SimRod E-Powertrain Modelling and Identification

Nowadays, mechatronic systems modelling and identifications methods are go- ing towards more challenging and complex scenarios. Practically, model based testing (MBT) approaches are mostly employed in order to face several kind of requirements and features of specific systems designs. Moreover, generally, the development of these techniques allows to move to new testing methodologies, such as X-in-the-Loop (XiL). Moving to the automotive world and, specifically, to modern electric vehicle (EV) powertrain systems, advanced and complex methods are continuously designed and developed for centering goals in terms of safety and production requirements. Indeed, high reliability with a reduced amount of costs, efforts and time are increas- ingly widespread requirements. Physical vehicle prototypes testing is an expensive and time consuming process and the instrumentation needed for multi-physical acquisitions is not always easy to implement in on-road test scenarios so that, obviously, virtual approaches are becoming increasingly preferable. This thesis project, carried out with the collaboration of Politecnico di Torino and Siemens Digital Industries Software (DISW), presents a specific Model based approach for the development of a software simulation model of an EV powertrain test setup in order to reproduce specific behaviors of the real test setup in a virtual environment. This classical powertrain test setup is upgraded for the implementation of an XiL setup preserving all the safety features of the starting test bench. The main purpose of this thesis project, indeed, is starting the creation of a reliable predictive model capable to perform a preventive system simulation before the real commission of advanced test campaigns. In order to get this objective, the experimental activity starts with the general analysis of the test setup, then it moves to the implementation of a test/measurements campaign exploited for the latter identification and/or modelling activity. The identified test setup features are implemented on the software model and, at the end, simulated results are validated.