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Technical Brief

Multidisciplinary and Multifidelity Design Optimization of Electric Vehicle Battery Thermal Management System

[+] Author and Article Information
Xiaobang Wang

School of Mechanical Engineering,
Dalian University of Technology,
Dalian 116024, LN, China;
Department of Mechanical Engineering,
The University of Texas at Dallas,
Richardson, TX 75080
e-mail: wxbang@mail.dlut.edu.cn

Yuanzhi Liu

Department of Mechanical Engineering,
The University of Texas at Dallas,
Richardson, TX 75080
e-mail: yuanzhi.liu@utdallas.edu

Wei Sun

School of Mechanical Engineering,
Dalian University of Technology,
Dalian 116024, LN, China
e-mail: sunwei@dlut.edu.cn

Xueguan Song

School of Mechanical Engineering,
Dalian University of Technology Dalian,
Dalian 116024, LN, China
e-mail: sxg@dlut.edu.cn

Jie Zhang

Department of Mechanical Engineering,
The University of Texas at Dallas,
Richardson, TX 75080
e-mail: jiezhang@utdallas.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received August 19, 2017; final manuscript received May 24, 2018; published online June 22, 2018. Assoc. Editor: Gary Wang.

J. Mech. Des 140(9), 094501 (Jun 22, 2018) (8 pages) Paper No: MD-17-1569; doi: 10.1115/1.4040484 History: Received August 19, 2017; Revised May 24, 2018

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 subsystems 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 subsystems and/or subdisciplines 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.

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Figures

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Fig. 1

Variable fidelity-based MDO architecture

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Fig. 2

An overall flow chart of the variable-fidelity-based MDO process

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Fig. 3

The switching metric of the AMS. PDF: probability density function.

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Fig. 4

Variable-fidelity models for the BTMS

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Fig. 5

A ten battery cell air-based BTMS model

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Fig. 6

Distributions of errors in the low fidelity surrogate models of Tmax, ΔT, and Δp

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Fig. 7

Distributions of error in the tuned low fidelity models of Tmax, ΔT, and Δp

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Fig. 8

Variable-fidelity based MDO architecture using MDF

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Fig. 9

The convergence history of objective and constraint violation during the variable-fidelity MDO of the BTMS: (a) the optimization history of variable-fidelity MDO using MDF and (b) the constraint violations history of variable-fidelity MDO using MDF

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Fig. 10

Identification history during the variable-fidelity MDO of the BTMS: (a) identification history in the phase of the low fidelity models and (b) identification history in the phase of the tuned low fidelity models

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Fig. 11

The temperature distribution of the final optimal solution using AMS-based variable-fidelity MDO

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Fig. 12

The temperature distribution of the final optimal solution using Co-Kriging-based variable-fidelity MDO

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