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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 楊鏡堂(Jing-Tang Yang) | |
dc.contributor.author | Yi-Wen Chen | en |
dc.contributor.author | 陳怡妏 | zh_TW |
dc.date.accessioned | 2023-03-19T23:16:32Z | - |
dc.date.copyright | 2022-07-22 | |
dc.date.issued | 2022 | |
dc.date.submitted | 2022-07-19 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/85432 | - |
dc.description.abstract | 本文分為三個部分,逐步建立適用於鋰離子電池儲能系統且具應用價值的分散式進口操控策略。第一部分參考資料中心的散熱方式,創新應用於電池儲能系統,具體改善現有商用儲能系統散熱效果不佳的熱流場因素。第二部分以電池模組的最高溫度、最大溫差作為指標,提出分散式的進口操控策略,有效消除局部熱點。第三部分提出兼具安全性與商業價值的散熱策略,引入機器學習演算法,驗證影響因素,並將電池的安全溫度、散熱系統的消耗功率納入控制策略的優劣評斷。 本文以商業軟體ANSYS Fluent進行數值模擬與分析,設計時引入資料中心提升散熱效率的方式且調整相異之處,比較三種進出風口配置,選定散熱效果最佳的配置作為本研究的物理模型,與本文參考的現有商用儲能貨櫃車之物理模型相比,最高溫度與最大溫差改善幅度達12.5 %和62.4 %。本研究解析各進口條件下的電池模組溫度,歸納出進口風量對於改善電池的最高溫度及最大溫差有最顯著效果,進口溫度僅對最高溫度改善較明顯。分析過程引入機器學習演算法協助印證兩進口條件對電池模組溫度分布的影響,建立各進口條件組合下,電池模組表面溫度是否超過安全溫度的預測。 更進一步以進口風量與進口溫度的影響為基礎,發展分散式進口控制策略,獨立設定進風口的風量或溫度。結果顯示,複合進口溫度控制策略可有效使目標機櫃的最高溫度降低8 °C以上,且未產生新熱點。與均勻調低進口溫度相比,此策略可省下10.99 kW的散熱系統消耗功率,為均勻調低進口溫度的37.1 %,大幅降低商業營運成本。 本文參考資料中心的散熱方式,改善商用的電池儲能系統配置;引入機器學習的演算法分析影響流場的因素,並建立電池儲能系統的分散式進口控制策略,有效節省空調系統與風扇的功率消耗,同時避免電池模組的溫度高於安全溫度。 | zh_TW |
dc.description.abstract | This study proposes a control strategy based on the temperature distribution of battery modules and power consumption of air conditioning system, aims to improve the safety of battery energy storage system and profitability of air conditioning system. The first part of this study inspired by the cooling the solution for data center, based on the similarity between data center and battery energy storage system, this study re-designs the inlet and outlet of battery energy storage system. The second part establishes the distributed air supply strategy to avoid local hot spot, takes the maximum temperature and maximum temperature difference as the index for temperature distribution. The last part of the study duduces the strategy based on the power consumption, takes the profitability of air conditioning system into consideration. In this study I utilize ANSYS Fluent to calculate the flow field and temperature distribution. Inspired by data center, this study rearranges the configuration of battery energy storage system, the rearrangement shows the maximum temperature and the maximum temperature difference reduce 12.5% and 62.4%, respectively. The machine learning algorithm is utilized in the analysis of this study. Results show that the inlet volume flow rate has significant impact among the maximum temperature and the maximum temperature difference of battery modules. However, only the maximum temperature decreases as the inlet temperature decreases. To verify the influence of inlet conditions among the temperature distribution, the algorithm of decision tree is utilized in this study. The algorithm of support vector machine is used to establish a model, which can predict whether the surface temperature of the battery module exceeds the safe temperature under various inlet conditions. The