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

Alguns softwares de análise e otimização são suportados pela Universidade de Tsinghua, Universidade de Konkuk, Universidade Nacional de Chonnam, Universidade de Mokpo e Universidade de Chiba.

NOTA: O roteiro de tecnologia de pesquisa é mostrado na Figura 1, onde o software de modelagem, simulação e otimização é usado. Os materiais necessários são mostrados na Tabela de Materiais.
1. Criando o modelo 3D
NOTA: Usamos o Solidworks para criar o modelo 3D.
<ol><li>Desenhe um retângulo de 252 mm x 175 mm, clique em Extrude Boss/Base e digite 73. Crie um novo plano a 4 mm da superfície externa.</li>
	<li>De...

Optimizing Battery Heat Management in Dusty Environments

<ol>
	<li>Xia, G., Cao, L., Bi, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+review+on+battery+thermal+management+in+electric+vehicle+application.">A review on battery thermal management in electric vehicle application.</a> Journal of Power Sources. 367 (1), 90-105 (2017).</li><li>Mahamud, R., Park, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reciprocating+air+flow+for+Li-ion+battery+thermal+management+to+improve+temperature+uniformity.">Reciprocating air flow for Li-ion battery thermal management to improve temperature uniformity.</a> Journal of Power Sources. 196 (13), 5685-5696 (2011).</li><li>Kumar, R., Goel, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+study+on+thermal+management+system+of+lithium-ion+batteries+for+electrical+vehicles:+A+critical+review.">A study on thermal management system of lithium-ion batteries for electrical vehicles: A critical review.</a> Journal of Energy Storage. 71, 108025 (2023).</li><li>Fan, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Experimental+study+on+the+thermal+management+performance+of+air+cooling+for+high+energy+density+cylindrical+lithium-ion+batteries.">Experimental study on the thermal management performance of air cooling for high energy density cylindrical lithium-ion batteries.</a> Applied Thermal Engineering. 155, 96-109 (2019).</li><li>Mohammadian, S. 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J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Targeting+plug-in+hybrid+electric+vehicle+policies+to+increase+social+benefits.">Targeting plug-in hybrid electric vehicle policies to increase social benefits.</a> Energy Policy. 38 (2), 705-708 (2010).</li><li>Avadikyan, A., Llerena, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+real+options+reasoning+approach+to+hybrid+vehicle+investments.">A real options reasoning approach to hybrid vehicle investments.</a> Technological Forecasting and Social Change. 77 (4), 649-661 (2010).</li><li>Chen, K., Chen, Y., Li, Z., Yuan, F., Wang, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Design+of+the+cell+spacings+of+battery+pack+in+parallel+air-+cooled+battery+thermal+management+system.">Design of the cell spacings of battery pack in parallel air- cooled battery thermal management system.</a> International Journal of Heat and Mass Transfer. 127, 393-401 (2018).</li><li>Jiang, Z. Y., Qu, Z. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Lithium+-+ion+battery+thermal+management+using+heat+pipe+and+phase+change+material+during+discharge+-+charge+cycle:+A+comprehensive+numerical+study.">Lithium - ion battery thermal management using heat pipe and phase change material during discharge - charge cycle: A comprehensive numerical study.</a> Applied Energy. 242, 378-392 (2019).</li><li>Saw, L. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computational+fluid+dynamic+and+thermal+analysis+of+Lithium-ion+battery+pack+with+air+cooling.">Computational fluid dynamic and thermal analysis of Lithium-ion battery pack with air cooling.</a> Applied energy. 177, 783-792 (2016).</li><li>Park, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+design+of+air+flow+configuration+for+cooling+lithium+-+ion+battery+in+hybrid+electric+vehicles.">A design of air flow configuration for cooling lithium - ion battery in hybrid electric vehicles.</a> Journal of Power Sources. 239 (10), 30-36 (2013).</li><li>Wang, Q., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Thermal+runaway+caused+fire+and+explosion+of+lithium-ion+battery.">Thermal runaway caused fire and explosion of lithium-ion battery.</a> Journal of power sources. 208, 210-224 (2012).