Fuel-Saving Potential of Hybrid Electric Vehicles Using Surroundings Sensor System Information

Operation strategies used in hybrid electric vehicles need a traffic speed profile for an initial operation strategy planning, which can be obtained from real-time traffic information services. However, microscopic traffic flow is unrepresentable in this initial planning which leads to deviations in the batteries’ state of charge. Therefore, the presented method plans partially automated micro maneuvers to correct the deviations during the drive using local prediction based on the surroundings sensor system, which is assumed to offer exact data of the future speed profile within its visual range. The method is applied to various driving cycles such as the WLTP and to real speed profiles in cities and on highways. Comparing driving cycle runs with and without the use of local prediction shows a fuel-saving potential of up to 11% in emission test cycles. Moreover, validation with real data confirms the results and indicates a higher potential in urban areas.