Recent advances in performance enhancement techniques and the perspective of solar energy for automobile air-conditioning system-a critical review

Patel, Hemantchandra N.; Modi, Kalpesh V.

doi:10.1016/j.solener.2022.06.003

Cited by 10 publications

(1 citation statement)

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“…The efficiency and effectiveness of AAC can significantly impact its performance, energy consumption, and environmental impact, making the optimization of AAC performance an increasingly important area of research in the automotive industry [1]. The change in temperature within a vehicle's interior can cause discomfort for passengers, making AAC even more crucial in maintaining a comfortable temperature inside the cabin; however, the operation of AAC consumes power either from the vehicle's engine or its battery, which can have an impact on the fuel economy, cruising range, or overall performance of the automobile [2]. With the extensive application of AAC in residences, industries, and vehicles, the energy consumed is significant; therefore, researchers are prompted to design efficient and green devices with better efficiency to conserve energy and protect the environment.…”

Section: Introductionmentioning

confidence: 99%

Improving Automotive Air Conditioning System Performance Using Composite Nano-Lubricants and Fuzzy Modeling Optimization

Alahmer

Ghoniem

2023

Sustainability

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This study aims to enhance the effectiveness of automobile air conditioning (AAC) systems through the use of composite nano-lubricants and fuzzy modeling optimization techniques. Composite nano-lubricants, which consist of varied metal oxide ingredients and content ratios, are projected to surpass single-component nano-lubricants in terms of improving the performance of AAC systems. Fuzzy modeling is used to simulate the AAC system based on experimental data using three input parameters: volume concentration of nano-lubricants (%), the refrigerant charge (g), and compressor speed (rpm). The output performance of the AAC system is measured using four parameters: cooling capacity (CC) in kW, compressor work (CW) in kJ/kg, coefficient of performance (COP), and power consumption (PC) in kW. Optimization is performed using the marine predators algorithm (MPA) to identify the best values for the input control parameters. The objective function is to minimize CW, COP, and PC while simultaneously maximizing CC and COP. Results showed that the performance of the AAC system improved from 85% to 88% compared to the experimental dataset, highlighting the potential benefits of using composite nano-lubricants and fuzzy modeling optimization for improving the energy efficiency of AAC systems. Furthermore, a comprehensive comparison with ANOVA was performed to demonstrate the superiority of the fuzzy modeling approach. The results indicate that the fuzzy model outperforms ANOVA, as evidenced by a reduced root mean square error (RMSE) for all data, from 0.412 using ANOVA to 0.0572 using fuzzy. Additionally, the coefficient of determination for training increased from 0.9207 with ANOVA to 1.0 with fuzzy, further substantiating the success of the fuzzy modeling phase.

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Section: Introductionmentioning

confidence: 99%