Optimization of fuel consumption of a Hybrid Electric Vehicle with Adaptive Cruise Control

One of the major goals of automotive industry in recent years has been the improvement of the energy efficiency of vehicles, due to the increasing energy costs and the restrictions on emissions. This thesis investigates the potential in fuel consumption reduction of a 48V P1 mild-hybrid IVECO Daily equipped with an Eco-Adaptive Cruise Control (Eco-ACC). From the energy analysis of the powertrain on different driving cycles, a major contribution to fuel usage is due to high torque requests in correspondence of high acceleration and deceleration. Fuel consumption strictly depends on driving behaviour and therefore one of the strategies to increase efficiency is to adjust the driving style by using Advanced Driver Assistance System (ADAS) to enhance eco-driving. ADAS technologies are a set of features that reduce human duties in driving, improving safety, comfort and emissions. In particular, acceleration values can be managed by an Eco-ACC. In classical Adaptive Cruise Control, the speed of the vehicle is controlled by acting on the accelerator command, based on the distance from preceding vehicles and on its speed, along with the driver settings. In literature, many examples of ACC are proposed and exploited for fuel consumption reduction, developing numerous control algorithms for this purpose. In this work different formulations of Eco-ACC have been integrated with a forward vehicle model developed in Matlab/Simulink, testing the different algorithms tuned to obtain a good balance between fuel consumption, car-following performance and safety. The eco-approach has been tested on standard driving cycles, as well as on critical situations’ scenarios, and the results are compared with a baseline vehicle without any ADAS. It has been observed that the integration of Eco-ACC leads to a reduction of the fuel consumption between 10% and 15% in urban and rural scenarios, while the improvement is limited in case of application to a highway scenario.