Energy-Optimal Control of an Automotive Air Conditioning System for Ancillary Load Reduction

Quansheng Zhang, Stephanie Stockar, Marcello Canova

Research output: Contribution to journalArticle

13 Scopus citations

Abstract

The air conditioning (A/C) system is currently the largest ancillary load in passenger cars, with a significant impact on fuel economy and CO2 emissions. Considerable energy savings could be attained by simply adopting supervisory energy management algorithms that operate the A/C system to reduce power consumption of the compressor, while maintaining the cabin comfort requirements. This paper proposes a model-based approach to the design of a supervisory energy management strategy for automotive A/C systems. Starting from an energy-based model of the A/C system that captures the complex dynamics of the refrigerant in the heat exchangers and the compressor power consumption, a constrained multiobjective optimal control problem is formulated to jointly account for fuel consumption, cabin comfort, and system durability. The tradeoff between fuel economy, performance, and durability is analyzed by performing a Pareto analysis of a family of solutions generated using dynamic programming. A forward-looking optimal compressor clutch policy is then obtained by developing an original formulation of Pontryagin's minimum principle for hybrid dynamical systems. The simulation results demonstrate that the proposed control strategy allows for fuel economy improvement while retaining system performance and driver comfort.

Original languageEnglish (US)
Article number7104126
Pages (from-to)67-80
Number of pages14
JournalIEEE Transactions on Control Systems Technology
Volume24
Issue number1
DOIs
StatePublished - Jan 2016

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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