</li><li>Rao, Z., Wang, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+review+of+power+battery+thermal+energy+management.">A review of power battery thermal energy management.</a> Renewable and Sustainable Energy Reviews. 15 (9), 4554-4571 (2011).</li><li>Chen, K., Wu, W., Yuan, F., Chen, L., Wang, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cooling+efficiency+improvement+of+air-cooled+battery+thermal+management+system+through+designing+the+flow+pattern.">Cooling efficiency improvement of air-cooled battery thermal management system through designing the flow pattern.</a> Energy. 167, 781-790 (2019).</li><li>Lan, X., Li, X., Ji, S., Gao, C., He, Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Design+and+optimization+of+a+novel+reverse+layered+air-cooling+battery+management+system+using+U+and+Z+type+flow+patterns.">Design and optimization of a novel reverse layered air-cooling battery management system using U and Z type flow patterns.</a> International Journal of Energy Research. 46 (10), 14206-14226 (2022).</li><li>Singh, G., Wu, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effect+of+different+inlet/outlet+port+configurations+on+the+thermal+management+of+prismatic+Li-ion+batteries.">Effect of different inlet/outlet port configurations on the thermal management of prismatic Li-ion batteries.</a> Journal of Heat Transfer. 144 (11), 112901 (2022).</li><li>Zhang, J., Wu, X., Chen, K., Zhou, D., Song, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Experimental+and+numerical+studies+on+an+efficient+transient+heat+transfer+model+for+air-cooled+battery+thermal+management+systems.">Experimental and numerical studies on an efficient transient heat transfer model for air-cooled battery thermal management systems.</a> Journal of Power Sources. 490, 229539 (2021).</li><li>Qian, X., Xuan, D., Zhao, X., Shi, Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Heat+dissipation+optimization+of+lithium-ion+battery+pack+based+on+neural+networks.">Heat dissipation optimization of lithium-ion battery pack based on neural networks.</a> Applied Thermal Engineering. 162, 114289 (2019).</li><li>Chen, K., Wang, S., Song, M., Chen, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Structure+optimization+of+parallel+air-cooled+battery+thermal+management+system.">Structure optimization of parallel air-cooled battery thermal management system.</a> International Journal of Heat and Mass Transfer. 111, 943-952 (2017).</li><li>Liu, Y., Zhang, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Self-adapting+J-type+air-based+battery+thermal+management+system+via+model+predictive+control.">Self-adapting J-type air-based battery thermal management system via model predictive control.</a> Applied Energy. 263, 114640 (2020).</li><li>Baveja, R., Bhattacharya, J., Panchal, S., Fraser, R., Fowler, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Predicting+temperature+distribution+of+passively+balanced+battery+module+under+realistic+driving+conditions+through+coupled+equivalent+circuit+method+and+lumped+heat+dissipation+method.">Predicting temperature distribution of passively balanced battery module under realistic driving conditions through coupled equivalent circuit method and lumped heat dissipation method.</a> Journal of Energy Storage. 70, 107967 (2023).</li><li>Singh, L. K., Kumar, R., Gupta, A. K., Sharma, A. K., Panchal, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computational+study+on+hybrid+air-PCM+cooling+inside+lithium-ion+battery+packs+with+varying+number+of+cells.">Computational study on hybrid air-PCM cooling inside lithium-ion battery packs with varying number of cells.</a> Journal of Energy Storage. 67, 107649 (2023).</li><li>Fan, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multi-objective+optimization+design+and+experimental+investigation+for+a+prismatic+lithium-ion+battery+integrated+with+a+multi-stage+Tesla+valve-based+cold+plate.">Multi-objective optimization design and experimental investigation for a prismatic lithium-ion battery integrated with a multi-stage Tesla valve-based cold plate.</a> Processes. 11 (6), 1618 (2023).</li><li>Feng, Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimization+of+the+Cooling+Performance+of+Symmetric+Battery+Thermal+Management+Systems+at+High+Discharge+Rates.">Optimization of the Cooling Performance of Symmetric Battery Thermal Management Systems at High Discharge Rates.</a> Energy Fuels. 37 (11), 7990-8004 (2023).