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博碩士論文 etd-0726116-160207 詳細資訊
Title page for etd-0726116-160207
論文名稱
Title
磷酸鋰鐵電池之線上健康狀態估測
On-line State-of-Health Estimation for LiFePO4 Battery
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
79
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2016-07-15
繳交日期
Date of Submission
2016-08-26
關鍵字
Keywords
電池健康狀態、磷酸鋰鐵電池、健康狀態估測、類神經網路
Artificial Neural Network, SOH Estimation, LiFePO4 Battery, State-of-Health
統計
Statistics
本論文已被瀏覽 5735 次,被下載 459
The thesis/dissertation has been browsed 5735 times, has been downloaded 459 times.
中文摘要
目前電動載具及儲能系統對電池的需求正在增加,而電池在長時間的使用後,其實際可用容量將會降低,為了因應電池實際可用容量對電動載具及儲能系統的影響,因此於電動載具及儲能系統運轉時,得知電池的健康狀態是相當重要的。本論文提出一個線上健康狀態估測方法,不同於傳統離線估測方法,不需將電池從系統中卸除並連接至其他裝置。在本文提出的估測流程中,主要是根據以離線方式進行老化實驗資料獲得老化指標及電池等效電路模型參數,進而使用這些老化指標及模型參數去估測電池健康狀態。透過最小平方差法來從實驗資料決定電池模型參數,計算不同健康狀態下的電池模型參數後,使用類神經網路建立健康狀態與模型參數的關係,進而建立健康狀態估測方法。在本文提出的估測流程中,使用迴歸模型建立電池開路電壓、電量狀態及健康狀態的關係。計算電池開路電壓和等效電路模型參數之後,再使用類神經網路進行估測,以達到即時線上估測。本文以5顆磷酸鋰鐵電池芯於不同線上使用情境下進行估測,測試結果顯示平均絕對誤差為1.7732%。
Abstract
The demand of batteries for electric vehicle (EV) and Energy Storage System (ESS) is increasing. After battery has been used for a long time, the actual available capacity of battery will decrease, so State-of-Health (SOH) estimation is important in EV and ESS operations. An on-line SOH estimation method is proposed in the thesis. It is different from the conventional off-line estimation methods that need to remove battery from the system and connect to other devices. The key component in the proposed SOH estimation procedure is to obtain aging indicators according to the data from aging experiment performed off-line, and then use the indicators, including model parameters in a battery equivalent circuit to estimate SOH. Test data are used to determine the model parameter values during different battery ages by least square error method. The battery characteristic parameters computed at each age of the battery are then used in an Artificial Neural Network (ANN) to train and setup the automatic SOH estimator. In the proposed procedure, a regression model is used to determine the relationship of battery open-circuit voltage with State-of-Charge (SOC) and SOH. An on-line SOH estimation can be achieved after the battery open-circuit voltage and the equivalent circuit model parameters are calculated real time and fed into the ANN model. Test results indicate that the average absolute error of the proposed SOH estimator under different usage scenarios is 1.7732% based on 5 LiFePO4 batteries.
目次 Table of Contents
論文審定書 i
誌謝 ii
中文摘要 iii
Abstract iv
目錄 v
圖目錄 vii
表目錄 ix
第一章 緒論 1
1.1 研究背景與動機 1
1.2 文獻回顧 3
1.3 本論文之成果 7
1.4 論文架構 10
第二章 電池等效電路模型參數估測 11
2.1 電池模型架構 11
2.1.1 電池等效電路模型簡介 11
2.1.2 改良型電池模型之數學描述 14
2.2 建立電池開路電壓與SOC及SOH的關係 16
2.2.1 多變數多項式迴歸模型簡介 16
2.2.2 建立開路電壓迴歸模型 18
2.3 建立SOH與電池參數間的關係 21
2.3.1 目標函數與限制條件 21
2.3.2 求解程序 23
第三章 電池健康狀態估測 25
3.1 利用老化實驗建立ANN的訓練資料集 25
3.1.1 實驗平台架構 25
3.1.2 老化實驗流程 28
3.1.3 ANN的訓練資料集分析 33
3.2 類神經網路模型 38
3.2.1 倒傳遞類神經網路簡介 40
3.2.2 健康狀態估測ANN架構 42
3.2.3 模型準確度驗證 43
3.3 健康狀態估測 44
第四章 估測結果與分析 47
4.1 測試案例 49
4.2 健康狀態估測結果 51
4.3 電池模型參數分析 60
第五章 結論及未來研究方向 63
5.1 結論 63
5.2 未來研究方向 64
參考文獻 65
參考文獻 References
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