Optimizing a start-stop system to minimize fuel consumption using machine learning

Abstract

Many people are working on improving the efficiency of car's engines. One approach to maximizing efficiency has been to create start-stop systems. These systems shut the car's engine off when the car comes to a stop, saving fuel that would be used to keep the engine running. However, these systems introduce additional energy costs, which are associated with the engine restarting. These energy costs must be balanced by the system. In this thesis I describe my work with Ford to improve the performance of their start-stop controller. In this thesis I discuss optimizing a controller for both the general population as well as for individual drivers. I use reinforcement-learning techniques in both cases to find the best performing controller. I find a 27% improvement on Ford's current controller when optimizing for the general population, and then find an additional 1.6% improvement on the improved controller when optimizing for an individual.