An Integrated Methodology for 0D Map-Based Powertrain Modelling Applied to a 48 V Mild-Hybrid Diesel Passenger Car

Nowadays, the 48 V vehicle architecture seems to be the perfect bridge between the 12 V system and the costly High Voltage (HV) electrification towards the crucial goal of CO2 and pollutants emissions reduction in combination with enhanced performance. However, this approach leads to an increased complexity in the interaction between different sub-systems targeting the optimization of the Energy Management System (EMS). Therefore, it becomes essential to perform a preliminary hardware assessment, exploring the interactions between the different components and quantifying the cost vs benefit trade-off. To this purpose, an integrated experimental/numerical methodology has been adopted: a comprehensive map-based Hybrid Electric Vehicle (HEV) model has been built, allowing the simulation of a variety of hybrid architectures, including both HV and 48 V systems. It comprises an embedded EMS model, calibrated by means of dedicated test campaigns carried out on benchmarking vehicles, under both steady-state and transient conditions. Furthermore, the main electrical subsystems have been characterized during the same experimental campaigns with a minimum and non-invasive instrumentation effort. Specifically, this activity investigates the features and performance of a 48 V mild-hybrid Diesel P0 architecture. The aim of this activity is to achieve an accurate determination of the energy and fuel consumption, as well as of the CO2 emissions, over standard driving cycles, by means of a 0D model calibrated with a dedicated test campaign. The obtained results indicate that the developed 0D model can be used to predict the powertrain behavior for different vehicle mission profiles such as extended Real Driving Emissions (RDE) tests. Furthermore, it enables the possibility to assess, on a virtual test rig, the impact of modifications of the components specifications in order to evaluate alternative and feasible designs that can fit customer needs.