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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 顏家鈺 | |
| dc.contributor.author | Sheng-Yu Huang | en |
| dc.contributor.author | 黃勝煜 | zh_TW |
| dc.date.accessioned | 2021-06-17T03:11:24Z | - |
| dc.date.available | 2023-07-23 | |
| dc.date.copyright | 2018-07-23 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-07-17 | |
| dc.identifier.citation | 參考文獻
[1] S. S. Zhang, 'The effect of the charging protocol on the cycle life of a Li-ion battery,' Journal of power sources, vol. 161, pp. 1385-1391, 2006. [2] Anseán, D., et al. 'Fast charging technique for high power lithium iron phosphate batteries: A cycle life analysis.' Journal of Power Sources 239 (2013): 9-15. [3] [Lithium-ion battery data sheet]. (n.d.). Revived from http://na.industrial.panasonic.com/sites/default/pidsa/files/ncr18650.pdf [4] Dees, Dennis W., Vincent S. Battaglia, and André Bélanger. 'Electrochemical modeling of lithium polymer batteries.' Journal of power sources 110.2 (2002): 310-320. [5] Song, Li, and James W. Evans. 'Electrochemical‐Thermal Model of Lithium Polymer Batteries.' Journal of the Electrochemical Society 147.6 (2000): 2086-2095. [6] Ledovskikh, A., et al. 'Modelling of rechargeable NiMH batteries.' Journal of alloys and compounds 356 (2003): 742-745. [7] M. B. Pinson and M. Z. Bazant, 'Theory of SEI formation in rechargeable batteries: capacity fade, accelerated aging and lifetime prediction,' Journal of the Electrochemical Society, vol. 160, pp. A243-A250, 2013. [8] B Parthiban, Thirumalai, R. Ravi, and N. Kalaiselvi. 'Exploration of artificial neural network [ANN] to predict the electrochemical characteristics of lithium-ion cells.' Electrochimica Acta 53.4 (2007): 1877-1882. [9] Shen, W. X., et al. 'Adaptive neuro-fuzzy modeling of battery residual capacity for electric vehicles.' Industrial Electronics, IEEE Transactions on 49.3 (2002): 677-684. [10] Salkind, Alvin J., et al. 'Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology.' Journal of Power Sources 80.1 (1999): 293-300. [11] H. W. Lin, M. Tegmark, and D. Rolnick, 'Why does deep and cheap learning work so well?,' Journal of Statistical Physics, vol. 168, pp. 1223-1247, 2017. [12] http://www.cc.ntu.edu.tw/chinese/epaper/0038/20160920_3805.html [13] Y. Zhang, R. Xiong, H. He, and Z. Liu, 'A LSTM-RNN method for the lithuim-ion battery remaining useful life prediction,' in Prognostics and System Health Management Conference (PHM-Harbin), 2017, 2017, pp. 1-4. [14] Rao, Ravishankar, Sarma Vrudhula, and Daler N. Rakhmatov. 'Battery modeling for energy aware system design.' Computer 36.12 (2003): 77-87. [15] Broussely, M., et al. 'Main aging mechanisms in Li ion batteries.' Journal of power sources 146.1 (2005): 90-96. [16] Haifeng, Dai, Wei Xuezhe, and Sun Zechang. 'A new SOH prediction concept for the power lithium-ion battery used on HEVs.' Vehicle Power and Propulsion Conference, 2009. VPPC'09. IEEE. IEEE, 2009. [17] F. A. Gers, J. Schmidhuber, and F. Cummins, 'Learning to forget: Continual prediction with LSTM,' 1999. [18] A. Krizhevsky, I. Sutskever, and G. E. Hinton, 'Imagenet classification with deep convolutional neural networks,' in Advances in neural information processing systems, 2012, pp. 1097-1105. [19] V. Nair and G. E. Hinton, 'Rectified linear units improve restricted boltzmann machines,' in Proceedings of the 27th international conference on machine learning (ICML-10), 2010, pp. 807-814. [20] S. Hochreiter and J. Schmidhuber, 'Long short-term memory,' Neural computation, vol. 9, pp. 1735-1780, 1997. [21] Y. Li, Y. Fu, H. Li, and S.-W. Zhang, 'The improved training algorithm of back propagation neural network with self-adaptive learning rate,' in Computational Intelligence and Natural Computing, 2009. CINC'09. International Conference on, 2009, pp. 73-76. [22] J. Duchi, E. Hazan, and Y. Singer, 'Adaptive subgradient methods for online learning and stochastic optimization,' Journal of Machine Learning Research, vol. 12, pp. 2121-2159, 2011. [23] Rakhmatov, Daler, Sarma Vrudhula, and Deborah Wallach. 