Prediction Error Applied to Hybrid Electric Vehicle Optimal Fuel Economy

Fuel economy (FE) improvements for hybrid electric vehicles using a predictive Optimal Energy Management Strategy (Optimal EMS) is an active subject of research. Recent developments have focused on real-time prediction-based control strategies despite the lack of research demonstrating the aspects of prediction that are most important for FE improvements. In this paper, driving-derived nonstochastic prediction errors are applied to a globally optimal control strategy implemented on a validated model of a 2010 Toyota Prius, and the FE results are reported for each type of prediction error. This paper first outlines the real-world drive cycle development, then the baseline model development that simulates a 2010 Toyota Prius, followed by an implementation of dynamic programming (DP) to derive the globally optimal control, and finally the use of the DP solution to evaluate prediction errors. FE comparisons are reported for perfect prediction, prediction errors from 14 alternate drive cycles, and prediction errors from 6 alternate vehicle parameters. The results show that FE improvements from the Optimal EMS are maintained under mispredicted stops, traffic, and vehicle parameters, while route changes and compounded drive cycle mispredictions may result in FE improvements being lost. Taken together, these results demonstrate that implementation of an Optimal EMS can result in a reliable FE improvement.