Data-Driven Modelling of a Plug-In Hybrid Electric Vehicle

Growing need for environmentally friendly transportation has led to the rise of hybrid electric vehicles (HEVs). Plug-in hybrid electric vehicles (PHEVs), which are a sub-class of HEVs, provide lower fuel consumption and exhaust gas emissions thanks to their external charging and higher range of electric drive capabilities. Therefore, they are increasingly popular in the automotive industry nowadays. To make a rapid assessment of the control or the energy management strategies applied to a PHEV, the generation of a vehicle model has a crucial role. This paper proposes a rapid-responsive, data-driven, kinematic, and map-based PHEV model in a Python environment. Approximately 2000 km of real-world vehicle measurements were used instead of driving cycles, which enables a wide range of driving maneuvers to be used for modeling and testing purposes. Results of the vehicle model were compared in two categories to express the charge-sustaining (CS) and charge-depleting (CD) behaviors of the model.