</li><li>Talele, V., Moral&#305;, U., Patil, M. S., Panchal, S., Mathew, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimal+battery+preheating+in+critical+subzero+ambient+condition+using+different+preheating+arrangement+and+advance+pyro+linear+thermal+insulation.">Optimal battery preheating in critical subzero ambient condition using different preheating arrangement and advance pyro linear thermal insulation.</a> Thermal Science and Engineering Progress. 42, 101908 (2023).</li><li>Kenny, Q. Y., Li, W., Sudjianto, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Algorithmic+construction+of+optimal+symmetric+Latin+hypercube+designs.">Algorithmic construction of optimal symmetric Latin hypercube designs.</a> Journal of statistical planning and inference. 90 (1), 145-159 (2000).</li><li>Oliveira Jr, H. A., Petraglia, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+optimization+using+dimensional+jumping+and+fuzzy+adaptive+simulated+annealing.">Global optimization using dimensional jumping and fuzzy adaptive simulated annealing.</a> Applied Soft Computing. 11 (6), 4175-4182 (2011).</li><li>Ingber, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Very+fast+simulated+re-annealing.">Very fast simulated re-annealing.</a> Mathematical and computer modelling. 12 (8), 967-973 (1989).</li><li>Yu, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Experimental+study+on+transient+thermal+characteristics+of+stagger-arranged+lithium-ion+battery+pack+with+air+cooling+strategy.">Experimental study on transient thermal characteristics of stagger-arranged lithium-ion battery pack with air cooling strategy.</a> International Journal of Heat and Mass Transfer. 143, 118576 (2019).</li><li>Li, W., Xiao, M., Peng, X., Garg, A., Gao, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+surrogate+thermal+modeling+and+parametric+optimization+of+battery+pack+with+air+cooling+for+EVs.">A surrogate thermal modeling and parametric optimization of battery pack with air cooling for EVs.</a> Applied Thermal Engineering. 147, 90-100 (2019).</li><li>Chen, K., Zhang, Z., Wu, B., Song, M., Wu, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+air-cooled+system+with+a+control+strategy+for+efficient+battery+thermal+management.">An air-cooled system with a control strategy for efficient battery thermal management.</a> Applied Thermal Engineering. 236, 121578 (2023).</li><li>Zhao, L., Li, W., Wang, G., Cheng, W., Chen, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+novel+thermal+management+system+for+lithium-ion+battery+modules+combining+direct+liquid-cooling+with+forced+air-cooling.">A novel thermal management system for lithium-ion battery modules combining direct liquid-cooling with forced air-cooling.</a> Applied Thermal Engineering. 232, 120992 (2023).</li><li>Oyewola, O. M., Awonusi, A. A., Ismail, O. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Design+optimization+of+Air-Cooled+Li-ion+battery+thermal+management+system+with+Step-like+divergence+plenum+for+electric+vehicles.">Design optimization of Air-Cooled Li-ion battery thermal management system with Step-like divergence plenum for electric vehicles.</a> Alexandria Engineering Journal. 71, 631-644 (2023).</li><li>Chen, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Design+of+parallel+air-cooled+battery+thermal+management+system+through+numerical+study.">Design of parallel air-cooled battery thermal management system through numerical study.</a> Energies. 10 (10), 1677 (2017).</li><li>Lyu, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+new+structure+optimization+method+for+forced+air-cooling+system+based+on+the+simplified+multi-physics+model.">A new structure optimization method for forced air-cooling system based on the simplified multi-physics model.</a> Applied Thermal Engineering. 198, 117455 (2021).</li><li>Zhang, W. 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O BTMS utilizado neste estudo foi estabelecido com base no sistema de resfriamento a ar devido ao seu baixo custo e simplicidade da estrutura. Devido à baixa capacidade de transferência de calor, o desempenho do sistema de resfriamento a ar é menor do que o do sistema de resfriamento líquido e do sistema de resfriamento de material de mudança de fase. No entanto, o sistema de resfriamento líquido tem a desvantagem de vazamento de refrigerante, e o sistema de resfriamento de material de mudança de fase tem alta mas...

