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

一部の解析および最適化ソフトウェアは、清華大学、建国大学、全南大学校、木浦大学、千葉大学によってサポートされています。

注:研究技術ロードマップを 図1に示し、モデリング、シミュレーション、および最適化ソフトウェアを使用しています。必要な材料は材料 表に示されています。
1. 3Dモデルの作成
注:3Dモデルの作成にはSolidworksを使用しました。
<ol><li>252 mm x 175 mm の長方形を描画し、 ボス/ベースを押し出し(Extrude Boss/Base)をクリック?...

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>

本研究で用いたBTMSは、低コストで構造がシンプルな空冷方式をベースとして確立したものである。熱伝達能力が低いため、空冷システムの性能は、液体冷却システムや相変化材料冷却システムの性能よりも低くなります。しかし、液冷システムには冷媒漏れという欠点があり、相変化材料冷却システムは質量が大きく、エネルギー密度が低い29。これらの冷却システムには?...

自動車産業の急速な発展に伴い、従来の燃料車は再生不可能な資源を大量に消費し、深刻な環境汚染とエネルギー不足を引き起こしています。最も有望な解決策の1つは、電気自動車(EV)の開発です1,2。
EVに使用される動力用バッテリーは、従来の燃料車に取って代わる鍵となる電気化学エネルギーを蓄えることができます。EVに使われる動力電池には、リチウムイオン電池(LIB)、ニッケル水素電池(NiMH)、電気二重層キャパシタ(EDLC)※3などがあります。リチウムイオン電池は、他の電池と比較して、エネルギー密度が高く、効率が高く、ライフサイクルが長いなどの利点があるため、現在、EVのエネルギー貯蔵ユニットとして広く使用されています4,5,6,7。
ただし、化学反応熱とジュール熱により、急速充電や高強度放電中に大量の熱が蓄積し、バッテリー温度が上昇しやすくなります。LIBの理想的な動作温度は20〜40°C8,9です。バッテリーストリング内のバッテリー間の最大温度差は、5°C10,11を超えてはなりません。そうしないと、バッテリー間の温度の不均衡、経年劣化の加速、さらには過熱、火災、爆発などの一連のリスクにつながる可能性があります12。したがって、解決すべき重要な課題は、電池パックの温度と温度差を狭い範囲で制御できる効率的な電池熱管理システム(BTMS)の設計と最適化です。
典型的なBTMSには、空冷、水冷、および相変化材料冷却が含まれます13。これらの冷却方法のうち、空冷式は、低コストで構造が簡便であることから広く用いられている14。空気の比熱容量が限られているため、空冷システムではバッテリーセル間で高温と大きな温度差が発生しやすくなります。空冷式BTMSの冷却性能を向上させるためには、効率的なシステムを設計する必要がある15,16,17。Qianら18は、バッテリーパックの最高温度と温度差を収集して、対応するベイジアンニューラルネットワークモデルをトレーニングし、シリーズ空冷バッテリーパックのセル間隔を最適化するために使用されます。Chen et al.19 は、Z 型並列空冷システムにおける入口分岐プレナムと出口収束プレナムの幅を最適化するために、ニュートン法と流動抵抗ネットワーク モデルを使用したことを報告しました。その結果、システムの温度差が45%減少しました。Liuら20は、J-BTMSの冷却ダクトの5つのグループをサンプリングし、アンサンブルサロゲートベースの最適化アルゴリズムによってセル間隔の最適な組み合わせを得ました。Baveja et al.21 は、受動的にバランスの取れたバッテリーモジュールをモデル化し、この研究では、モジュールレベルの受動的バランスに対する熱予測の影響と、その逆の効果について説明しました。Singhら22は、電気化学-熱連成モデリングを使用して設計された、カプセル化された相変化材料と強制対流空冷を使用したバッテリー熱管理システム(BTMS)を調査しました。Fanら23は、マイクロ流体用途で高い認知度を持つ角型リチウムイオン電池に、より安全な温度範囲を提供するために、多段テスラバルブ構成からなる液体冷却プレートを提案しました。Feng et al. 24 は、変動係数法を使用して、入口流量とバッテリーのクリアランスが異なるスキームを評価しました。Talele et al.25 は、加熱フィルムの最適な配置に基づいて潜在的な発生する熱を蓄えるために、壁強化パイロライニング断熱材を導入しました。
空冷BTMSを使用すると、外部環境中の金属粉塵粒子、鉱物粉塵粒子、建材粉塵粒子、およびその他の粒子が送風機によって空冷BTMSに持ち込まれ、バッテリーの表面がDPMで覆われる可能性があります。放熱計画がないと、バッテリーの温度が高すぎて事故の原因になります。シミュレーション後、指定された気流速度と粉塵のない環境でのバッテリーパックの最高温度を、粉塵の多い環境での予想温度として取ります。まず、Cレートとは、バッテリーが指定された時間内に定格容量を解放するときに必要な電流値を指し、データ値のバッテリーの定格容量の倍数に等しくなります。この論文では、シミュレーションに2Cレート放電を使用します。定格容量は10Ah、公称電圧は3.2Vで、正極材料にはリン酸鉄リチウム(LiFePO4)、負極材料にはカーボンを使用しています。電解液には、電解質リチウム塩、高純度有機溶媒、必要な添加剤、その他の原料が含まれています。入口における異なる速度の組み合わせを表すランダム配列をOLHAで決定し、曲線フィッティングの精度を確認する条件で、バッテリーパックの最高温度と入口流速の組み合わせの間の2次関数を設定しました。ラテンハイパーキューブ(LH)設計は、McKayらによって提案されて以来、多くのコンピュータ実験に適用されてきました26。LH は N x p 行列 L で与えられ、L の各列は整数 1 から N の順列で構成されます。本論文では、計算負荷を軽減するために、最適なラテンハイパーキューブサンプリング法を用いる。この方法では、層化サンプリングを使用して、サンプリングポイントがすべてのサンプリング内部をカバーできるようにします。
次の工程では、エネルギー消費を同時に考慮した条件で、ASAMに基づいて粉塵の多い環境下でバッテリーパックの最高温度を下げるように入口流速の組み合わせを最適化しました。適応型シミュレーテッドアニーリングアルゴリズムは広く開発され、多くの最適化問題で広く使用されています27,28。このアルゴリズムは、特定の確率で最悪の解を受け入れることで、局所的な最適解に陥ることを回避できます。大域的最適は、合格確率と温度を定義することによって達成されます。計算速度は、これら 2 つのパラメーターを使用して調整することもできます。最後に、最適化の精度を確認するために、最適な結果をシミュレーションソフトウェアから得られた解析結果と比較しました。
本稿では、ダストカバーにより温度が上昇する電池パックについて、電池ボックスの入口流量の最適化手法を提案する。目的は、エネルギー消費が少ない場合に、ほこりで覆われたバッテリーパックの最高温度を、ほこりで覆われていないバッテリーパックの最高温度未満に下げることです。

