Optimizing a Start-Stop Controller Using Policy Search

Abstract

We applied a policy search algorithm to the problem of optimizing a start-stop controller—a controller used in a car to turn off the vehicle’s engine, and thus save energy, when the vehicle comes to a temporary halt. We were able to improve the existing policy by approximately 12% using real driver trace data. We also experimented with using multiple policies, and found that doing so could lead to a further 8% improvement if we could determine which policy to apply at each stop. The driver’s behaviors before stopping were found to be uncorrelated with the policy that performed best; however, further experimentation showed that the driver’s behavior during the stop may be more useful, suggesting a useful direction for adding complexity to the underlying start-stop policy.