臺灣博碩士論文加值系統

本論文探討具有老化效應的鋰離子(Li-ion)電池的殘電量估測。吾人採用一階 等效電路模型(ECM)描述電池特性，並採用開路電壓測試法(OCV)及直流內電阻(DCIR)方法找出 的離線參數。此外，吾人更進一步地採用參數適應法修正 ECM 的在線參數值，由此可看出 ECM 的參數值隨著電池的 及容量衰減而變化。本論文建立五種老化的 ECM 模型以描述電池的老化效應，並採用模糊Ｃ回歸模型(FCRM)描述　ECM　之參數與 SOC 之關聯性。吾人採用自適應擴展卡爾曼濾波器（AEKF）搭配 ECM 來估計電池的老化效應，且將選擇具有最小平方誤差的最佳老化模型做為測試電池的模型。最後，吾人分別採用自適應擴展卡爾曼濾波器與粒子演算法估測電池SOC，實驗結果顯示本文所提出的方法具有良好的估測成效。

In thesis, the state of charge (SOC) of lithium-ion (Li-ion) battery with aging effect will be estimated. The first-order RC equivalent circuit model (ECM) is adopted to describe the characteristics of the battery. The open-circuit voltage (OCV) method and direct current internal resistance (DCIR) test are applied to find the numerical values of the off-line parameters of the ECM. Further, a parameter updating method is applied to find and modify the numerical values of the on-line parameters of the ECM. It is observed that the numerical values of the parameters of the ECM vary with both the ECM and the capacity fading of the battery. Five aging ECM models are built to reflect the aging effect of the battery. The fuzzy c-regression models (FCRMs) are adopted to describe the relationships between the parameters of the ECM and the SOC. Finally, the adaptive extended Kalman filter (AEKF) with parameter characteristics will be employed to estimate the SOC of the battery with aging effect. The best aging model which has the minimum squares error will be selected as the model of the tested battery. The experimental results adopting AEKF and the modified particle filter (PF) will show the effectiveness of the proposed method.

ENGLISH ABSTRACT III
CHINESE ABSTRACT IV
TABLE OF CONTENTS V
LIST OF FIGURES VII
LIST OF TABLES X
1. INTRODUCTION 1
2. LITHIUM BATTERY MODEL 4
2.1 Lithium Battery Model for SOC Estimation 4
2.2 Equivalent Circuit Model 4
3. PARAMETER ESTIMATION 9
3.1 Experimental Setup 9
3.2 Measure of Rated Capacity 11
3.3 Open Circuit Voltage Test 13
3.4 Direct Current Internal Resistance ( ) Test 16
3.5 FCRM Clustering Algorithms 24
3.6 The Parameters Updating Method 30
4. SOC ESTIMATION ALGORITHM 33
4.1 Adaptive Extended Kalman Filter 33
4.2 Particle Filter 37
5. EXPERIMENTAL RESULTS 41
5.1 Federal Urban Driving Schedule (FUDS) Test 41
5.2 SOC Estimation by AEKF 43
5.2.1 SOC Estimation by AEKF with Static Test 44
5.2.2 SOC Estimation by AEKF with Dynamic Test 47
5.3 SOC Estimation by PF 50
6. CONCLUSIONS 55
REFERENCES 56

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