The automotive industry plays a crucial role in battling the climate crisis and reducing emissions. Medium-duty and heavy-duty vehicles alone contribute to one-fourth of global emissions in the transport sector and there is a positive trend in the demand for these vehicles. Battery Electric Trucks have the potential to transform the growing logistics industry.

DAF Trucks N.V., one of the largest truck manufacturers in Europe, aims to transition to zero-emission vehicles and advance in this future of electric mobility. This project aims to model the powertrain of their CF Electric model and explore viable options for future designs. The thesis work can be defined in three parts.

The first part of the research focuses on modelling the electric powertrain in MATLAB/Simulink. Here, more focus is given in the calculation of individual motor and inverter losses. These losses are reverse engineered and extracted from the motor drive efficiency map using Particle Swarm Optimisation (PSO) algorithm.

The second part of the project focuses on performing sensitivity analysis to identify the key parameters that have the most influence on the energy efficiency of the Battery Electric Vehicle (BEV). This helps to prioritise and focus on optimisation of these parameters for future models. A comparison is drawn between the percentage change in the range each parameter has with small changes.

Lastly, in the third part of the thesis, different powertrain architectures are studied and modelled to under- stand their influence on the range of the BEV and the challenges involved in implementing such layouts.