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，然后单击 “线性草绘图案”。在间距和实例数中分别输入 22 和 6。选择矩形的所有四个边，然后单击 “确定”。输入 180 in angle 并再次运行。此步骤用于模型中心的对称性。</li>
	<li>单击“拉伸切割”（Extrude Cut），输入 65，然后单击“确定”（OK）。单击“拉伸凸台/底座”（Extrude Boss/Base） 并输入 65，取消选中“合并结果”（Merge result），然后单击“反向”（Reverse Direction） 和“确定”（OK）。 注： 取消选中合并结果后，拉伸图元将成为一个单独的图元。总共有 23 个零件，包括 11 个电池、11 个尘埃颗粒物和 1 个气域。</li>
	<li>绘制一个 16 mm x 1 mm 的矩形。重复步骤 1.2 和 1.3。</li>
	<li>绘制一个 63 mm x 15 mm 的矩形，单击矩形的上边缘和线性草绘图案。输入 21、3 和 270，然后单击确定。单击“分割线”（Split Line） 和立方体的面，单击“确定”（OK）。</li>
	<li>绘制一个 63 mm x 15 mm 的矩形。单击 “分割线”（Split Line ） 和立方体的面，单击“确定”（OK）。</li>
	<li>单击 “文件 ”并将其另存为 X_T 文件。 注：指定尺寸：L盒：73 mm;W箱：252 mm;H箱：175毫米;Lb， Ld：65 毫米;宽b， 宽d：10 mm;高b：131 mm;Hd：1 mm;长i：63 mm;宽i：15 mm;d1、d2：5 mm、d3：6 mm 如 图 2 所示。</li>
	<li>通过单击 “工具箱”（Toolbox） >“组件系统”（Component Systems） >“网格”（Mesh ） 将网格元件拖动到项目原理图区域。通过单击 “几何”（Geometry） 导入以前保存的 X_T 文件。</li>
	<li>进入网格设计建模器窗口，单击“生成”（ Generate），再次显示电池组模型，包括 23 个零件作为独立主体。</li>
	<li>在树形轮廓中选择电池的全部 23 个部分作为新部分命名为电池部分，将 23 个部分的所有尘埃颗粒物命名为灰尘部分，将空气腔体作为空气部分，以方便后续隐藏和命名对象。</li>
	<li>首先，右键单击 BatteryPart 和 DustPart ，然后选择 隐藏部分 ，以便弹出窗口仅显示空气部分。</li>
	<li>将鼠标移动到选择工具栏，选择“ 选择过滤器：主体”，在图形区域的气腔模型上单击鼠标右键，选择 “命名选择”，然后将细节视图区域上的气腔模型重命名为气域。</li>
	<li>切换到 选择过滤器：面，右键单击并重命名已从下到上分成三部分的曲面，分别为 inlet1、inlet2 和 inlet3，这三个面右侧的单独曲面分别命名为 outlet，剩余的外曲面分别命名为 outerBorder。</li>
	<li>将 “选择模式 ”切换到 “框选择”，单击 Y 轴 以获得合适的气腔模型视图，以便于选盒，使用框选择将所有内表面重命名为空腔表面 1 并将其编号为空腔表面 11。</li>
	<li>要仅显示 batteryPart，请右键单击 airPart 并选择 Hide Part。右键单击“ batteryPart ”，然后在弹出的快捷菜单中选择“ 显示部件 ”。</li>
	<li>将鼠标移动到选择工具栏以选择 “选择过滤器：主体”，将 “选择模式 ”切换为 “单选”，右键单击“图形”区域上的 “每个电池型号 ”以选择 “命名选择”，将详细信息视图区域中的 11 个电池型号分别重命名为 batteryDomain1 到 batteryDomain11。</li>
	<li>此外，每个电池型号都有六个侧面，然后切换到 “选择过滤器：面”，右键单击 编号电池域的每一侧 以选择 “命名选择 ”并根据电池侧的方向重命名它们。例如，将编号的 batteryDomain1 的六个边重命名为 batteryDomain1_Upper、batteryDomain1_Lower、batteryDomain1_Left、batteryDomain1_Right、batteryDomain1_Front 和 batteryDomain1_Back。</li>
	<li>要仅显示 dustPart，请右键单击 batteryPart 并选择 Hide Part。右键单击 dustPart ，然后在弹出的快捷菜单中选择“ 显示部件 ”。</li>