Com o rápido desenvolvimento da indústria automobilística, os veículos a combustível tradicionais consomem muitos recursos não renováveis, resultando em grave poluição ambiental e escassez de energia. Uma das soluções mais promissoras é o desenvolvimento de veículos elétricos (VEs)1,2.
As baterias de energia usadas para veículos elétricos podem armazenar energia eletroquímica, que é a chave para substituir os veículos a combustível tradicionais. As baterias de potência usadas em veículos elétricos incluem bateria de íons de lítio (LIB), bateria de níquel-hidreto metálico (NiMH) e capacitor elétrico de camada dupla (EDLC)3. Comparadas às outras baterias, as baterias de íons de lítio são atualmente amplamente utilizadas como unidades de armazenamento de energia em veículos elétricos devido às suas vantagens, como alta densidade energética, alta eficiência e longo ciclo de vida 4,5,6,7.
No entanto, devido ao calor de reação química e ao calor Joule, é fácil acumular uma grande quantidade de calor e aumentar a temperatura da bateria durante o carregamento rápido e a descarga de alta intensidade. A temperatura ideal de operação da LIB é de 20-40 °C 8,9. A diferença de temperatura máxima entre as baterias de uma corda de bateria não deve exceder 5 °C10,11. Caso contrário, pode levar a uma série de riscos, como desequilíbrio de temperatura entre as baterias, envelhecimento acelerado, até superaquecimento, incêndio, explosão, e assim por diante12. Portanto, a questão crítica a ser resolvida é projetar e otimizar um sistema eficiente de gerenciamento térmico da bateria (BTMS) que possa controlar a diferença de temperatura e temperatura da bateria dentro de um estreito.
Os BTMS típicos incluem resfriamento a ar, resfriamento a água e resfriamento de material de mudança de fase13. Dentre esses métodos de resfriamento, o tipo de resfriamento a ar é amplamente utilizado devido ao seu baixo custo e simplicidade da estrutura14. Devido à limitada capacidade térmica específica do ar, altas temperaturas e grandes diferenças de temperatura são fáceis de ocorrer entre células de bateria em sistemas refrigerados a ar. Para melhorar o desempenho de resfriamento do BTMS refrigerado a ar, é necessário projetar um sistema eficiente 15,16,17. Qian et al.18 coletaram a temperatura máxima e a diferença de temperatura da bateria para treinar o modelo de rede neural bayesiana correspondente, que é usado para otimizar os espaçamentos celulares da bateria resfriada a ar da série. Chen et al.19 relataram o uso do método de Newton e do modelo de rede de resistência ao fluxo para otimização das larguras do plenum de divergência de entrada e do plenum de convergência de saída no sistema paralelo refrigerado a ar tipo Z. Os resultados mostraram uma redução de 45% na diferença de temperatura do sistema. Liu et al.20 amostraram cinco grupos de dutos de resfriamento no J-BTMS e obtiveram a melhor combinação de espaçamentos celulares pelo algoritmo de otimização baseado em ensemble surrogate. Baveja et al.21 modelaram um módulo de bateria passivamente balanceado, e o estudo descreveu os efeitos da predição térmica no balanceamento passivo em nível de módulo e vice-versa. Singh et al.22 investigaram um sistema de gerenciamento térmico de baterias (BTMS) que usava material encapsulado de mudança de fase juntamente com resfriamento de ar convectivo forçado projetado usando a modelagem eletroquímico-térmica acoplada. Fan et al.23 propuseram uma placa de resfriamento líquido compreendendo uma configuração de válvula Tesla de vários estágios para fornecer uma faixa de temperatura mais segura para uma bateria de íons de lítio do tipo prismática com alto reconhecimento em aplicações microfluídicas. Feng e col. 24 utilizaram o método do coeficiente de variação para avaliar os esquemas com diferentes vazões de entrada e folgas das baterias. Talele et al.25 introduziram o isolamento térmico de revestimento pirotécnico aprimorado de parede para armazenar o aquecimento potencial gerado com base na colocação ideal de filmes de aquecimento.
Quando se usa BTMS de resfriamento a ar, partículas de poeira metálica, partículas de poeira mineral, partículas de poeira de materiais de construção e outras partículas no ambiente externo serão trazidas para o BTMS de resfriamento a ar pelo soprador, o que pode fazer com que a superfície das baterias seja coberta com DPM. Se não houver um plano de dissipação de calor, pode causar acidentes devido à temperatura excessivamente alta da bateria. Após a simulação, tomamos a temperatura máxima da bateria em uma velocidade de fluxo de ar especificada e ambiente livre de poeira como a temperatura esperada em um ambiente empoeirado. Primeiro, C-rate refere-se ao valor atual necessário quando a bateria libera sua capacidade nominal dentro do tempo especificado, que é igual a um múltiplo da capacidade nominal da bateria no valor de dados. Neste trabalho, a simulação utiliza a taxa de descarga de 2C. A capacidade nominal é de 10 Ah, e a tensão nominal é de 3,2 V. O fosfato de ferro de lítio (LiFePO4) é usado como o material do eletrodo positivo, e o carbono é usado como o material do eletrodo negativo. O eletrólito tem sal de lítio eletrolítico, um solvente orgânico de alta pureza, aditivos necessários e outras matérias-primas. A matriz aleatória representando as diferentes combinações de velocidades nas entradas foi determinada através do OLHA, e uma função de 2ª ordem entre a temperatura máxima da bateria e a combinação de velocidades de fluxo de entrada foi configurada sob a condição de verificar a precisão do ajuste da curva. Desenhos de hipercubos latinos (LH) têm sido aplicados em muitos experimentos computacionais desde que foram propostos por McKay et al.26. Um LH é dado por uma matriz N x p L, onde cada coluna de L consiste de uma permutação dos inteiros 1 a N. Neste artigo, o método ótimo de amostragem de hipercubo latino é usado para reduzir a carga computacional. O método utiliza amostragem estratificada para garantir que os pontos de amostragem possam abranger todos os elementos internos de amostragem.
Na etapa seguinte, a combinação de velocidade de fluxo de entrada foi otimizada para diminuir a temperatura máxima da bateria em um ambiente empoeirado baseado no ASAM sob a condição de considerar o consumo de energia simultaneamente. O algoritmo de recozimento simulado adaptativo tem sido extensivamente desenvolvido e amplamente utilizado em muitos problemas de otimização27,28. Este algoritmo pode evitar ficar preso em um ótimo local, aceitando a pior solução com uma certa probabilidade. O ótimo global é alcançado pela definição da probabilidade de aceitação e temperatura; A velocidade de cálculo também pode ser ajustada usando esses dois parâmetros. Finalmente, para verificar a precisão da otimização, o resultado ótimo foi comparado com o resultado da análise obtido do software de simulação.
Neste trabalho, um método de otimização para a vazão de entrada da caixa de bateria é proposto para a bateria cujo aumento de temperatura devido à cobertura de poeira. O objetivo é reduzir a temperatura máxima da bateria coberta de poeira para abaixo da temperatura máxima da bateria não coberta de poeira, no caso de baixo consumo de energia.