distributed air supply strategy bases on the influence of inlet conditions is proposed in this study. The inlet volume and inlet temperature of each inlet will be assigned respectively. Results show that the compound inlet temperature control, which reduce the inlet temperature of two specific inlets, can at least cool down the target cabinet 8 °C without new hot spot. Compare to reduce all inlet temperature, the power consumption of the compound inlet temperature control is 13190.7W, 53.6% less then reduce all inlet temperature. This study rearranges the configuration of battery energy storage system with the solutions inspired by data center, and utilizes machine learning algorithm to decouple the influence factors of flow field. Moreover, this study builds up a distributed air supply strategy to maintain the module temperature within safety limit with less power consumption, reduces the cost of the ventilation system. | en |
dc.description.provenance | Made available in DSpace on 2023-03-19T23:16:32Z (GMT). No. of bitstreams: 1 U0001-0607202214424500.pdf: 31869949 bytes, checksum: c17db08265264a238c6d08c48161eae1 (MD5) Previous issue date: 2022 | en |
dc.description.tableofcontents | 口試委員會審定書 i 誌謝 ii 摘要 iii Abstract iv 目錄 vi 圖目錄 ix 表目錄 xiv 符號表 xv 第一章 前言 1 1-1 研究背景 1 1-2 研究動機 1 第二章 文獻回顧 3 2-1電池 3 2-1.1 電池的種類 5 2-2 鋰離子電池熱相關議題 8 2-2.1 溫度過高 8 2-2.2 溫度分布不均 9 2-2.3 老化過程 10 2-2.4 電池組散熱 10 2-3 儲能系統 13 2-3.1 各儲能系統簡介 14 2-3.2 電池儲能系統簡介 17 2-3.3 電池儲能系統的散熱策略 22 2-4 資料中心散熱 26 2-4.1 冷空氣旁路 27 2-4.2 熱空氣回流 28 2-4.3 負壓 29 2-5 計算流體力學 29 2-5.1 紊流模型 30 第三章 研究方法 34 3-1 物理模型 34 3-2 統御方程式 38 3-2.1 質量守恆方程式 39 3-2.2 動量守恆方程式 39 3-2.3 能量守恆方程式 39 3-3 邊界條件與網格設置 39 3-3.1 邊界條件 39 3-3.2 電池模組發熱模型 40 3-3.3 網格設置 42 3-4 獨立性驗證 44 3-4.1 網格獨立性驗證 44 3-4.2 時間步階獨立性驗證 45 3-4.3 計算域獨立性驗證 47 3-5 耗電功率計算與策略優劣評斷 47 3-5.1 空調消耗功率計算 48 3-5.2 風扇消耗功率計算 49 3-5.3 優劣評斷參數Ind計算 49 3-6 控制策略之方法 49 3-6.1 決策樹 50 3-6.2 支持向量機 52 第四章 結果與討論 54 4-1 物理模型設定 54 4-1.1 幾何配置改善成果 54 4-1.2 電池定量發熱控制 61 4-2 進口條件影響的分析 66 4-2.1 進口風量之影響 67 4-2.2 進口溫度之影響 67 4-2.3 決策樹 69 4-2.4 支持向量機 71 4-3 非均勻進口控制策略 73 4-3.1 進口風量的獨立控制策略 73 4-3.2 進口溫度的獨立控制策略 77 第五章 結論與未來展望 104 5-1 結論 104 5-2 未來展望 106 5-3 甘特圖 107 參考文獻 108 | |
dc.language.iso | zh-TW | |
dc.title | 改善電池儲能系統溫度分布之進風口控制策略 | zh_TW |
dc.title | Strategy for Improving Temperature Distribution of Battery Storage System through Inlet Condition Settings | en |
dc.type | Thesis | |
dc.date.schoolyear | 110-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 葉思沂(Szu-I Yeh),呂明璋(Ming-Chang Lu),王安邦(An-Bang Wang) | |
dc.subject.keyword | 電池儲能系統,計算流體力學,熱管理,機器學習,分散式供氣, | zh_TW |
dc.subject.keyword | battery energy storage system,computational fluid dynamics,heat management,machine learning,distributed air supply, | en |
dc.relation.page | 116 | |
dc.identifier.doi | 10.6342/NTU202201302 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2022-07-19 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 機械工程學研究所 | zh_TW |
dc.date.embargo-lift | 2023-08-01 | - |
顯示於系所單位: | 機械工程學系 |
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