Journal
APPLIED THERMAL ENGINEERING
Volume 89, Issue -, Pages 101-114Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.applthermaleng.2015.06.002
Keywords
Air conditioning system; Artificial neural network; Electric vehicle; Electronic expansion valve; Performance prediction
Funding
- National Science and Technology Support Program of China [2013BAG03B01]
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In this study, electric vehicle air conditioning system (EVACS) performances with scroll compressor and electronic expansion valve (EEV) were experimentally investigated by varying scroll compressor speed, EEV opening and environment temperature. An artificial neural network (ANN) model for EVACS performances (such as refrigerant mass flow rate, condenser heat rejection, refrigeration capacity and compressor power consumption) prediction was developed based on experimental data. The ANN model was tested with two transfer functions (logsig and tansig) and different hidden neurons (3-13) using Levemberg-Marquardt algorithm. The optimized ANN was determined as a configuration of 4-13-4 with logsig transfer function, which demonstrated the best capability with mean relative errors, root mean square errors and correlation coefficients in the range of 0.92-2.71%, 0.0044-0.0141 and 0.9975-0.9998, respectively. (C) 2015 Elsevier Ltd. All rights reserved.
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