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研究生:阮彙權
研究生(外文):Juan, Hui-Chuan
論文名稱:鋰離子電池參數估算方法之研究
論文名稱(外文):A Study on Parameters Estimation of Li-ion Battery
指導教授:鐘太郎施武陽
指導教授(外文):Jong, Tai-LangSean, Wu-Yang
學位類別:碩士
校院名稱:國立清華大學
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:2010
畢業學年度:98
語文別:中文
論文頁數:206
中文關鍵詞:鋰離子電池模型擴展式卡門濾波器適應性觀測器參數萃取純電動車
外文關鍵詞:Li-ion Battery ModelExtended Kalman FilterAdaptive ObserverParameter ExtractionElectric Vehicle
相關次數:
  • 被引用被引用:5
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電池在近代的工程科技發展上,扮演了重要的角色,而因為電池的特性,也使其在數十年來廣泛地被運用在電子電路或機電相關的系統上,而為了要能有效地使用電池,管理與監控電池的使用情形就越顯重要,如電池的老化情形等,種種現象將會反映在電池的許多參數上,故必須萃取電池內部參數,如:電池內部阻抗等,以進一步強化後端系統之設計,而電池參數的萃取方式又可以分為兩種:線上方式與離線方式,本論文即是針對線上之電池估算方式加以研究。

本論文主要討論鋰離子電池的參數估算方式,首先在Simulink環境中建立所需使用之多種鋰離子電池模型,並再對其進行驗證,證實電池模型可與實測數據吻合,間接證明其模型正確性。並且使用所建立之電池模型,運用在不同的負載上進行一系列之系統化模擬,使用之模型包含簡化型電池模型,至更為接近真實電池狀態之完整鋰離子電池模型。

在電池參數估算方法方面,將對兩種參數估算方法做模擬與比較,分別為適應性觀測器法(Adaptive Observer)以及擴展式卡門濾波器(Extended Kalman Filter,EKF)估算法,對系統之參數與狀態進行估算,希望能藉由此兩種參數估算方法,再配合完整之系統化模擬,觀察系統的整體參數估算情形。

模擬情形包含使用不同型態之負載,觀察負載變化情形對估算結果之影響,除此之外,因考慮將此估算方法實用於純電動車(Electric Vehicle, EV)中,故又再設計一隨機變化之負載,進而貼近一般電動車的行車充放電使用情形。

而對於適應性觀測器估算方法,我們也將使用不同之數值方法進行離散化並加以比較,進而加強實作上的應用,最後,本論文將電池參數估算方法作結論並討論未來之發展。

Battery plays a crucial role in nowadays technology and is extensively used in electrical and electronic systems. In order to utilize battery efficiently and optimally, it is important to be able to provide good control and management functions of the battery while it is in use or charging. Most of those functions are based on the battery operational characteristics as well as accurate measurements or estimations of battery status and/or parameters. For example, parameters such as battery internal resistance could reflect battery status which is very significant information for battery management system. Therefore, it is important that battery parameters can be measured or estimated by different approaches either on-line or off-line.

The main focus of this thesis is to investigate and compare two different on-line methods for estimating battery parameters. One is the adaptive observer for dynamical estimation, and the other is the extended Kalman filter for state and parameters estimation. We first carefully examine the derivations and build Matlab simulation programs of the two methods, respectively, and then design and perform a series of systematic simulations for these two methods to study their advantages and disadvantages in battery parameter estimation.

Moreover, we also build three Li-ion battery models in Simulink for the aforementioned series of systematic simulations. The battery models can simulate dynamic battery characteristics in Simulink environment under different battery simplification assumptions. These models also can be used for various simulations in different system design applications, e.g., electric vehicle or power system design. By using these Li-ion models, system designers could have a much more accurate simulation result for their systems.

Abstract ii
中文摘要 iii
致謝 iv
目錄 v
附圖 vii
附表 xx
第一章 簡介 1
1.1電池的重要性 1
1.2電池介紹與鋰離子電池 1
1.3電池之等效模型 7
1.4電池管理系統與電池健康度的重要性及其功能 10
1.5電池參數萃取之方法 10
1.5.1電池等效電路之性質 11
1.5.2離線量測電池參數 11
1.5.3線上估算電池參數 13
1.6本論文研究範圍與簡略國內文獻回顧 14
1.7本論文貢獻 14
1.8論文章節 20
第二章 Simulink鋰離子電池之動態模型建立與驗證 22
2.1模擬用之三種鋰離子電池模型介紹 22
2.2鋰離子電池動態Simulink模型建立 26
2.3鋰離子電池動態模型驗證 28
第三章 鋰離子電池參數估算:適應性觀測器法 36
3.1理論介紹 36
3.1.1適應性觀測器法推導 36
3.1.2適應性觀測器的三種離散化方式 43
3.1.3與原適應性觀測器模擬結果比較 51
3.2連續時間系統模擬 66
3.2.1適應性觀測器的Simulink模型 66
3.2.2系統化模擬之說明 67
3.2.3適應性觀測器的參數估算模擬-電池模型一 69
3.2.4適應性觀測器的參數估算模擬-電池模型二 77
3.2.5適應性觀測器的參數估算模擬-電池模型三 88
3.2.6結論與討論 96
3.3離散時間系統模擬 97
3.3.1離散時間之適應性觀測器的參數估算模擬-電池模型一 98
3.3.2離散時間之適應性觀測器的參數估算模擬-電池模型二 116
3.3.3離散時間之適應性觀測器的參數估算模擬-電池模型三 132
3.3.4鋰離子電池實測數據模擬 157
3.3.5結論與討論 160
第四章 鋰離子電池參數估算:擴展式卡門濾波器(EKF) 162
4.1卡門濾波器(Kalman Filter)介紹 162
4.2卡門濾波器用於鋰離子電池估算的推導 167
4.3離散時間系統模擬 175
4.3.1擴展式卡門濾波器的參數估算模擬-電池模型一 176
4.3.2擴展式卡門濾波器的參數估算模擬-電池模型二 181
4.3.3擴展式卡門濾波器的參數估算模擬-電池模型三 186
4.4與適應性觀測器方法之比較 192
第五章 結論與未來發展 195
5.1結論 195
5.2未來發展 195
參考文獻 197
附錄: (Simscape與SimPowerSystems之比較與驗證) 199
附錄參考文獻 206


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