充電方法對於鋰離子電池的性能與壽命有很大的影響，因此可以加快充電時間並提高充電效率之高等演算法顯的十分重要。根據文獻所提，五階段定電流充電法(FSCC)與傳統定電流定電壓(CC-CV)充電法相比，其具有充電時間較短且充電效率高等優點，因此如何決定FSCC充電法的充電設定之技術是非常重要的。
本文採用粒子群演算法(PSO)尋找FSCC的最佳化充電電流設定，且所提方法只需使用鋰離子電池等效模型，因此不需進行多次耗時的實驗。此外，提出之策略考慮了充電時間與充電損失，而所提五階段定電流充電法與CC-CV相比，其平均溫升高4.798%，充電時間可縮短39.53%。

關鍵字：五階段定電流充電法、粒子群演算法、電池溫升

Charging method has a great impact on the performance and the longevity of lithium-ion batteries. Therefore, an advanced charging algorithm which can reduce the charging time and improve the charging efficiency becomes important. According to the literature, five-stage constant current (FSCC) charging method boasts the benefits such as short charging time and high charging efficiency comparing with conventional constant current-constant voltage (CC-CV) charging method. Hence, an efficient technique which can determine the setting values of the FSCC method is essential. In this thesis, particle swarm optimization (PSO) method is adopted to search for the optimal charging pattern of the FSCC charging method. The proposed approach uses only the equivalent model of the lithium-ion battery; hence, time-consuming experiments is not required. The presented strategy takes both the charging time and temperature rise into account. Comparing with CC-CV method, the average temperature rise increase of 4.798% and the charging time can be shortened by 39.53%.

Keyword: Five-stage Constant Current, Particle Swarm Optimization, Battery Temperature

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