Reinforcement Learning-based Thermal Comfort Control for Vehicle Cabins
share
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(λ) 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 40 controller, energy consumption is reduced by 13 spent thermally comfortable is increased by 23 this is a viable approach that promises to translate into substantial comfort and energy improvements in the car.
READ FULL TEXT