本論文利用卡曼濾波器執行鋰離子電池充放電中的電量狀態估測，利用鋰離子電化學動態特性將電極假設為單一球型粒子模型，進而得到電極的表面濃度，再將此表面濃度透過開路電壓方程式來計算電池的電壓及電量狀態。本論文提供非線性的無跡卡濾波及線性卡漫濾波之鋰離子電池電量狀態估測的運算法則,並在MATLAB環境上執行穩定電流及非穩定電流充放電過程中的電壓及電量狀態估測,最後再加入隨機雜訊執行充放電之電量估測,驗證此電量估測法則的可行性。 This research investigates the state-of-charge (SOC) estimation of Li-ion battery using Kalman filters. The dynamic model for the SOC estimation process is constructed based on a single spherical particle electrochemical model. The surface concentration of the positive electrode is obtained first. The battery voltage and SOC estimations are computed accordingly using the Li-ion battery electrochemical model. The nonlinear unscented Kalman filter and linear Kalman filter based SOC estimation algorithms are discussed in the thesis. The results for battery charging/discharging processes using constant and varying currents with random noises are included in the thesis.