Name: Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
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Noen analyse- og optimaliseringsprogrammer støttes av Tsinghua University, Konkuk University, Chonnam National University, Mokpo University og Chiba University.

MERK: Veikartet for forskningsteknologi er vist i figur 1, der modellerings-, simulerings- og optimaliseringsprogramvaren brukes. De nødvendige materialene vises i materialfortegnelsen.
1. Opprette 3D-modellen
MERK: Vi brukte Solidworks til å lage 3D-modellen.
<ol><li>Tegn et rektangel på 252 mm x 175 mm, klikk Ekstruder boss/base, og skriv inn 73. Opprett et nytt plan 4 mm fra ytre overflaten.</li>
	<li>Tegn et rektan...

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. K., He, Y. L., Zhang, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Internal+cooling+of+a+lithium-ion+battery+using+electrolyte+as+coolant+through+microchannels+embedded+inside+the+electrodes.">Internal cooling of a lithium-ion battery using electrolyte as coolant through microchannels embedded inside the electrodes.</a> Journal of Power Sources. 293, 458-466 (2015).</li><li>Skerlos, S. J., Winebrake, J. 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. C., Liang, Z. C., Ling, G. Z., Huang, L. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Influence+of+phase+change+material+dosage+on+the+heat+dissipation+performance+of+the+battery+thermal+management+system.">Influence of phase change material dosage on the heat dissipation performance of the battery thermal management system.</a> Journal of Energy Storage. 41, 102849 (2021).</li><li>Li, M. L., Zang, M. Y., Li, C. Y., Dai, H. Y. <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+structure+of+air+cooling+heat+dissipation+for+Li-ion+batteries.">Optimization of structure of air cooling heat dissipation for Li-ion batteries.</a> Battery Bimonthly. 50 (3), 1001 (2020).</li></ol>

BTMS som ble brukt i denne studien ble etablert basert på luftkjølesystemet på grunn av dets lave kostnader og enkelhet i strukturen. På grunn av den lave varmeoverføringskapasiteten er ytelsen til luftkjølesystemet lavere enn for væskekjølesystemet og kjølesystemet for faseendringsmateriale. Det flytende kjølesystemet har imidlertid ulempen med kjølemediumlekkasje, og kjølesystemet for faseendringsmateriale har høy masse og lav energitetthet29. Disse kjølesystemene har sine fordeler...

