (E
C
) of Battery Electric Buses (BEBs) based on bus, route, driver aggressiveness, and environmental parameters. An ADVISOR simulation tool is utilized to estimate E
C
for 10,800 operation scenarios resulted from a fractional-factorial design. The scenarios are used in a DLNN model with a goodness-of-fit of 0.993. The results show that road gradient sharply increases the E
C
, while driver aggressiveness parameters considerably affect the E
C
. The outcomes provide a substantial indication for the operation of BEBs transit networks concerning the consumed energy.