Optimization of the Operating Strategy of a BAS Hybrid Diesel Powertrain on Type-Approval and Real-World Representative Driving Cycles

Hybrid Electric Vehicles (HEVs) can be considered one of the most promising ways of improving the sustainability of the road transport sector. They are equipped with an Internal Combustion Engine (ICE) coupled to an electro-mechanical system. This study has focused on a parallel-hybrid diesel powertrain featuring a high-voltage Belt Alternator Starter (BAS). This layout allows regenerative braking, Stop&Start, load point shift and electric power assistance to the ICE. However, a dedicated optimization of the operating strategy is required to exploit all the expected advantages of the considered HEV. The project has entailed the implementation of a zero-dimensional model of the hybrid powertrain in GT-Drive and Matlab environments. Genetic Algorithm (GA) based techniques have been used to define a novel benchmark operating strategy and to calibrate a real-time optimizer. The benchmark and real-time optimization approaches have been applied to reduce the total FC and NO_x emissions as well as to diminish the local combustion noise peaks. Different mission profiles have been considered, i.e. the New European Driving Cycle (NEDC) and three Artemis driving routes. The results show the effectiveness of the proposed methods and the improvements obtained in fuel economy, NO_x emissions and combustion noise.