Name: Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
Uploaded: 2023-11-03T14:40:00
Description: Watch this Scientific Journal Video about Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption at JoVE.com

清华大学、建国大学、全南国立大学、木浦大学和千叶大学支持一些分析和优化软件。

注：研究技术路线图如 图1所示，其中使用了建模、仿真和优化软件。所需材料显示在 材料表中。
1. 创建 3D 模型
注意：我们使用 Solidworks 创建 3D 模型。
<ol><li>绘制一个 252 mm x 175 mm 的矩形，单击“ 拉伸凸台/底”（Eltrude Boss/Base），然后输入 73。创建一个距外表面 4 mm 的新平面。</li>
	<li>绘制一个 131 mm x 16 mm 的矩形，然?...

Optimizing Battery Heat Management in Dusty Environments

<ol>
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本研究中使用的BTMS是基于空气冷却系统建立的，因为它成本低，结构简单。由于传热能力低，风冷系统的性能低于液冷系统和相变材料冷却系统。然而，液体冷却系统存在制冷剂泄漏的缺点，相变材料冷却系统质量大，能量密度低29。这些冷却系统各有优缺点。因此，可以通过将风冷系统与液体冷却系统或相变材料冷却系统相结合来建立BTMS，以提高冷却性能。
<p class="jove_cont...

随着汽车工业的快速发展，传统燃油车消耗了大量的不可再生资源，造成严重的环境污染和能源短缺。最有前途的解决方案之一是电动汽车 （EV） 的开发1,2。
用于电动汽车的动力电池可以储存电化学能量，这是替代传统燃油汽车的关键。电动汽车中使用的动力电池包括锂离子电池（LIB）、镍氢电池（NiMH）和双电层电容器（EDLC）3。与其他电池相比，锂离子电池因其高能量密度、高效率和长生命周期等优点，目前被广泛用作电动汽车中的储能单元4,5,6,7。
但是，由于化学反应热和焦耳热，在快速充电和高强度放电时容易积聚大量热量并提高电池温度。LIB的理想工作温度为20-40°C 8,9。电池组中电池之间的最大温差不应超过 5 °C10,11。否则，可能会导致电池之间的温度不平衡、加速老化、甚至过热、火灾、爆炸等一系列风险12.因此，需要解决的关键问题是设计和优化一个高效的电池热管理系统（BTMS），该系统可以将电池组的温度和温差控制在狭窄的范围内。
典型的BTMS包括风冷、水冷和相变材料冷却13。在这些冷却方法中，风冷式因其成本低、结构简单等特点而被广泛使用14。由于空气的比热容有限，风冷系统中的电芯之间容易出现高温和较大的温差。为了提高风冷BTMS的冷却性能，有必要设计一个高效的系统15,16,17。Qian等18收集了电池组的最大温度和温差，以训练相应的贝叶斯神经网络模型，用于优化串联风冷电池组的电池间距。Chen等19报道了在Z型并联风冷系统中使用Newton方法和流动阻力网络模型优化入口发散室和出口辐合室的宽度。结果显示，该系统的温差降低了45%。Liu等20在J-BTMS中对5组冷却管道进行了采样，并通过基于集成代理的优化算法获得了单元间距的最佳组合。Baveja等人[21]对被动平衡电池模块进行了建模，该研究描述了热预测对模块级被动平衡的影响，反之亦然。Singh等人[22]研究了一种电池热管理系统（BTMS），该系统使用封装相变材料以及使用耦合电化学-热建模设计的强制对流空气冷却。Fan等23提出了一种包含多级特斯拉阀配置的液体冷却板，为微流控应用中具有高识别度的棱柱形锂离子电池提供更安全的温度范围。Feng等[24]采用变异系数法对不同入口流速和电池间隙的方案进行了评价。Talele 等人 25 引入了墙体增强的热火衬里隔热材料，以根据加热膜的最佳放置来存储潜在的热量。
当使用风冷BTMS时，外部环境中的金属粉尘颗粒、矿物粉尘颗粒、建材粉尘颗粒等颗粒会被鼓风机带入风冷BTMS，这会导致电池表面被DPM覆盖。如果没有散热计划，可能会因电池温度过高而造成事故。经过模拟，我们将电池组在指定气流速度和无尘环境中的最高温度作为多尘环境中的预期温度。首先，C-rate是指电池在规定时间内释放其额定容量时所需的电流值，该值等于数据值中电池额定容量的倍数。本文采用2C倍率放电进行仿真。额定容量为10Ah，额定电压为3.2V，正极材料采用磷酸铁锂（LiFePO4），负极材料采用碳。电解液有电解液锂盐、高纯度有机溶剂、必要的添加剂等原料。通过OLHA确定代表入口处不同速度组合的随机阵列，并在检查曲线拟合精度的条件下，建立电池组最高温度与入口流速组合之间的二阶函数。自 McKay 等人 26 提出以来，拉丁超立方体 （LH） 设计已应用于许多计算机实验中。LH 由 N x p 矩阵 L 给出，其中 L 的每一列都由整数 1 到 N 的排列组成。该文采用最优拉丁超立方体采样方法减轻计算负担。该方法采用分层抽样，确保采样点能够覆盖所有采样内部。
在后续步骤中，在同时考虑能耗的条件下，基于ASAM优化了进气流速组合，以降低多尘环境中电池组的最高温度。自适应模拟退火算法在许多优化问题中得到了广泛的发展和广泛应用27,28。该算法可以通过接受具有一定概率的最差解来避免陷入局部最优。通过定义验收概率和温度来实现全局最优;也可以使用这两个参数来调整计算速度。最后，为了验证优化的准确性，将最佳结果与仿真软件得到的分析结果进行了比较。
该文针对因防尘罩而升温的电池组，提出了一种电池箱入口流量的优化方法。目的是在能耗低的情况下，将有灰尘覆盖的电池组的最高温度降低到低于无尘覆盖电池组的最高温度。

