Optimal energy management of hybrid electric vehicles has previously been shown to increase fuel economy (FE) by approximately 20% thus reducing dependence on foreign oil, reducing greenhouse gas (GHG) emissions, and reducing Carbon Monoxide (CO) and Mono Nitrogen Oxide (NOx) emissions. This demonstrated FE increase is a critical technology to be implemented in the real world as Hybrid Electric Vehicles (HEVs) rise in production and consumer popularity. This review identifies two research gaps preventing optimal energy management of hybrid electric vehicles from being implemented in the real world: sensor and signal technology and prediction scope and error impacts. Sensor and signal technology is required for the vehicle to understand and respond to its environment; information such as chosen route, speed limit, stop light locations, traffic, and weather needs to be communicated to the vehicle. Since optimal control requires accurate prediction of the vehicle environment and drive cycle, prediction scope and error impact analysis is needed to understand the required accuracy of sensor and signal information received by the vehicle as well as the accuracy of the optimal control computed. This review presents the current state of research and solutions in development for each of these research gaps. Once these research gaps have been filled, HEVs may have the potential to substantially increase the FE standard and remove ICE vehicles as the leading consumer of petroleum and leading contributor of GHG, CO, and NOx emissions.