# Reinforcement Learning-based Thermal Comfort Control for Vehicle Cabins

## TL;DR

This paper introduces a reinforcement learning approach to optimize vehicle cabin thermal comfort control, significantly improving energy efficiency and comfort levels compared to traditional methods.

## Contribution

It formulates vehicle thermal comfort control as a Markov Decision Process and demonstrates its effectiveness through simulation, outperforming existing control strategies.

## Key findings

- Energy consumption reduced by 13%
- Thermal comfort increased by 23%
- Outperforms traditional controllers in simulations

## Abstract

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.