Highlights ► We develop an energy optimization strategy for power-split PHEV. ► The strategy utilize predicted driving conditions provided by Intelligent Transportation Systems. ► Optimization time window sizes are identified for different driving conditions. ► Impact of error in prediction are determined on the integrated system.

Abstract To demonstrate the greater capabilities and benefits achievable with a plug-in hybrid electric vehicle (PHEV), an energy optimization strategy for a power-split drivetrain PHEV, which utilizes a predicted speed profile, is presented. In addition, the paper reports an analysis and evaluation of issues related to real time control implementation for the modeled PHEV system, which include the optimization window sizes and the impact of prediction errors on the energy optimization strategy performance. The optimization time window sizes were identified and validated for different driving cycles under different operating modes and total length of travel. With the identified optimization windows size, improvements in fuel consumption were realized; the highest improvement was for Urban Dynamometer Driving Schedule (UDDS), with a range of improvement of 14–31%, followed by a 1–15% range of improvement for Highway Fuel Economy Driving Schedule (known as HWFET) and a 1–8% range of improvement for US06 (also known as Supplemental Federal Test Procedure). While no correlation was observed between the error rate and the rate of increased fuel consumption, this PHEV system still yielded energy savings with errors in the speed prediction, which is an indication of robustness of this PHEV model.