Widely published research shows that significant fuel economy improvements through optimal control of a vehicle powertrain are possible if the future vehicle velocity is known and real-time optimization calculations can be performed. In this research, however, we seek to advance the field of optimal powertrain control by limiting future vehicle operation knowledge and using no real-time optimization calculations. We have realized optimal control of acceleration events (AEs) in real-time by studying optimal control trends across 384 real world drive cycles and deriving an optimal control strategy for specific acceleration event categories using dynamic programming (DP). This optimal control strategy is then applied to all other acceleration events in its category, as well as separate standard and custom drive cycles using a look-up table. Fuel economy improvements of 2% average for acceleration events and 3.9% for an independent drive cycle were observed when compared to our rigorously validated 2010 Toyota Prius model. Our conclusion is that optimal control can be implemented in real-time using standard vehicle controllers assuming extremely limited information about future vehicle operation is known such as an approximate starting and ending velocity for an acceleration event.