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

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

本研究は、電池冷却ボックスの入口に気流速度を割り当てることで、セル表面を覆う塵埃粒子状物質によるセル温度上昇や性能低下の問題を解決し、低エネルギー消費を目指します。指定された気流速度とほこりのない環境でのバッテリーパックの最高温度を、ほこりの多い環境での予想温度として取ります。粉塵の多い環境におけるバッテリーパックの最高温度は、シミュレーションソフトウェアで構築された解析モデルの境界条件である、異なる吸気流速度で解かれます。吸気口のさまざまな気流速度の組み合わせを表す配列は、最適ラテンハイパーキューブアルゴリズム(OLHA)によってランダムに生成され、目的の温度を超える温度に対応する速度の下限と上限が最適化ソフトウェアで設定されます。最適化ソフトウェアのフィッティングモジュールを使用して、速度の組み合わせと最高温度の間のおおよそのQRSMを確立します。QRSMはASAMに基づいて最適化されており、最適な結果はシミュレーションソフトウェアで得られた解析結果とよく一致しています。最適化後、中央入口の流量を5.5 m/sから5 m/sに変更し、総気流速度を3%低下させます。ここでのプロトコルは、確立されたバッテリー管理システムのエネルギー消費と熱性能を同時に考慮した最適化方法を提示し、最小限の運用コストでバッテリーパックのライフサイクルを改善するために広く使用できます。

