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研究生:陳經緯
研究生(外文):Jing-Wei Chen
論文名稱:鉛酸電池之智慧型充電狀態與壽命狀態估測器研製
論文名稱(外文):Design and Implementation of Intelligent State of Charge and State of Health Estimators for Lead-Acid Batteries
指導教授:趙貴祥
指導教授(外文):Kuei-Hsiang Chao
學位類別:碩士
校院名稱:國立勤益科技大學
系所名稱:電機工程系
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:2009
畢業學年度:97
語文別:中文
論文頁數:123
中文關鍵詞:可拓理論可拓物元模型充電狀態估測壽命狀態估測可程式系統晶片改良式可拓類神經網路
外文關鍵詞:Extension theoryExtension matter-element modelState of charge estimatorState of health estimatorProgrammable system-on-chipModified extension neural network
相關次數:
  • 被引用被引用:4
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:1
本論文主要目的在於從事鉛酸電池之智慧型充電狀態(State of charge, SOC)與壽命狀態(State of health, SOH)估測器研製。首先針對各種影響鉛酸電池充電狀態與壽命狀態之因素進行研究,進而選出最適合鉛酸電池在各種使用方式下進行充電狀態與壽命狀態估測之特徵。藉由對蓄電池多次之充放電實驗,以擷取在各種充電狀態與壽命狀態下特徵數值之分佈情形,並且考慮蓄電池老化對其所造成之影響。然後將鉛酸電池之充電狀態分為21種類別,並以電池之開路電壓(Open-circuit voltage)、內電阻(Internal resistance)及短路電流(Short-circuit current) 3種特徵之實驗數據建立可拓物元模型(Extension matter-element model),進而以可拓評價方法(Extension evaluation method)對蓄電池之充電狀態進行估測。至於蓄電池之壽命狀態估測,本論文將其分為4種類別,並以電池之放電驟降電壓(Coup de fouet voltage)、內電阻與放電瞬間電流(Transient current) 3種特徵之實驗數據建立改良式可拓物元模型,以本論文所提之改良式可拓類神經網路(Modified extension neural network, MENN)對蓄電池之壽命狀態進行估測。最後,以可程式系統晶片(Programmable system on chip, PSoC)實現此即時式之鉛酸電池充電狀態及壽命狀態估測器,使其對於鉛酸電池的充電狀態及壽命狀態估測具有量測精確度高、估測速度快及硬體電路體積小之優點。
The main purpose of this paper is to design and implement intelligent state-of-charge (SOC) and state-of-health (SOH) estimators for lead-acid batteries. Firstly, many cycles of charging and discharging experiments for lead-acid batteries are made to measure and record the distributed range of characteristic values in each SOC and SOH cycle. And the impacts of battery life on these characteristics are inspected, also. Then, the battery capacity is classified into 21 states, and the measured internal resistance, open-circuit voltage and short-circuit current are adopted to construct an extension evaluation method based on the extension matter-element model for differentiating out the SOC of the lead-acid batteries. As to the battery cycle life is classified into 3 states, and the measured Coup de fouet voltage, internal resistance and transient current are adopted to construct a modified extension neural network (MENN) based on the modified extension matter-element model for differentiating out the SOH of the lead-acid batteries. Finally, a programmable system-on-chip (PSoC) is used to implement the intelligent SOC and SOH estimators of lead-acid batteries for achieving the advantages of high precision, fast speed and small size of hardware circuits.
