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Technical Brief

Multidisciplinary and Multi-fidelity Design Optimization of Electric Vehicle Battery Thermal Management System

[+] Author and Article Information
Xiaobang Wang

Dalian University of Technology, Dalian, LN 116024, China; The University of Texas at Dallas, Richardson, TX 75080, USA
wxbang@mail.dlut.edu.cn

Yuanzhi Liu

The University of Texas at Dallas, Richardson, TX 75080, USA
yuanzhi.liu@utdallas.edu

Wei Sun

Dalian University of Technology, Dalian, LN 116024, China
sunwei@dlut.edu.cn

Xueguan Song

Dalian University of Technology, Dalian, LN 116024, China
sxg@dlut.edu.cn

Jie Zhang

The University of Texas at Dallas, Richardson, TX 75080, USA
jiezhang@utdallas.edu

1Corresponding author.

ASME doi:10.1115/1.4040484 History: Received August 19, 2017; Revised May 24, 2018

Abstract

Battery thermal management system (BTMS) is a complex and highly-integrated system which is used to control the battery thermal conditions in electric vehicles (EVs). The BTMS consists of many sub-systems that belong to different disciplines, which poses challenges to BTMS optimization using conventional methods. This paper develops a general variable-fidelity based multidisciplinary design optimization (MDO) architecture and optimizes the BTMS by considering different systems/disciplines from the systemic perspective. Four sub-systems and/or sub-disciplines are modeled, including the battery thermodynamics, fluid dynamics, structure, and lifetime model. To perform the variable-fidelity based MDO of the BTMS, two computational fluid dynamics (CFD) models with different levels of fidelity are developed. A low fidelity surrogate model and a tuned low fidelity model are also developed using an automatic surrogate model selection method, the Concurrent Surrogate Model Selection (COSMOS). An adaptive model switching (AMS) method is utilized to realize the adaptive switch between variable-fidelity models. The objectives are to maximize the battery lifetime and to minimize the battery volume, the fan's power, and the temperature difference among different cells. The results show that the variable-fidelity MDO can balance the characteristics of the low fidelity mathematical models and the computationally expensive simulations, and find the optimal solutions efficiently and accurately.

Copyright (c) 2018 by ASME
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