Development of Adaptive-ECMS and predictive functions for Plug-in HEVs to Handle Zero-Emission Zones Using Navigation Data

The paper deals with the reduction of pollutant emissions in urban areas by considering a Zero-Emission Zone (ZEZ) in which hybrid electric vehicles (HEVs) are allowed to be driven without using the internal combustion engine, as several cities have planned to realize in the next decades. Moreover, since vehicle connectivity has spread more and more in the last years, a vehicle-to-network (V2N) communication system has been taken into account to retrieve real-time navigation data from a map service provider and thus reconstructing the so-called electronic horizon, which is a reconstruction of the future conditions of the vehicle on the road ahead. The speed profile and the road slope are used as input for an on-board predictive control strategy of a plug-in HEV (PHEV). In particular, a dedicated algorithm predicts the amount of necessary energy to complete the city event in full-electric mode, giving a state of charge (SoC) target value. With this aim, an adaptive equivalent consumption minimization strategy (A-ECMS) has been modified to use navigation data for approaching the ZEZ with the target SoC. The paper finally quantifies the benefits of such an approach in terms of CO₂ emissions by comparing it with a heuristic, rule-based one, which represents the standard OEM solution.