Prediction of the operational performance of a vehicle seat thermal management system using statistical and machine learning techniques

Abstract

To assess and forecast the operational performance of a modified car seat for thermal management using an air conditioning system, statistical and machine learning (ML) models were used. By extending evaporator/condenser coils beneath the back and cushion surfaces of the car seat and using operational data on the HVAC system, such as seat temperature readings, an interval of operation was gathered. Using a data mining approach, statistically relevant factors and varying the compressor speed from 500 to 1600 rpm under various scenarios to model the system were selected. Utilizing key feature variables, our data-driven approach yielded predictions with favorable accuracy for the Coefficient of Performance (COP) of the HVAC system. By using the Akaike Information Criterion (AIC) to improve the Linear Regression (LR) model, the Root Mean Square Error (RMSE) dropped to 0.20, the Mean Absolute Error (MAE) dropped to 0.16, and the Coefficient of Determination (R2) increased to 98 %. The Random Forest (RF) model, optimized with hyperparameters, demonstrated moderate predictive capability, with RMSE (0.52), MAE (0.37), and R2 (94 %). Furthermore, polynomial feature augmentation, individual and combined predictor analysis, and iterative predictor combinations all improved predictive accuracy. Detailed information on the algorithms was given for the sake of other researchers.