Med den raske utviklingen av bilindustrien bruker tradisjonelle drivstoffbiler mye ikke-fornybare ressurser, noe som resulterer i alvorlig miljøforurensning og energimangel. En av de mest lovende løsningene er utviklingen av elektriske kjøretøy (EV)1,2.
Strømbatteriene som brukes til elbiler kan lagre elektrokjemisk energi, som er nøkkelen til å erstatte tradisjonelle drivstoffbiler. Strømbatterier som brukes i elbiler inkluderer litiumionbatteri (LIB), nikkelmetallhydridbatteri (NiMH) og elektrisk dobbeltlagskondensator (EDLC)3. Sammenlignet med de andre batteriene, er litiumionbatterier for tiden mye brukt som energilagringsenheter i elbiler på grunn av fordelene som høy energitetthet, høy effektivitet og lang livssyklus 4,5,6,7.
På grunn av kjemisk reaksjonsvarme og Joule-varme er det imidlertid lett å akkumulere en stor mengde varme og øke batteritemperaturen under hurtiglading og høyintensiv utladning. Den ideelle driftstemperaturen til LIB er 20-40 °C 8,9. Den maksimale temperaturforskjellen mellom batteriene i en batteristreng bør ikke overstige 5 °C10,11. Ellers kan det føre til en rekke risikoer som temperaturubalanse mellom batteriene, akselerert aldring, til og med overoppheting, brann, eksplosjon og så videre12. Derfor er det kritiske problemet som skal løses, å designe og optimalisere et effektivt batteristyringssystem (BTMS) som kan kontrollere temperaturen og temperaturforskjellen til batteripakken innenfor en smal.
Typiske BTMS inkluderer luftkjøling, vannkjøling og faseendringsmaterialkjøling13. Blant disse kjølemetodene er luftkjølingstypen mye brukt på grunn av dens lave kostnader og enkelhet i strukturen14. På grunn av luftens begrensede spesifikke varmekapasitet er det lett å oppstå høye temperaturer og store temperaturforskjeller mellom battericeller i luftkjølte systemer. For å forbedre kjøleytelsen til luftkjølte BTMS, er det nødvendig å designe et effektivt system 15,16,17. Qian et al.18 samlet batteripakkens maksimale temperatur- og temperaturforskjell for å trene den tilsvarende bayesianske nevrale nettverksmodellen, som brukes til å optimalisere celleavstandene til seriens luftkjølte batteripakke. Chen et al.19 rapporterte ved hjelp av Newton-metoden og strømningsmotstandsnettverksmodellen for optimalisering av bredden på innløpsdivergensplenum og utløpskonvergensplenum i Z-type parallelt luftkjølt system. Resultatene viste en 45% reduksjon i temperaturforskjellen i systemet. Liu et al.20 samplet fem grupper av kjølekanalene i J-BTMS og oppnådde den beste kombinasjonen av celleavstander ved ensemble-surrogatbasert optimaliseringsalgoritme. Baveja et al.21 modellerte en passivt balansert batterimodul, og studien beskrev effekten av termisk prediksjon på passiv balansering på modulnivå og omvendt. Singh et al.22 undersøkte et batteri termisk styringssystem (BTMS) som brukte innkapslet faseendringsmateriale sammen med tvungen konvektiv luftkjøling designet ved hjelp av den koblede elektrokjemisk-termiske modelleringen. Fan et al.23 foreslo en flytende kjøleplate som består av en flertrinns Tesla-ventilkonfigurasjon for å gi et sikrere temperaturområde for et prismatisk litiumionbatteri med høy anerkjennelse i mikrofluidiske applikasjoner. Feng et al. 24 brukte variasjonskoeffisientmetoden for å evaluere ordningene med forskjellige innløpsstrømningshastigheter og batteriklaringer. Talele et al.25 introduserte veggforsterket varmeisolasjon for pyroforing for å lagre potensiell generert oppvarming basert på optimal plassering av varmefilmer.
Når man bruker luftkjølende BTMS, vil metallstøvpartikler, mineralstøvpartikler, byggematerialer, støvpartikler og andre partikler i det ytre miljøet føres inn i den luftkjølende BTMS av blåseren, noe som kan føre til at overflaten på batteriene dekkes med DPM. Hvis det ikke er noen varmespredningsplan, kan det føre til ulykker på grunn av for høy batteritemperatur. Etter simulering tar vi den maksimale temperaturen på batteripakken i en spesifisert luftstrømhastighet og støvfritt miljø som forventet temperatur i et støvete miljø. For det første refererer C-rate til gjeldende verdi som kreves når batteriet frigjør sin nominelle kapasitet innen den angitte tiden, som er lik et multiplum av batteriets nominelle kapasitet i dataverdien. I denne artikkelen bruker simuleringen 2C-hastighetsutladning. Nominell kapasitet er 10 Ah, og den nominelle spenningen er 3, 2 V. Litiumjernfosfat (LiFePO4) brukes som det positive elektrodematerialet, og karbon brukes som det negative elektrodematerialet. Elektrolytten har elektrolyttlitiumsalt, et organisk løsningsmiddel med høy renhet, nødvendige tilsetningsstoffer og andre råvarer. Den tilfeldige matrisen som representerte de forskjellige hastighetskombinasjonene ved innløpene ble bestemt gjennom OLHA, og en 2. ordens funksjon mellom batteripakkens maksimale temperatur og innløpshastighetskombinasjonen ble satt opp under forutsetning av å kontrollere nøyaktigheten av kurvetilpasningen. Latin hypercube (LH) design har blitt brukt i mange dataeksperimenter siden de ble foreslått av McKay et al.26. En LH er gitt av en N x p-matrise L, hvor hver kolonne av L består av en permutasjon av heltallene 1 til N. I dette papiret brukes den optimale latinske hyperkubeprøvemetoden for å redusere beregningsbyrden. Metoden bruker stratifisert prøvetaking for å sikre at prøvetakingspunktene kan dekke alle prøvetakingsinterne.
I det følgende trinnet ble kombinasjonen av innløpsstrømningshastighet optimalisert for å redusere batteripakkens maksimale temperatur i et støvete miljø basert på ASAM under forutsetning av å vurdere energiforbruket samtidig. Den adaptive simulerte glødealgoritmen har blitt omfattende utviklet og mye brukt i mange optimaliseringsproblemer27,28. Denne algoritmen kan unngå å bli fanget i et lokalt optimalum ved å akseptere den verste løsningen med en viss sannsynlighet. Det globale optimale oppnås ved å definere akseptsannsynlighet og temperatur; Beregningshastigheten kan også justeres ved hjelp av disse to parameterne. Til slutt, for å kontrollere nøyaktigheten av optimaliseringen, ble det optimale resultatet sammenlignet med analyseresultatet oppnådd fra simuleringsprogramvaren.
I dette papiret foreslås en optimaliseringsmetode for innløpsstrømningshastigheten til batteriboksen for batteripakken hvis temperatur stiger på grunn av støvdeksel. Hensikten er å redusere maksimumstemperaturen på den støvbelagte batteripakken til under maksimumstemperaturen på den ikke-støvbelagte batteripakken i tilfelle lavt energiforbruk.