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Ansys-Workbench</td>
			<td>ANSYS</td>
			<td>N/A</td>
			<td>Multi-purpose finite element method computer design program software.https://www.ansys.com</td>
		</tr>
		<tr>
			<td>Isight</td>
			<td>Engineous Sogtware</td>
			<td>N/A</td>
			<td>Comprehensive computer-aided engineering software.https://www.3ds.com</td>
		</tr>
		<tr>
			<td>NVIDIA GPU</td>
			<td>NVIDIA</td>
			<td>N/A</td>
			<td>An NVIDIA GPU is needed as some of the software frameworks below will not work otherwise. https://www.nvidia.com</td>
		</tr>
		<tr>
			<td>Software</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>SOLIDWORKS</td>
			<td>Dassault Systemes</td>
			<td>N/A</td>
			<td>SolidWorks provides different design solutions, reduces errors in the design process, and improves product quality 
			www.solidworks.com</td>
		</tr>
	</tbody>
</table>

optimization of an air-based heat management system for dusty particulate matter-covered lithium-ion battery packs

本研究旨在解决在低能耗目标下，通过分配电池冷却箱入口处的气流速度，解决含尘颗粒物覆盖电池表面导致电池温度升高和性能下降的问题。我们将电池组在指定气流速度和无尘环境中的最高温度作为多尘环境中的预期温度。求解不同进气流速下多尘环境下电池组的最高温度，这是仿真软件构建的分析模型的边界条件。通过最优拉丁超立方体算法（OLHA）随机生成代表不同进气口气流速度组合的阵列，其中在优化软件中设置了高于所需温度的温度对应的速度下限和上限。我们使用优化软件的拟合模块在速度组合和最高温度之间建立近似QRSM。基于ASAM对QRSM进行了优化，最优结果与仿真软件得到的分析结果吻合较好。优化后，中间入口流量由5.5 m/s变为5 m/s，总气流速度降低3%。本文提出了一种同时考虑已建立的电池管理系统的能耗和热性能的优化方法，可以广泛用于以最低的运行成本提高电池组的生命周期。

