Reinforcement Learning-based Thermal Comfort Control for Vehicle Cabins
  Vehicle climate control systems aim to keep passengers thermally comfortable.
However, current systems control temperature rather than thermal comfort and
tend to be energy hungry, which is of particular concern when considering
electric vehicles. This paper poses energy-efficient vehicle comfort control as
a Markov Decision Process, which is then solved numerically using
Sarsa({\lambda}) and an empirically validated, single-zone, 1D thermal model of
the cabin. The resulting controller was tested in simulation using 200 randomly
selected scenarios and found to exceed the performance of bang-bang,
proportional, simple fuzzy logic, and commercial controllers with 23%, 43%,
40%, 56% increase, respectively. Compared to the next best performing
controller, energy consumption is reduced by 13% while the proportion of time
spent thermally comfortable is increased by 23%. These results indicate that
this is a viable approach that promises to translate into substantial comfort
and energy improvements in the car.
