Model Predictive Control for Automotive Climate Control Systems via Value Function Approximation

TL;DR

This paper introduces a Model Predictive Control approach with value function approximation to optimize energy use in automotive climate control systems, aiming to extend electric vehicle range.

Contribution

It presents a novel MPC-based energy management strategy for vehicle thermal systems using a nonlinear refrigerant model and a terminal cost approximation for real-time application.

Findings

Reduced energy consumption in climate control systems.

Effective thermal tracking with optimized control.

Real-time implementation feasibility demonstrated.

Abstract

Among the auxiliary loads in light-duty vehicles, the air conditioning system is the single largest energy consumer. For electrified vehicles, the impact of heating and cooling loads becomes even more significant, as they compete with the powertrain for battery energy use and can significantly reduce the range or performance. While considerable work has been made in the field of optimal energy management for electrified vehicles and optimization of vehicle velocity for eco-driving, few contributions have addressed the application of energy-optimal control for heating and cooling loads. This paper proposes an energy management strategy for the thermal management system of an electrified powertrain, based on Model Predictive Control. Starting from a nonlinear model of the vapor compression refrigeration system that captures the dynamics of the refrigerant in the heat exchangers and the power consumption of the system, a constrained multi-objective optimal control problem is formulated to reduce energy consumption while tracking a desired thermal set point. An efficient implementation of MPC is proposed for real-time applications by introducing a terminal cost obtained from the approximation of the global optimal solution.