Model Predictive Climate Control of Connected and Automated Vehicles for Improved Energy Efficiency
It addresses energy efficiency in A/C systems for future electric and hybrid vehicles, but the improvement is incremental.
This paper develops a nonlinear model predictive control (NMPC) for automotive air conditioning (A/C) systems in connected and automated vehicles, achieving up to 9% energy efficiency improvements by utilizing speed preview and coordinated cabin temperature constraint adjustments.
This paper considers an application of model predictive control to automotive air conditioning (A/C) system in future connected and automated vehicles (CAVs) with battery electric or hybrid electric powertrains. A control-oriented prediction model for A/C system is proposed, identified, and validated against a higher fidelity simulation model (CoolSim). Based on the developed prediction model, a nonlinear model predictive control (NMPC) problem is formulated and solved online to minimize the energy consumption of the A/C system. Simulation results illustrate the desirable characteristics of the proposed NMPC solution such as being able to enforce physical constraints of the A/C system and maintain cabin temperature within a specified range. Moreover, it is shown that by utilizing the vehicle speed preview and through coordinated adjustment of the cabin temperature constraints, energy efficiency improvements of up to 9% can be achieved.