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Dynamic Modelling and Performance Prediction of a Multi-unit Baseline Air Conditioning System for a Generic Bus under Part-Load Conditions

Journal Article
02-14-02-0015
ISSN: 1946-391X, e-ISSN: 1946-3928
Published February 26, 2021 by SAE International in United States
Dynamic Modelling and Performance Prediction of a Multi-unit Baseline Air Conditioning System for a Generic Bus under Part-Load Conditions
Citation: Afrasiabian, E., Douglas, R., and Best, R., "Dynamic Modelling and Performance Prediction of a Multi-unit Baseline Air Conditioning System for a Generic Bus under Part-Load Conditions," SAE Int. J. Commer. Veh. 14(2):2021, https://doi.org/10.4271/02-14-02-0015.
Language: English

Abstract:

A dynamic model of a multi-unit air conditioning (AC) system in a generic bus was developed to investigate different control strategies on the system performance and the cabin comfort level. In this study, a part-load condition was considered, where adopting a proper strategy for governing a multi-unit system is important. Simulink and Simscape toolbox from MATLAB (R2019a) were used to build up the real-time model by integrating a cooling system with a cabin sub-model. The cooling system consists of two independently controlled units, based on a Vapor Compression Cycle (VCC). The cabin is modelled using a moisture air network and is coupled with the cooling system to exchange heat with the refrigerant through the evaporators. Moreover, the sensible and latent loads are incorporated into the cabin by a thermal network. Six different strategies were implemented using different criteria to investigate the average power and Coefficient of Performance (COP) under a part-load condition. The comfort level was obtained in terms of the Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD) indices. Results are suggestive of a link between the implemented control strategy of a multi-unit AC system and its performance. Results showed that five out of the six proposed strategies might be chosen, depending on the adopted trade-off policy between the comfort level and the system energy demand. In this way, the numerical approach introduced here, along with the combination of the presented findings, provides good support for the decision-making on thermal management inside the cabin, based on the energy consumption and the thermal comfort level.