With the rapid development of the automobile industry, traditional fuel vehicles consume a lot of non-renewable resources, resulting in serious environmental pollution and energy shortage. One of the most promising solutions is the development of electric vehicles (EVs)1,2.
The power batteries used for EVs can store electrochemical energy, which is the key to replacing traditional fuel vehicles. Power batteries used in EVs include lithium-ion battery (LIB), nickel-metal hydride battery (NiMH), and electric double-layer capacitor (EDLC)3. Compared to the other batteries, lithium-ion batteries are currently widely used as energy storage units in EVs owing to their advantages such as high energy density, high efficiency, and long life cycle4,5,6,7.
However, due to chemical reaction heat and Joule heat, it is easy to accumulate a large amount of heat and increase the battery temperature during rapid charging and high-intensity discharging. The ideal operating temperature of LIB is 20-40 &#176;C8,9. The maximum temperature difference between the batteries in a battery string should not exceed 5 &#176;C10,11. Otherwise, it may lead to a series of risks such as temperature imbalance between the batteries, accelerated aging, even overheating, fire, explosion, and so on12. Therefore, the critical issue to be resolved is designing and optimizing an efficient battery thermal management system (BTMS) that can control the temperature and temperature difference of the battery pack within a narrow.
Typical BTMS include air cooling, water cooling, and phase change material cooling13. Among these cooling methods, the air cooling type is widely used because of its low cost and simplicity of the structure14. Due to the limited specific heat capacity of air, high temperature and large temperature differences are easy to occur between battery cells in air-cooled systems. In order to improve the cooling performance of air-cooled BTMS, it is necessary to design an efficient system15,16,17. Qian et al.18 collected the battery pack&#39;s maximum temperature and temperature difference to train the corresponding Bayesian neural network model, which is used to optimize cell spacings of the series air-cooled battery pack. Chen et al.19 reported using the Newton method and the flow resistance network model for optimization of the widths of the inlet divergence plenum and the outlet convergence plenum in the Z-type parallel air-cooled system. The results showed a 45% reduction in the temperature difference of the system. Liu et al.20 sampled five groups of the cooling ducts in the J-BTMS and obtained the best combination of cell spacings by the ensemble surrogate-based optimization algorithm. Baveja et al.21 modeled a passively balanced battery module, and the study described the effects of thermal prediction on module-level passive balancing and vice versa. Singh et al.22 investigated a battery thermal management system (BTMS) that used encapsulated phase change material along with forced convective air cooling designed using the coupled electrochemical-thermal modeling. Fan et al.23 proposed a liquid cooling plate comprising a multi-stage Tesla valve configuration to provide a safer temperature range for a prismatic-type lithium-ion battery with high recognition in microfluidic applications. Feng et al. 24 used the coefficient of variation method to evaluate the schemes with different inlet flow rates and battery clearances. Talele et al.25 introduced wall-enhanced pyro lining thermal insulation to store potential generated heating based on optimal placement of heating films.
When one uses air-cooling BTMS, metal dust particles, mineral dust particles, building materials dust particles, and other particles in the external environment will be brought into the air-cooling BTMS by the blower, which can cause the surface of the batteries to be covered with DPM. If there is no heat dissipation plan, it may cause accidents due to the excessively high battery temperature. After simulation, we take the maximum temperature of the battery pack in a specified airflow velocity and dust-free environment as the expected temperature in a dusty environment. First, C-rate refers to the current value required when the battery releases its rated capacity within the specified time, which is equal to a multiple of the battery&#39;s rated capacity in the data value. In this paper, the simulation uses 2C rate discharge. The rated capacity is 10 Ah, and the nominal voltage is 3.2 V. Lithium iron phosphate (LiFePO4) is used as the positive electrode material, and carbon is used as the negative electrode material. The electrolyte has electrolyte lithium salt, a high-purity organic solvent, necessary additives, and other raw materials. The random array representing the different velocity combinations at the inlets was determined through the OLHA, and a 2nd order function between the maximum temperature of the battery pack and the inlet flow velocity combination was set up under the condition of checking the accuracy of the curve fitting. Latin hypercube (LH) designs have been applied in many computer experiments since they were proposed by McKay et al.26. An LH is given by an N x p-matrix L, where each column of L consists of a permutation of the integers 1 to N. In this paper, the optimal Latin hypercube sampling method is used to reduce the computational burden. The method uses stratified sampling to ensure that the sampling points can cover all the sampling internals.
In the following step, the inlet flow velocity combination was optimized to decrease the maximum temperature of the battery pack in a dusty environment based on the ASAM under the condition of considering energy consumption simultaneously. The adaptive simulated annealing algorithm has been extensively developed and widely used in many optimization problems27,28. This algorithm can avoid getting trapped in a local optimum by accepting the worst solution with a certain probability. The global optimum is achieved by defining the acceptance probability and temperature; the calculation speed can also be adjusted by using these two parameters. Finally, for checking the accuracy of the optimization, the optimal result was compared with the analysis result obtained from the simulation software.
In this paper, an optimization method for the inlet flow rate of the battery box is proposed for the battery pack whose temperature rises due to dust cover. The purpose is to reduce the maximum temperature of the dust-covered battery pack to below the maximum temperature of the non-dust-covered battery pack in the case of low energy consumption.

