Optimal Energy-Efficient Predictive Controllers for Automotive Air Conditioning Systems

Abstract

This work provides robust model predictive controllers for a vehicle climate control (A/C-R) system featuring a three-speed, constantly variable compressor that improves temperate stability while lowering energy consumption. First, a simple control-oriented paradigm is used to explain the A/C-R system. As a consequence, the suggested model is used to build a discrete Variable Structure Gamepad (MPC) for something like an A/C-R circuit with a three-speed blower. To guarantee its longevity in both standard and cold conditions, a sufficient terminal weight is chosen. A case study can be carried out under various heating load scenarios. The advantages of a on/off microcontroller and the standalone Lpc are combined to produce two hybrid remotes that will be more reliable in any external temperature setting. A continuously MPC is also being created for ecosystems with continually altering parts. Finally, the theoretical and computational results of the innovative regulators, and also the conclusions of the classic on/off controller, are compared and contrasted to demonstrate that the recommended remotes can save up to 23% more energy than the conventional on/off control system.

Authors and Affiliations

Dr. Satish Saini

Keywords

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  • EP ID EP746561
  • DOI 10.55524/ijircst.2022.10.2.5
  • Views 7
  • Downloads 0

How To Cite

Dr. Satish Saini (2022). Optimal Energy-Efficient Predictive Controllers for Automotive Air Conditioning Systems. International Journal of Innovative Research in Computer Science and Technology, 10(2), -. https://europub.co.uk/articles/-A-746561