Prediction of AI-Based Personal Thermal Comfort in a Car Using Machine-Learning Algorithm

Yeong Jo Ju,Jeong Ran Lim,Euy Sik Jeon

doi:10.3390/electronics11030340

Abstract

Defining a passenger’s thermal comfort in a car cabin is difficult because of the narrow environment and various parameters. Although passenger comfort is predicted using a thermal-comfort scale in the overall cabin or a local area, the scale’s range of passenger comfort may differ owing to psychological factors and individual preferences. Among the many factors affecting such comfort levels, the temperature of the seat is one of the direct and significant environmental factors. Therefore, it is necessary to predict the cabin environment and seat-related personal thermal comfort. Accordingly, machine learning is used in this research to predict whether a passenger’s seat-heating-operation pattern can be predicted in a winter environment. The experiment measures the ambient factor and collects data on passenger heating-operation patterns using a device in an actual winter environment. The temperature is set as the input parameter in the measured data and the operation pattern is used as the output parameter. Based on the parameters, the predictive accuracy of the heating-operation pattern is investigated using machine learning. The algorithms used in the machine-learning train are Tree, SVM, and kNN. In addition, the predictive accuracy is tested using SVM and kNN, which shows a high validation accuracy based on the prediction results of the algorithm. In this research, the parameters predicting the personal thermal comfort of three passengers are investigated as a combination of input parameters, according to the passengers. As a result, the predictive accuracy of the operation pattern according to the tested input parameter is 0.96, showing the highest accuracy. Considering each passenger, the predictive accuracy has a maximum deviation of 30%. However, we verify that it indicates the level of accuracy in predicting a passenger’s heating-operation pattern. Accordingly, the possibility of operating a heating seat without a switch operation is confirmed through machine learning. The primary-stage research result reveals whether it is possible to predict objective personal thermal comfort using the passenger seat’s heating-operation pattern. Based on the results of this research, it is expected to be utilized for system construction based on the AI prediction of operation patterns according to the passenger through machine learning.

Highlights

This article is an open access articleThe heating, ventilation, and air-conditioning (HVAC) system of a vehicle provides thermal comfort to passengers in various cabin environments
We verified whether personal thermal comfort is possible without a switch operation
The algorithm for the test was determined based on the validation accuracy and AUC according to the passenger

Summary

Introduction

The heating, ventilation, and air-conditioning (HVAC) system of a vehicle provides thermal comfort to passengers in various cabin environments. The cabin inside the vehicle is a highly asymmetrical environment. Factors, such as cabin temperature, air velocity, and air humidity around the passenger, play an essential role in determining passenger thermal comfort [1]. Heat from the engine is used for the HVAC of the cabin; in the case of EVs, the cabin’s thermal load consumes approximately 1/3 of the stored electricity. If there is not enough energy to satisfy the passenger’s thermal comfort, the use of the HVAC system should be reduced to reach the final destination or charging point. The speed must be reduced to satisfy the passenger’s thermal comfort [3]

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Conclusion