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

粉塵粒子状物質に覆われたリチウムイオン電池パックの空気熱管理システムの最適化

プロトコルに従うと、モデリング、メッシュ作成、シミュレーションを含む最初の3つの部分が最も重要であり、すべてバッテリーパックの最高温度を取得します。その後、サンプリングにより気流速度を調整し、最後に最適化により最適な流量の組み合わせを求めます。
図9は異なる環境における電...

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

ここでは、粉塵に覆われた粒子状物質に覆われたバッテリー熱管理システムに対応した近似二次応答曲面モデル(QRSM)を最適化し、システム入口の気流速度の組み合わせを調整することで温度低下を実現する適応型シミュレーテッドアニーリング法(ASAM)を紹介します。

This study aims to solve the problem of the cell temperature rise and performance decline caused by dusty particulate matter covering the surface of the ...

本研究は、電池冷却ボックスの少ない方に気流速度を割り当てることで、セル表面を覆う塵埃粒子状物質によるセル温度上昇と性能低下の問題を解決し、低エネルギー消費を目指します。蓄電池管理システムのエネルギー消費と熱性能を同時に考慮した最適化手法を確立した。また、最小限の運用コストでバッテリーパックのライフサイクルを改善するために広く使用できます。

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

Research

Education

JoVE Core

JoVE Journal

Electrical Engineering

Engineering

Unit 1

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

Transient Stability and System Controls

SCIENTISTS IN ACTION

This study aims to solve the problem of the cell temperature rise and performance decline caused by dusty particulate matter covering the surface of the cell through the allocation of airflow velocities at the inlets of the battery cooling box under the goal of low energy consumption. We take the maximum temperature of the battery pack at a specified airflow velocity and dust-free environment as the expected temperature in a dusty environment. The maximum temperature of the battery pack in a dusty environment is solved at different inlet airflow velocities, which are the boundary conditions of the analysis model constructed in the simulation software. The arrays representing the different airflow velocity combinations of inlets are generated randomly through the optimal Latin hypercube algorithm (OLHA), where the lower and upper limits of velocities corresponding to the temperatures above the desired temperature are set in the optimization software. We establish an approximate QRSM between the velocity combination and the maximum temperature using the fitting module of the optimization software. The QRSM is optimized based on the ASAM, and the optimal result is in good agreement with the analysis result obtained by the simulation software. After optimization, the flow rate of the middle inlet is changed from 5.5 m/s to 5 m/s, and the total airflow velocity is decreased by 3%. The protocol here presents an optimization method simultaneously considering energy consumption and thermal performance of the battery management system that has been established, and it can be widely used to improve the life cycle of the battery pack with minimum operating cost.

Here, we present the adaptive simulated annealing method (ASAM) to optimize an approximate quadratic response surface model (QRSM) corresponding to a dusty particulate matter-covered battery heat management system and fulfill the temperature drops back by adjusting the airflow velocities combination of system inlets.

工学

Simulation Analysis for Evaluating Temperature and Airflow Dynamics in Battery Packs 

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