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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90623完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳國慶 | zh_TW |
| dc.contributor.advisor | Kuo-Ching Chen | en |
| dc.contributor.author | 蘇庭緯 | zh_TW |
| dc.contributor.author | Ting-Wei Su | en |
| dc.date.accessioned | 2023-10-03T16:54:23Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-10-03 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-03 | - |
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(2019). A review of recurrent neural networks: LSTM cells and network architectures. Neural computation, 31(7), 1235-1270. [33] Zhou, M. Y., Zhang, J. B., Ko, C. J., & Chen, K. C. (2023). Precise prediction of open circuit voltage of lithium ion batteries in a short time period. Journal of Power Sources, 553, 232295. [34] Wang, Y., Cheng, Y., Xiong, Y., & Yan, Q. (2022). Estimation of battery open-circuit voltage and state of charge based on dynamic matrix control-extended Kalman filter algorithm. Journal of Energy Storage, 52, 104860. [35] Ma, Y., Shan, C., Gao, J., & Chen, H. (2022). A novel method for state of health estimation of lithium-ion batteries based on improved LSTM and health indicators extraction. Energy, 251, 123973. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90623 | - |
| dc.description.abstract | 電池管理系統會對鋰離子電池進行狀態監測，以防止電池發生過充電、過放電或是熱失控，確保電動車運行的效率和安全性。在本研究中，我們提出了一種計算架構以估計電池的充電狀態（State of Charge, SoC）、健康狀態（State of Health, SoH）和表面溫度。該框架結合三種不同演算法，包含向量型遞迴最小平方法(Vector-type Recursive Least Squares, VRLS)、自適應擴展卡爾曼濾波器（Adaptive Extended Kalman Filter, AEKF）以及深度神經網路（Deep Neural Network, DNN）。此架構應用在駕駛循環測試中依序分為三個計算步驟。首先，VRLS利用動態變化的電池電壓和電流識別等效電路模型(equivalent circuit model, ECM)的參數。接著，AEKF負責運用ECM的狀態方程式來估計電池的SoC和SoH。最後，DNN則以電壓、電流、SoC以及ECM的參數作為輸入，估計電池的表面溫度。該架構的特點是僅需要電池電壓和電流的量測資訊，而不需要額外感測器，能更泛用於實際情況。實驗結果表明，在此框架下，SoC和SoH的平均估計誤差小於0.03，測試數據中電池表面溫度的平均估計誤差可以小於0.5°C。 | zh_TW |
| dc.description.abstract | Battery management systems monitor the state of lithium-ion batteries to prevent overcharge, over-discharge, and thermal runaway, ensuring the efficiency and safety of electric vehicles. In this study, we propose a calculation framework for estimating the battery state of charge (SoC), state of health (SoH), and surface temperature. The framework combines three different algorithms: vector-type recursive least squares (VRLS), adaptive extended Kalman filter (AEKF), and deep neural network (DNN). The proposed framework is applied in drive cycle tests and consists of three calculation steps. First, VRLS identifies the parameters of the equivalent circuit model (ECM) using the dynamic variations of battery voltage and current. Then, AEKF utilizes the state equations of the ECM to estimate the battery SoC and SoH. Finally, DNN takes voltage, current, SoC, and ECM parameters as inputs to estimate the battery surface temperature. The key feature of this framework is that it only requires measurements of battery voltage and current, eliminating the need for additional sensors and enhancing its applicability in practical scenarios. Experimental results demonstrate that the average estimation errors of SoC and SoH are below 0.03, and the average estimation error of the battery surface temperature in the testing data can be less than 0.5°C. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-10-03T16:54:23Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-10-03T16:54:23Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 致謝 i
摘要 ii Abstract iii 目錄 iv 圖目錄 vi 表目錄 viii 第一章 序章 1 1.1 研究背景與動機 1 1.2 論文架構 2 第二章 文獻回顧 3 2.1 電池狀態估計方法回顧 3 2.1.1 關於SoC估計的文獻回顧 3 2.1.2 關於SoH(或電池容量)估計的文獻回顧 6 2.1.3 關於電池溫度估計的文獻回顧 9 2.2 等效電路模型與參數識別方法回顧 12 第三章 實驗步驟 16 3.1 電池規格與實驗儀器 16 3.2 實驗流程 17 3.2.1 容量測試 17 3.2.2 開路電壓測試 17 3.2.3 駕駛循環測試 18 3.2.4 循環老化實驗 19 第四章 電池SoC、SoH與表面溫度估計方法 21 4.1 一階ECM與參數識別 22 4.2 以AEKF估計電池SoC和SoH 26 4.3 以DNN估計電池表面溫度 29 第五章 實驗結果與分析 31 5.1 UOC - SoC函數 31 5.2 SoC和SoH的估計和修正方法 32 5.3 一階ECM參數與環境溫度的相關性分析 39 5.4 DNN模型的輸入時間長度分析 41 5.5 電池表面溫度估計結果 43 第六章 結論與未來展望 46 6.1 結論 46 6.2 未來展望 47 參考文獻 48 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 狀態估計 | zh_TW |
| dc.subject | 鋰離子電池 | zh_TW |
| dc.subject | 深度神經網路 | zh_TW |
| dc.subject | 卡爾曼濾波器 | zh_TW |
| dc.subject | 等效電路模型 | zh_TW |
| dc.subject | Kalman filter | en |
| dc.subject | equivalent circuit model | en |
| dc.subject | Lithium-ion battery | en |
| dc.subject | state estimation | en |
| dc.subject | deep neural network | en |
| dc.title | 聯合卡爾曼濾波器與深度學習於鋰離子電池多狀態估計 | zh_TW |
| dc.title | Multi-state estimation of lithium-ion batteries based on Kalman filter and deep learning | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 林祺皓;周鼎贏;梁世豪 | zh_TW |
| dc.contributor.oralexamcommittee | Chi-Hao Lin;Dean Chou;Shih-Hao Liang | en |
| dc.subject.keyword | 鋰離子電池,狀態估計,等效電路模型,卡爾曼濾波器,深度神經網路, | zh_TW |
| dc.subject.keyword | Lithium-ion battery,state estimation,equivalent circuit model,Kalman filter,deep neural network, | en |
| dc.relation.page | 52 | - |
| dc.identifier.doi | 10.6342/NTU202302494 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2023-08-07 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 應用力學研究所 | - |
| dc.date.embargo-lift | 2026-07-31 | - |
| 顯示於系所單位： | 應用力學研究所 | |
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