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Environmental conditions driven method for automobile cabin pre-conditioning with multi-satisfaction objectives.

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Optimizing vehicle cabin pre-conditioning is crucial for passenger comfort and energy efficiency. This study introduces a data-driven model using machine learning to determine optimal parameters for various climate conditions, ensuring passenger satisfaction.

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Area of Science:

  • Automotive Engineering
  • Thermal Management
  • Data Science

Background:

  • Passenger thermal comfort and energy efficiency are key challenges in vehicle cabin climate control.
  • Existing pre-conditioning methods struggle with varying environmental loads and complex cabin thermal characteristics.
  • Accurate identification of cabin thermal states under diverse conditions is difficult due to material properties and structural variations.

Purpose of the Study:

  • To develop a data-driven decision model for identifying cabin thermal characteristics and determining optimal pre-conditioning parameter schemes.
  • To enhance passenger comfort and energy efficiency through accurate temperature adjustments.
  • To address the limitations of fixed pre-conditioning parameters under dynamic environmental conditions.

Main Methods:

  • Collected year-round thermal data from a vehicle in a hot climate region (Middle East).
  • Utilized Support Vector Machines, Decision Tree, and K-nearest neighbor algorithms to classify climate scenes based on input conditions.
  • Developed a Comprehensive Evaluation Index (CEI) to quantify passenger satisfaction, considering Predicted Mean Vote (PMV), local temperature, air quality, and energy efficiency.

Main Results:

  • The proposed data-driven model effectively identifies different climate scenes within the vehicle cabin.
  • Classification algorithms accurately categorized climate levels based on recorded thermal data.
  • The developed pre-conditioning parameter schemes, guided by CEI, demonstrated effectiveness in satisfying multiple passenger comfort objectives.

Conclusions:

  • The data-driven decision model provides a feasible solution for optimizing vehicle cabin pre-conditioning.
  • The proposed CEI offers a robust method for numerically evaluating passenger satisfaction across multiple criteria.
  • The study successfully demonstrates the capability of the model to deliver effective pre-conditioning schemes for enhanced passenger comfort and energy savings.