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Lumped-Parameter Modeling of an Automotive Air Conditioning System for Energy Optimization and Management

[+] Author Affiliations
Quansheng Zhang, Marcello Canova

The Ohio State University, Columbus, OH

Paper No. DSCC2013-3835, pp. V001T04A003; 8 pages
  • ASME 2013 Dynamic Systems and Control Conference
  • Volume 1: Aerial Vehicles; Aerospace Control; Alternative Energy; Automotive Control Systems; Battery Systems; Beams and Flexible Structures; Biologically-Inspired Control and its Applications; Bio-Medical and Bio-Mechanical Systems; Biomedical Robots and Rehab; Bipeds and Locomotion; Control Design Methods for Adv. Powertrain Systems and Components; Control of Adv. Combustion Engines, Building Energy Systems, Mechanical Systems; Control, Monitoring, and Energy Harvesting of Vibratory Systems
  • Palo Alto, California, USA, October 21–23, 2013
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5612-3
  • Copyright © 2013 by ASME


The air conditioning (A/C) system is the largest ancillary load in passenger cars, with significant impact on fuel economy. In order to reduce the energy consumption of A/C systems, model-based optimization and optimal control design tools can be effectively applied to design of a supervisory energy management strategy. Significant challenges however lie in the design of a system model that is accurate enough to represent the nonlinear behavior of the system, yet sufficiently simple to enable the use of model-based control design methods.

This paper presents a low-order, energy-based model of an automotive A/C system that is able to predict the dynamics of the evaporator and condenser pressures and the compressor power consumption during typical thermostatic (on/off) operations. A characterization of the mass and energy transport in the heat exchangers is obtained using a lumped-parameter approximation, leading to a model with reasonable accuracy but greatly reduced complexity, hence for supervisory control design. The model was validated against experimental data obtained on a test vehicle, allowing one to evaluate the accuracy in predicting the pressure states and the power consumption.

Copyright © 2013 by ASME



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