Improving Vehicle Fleet Fuel Economy via Learning Fuel-Efficient Driving Behaviors

Reducing the fuel consumption of road vehicles has the potential to decrease environmental impact of transportation as well as achieve significant economical benefits. This paper proposes a novel methodology for improving the fuel economy of vehicle fleets via learning fuel-efficient driving behaviors. Vehicle fleets composed of large number of heavy vehicles routinely perform runs with different drivers over a set of fixed routes. While all drivers might achieve on-time and safe driving performance their actual driving behaviors and the subsequent fuel economy can vary substantially. The proposed Intelligent Driver System (IDS) utilizes vehicle performance data combined with GPS information on fixed routes to incrementally build a model of the historically most fuel efficient driving behavior. During driving, the calculated optimal velocity for specific location is compared to the current vehicle state and a fuzzy logic PD controller is used to compute the optimal control action. The control action can be projected to the drivers via a specialized HMI or used directly as a predictive cruise control to achieve overall fuel economy improvements. The method has been validated on a simulated heavy vehicle model, showing potential for substantial fuel economy improvements.