  In this paper, we propose a combined energy and comfort optimization (CECO)
strategy for the air conditioning (A/C) system of the connected and automated
vehicles (CAVs). By leveraging the weather and traffic predictions enabled by
the emerging CAV technologies, the proposed strategy is able to minimize the
A/C system energy consumption while maintaining the occupant thermal comfort
(OTC) within the comfort constraints, where the comfort is quantified by a
modified predictive mean vote (PMV) model adapted for an automotive
application. A general CECO problem is formulated and addressed using model
predictive control (MPC) and weather/traffic previews. Depending on the ways of
exploiting the preview information and enforcing the OTC constraint, different
MPCs are developed based on solving different variations of the general CECO
problem. The CECO-based MPCs are then tested in simulation using an automotive
A/C system simulation model (CoolSim) as the virtual testbed. The simulation
results show that, over SC03 driving cycle, the proposed CECO-based MPCs
outperform the baseline cabin temperature tracking controller, reducing the A/C
system energy consumption by up to 7.6%, while achieving better OTC according
to the PMV-based metrics. This energy saving in A/C system translates to 3.1%
vehicle fuel economy improvement. The trade-off between energy efficiency and
OTC for different control scenarios is also highlighted.
