An optimized ANN for the performance prediction of an automotive air conditioning system

Datta, Somenath; Das, Prasanta Kumar; Mukhopadhyay, Siddhartha

doi:10.1080/23744731.2018.1526014

Cited by 19 publications

(3 citation statements)

References 57 publications

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“…Work has also been done using artificial neural networks in automotive applications with the focus mainly on the automotive air conditioning system. Some models are used to predict system performance and cooling capacity (Hosoz and Ertunc, 2006;Kamar et al, 2013;Datta et al, 2019). Other work, such as that by Ng et al (2014b), makes use of MLP and radial basis network with experimental data to predict average cabin temperature.…”

Section: Discussionmentioning

confidence: 99%

Fast, Detailed, Accurate Simulation of a Thermal Car-Cabin Using Machine-Learning

et al. 2022

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Car-cabin thermal systems, including heated seats, air-conditioning, and radiant panels, use a large proportion of the energy budget of electric vehicles and thus reduce their effective range. Optimising these systems and their controllers might be possible with computationally efficient simulation. Unfortunately, state-of-the-art simulators are either too slow or provide little resolution of the cabin’s thermal environment. In this work, we propose a novel approach to developing a fast simulation by machine learning (ML) from measurements within the car cabin over a number of trials within a climatic wind tunnel. A range of ML approaches are tried and compared. The best-performing ML approach is compared to more traditional 1D simulation in terms of accuracy and speed. The resulting simulation, based on Multivariate Linear Regression, is fast (5 microseconds per simulation second), and yields good accuracy (NRMSE 1.8%), which exceeds the performance of the traditional 1D simulator. Furthermore, the simulation is able to differentially simulate the thermal environment of the footwell versus the head and the driver position versus the front passenger seat, but unlike a traditional 1D model cannot support changes to the physical structure. This fast method for obtaining computationally efficient simulators of car cabins will accelerate adoption of techniques such as Deep Reinforcement Learning for climate control.

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Section: Discussionmentioning

confidence: 99%

Fast, Detailed, Accurate Simulation of a Thermal Car-Cabin Using Machine-Learning

et al. 2022

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“…The cooling load of the central air-conditioning system is a typical nonlinear model with random characteristics such as slow time-varying, multi-interference, and uncertainty [2]. Airconditioning load forecasting mainly relies on various interference factors that affect the airconditioning cooling load to predict the air-conditioning cooling load at a certain time or within a certain period in the future.…”

Section: Main Factors Affecting Loadmentioning

confidence: 99%

Data Forecasting of Air-Conditioning Load in Large Shopping Malls Based on Multiple Nonlinear Regression

Wang

2022

Applied Mathematics and Nonlinear Sciences

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This article applies multiple nonlinear regression methods to establish a forecasting model for the load characteristics of air conditioning in shopping malls at different times. Based on Python data, determine the functional relationship of refrigerant parameters concerning pressure and temperature. The article uses kernel smoothing estimation technology to calculate the room temperature probability density distribution of users participating in DLC to characterize the user's comfort. The article's research results show that the average error between the regression analysis results of refrigerant parameters and the reference value is within 1%. This model is suitable for medium and long-term load forecasting. It has high prediction accuracy for the sudden change trend with a turning point.

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“…Fuzzy modeling is a mathematical approach that allows for the creation of a model that incorporates uncertainties and imprecision in the input data [22]. Optimization techniques, such as the grasshopper optimization, particle swarm optimization, whale optimization algorithm, sea-horse optimizer, and marine predators algorithm (MPA) can then be applied to the fuzzy model to identify the best values for the input controlling parameters and maximize the output performance [23][24][25]. Furthermore, Adopting proper control strategies can also boost the performance of air conditioning systems [26].…”

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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An optimized ANN for the performance prediction of an automotive air conditioning system

Cited by 19 publications

References 57 publications

Fast, Detailed, Accurate Simulation of a Thermal Car-Cabin Using Machine-Learning

Fast, Detailed, Accurate Simulation of a Thermal Car-Cabin Using Machine-Learning

Data Forecasting of Air-Conditioning Load in Large Shopping Malls Based on Multiple Nonlinear Regression

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

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