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研究生:黃晟瑋
研究生(外文):Huang, Sheng-Wei
論文名稱:以深度學習研究鋰電池的放電行為
論文名稱(外文):The Investigation of Discharge Behavior for Lithium Ion Battery with Deep Learning
指導教授:譚仕煒
指導教授(外文):Tan, Shih-Wei
口試委員:譚仕煒陳威鈿鄭國順施東河吳炳昇
口試委員(外文):Tan, Shih-Wei
口試日期:2020-07-11
學位類別:碩士
校院名稱:國立臺灣海洋大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:56
中文關鍵詞:深度學習多層感知器循環神經網路長短期記憶門控循環單元基因演算法
外文關鍵詞:Deep LearningMLPRNNLSTMGRUGenetic Algorithm
相關次數:
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本研究主要為利用深度學習(Deep Learning)演算法使鋰電池放電特性曲線自動建模,以鋰電池放電特性曲線為基礎,使用電池量測儀進行充放電來建立放電特性曲線,先以人工萃取參數方式試著找尋放電特性曲線,在以MLP(Multilayer Perceptron)、RNN(Recurrent Neural Network)、LSTM(Long Short-Term Memory)以及GRU(Gated Recurrent Unit)來做改良,但上述方法所得到的結果皆為圖表,而希望得到放電特性曲線方程式構成之參數,所以利用基因演算法(Genetic Algorithm,GA)找尋放電特性曲線方程式之參數。
In this study is mainly to use the deep learning algorithm to automatically model the discharge characteristic curve of the lithium battery. Based on the discharge characteristic curve of the lithium battery, the battery measurement instrument is used to charge and discharge to establish the discharge characteristic curve. The parameter method tries to find the discharge characteristic curve. It is improved by MLP (Multilayer Perceptron), RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit), but the results obtained by the above method All of them are graphs, and we hope to obtain the parameters composed of the discharge characteristic curve equation, so we use the Genetic Algorithm (GA) to find the parameters of the discharge characteristic curve equation.
致謝 I
摘要 II
Abstract III
目錄 IV
圖目錄 VI
表目錄 VIII
第一章 緒論 1
1.1 前言 1
1.2 研究動機 1
1.3 研究方法 2
1.4 論文架構 2
第二章 機器學習與深度學習介紹 3
2.1 機器學習與深度學習之差異 3
2.2 PYTHON 和 SPYDER介紹 4
2.3 深度學習演算法介紹 4
2.3.1 MLP(Multilayer Perceptron) 4
2.3.2 RNN(Recurrent Neural Network) 5
2.3.3 LSTM(Long Short-Term Memory) 5
2.3.4 GRU(Gated Recurrent Unit) 9
2.4 實驗架構 10
第三章 實驗步驟及結果 11
3.1電池特性 11
3.1.1鋰電池充放電研究 12
3.1.2鋰電池活化 12
3.1.3鋰電池的充放電 12
3.2人工萃取參數 13
3.3整理數據與變數介紹 14
3.3.1 批尺寸(Batch_Size) 15
3.3.2 迭代(Iteration) 15
3.3.3 時期(Epoch) 15
3.3.4 look_back 15
3.3.5 舉例 15
3.3.6 訓練分數 15
3.4實驗結果 16
3.4.1程式設定 16
3.4.2 MLP 17
3.4.3 RNN 29
3.4.4 LSTM 34
3.4.5 GRU 37
3.5 分析 40
第四章 基因演算法之實驗步驟及結果 41
4.1基因演算法介紹 41
4.2 方程式介紹 42
4.3 設定參數及範圍 43
4.3 實驗結果 47
4.3.1 L牌18650 47
4.4 P牌18650 49
4.5 S牌18650 51
第五章 結論 53
參考文獻 54
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[2] Yi-Zeng Hsieh, Shih-Wei Tan, Siang-Long Gu, Yu-Lin Jeng, "Prediction of Battery Discharge States Based on the Recurrent Neural Network," Journal of Internet Technology, vol. 21, no. 1 , pp. 113-120, Jan. 2020.

[3] Yi-Zeng Hsieh, Shih-Syun Lin, Yu-Cin Luo, Yu-Lin Jeng, Shih-Wei Tan, Chao-Rong Chen, Pei-Ying Chiang, “ARCS-Assisted Teaching Robots Based on Anticipatory Computing and Emotional Big Data for Improving Sustainable Learning Efficiency and Motivation”, 2020.

[4] Lee, E. A. (2008, May). Cyber physical systems: Design challenges. In Object oriented real-time distributed computing (isorc), 2008 11th ieee international symposium on (pp. 363-369). IEEE.

[5] S. W. Tan, Y. C. Shen, The Characteristics of Battery Discharge and Automodeling, 2016

[6] Bekir Karlik and A. Vehbi Olgac, Performance Analysis of Various Activation Functions in Generalized MLP Architectures of Neural Networks.

[7] M. Kashaninejad, A.A. Dehghani, M. Kashiri, Modeling of wheat soaking using two artificial neural networks (MLP and RBF)

[8] Mohammad Zare & Hamid Reza Pourghasemi & Mahdi Vafakhah & Biswajeet Pradhan, Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms.

[9] Shuai Li , Wanqing Li , Chris Cook , Ce Zhu , Yanbo Gao, Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN.

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[12] Hochreiter, Sepp, and Jürgen Schmidhuber. "Long short-term memory." Neural computation 9.8 (1997): 1735-1780

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[16] Ziqi Zhang, David Robinson, and Jonathan Tepper, Detecting Hate Speech on Twitter Using a Convolution-GRU Based Deep Neural Network.

[17] Abien Fred M. Agarap, A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data.

[18] Fathi M. Salem, Rahul Dey, Gate-variants of Gated Recurrent Unit(GRU) neural networks.

[19] Li Li, Zuo Zhang, Rui Fu, Using LSTM and GRU neural network methods for traffic flow prediction.

[20] Clinton Sheppard, “Genetic Algorithms with Python”, 2018

[21] Back, Th., Hoffmeister, F., and Schwefel, H. -P. (1991), A survey of evolution strategies, Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, 2-9

[22] VWonjae Lee, Hak-Young Kim, “Genetic Algorithm Implementation in Python”, Electronics and Telecommunications Research Institute

[23] An Object-Oriented Environment for Specification and Concurrent Execution of Genetic Algorithms, Vancouver, British Columbia, Canada, 5-110 October 1992.

[24] Madureira, A.; Ramos, C.; do Carmo Silva, S.,”A Coordination Mechanism for Real World Scheduling Problems using Genetic algorithms”, Evolutionary Computation, 2002. CEC ’02. Proceedings of the 2002 Congress on, 1, pp 175 –180, 2002.

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[28] What is the best multi-stage architecture for object recognition? Jarrett, K., Kavukcuoglu, K., Ranzato, M., and LeCun, Y. (2009a)

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[32] Abien Fred M. Agarap, Deep Learning using Rectified Linear Units (ReLU)
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