  Connected vehicles (CVs) have situational awareness that can be exploited for
control and optimization of the powertrain system. While extensive studies have
been carried out for energy efficiency improvement of CVs via eco-driving and
planning, the implication of such technologies on the thermal responses of CVs
has not been fully investigated. One of the key challenges in leveraging
connectivity for optimization-based thermal management of CVs is the relatively
slow thermal dynamics, which necessitate the use of a long prediction horizon
to achieve the best performance. Long-term prediction of the CV speed, unlike
the V2V/V2I-based short-range prediction, is difficult and error-prone. The
multiple timescales inherent to power and thermal systems call for a variable
timescale optimization framework with access to short- and long-term vehicle
speed preview. To this end, a model predictive controller (MPC) with a
multi-range speed preview for integrated power and thermal management (iPTM) of
connected hybrid electric vehicles (HEVs) is presented in this paper. The MPC
is formulated to manage the power-split between the engine and the battery
while enforcing the power and thermal (engine coolant and catalytic converter
temperatures) constraints. The MPC exploits prediction and optimization over a
shorter receding horizon and longer shrinking horizon. Over the longer
shrinking horizon, the vehicle speed estimation is based on the data collected
from the connected vehicles traveling on the same route as the ego-vehicle.
Simulation results of applying the MPC over real-world urban driving cycles in
Ann Arbor, MI are presented to demonstrate the effectiveness and fuel-saving
potentials of the proposed iPTM strategy under the uncertainty associated with
long-term predictions of the CV's speed.
