Model Predictive Climate Control of Connected and Automated Vehicles for Improved Energy Efficiency

Abstract

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.

Combined Energy and Comfort Optimization of Air Conditioning System in Connected and Automated Vehicles

“…2. Simulations of CoolSim model have revealed [9] that the A/C efficiency increases as vehicle speed increases. This is attributed to the condenser dissipating the heat more efficiently as the ram air speed increases.…”

“…This sensitivity will also be exploited in the CECO strategy developed in this paper to facilitate the efficient and comfortable A/C operation. A control-oriented discrete-time model of the A/C system has been developed in [9], and has the following form:…”

“…where the temperatures are defined as indicated in Fig. 1 and have units of K [9]. Two control inputs are the air flow rate through the blower,ṁ bl (in kg/s), and the evaporator wall temperature set-point, T s.p.…”

MPC-based Precision Cooling Strategy (PCS) for Efficient Thermal Management of Automotive Air Conditioning System

“…In [12], the analysis of A/C system was performed and the optimal compressor and fan speed controls have been investigated. We also studied the speed sensitivity of the A/C system efficiency in [13], which has been exploited to reduce the A/C system energy consumption via model predictive control (MPC). Reference [14] demonstrated the impact of uncertain traffic information on optimizing the A/C energy efficiency and evaluated the overall vehicle fuel economy over different driving cycles.…”

“…We note that realistic occupant comfort requirements are much more complicated than the P DACP,targ metric defined here and that research is currently ongoing to define better performance metrics for guiding the design of HVAC control systems in automotive applications. Besides the precise tracking of P DACP,targ which is intended to prevent over-cooling of the cabin, the idea similar to [13] of exploiting the speed sensitivity of A/C system efficiency will also be pursued. Specifically, the work presented in this paper may be directly compared with our previous work done in [13] since they both explore the speed sensitivity of the A/C system and apply the model predictive control (MPC) design framework.…”