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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

Denne studien tar sikte på å løse problemet med celletemperaturstigning og ytelsesnedgang forårsaket av støvete partikler som dekker overflaten av cellen gjennom tildeling av luftstrømningshastigheter ved innløpene til batterikjøleboksen under målet om lavt energiforbruk. Vi tar den maksimale temperaturen på batteripakken ved en spesifisert luftstrømshastighet og støvfritt miljø som forventet temperatur i støvete omgivelser. Batteripakkens maksimale temperatur i støvete omgivelser løses ved forskjellige luftstrømningshastigheter, som er grensebetingelsene for analysemodellen konstruert i simuleringsprogramvaren. Matrisene som representerer de forskjellige luftstrømhastighetskombinasjonene av innløp, genereres tilfeldig gjennom den optimale latinske hyperkubealgoritmen (OLHA), der de nedre og øvre grensene for hastigheter som tilsvarer temperaturene over ønsket temperatur er satt i optimaliseringsprogramvaren. Vi etablerer en omtrentlig QRSM mellom hastighetskombinasjonen og maksimumstemperaturen ved hjelp av tilpasningsmodulen til optimaliseringsprogramvaren. QRSM er optimalisert basert på ASAM, og det optimale resultatet stemmer godt overens med analyseresultatet oppnådd av simuleringsprogramvaren. Etter optimalisering endres strømningshastigheten til midtinnløpet fra 5,5 m/s til 5 m/s, og den totale luftstrømshastigheten reduseres med 3 %. Protokollen her presenterer en optimaliseringsmetode som samtidig vurderer energiforbruk og termisk ytelse til batteristyringssystemet som er etablert, og det kan brukes mye for å forbedre livssyklusen til batteripakken med minimale driftskostnader.

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 

Optimalisering av et luftbasert varmestyringssystem for støvete litium-ion-batteripakker dekket av partikler

Etter protokollen er de tre første delene de viktigste, som inkluderer modellering, meshing og simulering, alt for å få maksimal temperatur på batteripakken. Deretter justeres luftstrømshastigheten ved prøvetaking, og til slutt oppnås den optimale strømningshastighetskombinasjonen ved optimalisering.
Figur 9 viser sammenligningen av batteripakkens temperaturfordeling i forskjellige miljøer,...

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

Her presenterer vi den adaptive simulerte glødningsmetoden (ASAM) for å optimalisere en tilnærmet kvadratisk responsoverflatemodell (QRSM) som tilsvarer et støvete partikkelformet batterivarmestyringssystem og oppfylle temperaturfallene tilbake ved å justere luftstrømhastighetskombinasjonen av systeminntak.

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 ...

Denne studien tar sikte på å løse problemet med celletemperaturstigningen og ytelsesnedgangen forårsaket av støvete partikler som dekker overflaten av cellen gjennom tildeling av luftstrømningshastigheter ved mindre batterikjøleboks under målet om lavt energiforbruk. Det er etablert en optimaliseringsmetode som samtidig vurderer energiforbruk og termisk ytelse for batteristyringssystemet. Og det kan brukes mye til å forbedre livssyklusen til batteripakken med minimale driftskostnader.

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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.