An Economic Predictive Control approach for Autonomous EVs in Adaptive Cruise Control scenario

Concerns about the effects of climate change have recently driven research across various fields of applications. The automotive transportation sector is one of the most interesting from this point of view: government laws and regulatory policies push the automotive companies to invest in diminishing the emissions of the means of transportation. As matter of facts, the automotive industry has intensified its effort in development of Electric Vehicles (EVs) or Hybrid Electric Vehicles (HEVs) to be compliant with the new era of the transportation sector. Although the EVs represent a promising solution to ensure a more sustainable form of transportation, their limited driving range is a critical aspect, which calls for further investigation. Despite their higher energy efficiency compared to vehicles equipped with Internal Combustion Engines (ICEs), the storage of electric energy presents greater challenges than conventional fuel storage. This limitation needs to have a thrifty usage of the electric power. Furthermore, the time required to recharge a battery is significantly longer than the time needed to refuel a traditional fuel tank, representing another obstacle to the widespread adoption of EVs across various sectors. To address these necessities, the following work proposes a Nonlinear Model Predictive Control (NMPC) strategy for the lateral and longitudinal control of a vehicle dynamics and then shifts to an Economic NMPC to achieve simultaneously optimal control performances and energy saving. Tracking performances, comfort and safety considerations, and energy saving are opposing objectives: often, focusing blue solely on one objective might cause significant degradation of the other objective performance. Hence, the main objective of the thesis is to demonstrate the effectiveness of the economic approach in finding a compromise in the control action which can reduce the energy consumption and, at the same time, featuring satisfying tracking performances. The control algorithm is developed in a software environment, employing two main methodologies: a traditional tracking-based NMPC and an Economic NMPC. The controllers are tested in a simulated environment using real-world data for the controlled vehicle. Then the two controllers are compared from the standpoints of tracking performances, passenger safety and comfort and energy consumptions. Simulation results show the potential of the economic approach in different scenarios, with increasing level of complexity. Additionally, the EMPC simplifies the cost function, maintaining the same constraint set, such that the computational effort of the problem considerably decreases.