Development of a Predictive Control Function for a Plug-in Hybrid Electric Vehicle with Electrically-heated Catalyst

This master thesis work is focused on the development of a predictive EHC control function for a diesel plug-in hybrid electric vehicle equipped with a EURO 7 compliant exhaust aftertreatment system (EATS), with the purpose of showing the advantages provided by the implementation of a predictive control strategy with respect to a rule-based one. A preliminary step will be the definition of an accurate powertrain and EATS physical model, starting from already existing and validated applications. Then, a rule-based control strategy managing the torque split between the electric motor (EM) and the internal combustion engine (ICE) will be developed and calibrated, with the main target of limiting tailpipe NOx emission by taking into account EM and ICE operating conditions together with EATS conversion efficiency. The information available from vehicle connectivity will be used to reconstruct the future driving scenario, also referred to as electronic horizon (eHorizon), and in particular to predict ICE first start. Based on this knowledge, an EATS pre-heating phase can be planned to avoid low pollutant conversion efficiencies, thus preventing high NOx emission due to engine cold start. Consequently, the final NOx emission over the complete driving cycle will be strongly reduced, allowing to comply with the limits potentially set by the incoming EURO 7 regulation. Moreover, given the same NOx emission target, the gain achieved thanks to the implementation of an EHC predictive control function will allow to consider a simplified EATS layout, thus reducing the related manufacturing cost. The promising results achieved in terms of NOx emission reduction show the effectiveness of the application of a predictive control strategy focused on EATS thermal management and highlight the potential of a complete integration and parallel development of involved vehicle physical systems, control software and connectivity data management.

Analysis and detection of partial discharges in an electric motor of a hybrid supercar.

The objective of this thesis was the development of a new detection method of partial discharge (PD) activity in the stator of an electrical hybrid supercar fed by a silicon carbide converter, for which detection with common methods make it very difficult to separate PD pulses from switching noise. This work focused on the analysis and detection of partial discharges making use of an antenna, a peak detector, and an oscilloscope capable of capturing the electromagnetic pulses emitted during PD activity. Validation of the proposed method was done by comparing the partial discharge inception voltage (PDIV) detected by this system with the one obtained from an optical method of proven accuracy, with different rise times and samples. Further development of this method, if proved successful on a full stator, can help increasing the overall reliability of the car, potentially allowing for real time detection of PD activity and predictive maintenance before failure of the insulation system in a hybrid vehicle.