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研究生： Tadele Mamo Wolde
Tadele Mamo Wolde
論文名稱： 數據驅動法預測鋰離子電池健康狀態之研究
State of Health Prediction for Lithium-ion Batteries Using Some Data-Driven Methods
指導教授： 王福琨
Fu-Kwun Wang
口試委員: James T. Lin
James T. Lin
王福琨
Fu-Kwun Wang
葉瑞徽
Ruey Huei Yeh
歐陽超
Chao Ou-Yang
徐世輝
Shey-Huei Sheu
林義貴
Yi-Kuei Lin
學位類別： 博士
Doctor
系所名稱： 管理學院 - 工業管理系
Department of Industrial Management
論文出版年： 2020
畢業學年度： 108
語文別： 英文
論文頁數： 140
中文關鍵詞： 鋰離子電池 、預後方法健康狀態預測
外文關鍵詞： prognostic methods, state-of-health prediction
相關次數： 點閱：170  下載：2 
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    • 隨著設備的退化，測得的系統參數可用於顯示系統退化情況。因此，需要故障預測和健康管理的技術與方法來延長系統或組件的壽命。現行最可執行預測的方法與最終目標是準確預測系統或組件狀態，例如健康狀態（SOH），充電狀態（SOC）和剩餘使用壽命（RUL）。由於電力的儲能系統快速發展，考量成本、性能和耐用性，本論文介紹了鋰離子電池的健康狀態預測和剩餘使用壽命。本論文的目的是為電池的健康狀態預測和剩餘使用壽命進而開發改進的預測方法研究。論文內容包括對現有預測模型與演算法的全面比較，以探索改進的方法。在徹底查閱現有的預測模型與演算法之後，提出了四種改進鋰離子電池的預測模型與演算法。本研究詳細介紹了這些預測模型與演算法並通過使用公共數據集進行了驗證並討論了改進鋰離子電池健康狀態預測模型與演算法的研究方法，包括採用差分進化演算法的支持向量回歸，採用人工蜂群算法的梯度提升回歸，集成模型以及基於長短期記憶-注意力模型。長短期記憶-注意力模型用於估計電池SOC，而其他模型則用於預測鋰離子電池的SOH和RUL。性能指標，例如平均絕對百分比誤差（MAPE），平均絕對誤差（MAE），均方根誤差（RMSE），絕對誤差（AE）和最大誤差（MAX），用於比較預測模型與演算法的精準度。最後，與現行的預測模型與演算法相比，本研究提出的預測模型與演算法方法均顯示出優於現行預測模型與演算法的預測結果。

    Measured system parameters tend to change as equipment degrades, which can be used to characterize system degradation. For this reason, a prognostic and health management discipline is needed recently to manage and possibly extend the lifespan of technological systems or components. The ultimate goal of the most prognostic approach is to accurately predict system or component states such as the state of health (SOH), state of charge (SOC), and remaining useful life (RUL). This dissertation introduces a state of health prediction for lithium-ion batteries due to the rapid development, cost, performance, and durability of the energy storage system. The aim of this work is to develop improved prognostic approaches for battery state of health prediction. The approaches followed include comprehensive comparisons of existing prognostic approaches for exploring improved methods. Following a thorough review of existing prognostic methods, four improved prediction methods are proposed for lithium-ion batteries. Chapter 3 explains the details of these proposed methods, which are validated by using public datasets. The results of the improved state of health prediction methods for lithium-ion batteries are discussed in chapter four, which includes support vector regression with differential evolution algorithm, gradient boosted regression with artificial bee colony algorithm, ensemble model, and attention-based long short-term memory. The last model is used to estimate battery SOC, while the other models are used to predict the SOH and RUL of the lithium-ion batteries. Performance metrics, such as mean absolute percentage error (MAPE), mean absolute error (MAE), root mean square error (RMSE), absolute error (AE), and maximum error (MAX), are used to compare the prognostic approaches. In all cases, the proposed methods have shown improved performance compared to existing methods.

    摘要 ii Abstract iii Acknowledgment iv Table of Contents v List of Figures viii List of Tables x Acronyms xii Chapter One 1 Introduction 1 1.1. Background 1 1.2. Statement of the Problem 5 1.3. Objectives of the Study 6 1.4. Organization of the Dissertation 7 Chapter Two 8 State of Health Prediction Methods for Lithium-ion Batteries 8 2.1. State of Health Prediction 8 2.2. State of Charge Estimation 12 2.3. Differential Evolution and Artificial Bee Colony Algorithms 15 2.4. Metrics for Comparison of Prognostic Approaches 18 Chapter Three 19 Improved State of Health Prediction Methods for Lithium-ion Batteries 19 3.1. Support Vector Regression with DE Algorithm for SOH and RUL Prediction of Li-ion Batteries 19 3.2. Gradient Boosted Regression with ABC Algorithm for Capacity Degradation Trend and RUL Prediction of Prismatic Cell 23 3.3. Ensemble Model for Capacity Degradation Trend and RUL Prediction of Lithium-ion Batteries 28 3.4. Attention-based LSTM Model for Battery SOC Estimation 32 Chapter Four 38 State of Health Prediction for Lithium-ion Batteries 38 4.1. RUL Prediction Using SVR-DE Model 38 4.1.1. Datasets 38 4.1.2. SOH Prediction 39 4.1.3. RUL Prediction 42 4.1.4. Conclusions 43 4.2. Prismatic Cells Capacity Degradation and RUL Prediction Using GBR-ABC Model 44 4.2.1. Datasets 44 4.2.2. SOH Prediction 45 4.2.3. RUL Prediction 51 4.2.4. GBR-ABC Model Performance for Unseen Datasets 56 4.2.5. Conclusions 58 4.3. Ensemble Model for Capacity Degradation and RUL Prediction 59 4.3.1. Datasets 59 4.3.2. SOH Prediction 61 4.3.3. RUL Prediction 66 4.3.4. Model Validation for Unseen Datasets 69 4.3.5. Conclusions 70 4.4. SOC Estimation Using Attention-based LSTM Model 71 4.4.1. Datasets 71 4.4.2. SOC Estimation 72 4.4.3. Conclusions 83 Chapter Five 84 Conclusions and Future Study 84 5.1. Conclusions 84 5.2. Future Study 86 Appendices 87 Appendix I: R-code for SVR-DE Model 87 Appendix II: R-code for GBR-ABC Model 91 Appendix III: R-code for Ensemble Model. 94 Appendix IV: R-code for Attention-based LSTM Model. 107 References 114

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