	<li>将鼠标移动到选择工具栏以选择 “选择过滤器：实体”，右键单击“图形”区域上的每个尘土飞扬的颗粒物模型以选择 “命名选择”，将详细信息视图区域上的 11 个尘土飞扬的颗粒物模型重命名并编号为分别为 dpmDomain1 到 dpmDomain11。</li>
	<li>此外，每个尘埃颗粒物模型都有六个面;然后切换到“选择过滤器：面”，右键单击 “编号的 dpmDomains ”的每一侧，选择 “命名选择 ”，并根据尘土飞扬的颗粒物侧的方向重命名它们。例如，将编号为 dpmDomain1 的六条边重命名为 dpmDomain1_Upper、dpmDomain1_Lower、dpmDomain1_Left、dpmDomain1_Right、dpmDomain1_Front 和 dpmDomain1_Back。</li>
	<li>显示所有实体并再次返回初始窗口。</li>
</ol>2. 生成网格模型
注意：有限元网格划分是有限元数值模拟分析中非常重要的一步，它直接影响后续数值分析结果的准确性。然后对重命名的实体进行网格划分。
<ol><li>要独立地划分空气域、电池域和 dpm 域，请再次将两个 Mesh 组件从 Toolbox > Component Systems > Mesh 拖动到项目原理图区域，并将它们分别重命名为 airFEM、batteryFEM 和 dpmFEM。用鼠标左键按住 airFEM > Geometry 并将其拖动到 batteryFEM > Geometry。</li>
	<li>接下来，用鼠标左键按住 batteryFEM > Geometry 并将其拖动到 dpmFEM > Geometry。右键单击三个网格组件之间的 “线”（Lines ），然后选择 “删除”（Delete ） 以取消它们之间的关联。</li>
	<li>双击 airFEM的网格，进入网格划分窗口，右键点击 batteryPart 和 dustPart ，选择 Suppress Body，将物理首选项从mechanical改为CFD。通过单击“更新”（ Update and return） 返回初始窗口，通过面尺寸 2 mm 和主体尺寸 4 mm 生成 FEM 空气域模型。</li>
	<li>双击 batteryFEM 的网格，进入网格划分窗口，右键单击 airPart 和 dustPart 以选择 Suppress Body，并将物理首选项从 Mechanical 更改为 CFD。通过单击“更新”（ Update ） 生成 FEM 电池域模型，将尺寸调整为 2 mm，然后返回到初始窗口。</li>
	<li>双击 dpmFEM 的 Mesh，进入网格划分窗口，右键单击 airPart 和 batteryPart 以选择 Suppress Body，然后将物理首选项从 Mechanical 更改为 CFD。通过单击“更新”（ Update） 通过体大小调整 2 mm 来生成 FEM dpm 域模型，返回到初始窗口。 注意： 图3A 显示了空气域的网格， 图3B 显示了电池域的网格， 图3C 显示了dpm域的网格。</li>
	<li>将空气网格的最小尺寸设置为 4 毫米，将电池和尘埃颗粒网格的最小尺寸设置为 2 毫米。确保网格与解决方案无关，更改网格的最小像元大小，并执行网格敏感性研究。 注意：如 图 4 所示，随着电网数量从 519343 增加到 1053849，最大电池温度变化小于 0.6 K。 考虑到计算能力和准确性，以下分析基于具有931189网格的网格模型。</li>
</ol>3. 仿真分析
<ol><li>将 “流体流 ”从 “工具箱”>“分析系统”>“流体流 ”拖动到项目示意图区域中。按住 airFEM > Mesh，然后按 住 batteryFEM > Mesh 和 dpmFEM > Mesh ，然后用鼠标左键将它们拖动到 Fluid Flow > Setup。右键单击“ 流体流动>设置 ”，然后选择 “更新 ”进入设置窗口。</li>
	<li>验证有限元模型的有效性，并检查网格是否具有负体积。软件会自动建议模型的体积，合理的模型值为正。如果分割的网格或模型设置有任何问题，将弹出一条错误消息。</li>
	<li>激活传热模型中的能量方程。进入粘性模型和辐射模型的设置界面，选择 K-epsilon模型 和 离散纵坐标模型。 注：如 图5所示，比较四种粘性模型，Spalart-Allmaras模型的计算结果与其他模型有很大不同。标准 K-epsilon 模型的结果与其他 K-epsilon 模型的结果类似。具有更高稳定性和经济性的标准K-epsilon模型被广泛使用;以下分析基于标准 K-epsilon 模型。</li>
