Work

Fuel Efficiency Optimization in Adaptive Cruise Control: A Comparative Study of Model Predictive Control-Based Approaches

Year: 2024

Type: article

Abstract: This work investigates the fuel efficiency potential of Adaptive Cruise Control (ACC) systems, focusing on two optimization-based control approaches for internal combustion engine (ICE) vehicles. In particular, this study compares two model predictive control (MPC) designs. In the first approach, a strictly quadratic cost is adopted, and fuel consumption is indirectly minimized by adjusting the weights assigned to state tracking and control effort. In the second approach, a fuel consumption map is explicitly included in the MPC cost function, aiming to directly minimize it. Both approaches are compared to a globally optimal benchmark obtained with dynamic programming. Although these methods have been discussed in the literature, no systematic comparison of their relative performance has been conducted, which is the primary contribution of this article. The results demonstrate that, with proper tuning, the simpler quadratic approach can achieve comparable fuel savings to the approach with explicit fuel consumption minimization, with a maximum variation of 0.5%. These results imply that the first alternative is more suitable for online implementation, due to the more favorable characteristics of the associated optimization problem.

Source: Applied Sciences

DOI: 10.3390/app14219833

Authors: Angelo Borneo, Federico Miretti, Daniela Anna Misul

Institution: Politecnico di Torino

Language: English

Topic: Advanced Control Systems Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $2,728