Surrogate based multidisciplinary design optimization of lithium-ion battery thermal management system in electric vehicles

Wang, Xiaobang; Li, Mao; Liu, Yuanzhi; Sun, Wei; Song, Xueguan; Zhang, Jie

doi:10.1007/s00158-017-1733-1

Cited by 54 publications

(21 citation statements)

References 37 publications

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“…Battery thermal management system is a complex system that consists of many modules belonging to different disciplines [17]; therefore, a corresponding multidisciplinary analysis of the BTMS should be conducted beforehand when the variable-fidelity models are established. In this paper, four subsystems/disciplines are taken into account, including the battery thermodynamic, fluid dynamic (air), lifetime, and structure.…”

Section: Variable-fidelity Models Establishmentmentioning

confidence: 99%

“…More details on the disciplines selection and multidisciplinary analysis of BTMS can be found in Ref. [17].…”

Section: Variable-fidelity Models Establishmentmentioning

confidence: 99%

“…From the multidisciplinary analysis [17], the average temperature, T max ( C), the maximum temperature difference, DT ( C), and the pressure drop, Dp (Pa), are selected as the outputs. To build the low fidelity surrogate models, the Sobol sequence [18] is used to generate 200 training points.…”

Section: Low Fidelity Surrogate Modelsmentioning

confidence: 99%

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

Wang

Liu

Sun

et al. 2018

Journal of Mechanical Design

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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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Section: Variable-fidelity Models Establishmentmentioning

confidence: 99%

“…More details on the disciplines selection and multidisciplinary analysis of BTMS can be found in Ref. [17].…”

Section: Variable-fidelity Models Establishmentmentioning

confidence: 99%

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

Wang

Liu

Sun

et al. 2018

Journal of Mechanical Design

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“…Fan et al investigated the effect of different gap spacing and mass flow on the maximum temperature rise of the battery module . Wang et al studied the surrogate‐based optimization for air cooling system . Liu et al presented surrogate‐based optimization for J‐type air cooling system .…”

Section: Introductionmentioning

confidence: 99%

“…13 Wang et al studied the surrogate-based optimization for air cooling system. 14 Liu et al presented surrogatebased optimization for J-type air cooling system. 15 Generally, considering factors of manufacturing and cost, the air-based cooling system is preferable for most of the driving cycles.…”

Section: Introductionmentioning

confidence: 99%

Multi‐objective design optimization for mini‐channel cooling battery thermal management system in an electric vehicle

Peng

Xiao

et al. 2019

Int J Energy Res

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Summary Lithium‐ion battery packs have been generally used as the power source for electric vehicles. Heat generated during discharge and limited space in the battery pack may bring safety issues and negative effect on the battery pack. Battery thermal management system is indispensable since it can effectively moderate the temperature rise by using a simple system, thereby improving the safety of battery packs. However, the comprehensive investigation on the optimal design of battery thermal management system with liquid cooling is still rare. This article develops a comprehensive methodology to design an efficient mini‐channel cooling system, which comprises thermodynamics, fluid dynamics, and structural analysis. The developed methodology mainly contains four steps: the design of the mini‐channel cooling system and computational fluid dynamics analysis, the design of experiments and selection of surrogate models, formulation of optimization model, and multi‐objective optimization for selection of the optimum scheme for mini‐channel cooling battery thermal management system. The findings in the study display that the temperature difference decreases from 8.0878 to 7.6267 K by 5.70%, the standard temperature deviation decreases from 2.1346 to 2.1172 K by 0.82%, and the pressure drop decreases from 302.14 to 167.60 Pa by 44.53%. The developed methodology could be extended for industrial battery pack design process to enhance cooling effect thermal performance and decrease power consumption.

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Balanced structural optimization of air‐cooling battery module with single‐layer sleeved heat spreader plate

Zhang

et al. 2021

Intl J of Energy Research

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A balanced structural optimization on the strength of the response surface method is conducted for the battery module with a single-layer sleeved heat spreader plate (SHSP). The module contains 4 Â 5 cylindrical batteries, connected with the SHSP through the tubular sleeve structure. The SHSP structure, simple yet reliable in construction, not only enlarges the heat dissipation area of module but also facilitates temperature uniformity across the batteries through on-plate heat spreading. First, the numerical model is constructed to examine the thermal performance of module. The sensitivity of the structure parameters, including the SHSP thickness, sleeve structure height, caudal length of SHSP, and central distance of batteries, are examined. A balanced optimization is then conducted by varying the structural parameters to obtain the objective functions based on central composite design. Besides the temperature maxima and temperature nonuniformity, the nonthermal metrics such as the pressure differential and the mass of SHSP are also taken as the design objectives to obtain an optimal configuration between performance and energy efficiency. Moreover, the effect of inlet velocity on the module performance is studied. The results indicate that with the optimal SHSP configuration, the temperature nonuniformity of module can be minimized even under lower fan power consumption.

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Surrogate based multidisciplinary design optimization of lithium-ion battery thermal management system in electric vehicles

Cited by 54 publications

References 37 publications

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

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

Multi‐objective design optimization for mini‐channel cooling battery thermal management system in an electric vehicle

Balanced structural optimization of air‐cooling battery module with single‐layer sleeved heat spreader plate

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