'A model for battery lifetime analysis for organizing applications on a pocket computer.' Very Large Scale Integration (VLSI) Systems, IEEE Transactions on 11.6 (2003): 1019-1030. [24] Grewal, S., and D. A. Grant. 'A novel technique for modelling the state of charge of lithium ion batteries using artificial neural networks.'Telecommunications Energy Conference, 2001. INTELEC 2001. Twenty-Third International. IET, 2001. [25] H. Li, A. Ravey, A. N'Diaye, and A. Djerdir, 'State of health estimation of lithium-ion batteries under variable load profile,' in Industrial Electronics Society, IECON 2017-43rd Annual Conference of the IEEE, 2017, pp. 5287-5291. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/69252 | - |
| dc.description.abstract | 在現今注重綠色能源的氣氛下，電動車也隨之蓬勃發展，而電池作為電動車主要提供能源的核心裝置，因此關於電池各類特性響應需要著實監控。
電池經過多次使用後，各參數如：電池容量、內阻會有顯著的差異並影響能量見控制上有影響，因此建立具備電池老化估測之模型，有助於改善控制策略以達到節省能量，並知曉是否將之汰換。本論文循環充放電實驗以接近實車的測試模式為主，其考量不同充放電情況和使用次數對電池老化的影響，並運用TensorFlow™開源軟體庫建立人工智慧模型，預測出電池健康度。本論文將實驗數據分為訓練資料與測試資料，經過深層神經網路(Deep Neural Network)訓練後，並估測出不同實驗方式之結果，且最佳估測結果為方均誤差小於0.1。本論文另一電池模型為遞歸神經網路(Recurrent Neural Network)，能記憶之前時刻的資料，電池老化與時間序列有些許關聯，也能精準地預測SOH。 | zh_TW |
| dc.description.abstract | In development of Battery models are vital for the development of electric vehicles. The battery life change the battery response. As a result, the model, which has battery degradation factors helps improve the efficiency of other control algorithms and maintain the safe usage of the battery.
In this thesis, use charge-discharge cycle that is similar to a vehicle testing pattern and TensorFlow™ to build artificial neural network of battery cell which can predict the battery life. The battery model considers different parameters like patterns of charge current and the number of cycle which have an influence on the result of prediction. We have training data that is for a Deep Neural Network and test data that is identified for the model accuracy. The best result is that it can predict state of health within 0.1 of mean square error. In this thesis, the other model that is Recurrent Neural Network has the ability of remembering previous data. It has also good performance on estimating SOH. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T03:11:24Z (GMT). No. of bitstreams: 1 ntu-107-R05522809-1.pdf: 4983952 bytes, checksum: 16d0e6a867de441902c103b309d3ec6c (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 目錄
致謝 I 摘要 II Abstract III 圖目錄 VI 表目錄 IX 第 1 章 導論 1 1.1 研究動機 1 1.2 鋰離子電池 1 1.3 文獻回顧 3 1.3.1 電池壽命老化狀態 ( State of Health ) 3 1.3.1.1 電池壽命實驗測試 4 1.3.1.2 電池健康度模型估測 4 1.3.2 神經網路模型 6 1.3.2.1 Neural Network 6 1.3.2.2 Recurrent Neural Network 8 1.4 論文架構 9 第 2 章 二次電池性質總覽 10 2.1.1 電池容量 (Capacity) 10 2.1.2 電量狀態 (State of Charge) 10 2.1.3 非線性容量效應 (Nonlinear Capacity Effects) 11 2.1.4 溫度影響 12 2.1.5 電池老化效應 13 第 3 章 深度學習架構與理論基礎 14 3.1 深層神經網路模型架構 14 3.2 遞歸神經網路模型架構 24 第 4 章 電池深層神經網路建立與模擬結果 31 4.1 測試設備與電池規格 31 4.2 電池壽命循環測試 35 4.3 電池老化參數資料選用 37 4.4 資料標準化與DNN架構設定 40 4.5 模擬結果 45 4.5.1 DNN模型訓練 45 4.5.2 DNN模型估測結果 48 第 5 章 電池遞歸神經網路模型與老化估測 53 5.1 RNN模型架構設定 53 5.2 電池健康度模擬結果 57 5.2.1 RNN模型訓練 57 5.2.2 RNN模型估測結果 60 5.2.3 不同資料下之RNN模擬結果 65 5.2.4 神經網路模型結果比較 68 第 6 章 結論 69 參考文獻 70 | |
| dc.language.iso | zh-TW | |
| dc.subject | 鋰離子電池 | zh_TW |
| dc.subject | 長短期記憶模型 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 電池健康度估測 | zh_TW |
| dc.subject | Lithium-ion Battery | en |
| dc.subject | SOH | en |
| dc.subject | Deep Learning | en |
| dc.subject | Long Short-Term Memory | en |
| dc.title | 以深度學習方法估測鋰電池健康度 | zh_TW |
| dc.title | Estimation of State of Health of Lithium-ion Battery Using Deep Learning | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳柏安,傅增棣 | |
| dc.subject.keyword | 鋰離子電池,電池健康度估測,深度學習,長短期記憶模型, | zh_TW |
| dc.subject.keyword | Lithium-ion Battery,SOH,Deep Learning,Long Short-Term Memory, | en |
| dc.relation.page | 72 | |
| dc.identifier.doi | 10.6342/NTU201801400 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2018-07-17 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 機械工程學研究所 | zh_TW |
| 顯示於系所單位： | 機械工程學系 | |
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