Experimental Optimization of Charge Level in an Automotive Air Conditioning system under Steady State Conditions

Desai, Avinash D.; Sapali, S. N.; Parthasarathi, G. V.

doi:10.4271/2010-36-0018

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…Make the RBF neural network can accurately simulate the temperature of the windshield of fog universal and effective data samples for training. Therefore, the first sensor to the experimental calibration data samples and 48 sets of data representative sample library [3] .…”

Section: Iirbf Neural Network Sample Databasementioning

confidence: 99%

Research of Automotive Glass Fog System Based on RBF Neural Network

Zhang

2012

AMR

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By describing a danger from driving vehicles with fog on windshield, we give a concept of a new type of automatic windshield defogging system applying traditional sensor and RBF neural networks. In terms of an analysis on the source of fogging on automatic windshield, applying traditional sensor, we design a RBF neural networks. Then, via RBF neural networks mode, training and testing 48 series of data from an experiment. A result of MATLAB software demonstrates that this new system defog from automatic windshield swiftly and precisely by applying RBF neural networks.

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Section: Iirbf Neural Network Sample Databasementioning

confidence: 99%

Research of Automotive Glass Fog System Based on RBF Neural Network

Zhang

2012

AMR

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A state of art review on future low global warming potential refrigerants and performance augmentation methods for vapour compression based mobile air conditioning system

Prabakaran

Lal

Kim

2022

J Therm Anal Calorim

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Effects of Air Conditioner Use on Real-World Fuel Economy

Huff¹,

West²,

Thomas³

2013

SAE Technical Paper Series

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On-road and laboratory experiments with a 2009 FordExplorer and a 2009 Toyota Corolla were conducted to assess the fuel consumption penalty associated with air conditioner (A/C) use at idle and highway cruise conditions. Vehicle data were acquired on-road and on a chassis dynamometer. Data were gathered for various A/C settings and with the A/C off and the windows open. At steady speeds between 64.4 and 113 kph (40 and 70 mph), both vehicles consumed more fuel with the A/C on at maximum cooling load (compressor at 100% duty cycle) than when driving with the windows down. The Explorer maintained this trend beyond 113 kph (70 mph), while the Corolla fuel consumption with the windows down matched that of running the A/C at 121 kph (75 mph), and exceeded it at 129 kph (80 mph). The incremental fuel consumption rate penalty due to air conditioner use was nearly constant with a slight trend of increasing consumption with increasing vehicle (and compressor) speed. A lower fuel penalty due to A/C operation is observed at idle for both vehicles, likely due to the low compressor speed at this operating point, although the percentage increase due to A/C use is highest at idle.

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Experimental Optimization of Charge Level in an Automotive Air Conditioning system under Steady State Conditions

Cited by 4 publications

References 6 publications

Research of Automotive Glass Fog System Based on RBF Neural Network

Research of Automotive Glass Fog System Based on RBF Neural Network

A state of art review on future low global warming potential refrigerants and performance augmentation methods for vapour compression based mobile air conditioning system

Effects of Air Conditioner Use on Real-World Fuel Economy

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