Application of Pre-Computed Acceleration Event Control to Improve Fuel Economy in Hybrid Electric Vehicles

Application of predictive optimal energy management strategies to improve fuel economy in hybrid electric vehicles is an active subject of research. Acceleration events during a drive cycle provide particularly attractive opportunities for predictive optimal energy management because of their high energy cost and limited variability, which enables optimal control trajectories to be computed in advance. In this research, dynamic-programming derived optimal control matrices are implemented during a drive cycle on a validated model of a 2010 Toyota Prius to simulate application of pre-computed control to improve fuel economy over a baseline model. This article begins by describing the development of the vehicle model and the formulation of optimal control, both of which are simulated over the New York City drive cycle to establish baseline and upper-limit fuel economies. Then, optimal control strategies are computed for acceleration events in the drive cycle. The model is first simulated with optimal control during acceleration events, then with optimal control matrices applied to different acceleration events than originally derived, so as to represent mis-prediction and mis-application of optimal control. The results show that drive cycle fuel economy can be robustly improved by applying pre-computed control matrices to acceleration events. Overall, this article demonstrates that fuel economy improvements with predictive optimal energy management are achievable without precise prediction capabilities or real-time, on-vehicle computation of optimal control.