Reinforcement Learning-based Thermal Comfort Control for Vehicle Cabins
This addresses energy consumption and comfort issues for electric vehicle passengers, representing an incremental improvement over existing control methods.
The paper tackled energy-efficient thermal comfort control in vehicle cabins by formulating it as a Markov Decision Process and solving it with Sarsa(λ), resulting in performance increases of 23-56% over baseline controllers and a 13% reduction in energy consumption with a 23% increase in thermal comfort time.
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%, 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.