Target speed computation through predictive cruise control for vehicles energy consumption reduction

Energy reduction in automotive applications is addressed since many years by both academia and vehicle manufactures. Recently, the introduction of innovative technical solutions, based, for instance, on internet connectivity and Advanced Driver-Assistance Systems (ADAS), allowed the on-board implementation of complex Energy Management Strategies (EMS) based on advanced optimization algorithms. However, these solutions require, in most cases, high computational capabilities and are generally customized on vehicle type and addressed to hybrid powertrain configurations. In this regard, this paper presents a vehicle speed management algorithm that suggests in real time to the driver the speed that shall be followed to achieve fuel consumption reduction. The algorithm requires road information, such as speed limits and slope variation, but it can be applied to any vehicle (e.g., light-weight cars, busses, trucks, etc.) and powertrain configuration (conventional, hybrid, pure electric, etc.). The basic structure relies on Predictive Cruise Control (PCC) approach, without introducing any complex mathematical optimization algorithm. The algorithm performance is here investigated in simulated environment by analyzing different vehicle types and routes. Under all the addressed scenarios, the algorithm can allow fulfilling energy consumption reduction (within the range of 3%-11% for real scenarios) with respect to a given reference speed profile under the same travel time.