Name: Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
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Vissa analys- och optimeringsprogram stöds av Tsinghua University, Konkuk University, Chonnam National University, Mokpo University och Chiba University.

OBS: Färdplanen för forskningsteknik visas i figur 1, där programvaran för modellering, simulering och optimering används. De material som krävs visas i materialtabellen.
1. Skapa 3D-modellen
OBS: Vi använde Solidworks för att skapa 3D-modellen.
<ol><li>Rita en rektangel på 252 mm x 175 mm, klicka på Extrude Boss/Base och ange 73. Skapa ett nytt plan 4 mm från den yttre ytan.</li>
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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. 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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. 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BTMS som användes i denna studie fastställdes baserat på luftkylningssystemet på grund av dess låga kostnad och enkelhet i strukturen. På grund av den låga värmeöverföringskapaciteten är luftkylningssystemets prestanda lägre än för vätskekylsystemet och fasändringsmaterialkylsystemet. Vätskekylsystemet har dock nackdelen med köldmedieläckage, och fasförändringsmaterialets kylsystem har hög massa och låg energitäthet29. Dessa kylsystem har sina fördelar och nackdelar. Därf...

Med den snabba utvecklingen av bilindustrin förbrukar fordon som drivs med traditionella bränslen mycket icke-förnybara resurser, vilket resulterar i allvarliga miljöföroreningar och energibrist. En av de mest lovande lösningarna är utvecklingen av elfordon1,2.
Kraftbatterierna som används för elbilar kan lagra elektrokemisk energi, vilket är nyckeln till att ersätta traditionella bränslefordon. Kraftbatterier som används i elfordon inkluderar litiumjonbatterier (LIB), nickelmetallhydridbatterier (NiMH) och elektriska dubbelskiktskondensatorer (EDLC)3. Jämfört med de andra batterierna används litiumjonbatterier för närvarande i stor utsträckning som energilagringsenheter i elfordon på grund av deras fördelar som hög energitäthet, hög effektivitet och lång livscykel 4,5,6,7.
Men på grund av kemisk reaktionsvärme och Joule-värme är det lätt att ackumulera en stor mängd värme och öka batteritemperaturen under snabbladdning och högintensiv urladdning. Den idealiska driftstemperaturen för LIB är 20-40 °C 8,9. Den maximala temperaturskillnaden mellan batterierna i en batteristräng bör inte överstiga 5 °C10,11. Annars kan det leda till en rad risker som temperaturobalans mellan batterierna, accelererat åldrande, till och med överhettning, brand, explosion och så vidare12. Därför är den kritiska frågan som måste lösas att designa och optimera ett effektivt batterivärmehanteringssystem (BTMS) som kan kontrollera temperaturen och temperaturskillnaden i batteripaketet inom en snäv gräns.
Typiska BTMS inkluderar luftkylning, vattenkylning och fasändringsmaterialkylning13. Bland dessa kylmetoder används luftkylningstypen i stor utsträckning på grund av dess låga kostnad och enkelhet i strukturen14. På grund av luftens begränsade specifika värmekapacitet är höga temperaturer och stora temperaturskillnader lätta att uppstå mellan battericeller i luftkylda system. För att förbättra kylningsprestandan hos luftkylda BTMS är det nödvändigt att utforma ett effektivt system 15,16,17. Qian et al.18 samlade in batteripaketets maximala temperatur och temperaturskillnad för att träna motsvarande Bayesianska neurala nätverksmodell, som används för att optimera cellavstånden i seriens luftkylda batteripaket. Chen et al.19 rapporterade att Newton-metoden och flödesmotståndsnätverksmodellen användes för optimering av bredderna på inloppsdivergenslådan och utloppskonvergenslådan i det parallella luftkylda systemet av Z-typ. Resultaten visade en 45-procentig minskning av temperaturskillnaden i systemet. Liu et al.20 tog prov på fem grupper av kylkanalerna i J-BTMS och erhöll den bästa kombinationen av cellavstånd med den ensemblesurrogatbaserade optimeringsalgoritmen. Baveja et al.21 modellerade en passivt balanserad batterimodul, och studien beskrev effekterna av termisk förutsägelse på passiv balansering på modulnivå och vice versa. Singh et al.22 undersökte ett batterivärmehanteringssystem (BTMS) som använde inkapslat fasförändringsmaterial tillsammans med forcerad konvektiv luftkylning designad med hjälp av den kopplade elektrokemiska-termiska modelleringen. et al.23 föreslog en vätskekylplatta bestående av en flerstegs Tesla-ventilkonfiguration för att ge ett säkrare temperaturområde för ett litiumjonbatteri av prismatisk typ med hög igenkänning i mikrofluidiska applikationer. Feng et al. 24 använde variationskoefficientmetoden för att utvärdera systemen med olika inloppsflöden och batterispel. Talele et al.25 introducerade väggförstärkt pyrofoder värmeisolering för att lagra potentiellt genererad värme baserat på optimal placering av värmefilmer.
När man använder luftkylning BTMS kommer metalldammpartiklar, mineraldammpartiklar, dammpartiklar från byggmaterial och andra partiklar i den yttre miljön att föras in i luftkylnings-BTMS av fläkten, vilket kan göra att batteriernas yta täcks med DPM. Om det inte finns någon värmeavledningsplan kan det orsaka olyckor på grund av den för höga batteritemperaturen. Efter simulering tar vi den maximala temperaturen på batteripaketet i en specificerad luftflödeshastighet och dammfri miljö som den förväntade temperaturen i en dammig miljö. För det första hänvisar C-rate till det strömvärde som krävs när batteriet släpper sin nominella kapacitet inom den angivna tiden, vilket är lika med en multipel av batteriets nominella kapacitet i datavärdet. I det här dokumentet använder simuleringen 2C-urladdning. Den nominella kapaciteten är 10 Ah och den nominella spänningen är 3,2 V. Litiumjärnfosfat (LiFePO4) används som det positiva elektrodmaterialet och kol används som det negativa elektrodmaterialet. Elektrolyten har elektrolytlitiumsalt, ett organiskt lösningsmedel med hög renhet, nödvändiga tillsatser och andra råvaror. Den slumpmässiga matrisen som representerar de olika hastighetskombinationerna vid inloppen bestämdes genom OLHA, och en 2:a ordningens funktion mellan batteripaketets maximala temperatur och inloppsflödeshastighetskombinationen ställdes in under förutsättning att kurvans noggrannhet kontrollerades. Latinska hyperkuber (LH) har tillämpats i många datorexperiment sedan de föreslogs av McKay et al.26. En LH ges av en N x p-matris L, där varje kolonn av L består av en permutation av heltalen 1 till N. I den här artikeln används den optimala latinska hyperkubsamplingsmetoden för att minska beräkningsbördan. Metoden använder stratifierad provtagning för att säkerställa att provtagningspunkterna kan täcka alla provtagningsinterna delar.
I följande steg optimerades kombinationen av inloppsflödeshastighet för att minska batteripaketets maximala temperatur i en dammig miljö baserat på ASAM under förutsättning att energiförbrukningen beaktas samtidigt. Den adaptiva simulerade glödgningsalgoritmen har utvecklats i stor utsträckning och används i stor utsträckning i många optimeringsproblem27,28. Denna algoritm kan undvika att fastna i ett lokalt optimum genom att acceptera den sämsta lösningen med en viss sannolikhet. Det globala optimum uppnås genom att definiera acceptanssannolikheten och temperaturen; Beräkningshastigheten kan också justeras med hjälp av dessa två parametrar. Slutligen, för att kontrollera noggrannheten i optimeringen, jämfördes det optimala resultatet med analysresultatet från simuleringsprogramvaran.
I detta dokument föreslås en optimeringsmetod för batterilådans inloppsflöde för batteripaketet vars temperatur stiger på grund av dammskydd. Syftet är att sänka den maximala temperaturen för det dammtäckta batteripaketet till under den maximala temperaturen för det icke-dammtäckta batteripaketet vid låg energiförbrukning.

