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 optimal energy management because of their predictability and high energy cost. In this research, a small set of dynamic-programming-derived optimal control strategies for acceleration events is implemented during drive cycles on a validated model of a 2010 Toyota Prius and the fuel economy results are reported in comparison to the baseline model. This article begins by describing the development of the vehicle model and the implementation of dynamic programming to derive optimal control strategies for a large set of acceleration events. Then, a subset of the derived optimal control strategies, representing a wide variety of acceleration events based on beginning and ending velocity, is chosen. These control strategies are applied to acceleration events with similar beginning and ending velocity to simulate imperfect acceleration event prediction, first as standalone events, then in the context of drive cycles. Fuel economy results for full drive cycles are reported for the baseline vehicle model and for the model with imperfect prediction of acceleration events, perfect prediction of acceleration events, and perfect prediction of the full drive cycle. The results show that imperfect acceleration event prediction based on beginning and ending velocity reliably improves fuel economy for a full drive cycle. Overall, this article demonstrates that optimal energy management for acceleration events can result in fuel economy improvements even with limited prediction.