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 batteries, lithium-ion batteries are currently widely used as energy storage units in EVs owing to their advantages such as high energy density, high efficiency, and long life cycle4,5,6,7.
However, due to chemical reaction heat and Joule heat, it is easy to accumulate a large amount of heat and increase the battery temperature during rapid charging and high-intensity discharging. The ideal operating temperature of LIB is 20-40 &#176;C8,9. The maximum temperature difference between the batteries in a battery string should not exceed 5 &#176;C10,11. Otherwise, it may lead to a series of risks such as temperature imbalance between the batteries, accelerated aging, even overheating, fire, explosion, and so on12. Therefore, the critical issue to be resolved is designing and optimizing an efficient battery thermal management system (BTMS) that can control the temperature and temperature difference of the battery pack within a narrow.
Typical BTMS include air cooling, water cooling, and phase change material cooling13. Among these cooling methods, the air cooling type is widely used because of its low cost and simplicity of the structure14. Due to the limited specific heat capacity of air, high temperature and large temperature differences are easy to occur between battery cells in air-cooled systems. In order to improve the cooling performance of air-cooled BTMS, it is necessary to design an efficient system15,16,17. Qian et al.18 collected the battery pack&#39;s maximum temperature and temperature difference to train the corresponding Bayesian neural network model, which is used to optimize cell spacings of the series air-cooled battery pack. Chen et al.19 reported using the Newton method and the flow resistance network model for optimization of the widths of the inlet divergence plenum and the outlet convergence plenum in the Z-type parallel air-cooled system. The results showed a 45% reduction in the temperature difference of the system. Liu et al.20 sampled five groups of the cooling ducts in the J-BTMS and obtained the best combination of cell spacings by the ensemble surrogate-based optimization algorithm. Baveja et al.21 modeled a passively balanced battery module, and the study described the effects of thermal prediction on module-level passive balancing and vice versa. Singh et al.22 investigated a battery thermal management system (BTMS) that used encapsulated phase change material along with forced convective air cooling designed using the coupled electrochemical-thermal modeling. Fan et al.23 proposed a liquid cooling plate comprising a multi-stage Tesla valve configuration to provide a safer temperature range for a prismatic-type lithium-ion battery with high recognition in microfluidic applications. Feng et al. 24 used the coefficient of variation method to evaluate the schemes with different inlet flow rates and battery clearances. Talele et al.25 introduced wall-enhanced pyro lining thermal insulation to store potential generated heating based on optimal placement of heating films.
When one uses air-cooling BTMS, metal dust particles, mineral dust particles, building materials dust particles, and other particles in the external environment will be brought into the air-cooling BTMS by the blower, which can cause the surface of the batteries to be covered with DPM. If there is no heat dissipation plan, it may cause accidents due to the excessively high battery temperature. After simulation, we take the maximum temperature of the battery pack in a specified airflow velocity and dust-free environment as the expected temperature in a dusty environment. First, C-rate refers to the current value required when the battery releases its rated capacity within the specified time, which is equal to a multiple of the battery&#39;s rated capacity in the data value. In this paper, the simulation uses 2C rate discharge. The rated capacity is 10 Ah, and the nominal voltage is 3.2 V. Lithium iron phosphate (LiFePO4) is used as the positive electrode material, and carbon is used as the negative electrode material. The electrolyte has electrolyte lithium salt, a high-purity organic solvent, necessary additives, and other raw materials. The random array representing the different velocity combinations at the inlets was determined through the OLHA, and a 2nd order function between the maximum temperature of the battery pack and the inlet flow velocity combination was set up under the condition of checking the accuracy of the curve fitting. Latin hypercube (LH) designs have been applied in many computer experiments since they were proposed by McKay et al.26. An LH is given by an N x p-matrix L, where each column of L consists of a permutation of the integers 1 to N. In this paper, the optimal Latin hypercube sampling method is used to reduce the computational burden. The method uses stratified sampling to ensure that the sampling points can cover all the sampling internals.
In the following step, the inlet flow velocity combination was optimized to decrease the maximum temperature of the battery pack in a dusty environment based on the ASAM under the condition of considering energy consumption simultaneously. The adaptive simulated annealing algorithm has been extensively developed and widely used in many optimization problems27,28. This algorithm can avoid getting trapped in a local optimum by accepting the worst solution with a certain probability. The global optimum is achieved by defining the acceptance probability and temperature; the calculation speed can also be adjusted by using these two parameters. Finally, for checking the accuracy of the optimization, the optimal result was compared with the analysis result obtained from the simulation software.
In this paper, an optimization method for the inlet flow rate of the battery box is proposed for the battery pack whose temperature rises due to dust cover. The purpose is to reduce the maximum temperature of the dust-covered battery pack to below the maximum temperature of the non-dust-covered battery pack in the case of low energy consumption.