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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

Denna studie syftar till att lösa problemet med celltemperaturökningen och prestandaförsämringen orsakad av dammiga partiklar som täcker cellens yta genom allokering av luftflödeshastigheter vid inloppen till batterikylboxen under målet om låg energiförbrukning. Vi tar den maximala temperaturen på batteripaketet vid en specificerad luftflödeshastighet och dammfri miljö som den förväntade temperaturen i en dammig miljö. Batteripaketets maximala temperatur i en dammig miljö löses vid olika inloppsluftflödeshastigheter, vilket är randvillkoren för den analysmodell som konstrueras i simuleringsmjukvaran. Matriserna som representerar de olika kombinationerna av luftflödeshastighet genereras slumpmässigt genom den optimala Latin Hypercube-algoritmen (OLHA), där de nedre och övre gränserna för hastigheter som motsvarar temperaturerna över den önskade temperaturen ställs in i optimeringsprogramvaran. Vi fastställer en ungefärlig QRSM mellan hastighetskombinationen och den maximala temperaturen med hjälp av optimeringsprogrammets anpassningsmodul. QRSM är optimerad baserat på ASAM, och det optimala resultatet överensstämmer väl med det analysresultat som erhålls av simuleringsprogramvaran. Efter optimering ändras flödeshastigheten för det mellersta inloppet från 5,5 m/s till 5 m/s, och den totala luftflödeshastigheten minskas med 3 %. Protokollet presenterar här en optimeringsmetod som samtidigt tar hänsyn till energiförbrukning och termisk prestanda för det batterihanteringssystem som har etablerats, och det kan användas i stor utsträckning för att förbättra batteripaketets livscykel med minimal driftskostnad.

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 

Optimering av ett luftbaserat värmehanteringssystem för dammiga partikeltäckta litiumjonbatterier

Enligt protokollet är de tre första delarna de viktigaste, som inkluderar modellering, maskning och simulering, allt för att få den maximala temperaturen på batteripaketet. Därefter justeras luftflödeshastigheten genom provtagning, och slutligen erhålls den optimala flödeshastighetskombinationen genom optimering.
Figur 9 visar jämförelsen av batteripaketets temperaturfördelning i olika m...

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

Här presenterar vi den adaptiva simulerade glödgningsmetoden (ASAM) för att optimera en approximativ kvadratisk responsytmodell (QRSM) som motsvarar ett dammigt partikeltäckt batterivärmehanteringssystem och uppfylla temperatursänkningarna genom att justera luftflödeshastigheterna genom att justera luftflödeshastigheterna kombination av systeminlopp.

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 ...

Denna studie syftar till att lösa problemet med celltemperaturökningen och prestandaförsämringen som orsakas av de dammiga partiklar som täcker cellens yta genom allokering av luftflödeshastigheter vid den lägre batterikylboxen under målet låg energiförbrukning. En optimeringsmetod som samtidigt tar hänsyn till energiförbrukning och termisk prestanda för batterihanteringssystem har etablerats. Och det kan användas i stor utsträckning för att förbättra batteripaketets livscykel med minimal driftskostnad.

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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

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.

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.