鋰離子電池有著能量密度高、自放電率低、無記憶效應及循環壽命長等優點，使其成為可攜式電子產品及電動車電力來源的主流。然而，鋰離子電池的阻抗特性會受到老化程度、操作電壓及溫度等條件的影響，形成精確估測充電狀態(SOC)和健康狀態(SOH)等之挑戰。因此，本研究提出以無跡卡爾曼濾波器(UKF)結合適應性遞迴最小平方法(RLS)，同步達成電池阻抗參數及狀態之即時估測。具體來說，透過無跡卡爾曼濾波器估測電池充電狀態(SOC)、電壓、溫度及串聯電阻值，並以適應性遞迴最小平方法估測電容值等電池阻抗參數，即時更新卡爾曼濾波器中之模型參數。最後，分別以模擬和實驗驗證所提出的估測法則，使用三種不同老化程度之鋰離子電池在多種充放電行程下進行測試。結果顯示，所提出的估測法則，在不同老化程度及各種操作條件下，所達成的最大電壓誤差小於0.05 V，最大溫度誤差小於0.16 ℃，且參數估測準確率皆大於90 %。而由於所提出之估測法則能達成無跡卡爾曼濾波器中參數之即時線上更新，可預期在不同鋰離子電池的應用上皆有良好的移植性。

The lithium-ion battery has become the main power source for the portable electronic devices and electric vehicles (EVs) due to its advantages of high energy density, low self-discharge rate, zero to minimal memory effect and long cycle life. The impedance characteristics of lithium-ion batteries, however, depend heavily on the aging condition, operating voltage and temperature. As a result, accurate estimation of the battery states such as the state of charge (SOC) and state of health (SOH) remains a challenging task. In this thesis, the unscented Kalman filter (UKF) is integrated with the recursive least square (RLS) method to simultaneously achieve real-time estimation of the impedance parameters and battery states. Specifically, the UKF is used to estimate SOC, voltages, temperature and series resistance, whereas the RLS is employed for online estimation of the other impedance parameters, such as the capacitance, which are then used to update the parameters in the UKF. Finally, the proposed algorithm is examined, via both simulation and experiment, on batteries of three different aging conditions using various charging and discharging cycles. The results show that, under various aging and operating conditions, the proposed estimation algorithm achieves maximum estimate errors less than 0.05 V and 0.16 ℃ in voltage and temperature respectively, and the accuracy of parameter estimation is higher than 90 %. Since the parameters in the UKF are updated online, the propose algorithm is expected to attain desirable portability across different lithium-ion batteries.

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