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This paper presents a sensitivity analysis-based study aimed at robustly calibrating the parameters of an adaptive energy management strategy designed for a Plugin Hybrid Electric Vehicle (PHEV). The supervisory control is developed from the Pontryagin's Minimum Principle (PMP) approach and applied to a model of a GM Chevrolet Volt vehicle. The proposed controller aims at minimizing the fuel consumption of the vehicle over a given driving mission, by achieving a blended discharge strategy over the entire cycle. The calibration study is conducted over a wide set of driving conditions and it generates a look-up table and two constant values for the three controller parameters to be used in the in-vehicle implementation. Finally, the calibrated adaptive control strategy is validated against real driving cycles showing the effectiveness of the calibration approach.

The goal of the investigation is the evaluation of the environmental impact of hybrid and electric mobility in Lecce, a city of about 100,000 inhabitants in southern Italy. The investigation starts from the definition of specific driving cycles for the University campus and Lecce city center under different conditions of traffic and weather. The data acquired in this way are used to evaluate the performance of four typologies of vehicles: a gasoline city car (Smart Fortwo), the corresponding electric version (Smart ED), three range extenders and a plug-in hybrid electric vehicle operating with blended discharge. The simulation of the different power trains is performed with AVL-Cruise and validate through comparison with literature results on the European driving cycle.