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

TL;DR

This work tackles energy-efficient thermal management for automotive A/C by introducing a nonlinear NMPC-based precision cooling strategy that tracks a target discharge air cooling power trajectory $P_{DACP,targ}$ using a simplified predictive model built from a high-fidelity ACSim. It leverages speed preview from connected/automated vehicle technology to shift cooling load toward more efficient operating regions. The approach defines and uses the DACP and DACE metrics, derives a predictive model with states $T_{evap}$ and $W_{bl}$ and inputs $\triangle W_{bl}$ and $T_{evap,targ}$, and demonstrates a 4.9% reduction in A/C energy on a Ford benchmark with a cabin temperature rise less than 1°C, with potential further gains via speed-aware load shifting. The results indicate a practical, real-time capable strategy for reducing A/C auxiliary energy in HEVs/EVs by coordinating cooling with driving conditions and vehicle connectivity.

Abstract

In this paper, we propose an MPC-based precision cooling strategy (PCS) for energy efficient thermal management of automotive air conditioning (A/C) system. The proposed PCS is able to provide precise tracking of the time-varying cooling power trajectory, which is assumed to match the passenger comfort requirements. In addition, by leveraging the emerging connected and automated vehicles (CAVs) technology, vehicle speed preview can be incorporated in our A/C thermal management strategy for further energy efficiency improvement. This proposed A/C thermal management strategy is developed and evaluated based on a physics-based A/C system model (ACSim) from Ford Motor Company for the vehicles with electrified powertrains. In a comparison with Ford benchmark case over SC03 cycle, for tracking the same cooling power trajectory, the proposed PCS provides 4.9% energy saving at the cost of a slight increase in the cabin temperature (less than 1$^oC$). It is also demonstrated that by coordinating with future vehicle speed and shifting the A/C power load, the A/C energy consumption can be further reduced.

Figures (12)

• Figure 1: Schematic of the A/C system in a power-split HEV
• Figure 2: Schematics of ACSim simulation model
• Figure 3: Sensitivity of the ACSim model responses to vehicle speed.
• Figure 4: Total A/C energy consumption decreases as vehicle speed increases.
• Figure 5: Model validation results of $\Delta T_{evap}(k)=T_{evap}(k+1)-T_{evap}(k)$ and $T_{discharge}(k)$ for given sinusoidal excitations.