中文摘要 ------------------------------------------------i
英文摘要 -----------------------------------------------ii
誌謝 -------------------------------------------------iii
目錄 --------------------------------------------------iv
表目錄 ------------------------------------------------vii
圖目錄 -----------------------------------------------viii
第一章 緒論----------------------------------------------1
1.1 研究背景與動機---------------------------------------1
1.2 國內外文獻探討---------------------------------------3
1.3 研究方法--------------------------------------------5
1.4 論文大綱--------------------------------------------6
第二章 鉛酸電池之特性介紹----------------------------------7
2.1 前言------------------------------------------------7
2.2 鉛酸電池之基本原理-----------------------------------7
2.2.1 鉛酸電池之構造---------------------------------8
2.2.2 鉛酸電池之化學原理-----------------------------9
2.2.3 鉛酸電池之名詞術語----------------------------13
2.3 鉛酸電池之充放電特性--------------------------------17
2.3.1 鉛酸電池之容量標準----------------------------18
2.3.2 影響鉛酸電池容量之因素------------------------19
2.3.3 既有鉛酸電池之充電狀態檢測方法-----------------22
2.4 鉛酸電池之壽命特性----------------------------------25
2.4.1 鉛酸電池之壽命標準----------------------------26
2.4.2 影響鉛酸電池壽命之因素-------------------------27
2.4.3 既有鉛酸電池之壽命檢測方法---------------------29
第三章 鉛酸電池充電狀態與壽命狀態之特徵擷取-----------------35
3.1 前言----------------------------------------------35
3.2 鉛酸電池充電狀態之特徵------------------------------35
3.2.1 鉛酸電池內部電阻之檢測方式---------------------37
3.2.2 鉛酸電池充電狀態特徵擷取系統之整體架構-----------40
3.2.3 鉛酸電池充電狀態之特徵擷取結果及分析 ---------52
3.3 鉛酸電池壽命狀態之特徵-------------------------------56
3.3.1 鉛酸電池放電驟降電壓之檢測方式------------------57
3.3.2 鉛酸電池壽命狀態特徵擷取系統之整體架構-----------62
3.3.3 鉛酸電池壽命狀態特徵之擷取實驗與結果分析---------65
第四章 鉛酸電池之充電狀態檢測法----------------------------68
4.1 前言----------------------------------------------68
4.2 可拓理論-------------------------------------------68
4.2.1 可拓物元之概念--------------------------------70
4.2.2 可拓理論之評價方法-----------------------------72
4.3 所提可拓理論鉛酸電池充電狀態估測方法------------------75
4.3.1 鉛酸電池充電狀態之可拓物元模型-----------------75
4.3.2 基於可拓評價方法之鉛酸電池充電狀態估測----------76
4.4 鉛酸電池充電狀態之測試結果---------------------------79
第五章 鉛酸電池之壽命狀態檢測------------------------------84
5.1 前言-----------------------------------------------84
5.2 改良式可拓理論--------------------------------------85
5.2.1 可拓關聯函數之應用-----------------------------87
5.2.2 改良式可拓理論之辨識架構-----------------------91
5.2.3 基於類神經網路之學習機制-----------------------94
5.3 所提改良式可拓類神經網路之鉛酸電池壽命估測方法---------98
5.3.1 鉛酸電池壽命狀態之物元模型建立-----------------99
5.3.2 基於改良式可拓類神經網路之鉛酸電池壽命狀態估測--101
5.3.3 所提改良式類神經網路權重之訓練程序與學習率調整--104
5.4 鉛酸電池壽命狀態之測試結果--------------------------111
第六章 結論--------------------------------------------113
6.1 總結---------------------------------------------113
6.2 未來研究方向--------------------------------------114
參考文獻 ----------------------------------------------115
作者簡介 ----------------------------------------------122

[1]Y. Takahiro and K. Atsuo, “Residual Capacity Estimation of Sealed Lead-Acid Battery for Electric Vehicles,” Proceedings of Power Conversion Conference, Vol. 2, pp. 943-946, August 1997.
[2]S. Sato and A. Kawamura, “A New Estimation Method of State of Charge Using Terminal Voltage and Internal Resistance for Lead Acid Battery,” Proceedings of Power Conversion Conference, Vol. 2, pp. 565-570, April 2002.
[3]何文隆,電動車輛變動負載之電池殘電量研究,私立大葉大學,碩士論文,2003。
[4]張永昌,鉛酸電池殘量預測及壽命分析之研究,國立彰化師範大學,碩士論文,2003。
[5]Y. Morita, S. Yamamoto, S. H. Lee and N. Mizuno, “On-Line Detection of State-of-Charge in Lead Acid Battery Using Both Neural Network and On-Line Identification,” Proceedings of IEEE Industrial Electronics Conference, pp. 3379-3384, November 2006.
[6]C. R. Chen, K. H. Huang and H. C. Teng, “The Estimation of the Capacity of Lead-Acid Storage Battery Using Artificial Neural Networks,” Proceedings of IEEE International Systems Man and Cybernetics Conference, Vol. 2, pp. 1575-1579, October 2006.
