  Increasing the fuel economy of hybrid electric vehicles (HEVs) and extended
range electric vehicles (EREVs) through optimization-based energy management
strategies (EMS) has been an active research area in transportation. However,
it is difficult to apply optimization-based EMS to current in-use EREVs because
insufficient knowledge is known about future trips, and because such methods
are computationally expensive for large-scale deployment. As a result, most
past research has been validated on standard driving cycles or on recorded
high-resolution data from past real driving cycles. This paper improves an
in-use rule-based EMS that is used in a delivery vehicle fleet equipped with
two-way vehicle-to-cloud connectivity. A physics model-guided online Bayesian
framework is described and validated on large number of in-use driving samples
of EREVs used for last-mile package delivery. The framework includes: a
database, a preprocessing module, a vehicle model and an online Bayesian
algorithm module. It uses historical 0.2 Hz resolution trip data as input and
outputs an updated parameter to the engine control logic on the vehicle to
reduce fuel consumption on the next trip. The key contribution of this work is
a framework that provides an immediate solution for fuel use reduction of
in-use EREVs. The framework was also demonstrated on real-world EREVs delivery
vehicles operating on actual routes. The results show an average of 12.8% fuel
use reduction among tested vehicles for 155 real delivery trips. The presented
framework is extendable to other EREV applications including passenger
vehicles, transit buses, and other vocational vehicles whose trips are similar
day-to-day.
