Model Order Reduction of Vehicle Air-Conditioning Loop with Neural Networks

Dr. Shıvakumar Nagareddy; Ravi Subramanyam; Vijay Govindarasu; Veeramani Neelakandan

doi:10.64808/engineeringperspective.1817878

Model Order Reduction of Vehicle Air-Conditioning Loop with Neural Networks

Abstract

To meet the energy demand, passenger’s comfort and sustainability in the air conditioning system of vehicles, the Neural Network plays an intelligent role. In this study, the vehicle air conditioning loop was replaced with the reduced control model using a neural network including Proportional-Integral-Derivative temperature controller. The detailed model of vehicle air conditioning system with Proportional-Integral-Derivative controller and the reduced model with neural network were modelled and simulated using AMESim simulation tool. The five different driving cycles (NEDC, FTP75, US06, EPA-HWFET and SC03) were used to simulate the results of both detailed model and reduced model with neural network. The reduced control model with neural network was trained and validated through more history of data sets. Finally, the output simulation results such as vehicle cabin temperature, relative humidity at evaporator outlet, compressor shaft mechanical power, fidelity, CPU run time and compressor command were compared between detailed model and reduced control model with neural network among each driving cycle. The simulation results showed the greater reduction of CPU run time of a reduced control model with neural network than a detailed model of vehicle air-conditioning system. The same fidelity levels and speed up gains obtained in cabin temperature, relative humidity at evaporator outlet, compressor shaft mechanical power and compressor command. The drastically reduced CPU run time of a reduced model with neural network, results in lower computational complexity and its adaptability at dynamic operating situations make it suitable for real time embedded controlled air-conditioning control in vehicles. The validation of optimized parameters helps the manufacturers significantly break down the number of expensive and time consuming physical sub-models, resulting in adequate cost savings and a more sustainable development process.

Keywords

Supporting Institution

Chennai Institute of Technology, Chennai, India.

Project Number

nil

Ethical Statement

We authors confirm that the submitted research article is original and prepared with our own effort using AMEsim simulation software. Also, we conclude that the same article was not shared or submitted to any other journal or anywhere.

Thanks

Authors thank the management of Chennai Institute of Technology for providing an excellent the research and infrastructural facility available inside the campus to work on simulation study.

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Details

Primary Language

English

Subjects

Experimental Methods in Fluid Flow, Heat and Mass Transfer, Circuit Machines, Heat Transfer in Automotive

Journal Section

Research Article

Authors

Dr. Shıvakumar Nagareddy ^*
0000-0001-9084-9101
India

Ravi Subramanyam
0000-0003-1778-6516
India

Vijay Govindarasu
0009-0000-3220-8947
India

Veeramani Neelakandan
0009-0001-4853-7662
India

Publication Date

February 5, 2026

Submission Date

November 5, 2025

Acceptance Date

January 16, 2026

Published in Issue

Year 2026 Volume: 6 Number: 1

DOI

https://doi.org/10.64808/engineeringperspective.1817878

IZ

https://izlik.org/JA55NW43GW

Cite

RIS / Bibtex

APA

Nagareddy, D. S., Subramanyam, R., Govindarasu, V., & Neelakandan, V. (2026). Model Order Reduction of Vehicle Air-Conditioning Loop with Neural Networks. Engineering Perspective, 6(1), 33-42. https://doi.org/10.64808/engineeringperspective.1817878

AMA

1.Nagareddy DS, Subramanyam R, Govindarasu V, Neelakandan V. Model Order Reduction of Vehicle Air-Conditioning Loop with Neural Networks. engineeringperspective. 2026;6(1):33-42. doi:10.64808/engineeringperspective.1817878

Chicago

Nagareddy, Dr. Shıvakumar, Ravi Subramanyam, Vijay Govindarasu, and Veeramani Neelakandan. 2026. “Model Order Reduction of Vehicle Air-Conditioning Loop With Neural Networks”. Engineering Perspective 6 (1): 33-42. https://doi.org/10.64808/engineeringperspective.1817878.

EndNote

Nagareddy DS, Subramanyam R, Govindarasu V, Neelakandan V (February 1, 2026) Model Order Reduction of Vehicle Air-Conditioning Loop with Neural Networks. Engineering Perspective 6 1 33–42.

IEEE

[1]D. S. Nagareddy, R. Subramanyam, V. Govindarasu, and V. Neelakandan, “Model Order Reduction of Vehicle Air-Conditioning Loop with Neural Networks”, engineeringperspective, vol. 6, no. 1, pp. 33–42, Feb. 2026, doi: 10.64808/engineeringperspective.1817878.

ISNAD

Nagareddy, Dr. Shıvakumar - Subramanyam, Ravi - Govindarasu, Vijay - Neelakandan, Veeramani. “Model Order Reduction of Vehicle Air-Conditioning Loop With Neural Networks”. Engineering Perspective 6/1 (February 1, 2026): 33-42. https://doi.org/10.64808/engineeringperspective.1817878.

JAMA

1.Nagareddy DS, Subramanyam R, Govindarasu V, Neelakandan V. Model Order Reduction of Vehicle Air-Conditioning Loop with Neural Networks. engineeringperspective. 2026;6:33–42.

MLA

Nagareddy, Dr. Shıvakumar, et al. “Model Order Reduction of Vehicle Air-Conditioning Loop With Neural Networks”. Engineering Perspective, vol. 6, no. 1, Feb. 2026, pp. 33-42, doi:10.64808/engineeringperspective.1817878.

Vancouver

1.Dr. Shıvakumar Nagareddy, Ravi Subramanyam, Vijay Govindarasu, Veeramani Neelakandan. Model Order Reduction of Vehicle Air-Conditioning Loop with Neural Networks. engineeringperspective. 2026 Feb. 1;6(1):33-42. doi:10.64808/engineeringperspective.1817878

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https://doi.org/10.64808/engineeringperspective.1771169