[7]P. E. Pascoe and A. H. Anbuky, “Adaptive Fuzzy Coup de Fouet Based VRLA Battery Capacity Estimation,” Proceedings of IEEE International Systems Man and Cybernetics Conference, Vol. 4, pp. 2157-2162, October 2001.
[8]王文俊,認識Fuzzy,全華科技圖書公司,台灣,2001。
[9]S. Ahlers and A. Mukherjee, “Advances in Battery Management Using Neural Networks and Fuzzy Logic,” Technology Report, School of Electrical and Computer Engineering, Cornell University, 2003.
[10]蔡文、陽春燕、林傳初,可拓工程方法,全華科技圖書公司,台灣,1987。
[11]王志方,二次電池產業概況,台灣工業銀行產業分析資料庫,2008。
[12]孫清華編譯,最新可充電電池技術大全,全華科技圖書公司,台灣,2003。
[13]張永東,共振式蓄電池充電器之研製,私立崑山科技大學,碩士論文,2004。
[14]徐嘉章,鉛酸電池充電及殘餘電量估測之研究,國立勤益科技大學,碩士論文,2007。
[15]CSB密閉式鉛酸電池技術手冊,2005。http://www.csb-battery.com/chine/03_support/01_support.php.
[16]朱耕毅,以內阻中位數法估測汽車用鉛酸蓄電池殘電量之研究,國立勤益技術學院,碩士論文,2006。
[17]洪芳州,各類電池使用指南,全華科技圖書公司,台灣,1994。
[18]陳士椀,鉛酸電池殘餘放電時間預估之研究,國立彰化師範大學,碩士論文,2005。
[19]J. H. Aylor, A. Thieme and B. W. Johnso, “A Battery State-of-Charge Indicator for Electric Wheelchairs,” IEEE Transactions on Industrial Electronics, Vol. 39, No. 5, pp. 398-409, October 1992.
[20]陳財榮,UPS蓄電池容量之偵測技術,電子月刊,第三卷第二期,頁94-103,1997。
[21]李仁豪、趙貴祥、黃弘昇、賴信丞,“鉛酸電池容量測試器之研製”,第二屆生活智慧研討會,頁131-137,2007。
[22]M. J. Hlavac and D. Feder, “VRLA Battery Monitoring Using Conductance Technology,” Proceedings of International Telecommunications Energy Conference, pp. 284-291, 1995.
[23]黃士航,“密閉式鉛酸電池AC內阻之研究”,中華民國第二十三屆電力工程研討會,頁1552-1556,2002。
[24]陳松誼,電動機車殘存電量顯示之研究,國立台灣大學,碩士論文,2001。
[25]李世興編,電池活用手冊,全華科技圖書公司,台灣,1996。
[26]YUASA 電池使用手冊,2004。
[27]陳士椀、李義隆、陳財榮、陳德超、張家豪,”惡劣環境溫度對鉛酸電池充放電效率影響之研究",第二十五屆電力工程研討會,頁1666-1670,2004。
[28]P. E. Pascoe, H. Sirisena and A.H. Anbuky, “Coup de Fouet Based VRLA Battery Capacity Estimation,” Proceedings of Electronic Design Test and Applications Conference, Vol. 1, pp. 149-153, January 2002.
[29]G. E. M. D. C. Bandara, R. Ivanov and S. Gishin, “Intelligent Fuzzy Controller for a Lead-Acid Battery Charger,” Proceedings of IEEE International Systems Man and Cybernetics Conference, Vol. 6, pp. 185-189, October 1999.
[30]"XTR110 Precision Voltage to Current Converter Transmitter,” BurrBrown Corporation.
http://www.alldatasheet.com/datasheet-pdf/pdf/56889/BURR-BROWN/XTR110.html.
[31]"Technical Reference Manual for All PSoC Devices with a Base Part Number of CY8C2xxxx”.
http://sjapp20.mis.cypress.com:7001/publishedcontent/publish/design_resources/more_resources/contents/psoc_t
[32]H. Yakabayashi, I. Shimoura, Y. Matsuda and K. Onoue, “Development of Valve-Regulated Lead Acid Batteries for Power Storage,” Proceedings of International Telecommunications Energy Conference, pp. 383-390, 2003.
