The Effect of Trip Preview Prediction Signal Quality on Hybrid Vehicle Fuel Economy

This study seeks to develop an understanding of the sensitivity of sensing and prediction-derived vehicle fuel economy improvements to prediction signal quality. The trip pattern identification type of scenario control was selected for in-depth study. For this scenario control, we developed real-world derived drive cycles to test and demonstrate the effectiveness of the scenario control. Baseline models of the Prius HV were refined and used to develop a baseline fuel economy model. Optimal scenario control policies were derived assuming perfect signal quality and were implemented in the baseline vehicle fuel economy model to demonstrate the effectiveness of the scenario control under ideal conditions. Both the optimized and baseline vehicle models were then subjected to imperfections in the prediction signals with the objective of quantifying the absolute and relative performance of the scenario control policies, and the baseline vehicle control.