Heavy heavy-duty diesel truck (HHDDT) drive cycles for long-haul transport trucks
were developed over 20 years ago and have a renewed relevance for performance
assessment and technical forecasting for transport electrification. In this
study, a model was constructed from sparse data recorded from the real-life
on-road activity of a small fleet of class 8 trucks by fitting them into
separate driving-type segments constituting the complete HHDDT drive cycle.
Detailed 1-s resolution truck fleet raw data were also available for assessing
the drive cycle model.
Numerical simulations were conducted to assess the model for trucks powered by
both 1.0 MW charging and 300 kW-level e-Highway, accounting for elevation and
seasonally varying climate conditions along the Windsor–Quebec City corridor in
Canada. The modeling approach was able to estimate highway cruising speeds,
energy efficiencies, and battery pack lifetimes normally within 2% of values
determined using the detailed high-resolution on-road raw data.
HHDDT drive cycles derived from sparse data can be considered fully satisfactory
for long-haul truck simulations for electrification planning. Further, it was
shown that the virtual electric truck as specified here can perform the same
duty cycle as diesel trucks are currently doing in the same amount of time on
e-Hwys, and requiring less an hour per day additional time for the 1.0 MW
charger modality.