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	<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

Este estudo visa resolver o problema do aumento da temperatura celular e declínio de desempenho causado por material particulado empoeirado que cobre a superfície da célula através da alocação de velocidades de fluxo de ar nas entradas da caixa de resfriamento da bateria sob o objetivo de baixo consumo de energia. Tomamos a temperatura máxima da bateria em uma velocidade de fluxo de ar especificada e ambiente livre de poeira como a temperatura esperada em um ambiente empoeirado. A temperatura máxima da bateria em um ambiente empoeirado é resolvida em diferentes velocidades de fluxo de ar de entrada, que são as condições de contorno do modelo de análise construído no software de simulação. As matrizes que representam as diferentes combinações de velocidades de entrada do fluxo de ar são geradas aleatoriamente através do algoritmo de hipercubo latino ótimo (OLHA), onde os limites inferior e superior das velocidades correspondentes às temperaturas acima da temperatura desejada são definidos no software de otimização. Estabelecemos um QRSM aproximado entre a combinação de velocidades e a temperatura máxima usando o módulo de ajuste do software de otimização. O QRSM é otimizado com base no ASAM, e o resultado ótimo está em boa concordância com o resultado da análise obtida pelo software de simulação. Após a otimização, o fluxo da entrada média é alterado de 5,5 m/s para 5 m/s, e a velocidade total do fluxo de ar é reduzida em 3%. O protocolo aqui apresentado apresenta um método de otimização considerando simultaneamente o consumo de energia e o desempenho térmico do sistema de gerenciamento de baterias que foi estabelecido, e pode ser amplamente utilizado para melhorar o ciclo de vida da bateria com o mínimo custo operacional.

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 

Otimização de um sistema de gerenciamento de calor baseado em ar para baterias de íons de lítio cobertas de material particulado empoeirado

Seguindo o protocolo, as três primeiras partes são as mais importantes, que incluem modelagem, malha e simulação, tudo para obter a temperatura máxima da bateria. Em seguida, a velocidade do fluxo de ar é ajustada por amostragem e, finalmente, a combinação ótima de vazão é obtida por otimização.
A Figura 9 mostra a comparação da distribuição da temperatura da bateria em diferentes a...

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

Aqui, apresentamos o método de recozimento simulado adaptativo (ASAM) para otimizar um modelo de superfície de resposta quadrática aproximada (QRSM) correspondente a um sistema de gerenciamento de calor de bateria coberto de material particulado empoeirado e preencher as quedas de temperatura ajustando a combinação de velocidades de fluxo de ar das entradas do sistema.

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 ...

Este estudo visa resolver o problema do aumento da temperatura celular e o declínio de desempenho causado pelo material particulado empoeirado que cobre a superfície da célula através da alocação de velocidades de fluxo de ar na menor caixa de resfriamento da bateria sob o objetivo de baixo consumo de energia. Um método de otimização considerando simultaneamente o consumo de energia e o desempenho térmico do sistema de gerenciamento de baterias foi estabelecido. E pode ser amplamente utilizado para melhorar o ciclo de vida da bateria com o mínimo custo operacional.

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

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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.

Engenharia

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.