Longitudinal dynamic modelling and simulation of a full electric vehicle

This thesis is carried out in the context of a design of an electric vehicle. The purpose of the thesis is to model and to simulate the longitudinal dynamic behavior of a full electric vehicle, in order to design the battery pack and to evaluate the vehicle’s performance and range, through the standard driving cycles NEDC and WLTC. The work is developed in the Matlab-Simulink environment, the electric vehicle is modelled as three different blocks that interact with each other: a vehicle block, a motor block and a battery block. The modelled vehicle follows the driving cycles with two different approaches, a forward approach, where the vehicle follows strictly the imposed speed profile, and a backward approach, where the model simulates the driver that tries to follow the driving cycles. The first approach is used to evaluate the energy consumption to complete a standard cycle, this value will be exploited in the backward approach to calibrate the proportional integrative controller, that simulates the driver, and to design and verify the battery pack. The backward approach has the objective to evaluate the vehicle range, performance and to gives information about battery pack parameters as temperature, current and state of charge (SoC). In this model the greatest efforts are spent in the modelling of the battery pack block, which takes in consideration a thermal model and the open circuit voltage (OCV) against depth of discharge (DoD) cell characteristic. In the specific, the thermal model evaluates the temperature of the battery pack due to the dissipating power and provide a de-rating control on the traction torque. The full electric vehicle used to verify the model accuracy is the Renault Zoe, because several technical data are available. The vehicle’s range and performance obtained through Renault Zoe simulation are in acceptable range of accuracy (about 5%). To conclude, the model is exploited to support the battery pack design and to predict the performance and the range, in the standard driving cycles, of the of the electric vehicle in development.