Studies have shown that obtaining and utilizing information about the future state of vehicles can improve vehicle fuel economy (FE). However, there has been a lack of research into the impact of real-world prediction error on FE improvements, and whether near-term technologies can be utilized to improve FE. In this study, a speed prediction method utilizing simulated vehicle-to-vehicle (V2V) communication with real-world driving data was developed to understand if incorporating near-term technologies can be utilized in a predictive energy management strategy to improve vehicle FE. This speed prediction method informs a predictive powertrain controller to determine the optimal engine operation for various prediction durations. The optimal engine operation is input into a high-fidelity model of the FE of a Toyota Prius. A tradeoff analysis between prediction duration and prediction fidelity was completed to determine what duration of prediction resulted in the largest FE improvement. This study concludes that speed prediction and prediction-informed optimal vehicle energy management can produce FE improvements with real-world prediction error and drive cycle variability. Up to a 6% FE improvement over the baseline was realized and up to 85% of the FE benefit of perfect speed prediction was achieved with the proposed prediction method. Additionally, the results from this prediction method are compared to the results of a previous study that incorporates only local vehicle information in speed predictions.