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In This Article

  • Summary
  • Abstract
  • Introduction
  • Protocol
  • Representative Results
  • Discussion
  • Acknowledgements
  • Materials
  • References
  • Reprints and Permissions

Summary

Here, we present the adaptive simulated annealing method (ASAM) to optimize an approximate quadratic response surface model (QRSM) corresponding to a dusty particulate matter-covered battery heat management system and fulfill the temperature drops back by adjusting the airflow velocities combination of system inlets.

Abstract

This study aims to solve the problem of the cell temperature rise and performance decline caused by dusty particulate matter covering the surface of the cell through the allocation of airflow velocities at the inlets of the battery cooling box under the goal of low energy consumption. We take the maximum temperature of the battery pack at a specified airflow velocity and dust-free environment as the expected temperature in a dusty environment. The maximum temperature of the battery pack in a dusty environment is solved at different inlet airflow velocities, which are the boundary conditions of the analysis model constructed in the simulation software. The arrays representing the different airflow velocity combinations of inlets are generated randomly through the optimal Latin hypercube algorithm (OLHA), where the lower and upper limits of velocities corresponding to the temperatures above the desired temperature are set in the optimization software. We establish an approximate QRSM between the velocity combination and the maximum temperature using the fitting module of the optimization software. The QRSM is optimized based on the ASAM, and the optimal result is in good agreement with the analysis result obtained by the simulation software. After optimization, the flow rate of the middle inlet is changed from 5.5 m/s to 5 m/s, and the total airflow velocity is decreased by 3%. The protocol here presents an optimization method simultaneously considering energy consumption and thermal performance of the battery management system that has been established, and it can be widely used to improve the life cycle of the battery pack with minimum operating cost.

Introduction

With the rapid development of the automobile industry, traditional fuel vehicles consume a lot of non-renewable resources, resulting in serious environmental pollution and energy shortage. One of the most promising solutions is the development of electric vehicles (EVs)1,2.

The power batteries used for EVs can store electrochemical energy, which is the key to replacing traditional fuel vehicles. Power batteries used in EVs include lithium-ion battery (LIB), nickel-metal hydride battery (NiMH), and electric double-layer capacitor (EDLC)3. Compared to the other....

Protocol

NOTE: The research technology roadmap is shown in Figure 1, where the modeling, simulation, and optimization software are used. The materials required are shown in the Table of Materials.

1. Creating the 3D model

NOTE: We used Solidworks to create the 3D model.

  1. Draw a 252 mm x 175 mm rectangle, click Extrude Boss/Base, and enter 73. Create a new plane 4 mm from the outer surface.
  2. Draw a rectangle 131 m.......

Representative Results

Following the protocol, the first three parts are the most important, which include modeling, meshing, and simulation, all in order to get the maximum temperature of the battery pack. Then, the airflow velocity is adjusted by sampling, and finally, the optimal flow rate combination is obtained by optimization.

Figure 9 shows the comparison of battery pack temperature distribution in different e.......

Discussion

 The BTMS used in this study was established based on the air-cooling system due to its low cost and simplicity of the structure. Because of the low heat transfer capacity, the performance of the air-cooling system is lower than that of the liquid cooling system and phase change material cooling system. However, the liquid cooling system has the disadvantage of refrigerant leakage, and the phase change material cooling system has high mass and low energy density29. These cooling systems have .......

Acknowledgements

Some analysis and optimization software are supported by Tsinghua University, Konkuk University, Chonnam National University, Mokpo University, and Chiba University.

....

Materials

NameCompanyCatalog NumberComments
Ansys-WorkbenchANSYSN/AMulti-purpose finite element method computer design program software.https://www.ansys.com
IsightEngineous SogtwareN/AComprehensive computer-aided engineering software.https://www.3ds.com
NVIDIA GPUNVIDIAN/AAn NVIDIA GPU is needed as some of the software frameworks below will not work otherwise. https://www.nvidia.com
Software
SOLIDWORKSDassault SystemesN/ASolidWorks provides different design solutions, reduces errors in the design process, and improves product quality
www.solidworks.com

References

  1. Xia, G., Cao, L., Bi, G. A review on battery thermal management in electric vehicle application. Journal of Power Sources. 367 (1), 90-105 (2017).
  2. Mahamud, R., Park, C. Reciprocating air ....

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Battery PackLithium ion BatteryThermal ManagementAir based CoolingDust Particulate MatterOptimizationEnergy ConsumptionQRSMASAM

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