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption 

粉尘颗粒物覆盖锂离子电池组的空气热管理系统的优化

按照协议，前三个部分是最重要的，包括建模、网格划分和仿真，所有这些都是为了获得电池组的最高温度。然后，通过采样调节气流速度，最后通过优化得到最优的流速组合。
图9为不同环境下电池组温度分布的比较， 图10 为不同环境下第二电池温度分布的比较。如 <strong class="xf...

Watch this Scientific Journal Video about Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption at JoVE.com

在这里，我们提出了自适应模拟退火方法（ASAM），以优化对应于尘土飞扬的颗粒物覆盖的电池热管理系统的近似二次响应面模型（QRSM），并通过调整系统入口的气流速度组合来实现温度回落。

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 ...

本研究旨在解决在低能耗目标下，通过在电池冷却箱较小的位置分配气流速度，解决电池表面覆盖的含尘颗粒物导致电池温度升高和性能下降的问题。建立了一种同时考虑电池管理系统能耗和热性能的优化方法。并且可以广泛用于以最低的运行成本提高电池组的生命周期。

optimization-an-air-based-heat-management-system-for-dusty

Research

Education

JoVE Core

JoVE Journal

Electrical Engineering

Engineering

Unit 1

Optimization of An Air-Based Heat Management System for Dusty Particulate Matter-Covered Lithium-Ion Battery Packs

Transient Stability and System Controls

SCIENTISTS IN ACTION

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.

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.

科研

工程学

Simulation Analysis for Evaluating Temperature and Airflow Dynamics in Battery Packs 

To begin the fluid flow analysis, drag Fluid Flow from the toolbox and Analysis Systems into the project schematic zone. Then hold airFEM mesh, batteryFEM mesh, and dpmFEM mesh fluid flow with the left mouse button and move them to fluid flow setup. Right click on Fluid Flow, then set up and select Update to enter the Setup window.Confirm the validity of the FEM model and check if the mesh has a negative volume. Now, enter the setting interface of the Viscous model and the Radiation model and select the K-epsilon model and the Discrete Ordinates model respectively. Change the fluid type of the numbered battery domains to the solid type.Then on the Solid window, double click each battery domain to change the DPM material to the battery material. Subsequently, choose the source terms item and check the highlighted source terms to add an energy source by assigning the number and the number of energy sources and selecting constant type to input the source value. Change the fluid type of numbered DPM domains to solid type.Next, convert the type of all renamed surfaces, including the inner surfaces of the air domain, all sides of the battery domains, and DPM domains from the default wall to the interface. To generate mesh interfaces, click the Mesh Interfaces and enter the Create and Edit Mesh Interfaces Window. Match the cavity surfaces to all sides, except the upper sides of the battery domains and the lower sides of the DPM domains.Name and number them as interface1 to interface11 respectively. Then match the upper sides of the battery domains and the lower sides of the DPM domains. Name and number them as interface12 to interface22 respectively.To assign the outer border surface as the wall thermal boundary, set the heat transfer coefficient as five in the mixed thermal condition. Then change the material from default aluminum to the previously self-defined battery box material. In the Velocity Inlet window, set airflow velocities of all inlets to five meters per second.Then set the gauge pressure of the outlet to zero in the Pressure Outlet window. Next, set the state of the computing domain with an initial temperature of 300 kelvin and solution initialization type, best standard initialization. Set the number of iterations to 2000 and click Calculate to begin the simulation.To enter the CFD Post window, double click Fluid Flow followed by Results. Then from the toolbox, double click the icon of contour. In the location selector, choose all sides of the batteries and switch from pressure to temperature.Then click Apply to generate the temperature contour of the batteries. Click File, then Export. To select the temperature of the selected variables, then click the dropdown button of the locations to select the battery domains.Click OK followed by the Save button to quit. Battery pack temperature variation at different inlet airflow velocities demonstrated that the maximum battery pack temperature decreases with the increase of inlet airflow velocity. The comparison of the battery pack temperature distribution and the second battery temperature distribution in different environments showed that the temperature of the battery increases under dusty conditions due to the low thermal conductivity of DPM.