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Governments worldwide are taking actions aiming to achieve a sustainable transportation system that can comprise of minimal pollutant and GHG emissions. Particular attention is given to the real-world emissions, i.e. to the emissions achieved in the real driving conditions, outside of a controlled testing environment. In this framework, interest in vehicle fleet electrification is rapidly growing, as it is seen as a way to simultaneously reduce pollutant and GHG emissions, while on the other hand OEMs are facing a significant increase in the number of tests which are needed to calibrate this new generation of electrified powertrains over a variety of different driving scenarios.

Over the next decade, CO2 legislation will be more demanding and the automotive industry has seen in vehicle electrification a possible solution. This has led to an increasing need for advanced powertrain systems and systematic model-based control approaches, along with additional complexity. This represents a serious challenge for all the OEMs. This paper describes a novel reverse engineering methodology developed to estimate relevant powertrain data required for fuel consumption-oriented hybrid electric vehicle (HEV) modelling. The estimated quantities include high-voltage battery internal resistance, electric motor and transmission efficiency, gearshift thresholds, torque converter performance diagrams, engine fuel consumption map and front/rear hydraulic brake torque distribution. This activity provides a list of dedicated experimental tests, to be carried out on road or on a chassis dynamometer, aiming at powertrain characterization thanks to a suitable post-processing algorithm.