臺灣博碩士論文加值系統

相較於傳統的定電流定電壓(Constant-Current Constant-Voltage, CC-CV)充電方法，多階段定電流(Multi-Stage Constant-Current, MSCC)充電方法具有充電時間短和充電效率高等優點。然而，MSCC必須具備最佳的充電電流樣式(Optimal Charging Current Profile, OCCP)才能實現上述優勢。因此，本論文應用五種生物啟發式演算法(BIO-inspired optimization Algorithms BIOAs)，包括粒子群演算法(Particle Swarm Optimization, PSO)、改良型PSO (modified PSO, MPSO)、灰狼演算法(Grey Wolf Optimization, GWO)、改良型GWO (Modified GWO, MGWO)和水母演算法(Jellyfish Search Algorithm, JSA)，來解決MSCC的OCCP搜索問題。本研究充電策略目標為最短充電時間(Charging Time, CT)和最低充電損耗(Charging Loss, CL)，並利用所建立的MATLAB模擬平台進行搜尋OCCP的模擬。無需進行耗時的實際充放電實驗，藉由所建立的電池等效電路模型(Equivalent Circuit Model, ECM)，即可實現廣泛的搜索。
藉由模擬和實驗結果來驗證理論推導的正確性與性能提升，實驗結果顯示JSA具有最佳的適應值（Fitness Value, FV）。相較於傳統的CC-CV方法，所提出的五種BIOAs方法在充電時間、充電效率、充電能量、平均溫升和FV六個充電性能評估指標(Charging Performance Evaluation Indicators, CPEIs)的最大改善率(Improvement Rates, IRs)分別為21.10%、0.24%、2.85%、19.72%和 26.80%。此外，針對CPEIs的綜合評估，性能表現最佳的前三名分別為MPSO、MGWO和GWO方法。

Compared to the traditional constant-current constant-voltage (CC-CV) charging method, the multi-stage constant-current (MSCC) charging method has the advantages of short charging time and high charging efficiency. However, MSCC must have an optimal charging current profile (OCCP) to achieve the above advantages. Therefore, in this thesis, five bio-inspired optimization algorithms (BIOAs), including partiEe swarm optimization (PSO), modified PSO (MPSO), gray wolf optimization (GWO), modified GWO (MGWO) and jellyfish search algorithm (JSA), are applied to solve the OCCP search of MSCC. The objectives of the proposed charging strategy are the shortest charging time (CT) and the lowest charging loss (CL). A simulation platform based on MATLAB is established to simulate the search for OCCP. Without the need to conduct time-consuming charging-and-discharging experiments, extensive searches can be achieved through the built battery equivalent circuit model (ECM).
Simulation and experimental results demonstrate the correctness of the theoretical derivation and performance improvement. The experimental results show that JSA has the best fitness value (FV). Compared to the standard CC-CV method, the five proposed BIOAs methods have the greatest improvement rate in six charging performance evaluation indicators (CPEIs), including charging time, charging efficiency, charging energy, average temperature rise and FV, which are 21.10%, 0.24%, 2.85%, 19.72% and 26.80% respectively. In addition, for the comprehensive evaluation of CPEIs, the top three methods are MPSO, MGWO and GWO, respectively.

目錄
第一章 緒論
1.1 研究背景
1.2 研究動機與貢獻
1.3 文獻回顧
1.4 論文大綱
第二章 鋰離子電池充電技術介紹
2.1 鋰離子電池種類之介紹
2.2 鋰離子電池之常用專有名詞介紹
2.3 本文選用之鋰離子電池介紹
2.4 鋰離子電池充電技術介紹
2.4.1 定電壓充電方法
2.4.2 定電流充電方法
2.4.3 定電流-定電壓充電方法
2.4.4 衍生型定電壓-定電流充電方法
2.4.5 多階段定電流充電法
第三章 鋰離子電池等效電路模型介紹
3.1 鋰離子電池等效電路模型種類介紹
3.1.1 理想電池等效電路模型
3.1.2 線性電池等效電路模型
3.1.3 戴維寧電池等效電路模型
3.2 鋰離子電池之交流阻抗分析介紹
3.2.1 交流阻抗之分析流程介紹
3.2.2 交流阻抗之實驗規劃
3.2.3 交流阻抗之資料分析
第四章 生物啟發式演算法
4.1 粒子群演算法
4.2 灰狼演算法
4.3 水母演算法
第五章 最佳化多階段定電流充電之實現
5.1 多階段定電流充電法之推導
5.2 問題描述與目標函數推導
5.3 最佳化多階段定電流充電實現流程
第六章 模擬與實驗結果
6.1 模擬結果
6.2 實驗測試環境設置
6.3 最佳化多階段定電流充電方法實驗測試結果
6.4 各種充電方法充電性能評估指標分析
第七章 結論與未來展望
7.1 結論
7.2 未來展望
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