鋰離子電池已被大量使用在消費性產品的能量儲存上，例如：手機、航空、電動車以及再生能源之儲存裝置，而充電方法對鋰離子電池的壽命有很大之影響，因此設計出高品質的電池充電器是必須的，一個好的充電器需具備高充電效率、充電時間短且能延長電池循環壽命。
本文提出一種基於模型預測控制之電池充電法，此法主要之目標為縮短充電時間同時降低電池在充電過程中的溫升，本文使用庫侖積分來計算未來之剩餘容量，並透過訓練過後的類神經網路來預測未來之溫升，相較於定電流定電壓充電法相比，本文所提方法之充電時間可快2.8%，平均溫升最多可降低14.45%。

關鍵字：模型預測控制、類神經網路、鋰離子電池、電池溫升

Lithium-ion (Li-ion) batteries play a substantial role in energy storage solutions for modern-day technologies such as hand-held consumer electronics, aerospace, electric vehicles and energy backup for renewable energy systems. For Li-ion batteries, designing a high-quality battery charger is essential since charging method has significant influences on the performance and lifetime of Li-ion batteries. The objectives of a high-quality charger include higher charging efficiency, shorter charging time and prolonged cycle life.
In this thesis, a model predictive control based charging algorithm is proposed, the aim of the presented technique is to simultaneously reduce the charging time and the temperature rise during charging. In this study, coulomb counting method is utilized to calculate the future state-of-charge (SOC) and an artificial neural network (ANN) trained by experimental data is also applied to predict the future temperature rise. Comparing with widely employed constant current-constant voltage (CC-CV) charging method, the proposed charging technique can improve the charging time and the average temperature rise by 2.8 % and 14.45 %, respectively.

Keyword: Model Predictive Control, Artificial Neural Network, Li-ion battery, Battery Temperature

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