[33]R. D. Soileau, “A Diagnostic Testing Program for Large Lead Acid Storage Battery Banks,” IEEE Transactions on Industry Applications, Vol. 30, No. 1, pp. 193- 200, February 1994.
[34]T. Yamazaki, K. Sakurai and K. Muramoto, “Estimation of the Residual Capacity of Sealed Lead-Acid Batteries by Neural Network,” Proceedings of International Telecommunications Energy Conference, pp. 210-214, October 1998.
[35]A. H. Anbuky and P. E. Pascoe, “VRLA Battery State-of-Charge Estimation in Telecommunication Power Systems,” IEEE Transactions on Industrial Electronics, Vol. 47, No. 3 pp. 565-573, June 2000.
[36]P. E. Pascoe and A. H. Anbuky, “Standby Power System VRLA Battery Reserve Life Estimation Scheme,” IEEE Transactions on Energy Conversion, Vol. 20, No. 4, pp. 887-895, December 2005.
[37]A. Delaille, M. Perrin, F. Huet and L. Hernout, “Study of the Coup de Fouet of Lead-Acid Cells As a Function of Their State-of-Charge and State-of-Health,” Journal of Power Sources, Vol. 158, No. 2, pp. 1019-1028, August 2006.
[38]W. Cai, “The Extension Set and Incompatibility Problem,” Journal of Scientific Exploration, Vol. 1, pp. 81-93, 1983.
[39]王洪偉,策略生成的物元模型,系統工程理論與實踐, 11期,頁55-124,1999。
[40]楊春燕、何斌,可拓方法在新產品構思中的應用,系統工程理論與實踐,4期,頁120-124,1999。
[41]C. C. Chan, E. W. C. Lo and S. Weixiang, “The Available Capacity Computational Model Based on Artificial Neural Network for Lead-Acid Batteries in Electric Vehicles,” Journal of Power Sources, Vol. 87, pp. 201-204, 2000.
[42]M. H. Wang and C. Y. Ho, “Application of Extension Theory to PD Pattern Recognition of High Voltage Current Transformers,” IEEE Transactions on Power Delivery, Vol. 20, No. 3, pp. 1939-1946, July 2005.
[43]M. H. Wang, “Extension Neural Network for Power Transformer Incipient Fault Diagnosis,” IEEE Transactions on Generation Transmission and Distribution, Vol. 150, pp. 679-685, November 2003.
[44]M. H. Wang, “Partial Discharge Pattern Recognition of Current Transformers Using an ENN,” IEEE Transactions on Power Delivery, Vol. 20, No. 3, pp. 1984-1990, 2005.
[45]M. A. C. Valdez, J. A. O. Valera, Ma. Jojutla and O. P. Arteaga, “Estimating Soc in Lead-Acid Batteries Using Neural Networks in a Microcontroller-Based Charge-Controller,” Proceedings of International Joint Neural Network Conference, pp. 2713-2719, July 2006.
[46]W. X. Shen, C. C. Chan, E. W. C. Lo and K. T. Chau, “Adaptive Neuro-fuzzy Modeling of Battery Residual Capacity for Electric Vehicles,” IEEE Transactions on Industrial Electronics, Vol. 49, No. 3, pp. 677-684, June 2002.
[47]C. H. Wang, W. Y. Wang, T. T. Lee and P. S. Tseng, “Fuzzy B-Spline Membership Function (BMF) and Its Applications in Fuzzy-Neural Control,” IEEE Transactions on Systems Man and Cybernetics, Vol. 25, No. 5, pp. 841-851, May 1995.
[48]J. S. R. Jang, “Input Selection for ANFIS Learning,” Proceedings of 5th IEEE International Fuzzy System Conference, Vol. 2, pp. 1493-1499, September 1996.
[49]C. T. Lin and C. S. G. Lee, Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems, Englewood Cliffs, New Jersey: Prentice-Hall, 1999.
[50]S. K. Oh, W. Pedrycz and H. S. Park, “A New Approach to the Development of Genetically Optimized Multilayer Fuzzy Polynomial Neural Networks,” IEEE Transactions on Industrial Electronics, Vol. 53, No. 4, pp. 1309-1321, June 2006.

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