On quantifying the utility of look-ahead data for energy management

Abstract

Connectivity and technology integration focused on autonomy and safety in vehicles have equipped many vehicles with a host of sensors. Some of these sensors have the potential to aid energy management strategies by providing useful look-ahead data. Road features like speed limits, road grade, traffic sign locations etc. make up the look-ahead data for energy management. In this paper we quantify the utility of the look-ahead data in fuel economy improvements. The traffic-powertrain co-simulator provides the simulation framework necessary for our analysis. The methodology includes a look-ahead data based velocity predictor and an MPC based look-ahead controller to determine the fuel economy improvement with each look-ahead data. The results of the analysis indicate the existence of a Pareto front for fuel economy improvements with increased look-ahead data.