	<li>根据 表1设置空气材料、电池材料、DPM材料和电池箱材料具有不同属性的新材料。 注意： 在电池组内部，有三种不同的物理材料：空气作为流体，其余为固体。接下来，设置材料。<ol><li>通过双击每个电池域，将编号电池域的流体类型更改为“实心”类型，并将 dpm 材料更改为“实心”窗口中的电池材料。随后，选择“源术语”项，然后单击突出显示的“源术语”以添加能源，方法是分配能源数量中的数字，然后选择“常量类型”以输入209993 w/m 3 的值。</li>
		<li>将编号的 dpm 域的流体类型更改为 实体 类型。</li>
	</ol></li>
	<li>接下来，根据实际设定的流量和传热系数，设置几个不同域的仿真计算接口，如下所述。<ol><li>将所有重命名曲面的类型（包括空气域的内曲面和电池域的所有边，以及从默认壁面转换为界面的 dpm 域。成功完成上述步骤后，将立即生成网格接口。</li>
		<li>单击 Mesh Interfaces 并进入 Create/Edit Mesh Interfaces 窗口。将空腔表面与除电池域的上侧和 dpm domian 的下侧以外的所有侧相匹配。接下来，将它们分别命名为 interface1 到 interface11 并将它们编号为interface1。因此，可以在 air 域和电池 domians 以及 dpm 域之间创建 11 个网状接口。</li>
		<li>匹配电池域的上侧和 dpm 域的下侧。接下来，将它们分别命名为 interface12 到 interface22。然后，在电池域和 dpm 域之间创建 11 个网状接口。</li>
		<li>通过在混合热条件下将传热系数设置为 5，并将其材料从默认铝更改为先前自定义的电池盒材料，将外边界的曲面指定为壁热边界条件。</li>
		<li>在速度入口窗口中将所有入口的气流速度设置为 5 m/s，在压力出口窗口中将出口的表压设置为零。</li>
	</ol></li>
	<li>接下来，设置计算域在初始时刻的状态，例如初始温度为300K，这将影响计算收敛的过程。<ol><li>在初始化之前，将解决方案初始化的类型设置为标准初始化。</li>
		<li>将迭代次数设置为 2000。</li>
		<li>单击 “计算 ”进行模拟。返回初始窗口，直到模拟完成。</li>
	</ol></li>
	<li>上部分完成电池组内部温度和风速的仿真计算，然后在结果中显示仿真结果。在显示的结果中执行以下步骤。<ol><li>双击 Fluid Flow > Results ，进入 CFD post 窗口，然后单击工具箱中的 Contour 图标。</li>
		<li>在位置选择器中选择 电池的所有侧面 ，然后将压力更改为温度。然后单击 应用 以生成电池的温度等值线。</li>
		<li>单击 “文件”>“导出 ”以选择所选变量的温度。单击位置的 下拉按钮 以弹出位置选择器窗口，应在其中选择所有电池域。单击 “确定 ”并 “保存 ”按钮退出。 注意： 单击保存按钮时，将自动保存数据与所有电池网格节点的温度相对应的电子表格。</li>
		<li>打开电子表格以查找最大值，该值表示电池在多尘环境中的最高温度，所有气流入口为 5 m/s。</li>
		<li>获取电池在无尘状态下的最高温度作为预期温度，并与无尘状态下的最高温度进行比较;结果显示整个温度升高。 注意： 要获得无尘环境中电池的最高温度，应重新建立 图 6 所示的新电池组型号，并重复所有步骤 1.1-3.4.3。</li>
		<li>为了降低电池组内部的最高温度，将入口处的气流速度设置为5 m/s至6 m/s，增加5%，并计算尘土飞扬的电池的相应最高温度。 注意： 在更改参数值之前，应提前做好气流速度参数的灵敏度分析。如 图 7 和 表 2 所示，我们为七组不同入口气流速度组合中的每一组保持相同的总流量。由于气流速度分配的差异，最高温度仍有明显变化。换句话说，气流速度组合与最高温度之间存在很强的相关性。因此，这些速度参数可以用作设计变量。</li>
		<li>绘制温度-速度曲线，如 图8所示，其中红线表示温度特性曲线随气流速度的增加而减小，蓝线表示预期温度。</li>
		<li>保持气流速度增加 10%。当速度增量大于10%时，最高温度已经低于预期温度，但这并不能达到低能耗的目的。对于剩余的空气流速，通过优化将电池组的最高温度降低到预期温度，从而达到低能耗的目的。</li>
	</ol></li>
</ol>4. 最优拉丁超立方体采样与响应曲面建模
