Dynamic adaptation of a vehicle´s cruising speed with recurrent neural networks for enhancing fuel economy

This paper presents an architecture for optimally modulating the cruise speed around its set point. Our objective is to minimize the overall fuel consumption over a trip without impacting the overall trip time, by exploiting the vehicle dynamics and the terrain, specifically in this paper, the road grades. The overall trip time is defined as the time to complete the trip while driving at the constant cruise speed, which was set by the driver at the beginning of the trip. We test this architecture with data acquired by an instrumented vehicle driven on city and highway roads in Southeast Michigan. Our testing was very promising and showed that we can achieve up to 11% of overall fuel economy of which 10.8% are from exploiting the road grades.