An Optimization Study on the Operating Parameters of Liquid Cold Plate for Battery Thermal Management of Electric Vehicles

Lichuan Wei, Yanhui Zou, Feng Cao (), Zhendi Ma, Zhao Lu and Liwen Jin ()
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Lichuan Wei: School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Yanhui Zou: School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Feng Cao: School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Zhendi Ma: School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Zhao Lu: School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Liwen Jin: School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Energies, 2022, vol. 15, issue 23, 1-24

Abstract: The development of electric vehicles plays an important role in the field of energy conservation and emission reduction. It is necessary to improve the thermal performance of battery modules in electric vehicles and reduce the power consumption of the battery thermal management system (BTMS). In this study, the heat transfer and flow resistance performance of liquid cold plates with serpentine channels were numerically investigated and optimized. Flow rate ( m ˙ ), inlet temperature ( T in ), and average heat generation ( Q ) were selected as key operating parameters, while average temperature ( T ave ), maximum temperature difference (Δ T max ), and pressure drop (Δ P ) were chosen as objective functions. The Response Surface Methodology (RSM) with a face-centered central composite design (CCD) was used to construct regression models. Combined with the multi-objective non-dominated sorting genetic algorithm (NSGA-II), the Pareto-optimal solution was obtained to optimize the operation parameters. The results show that the maximum temperature differences of the cold plate can be controlled within 0.29~3.90 °C, 1.11~15.66 °C, 2.17~31.39 °C, and 3.43~50.92 °C for the discharging rates at 1.0 C, 2.0 C, 3.0 C, and 4.0 C, respectively. The average temperature and maximum temperature difference can be simultaneously optimized by maintaining the pressure drop below 1000 Pa. It is expected that the proposed methods and results can provide theoretical guidance for developing an operational strategy for the BTMS.

Keywords: battery thermal management system; response surface methodology; genetic algorithm; multi-objective optimization; serpentine cold plate (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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