注意： 对于 5 m/s-5.5 m/s 的保留流速，选择样品以在此流速范围内构建不同的流速组合。对速度组合进行仿真，得到最高温度。构造速度和最高温度的函数。
<ol><li>打开一个新的空电子表格，创建一个表，其第一列中的行分别命名为 inlet1、inlet2 和 inlet3，并将文件另存为sampling.xlsx。</li>
	<li>运行优化软件，将 电子表格 图标拖动到任务 1 的单个箭头上。接下来，双击 电子表格 图标以弹出组件编辑器-Excel 窗口。</li>
	<li>通过单击 “浏览 ”按钮导入sampling.xlsx，并通过单击 “添加此映射”将 inlet1、inlet2 和 inlet3 映射到 A1、A2 和 A3 作为参数。单击 “确定 ”按钮返回到初始窗口。</li>
	<li>将 DOE 图标拖到 Task1 中，然后双击它以弹出 Component Editor-DOE 窗口。选择 OptimOKal Latin Hypercube ，并在“常规”窗口中将 “点数 ”设置为 15。</li>
	<li>切换到 “因子 ”窗口，将 A1、A2 和 A3 的上限设置为上限，将 5 设置为下限。</li>
	<li>切换到 “设计矩阵”（Design Matrix ） 窗口，然后单击 “生成”（Generate ） 以生成与不同入口速度相对应的随机采样点。关闭优化软件。</li>
	<li>取随机采样点的速度组合数组进行计算，重复步骤3.5.5-3.7.5，得到由电池最高温度组成的相应温度数组。</li>
	<li>将速度组合数组的预测变量 x1、x2 和 x3 与温度数组的 y 组合在一起，形成一个新的变量表，如 表 3 所示，并将其另存为sample.txt文件。导入文件以拟合响应曲面模型。</li>
	<li>重新运行优化软件，并将 “近似 ”图标拖动到 Task1 的单个箭头上。双击 Task1 图标，弹出组件编辑器-近似窗口，选择 响应曲面模型。</li>
	<li>切换到“ 数据文件 ”窗口并导入包含预测变量的sample.txt文件。</li>
	<li>切换到 “参数” 窗口，然后单击 “扫描 ”以在数据文件窗口中打开参数，其中 x1、x2 和 x3 的预测变量定义为输入，y 定义为输出。</li>
	<li>切换到 “技术选项 ”窗口，然后按多项式顺序选择 二次函数 。切换到 “错误分析选项” 窗口，然后在错误分析方法中选择 “交叉验证 ”。</li>
	<li>切换到 “查看数据 ”窗口，然后单击 “立即初始化 ”以获取二次线性回归方程的系数。</li>
	<li>单击 “误差分析 ”按钮，弹出近似误差分析窗口，检查误差是否满足每种误差类型的可接受标准。关闭近似组件窗口。如果任意误差不能满足相应的可接受标准，则增加更多采样点参与模型拟合。</li>
</ol>5. 基于自适应模拟退火算法的近似拟合模型
注：接下来，使用软件和算法来找到近似模型的最优值
<ol><li>将 Optimization 图标拖到 Task1 中，然后双击它以弹出组件编辑器优化窗口。在优化技术中选择自适应模拟退火 （ASA）。 </li>
	<li>切换到“ 变量 ”窗口，将 5.5 设置为上限，将 5 设置为下限。</li>
	<li>切换到 “目标”（Objectives） 窗口并选择 “Y ”（Y） 参数，然后关闭组件编辑器优化窗口。</li>
	<li>单击 “运行优化 ”按钮，等待优化结果。</li>
</ol>

Optimizing Battery Heat Management in Dusty Environments

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作者没有什么可透露的。

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。

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

Transient Stability and System Controls

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901 次观看.  Wenzhou University. 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。

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

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.