Comparative Study of Physics-Based Modeling and Neural Network Approach to Predict Cooling in Vehicle Integrated Thermal Management System

Duwon Choi,Nankyu Lee,Jinil Park,Youngkuk An,Jonghwa Lee

doi:10.3390/en13205301

Abstract

Vehicle integrated thermal management system (VTMS) is an important technology used for improving the energy efficiency of vehicles. Physics-based modeling is widely used to predict the energy flow in such systems. However, physics-based modeling requires several experimental approaches to get the required parameters. The experimental approach to obtain these parameters is expensive and requires great effort to configure a separate experimental device and conduct the experiment. Therefore, in this study, a neural network (NN) approach is applied to reduce the cost and effort necessary to develop a VTMS. The physics-based modeling is also analyzed and compared with recent NN techniques, such as ConvLSTM and temporal convolutional network (TCN), to confirm the feasibility of the NN approach at EPA Federal Test Procedure (FTP-75), Highway Fuel Economy Test cycle (HWFET), Worldwide harmonized Light duty driving Test Cycle (WLTC) and actual on-road driving conditions. TCN performed the best among the tested models and was easier to build than physics-based modeling. For validating the two different approaches, the physical properties of a 1 L class passenger car with an electric control valve are measured. The NN model proved to be effective in predicting the characteristics of a vehicle cooling system. The proposed method will reduce research costs in the field of predictive control and VTMS design.

Highlights

Vehicle integrated thermal management system (VTMS) technologies have demonstrated their potential in a variety of fields, including powertrains, electrical systems such as motors and batteries, passenger comfort systems, and implementation of new powertrain technologies and emission control systems [1,2]
neural network (NN)-based models paper, we proposed a deep learning framework based on NNs in place of conventional physics-based
This paper proposed an NN model for a conventional cooling system modeling technique for VTMS

Summary

Introduction

Vehicle integrated thermal management system (VTMS) technologies have demonstrated their potential in a variety of fields, including powertrains, electrical systems such as motors and batteries, passenger comfort systems, and implementation of new powertrain technologies and emission control systems [1,2]. Among the numerous technologies related to improving fuel economy, when efficiency and improvement in fuel economy are considered compared to the increase in production cost of a vehicle, VTMS technologies are expected to cost less than $50 per 1% reduction in fuel consumption [3]; automakers are showing significant interest in the technology and are actively applying it to their products [4]. Adding sensors for system behavior analysis increases the cost. Manufacturers use several methods to minimize the number of sensors required. CFD-based analysis for prediction [5,6], simplified model-based prediction [7,8], and model-based analysis for optimization by relocating the sensor [9] have been utilized

Objectives

Results

Discussion

Conclusion