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

TL;DR

This paper introduces a predictive control strategy for vehicle air conditioning that leverages weather and traffic forecasts to reduce energy use while maintaining occupant comfort, demonstrated through simulation.

Contribution

It develops a novel combined energy and comfort optimization approach using model predictive control with forecast data for connected and automated vehicles.

Findings

Up to 7.6% reduction in A/C energy consumption.

Achieved 3.1% improvement in vehicle fuel economy.

Better occupant comfort compared to baseline control.

Abstract

In this paper, we propose a combined energy and comfort optimization (CECO) strategy for the air conditioning (A/C) system of the connected and automated vehicles (CAVs). By leveraging the weather and traffic predictions enabled by the emerging CAV technologies, the proposed strategy is able to minimize the A/C system energy consumption while maintaining the occupant thermal comfort (OTC) within the comfort constraints, where the comfort is quantified by a modified predictive mean vote (PMV) model adapted for an automotive application. A general CECO problem is formulated and addressed using model predictive control (MPC) and weather/traffic previews. Depending on the ways of exploiting the preview information and enforcing the OTC constraint, different MPCs are developed based on solving different variations of the general CECO problem. The CECO-based MPCs are then tested in simulation using an automotive A/C system simulation model (CoolSim) as the virtual testbed. The simulation results show that, over SC03 driving cycle, the proposed CECO-based MPCs outperform the baseline cabin temperature tracking controller, reducing the A/C system energy consumption by up to 7.6%, while achieving better OTC according to the PMV-based metrics. This energy saving in A/C system translates to 3.1% vehicle fuel economy improvement. The trade-off between energy efficiency and OTC for different control scenarios is also highlighted.