儘管超級電容有著功率密度高、工作溫度範圍廣及循環壽命長等優點，超級電容的阻抗特性仍會受到老化程度、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)擬定之多種估測策略，以達成超級電容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超級電容充電狀態(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超級電容阻抗參數，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不同的估測策略，使用三種不同老化程度之超級電容在多種充放電行程下進行測試。結果顯示，所提出的估測策略(2) 與估測策略(3)，在不同老化程度及各種操作條件下，都能夠達成最大電壓誤差小於0.1 V，最大溫度誤差小於0.04 ℃，且參數RS與參數C估測準確率皆大於90 %之條件。而由於所提出之估測策略能達成無跡卡爾曼濾波器中參數之即時線上更新，可預期在不同超級電容的應用上皆有良好的移植性。

Even though ultracapacitor has the advantages of high power density, wide operating
temperature range and long cycle life, the impedance characteristics of Ultracapacitor still depend heavily on the aging condition, operating voltage and temperature. As a result, accurate estimation of the capacitor 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 capacitor 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, three estimation strategy is examined, via both simulation and experiment, on ultracapacitors of three different aging conditions using various charging and discharging cycles. The results show that, under various aging and operating conditions, the proposed estimation strategy (2) and estimation strategy (3) achieve maximum estimate errors less than 0.1 V and 0.04 ℃ 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 ultracapacitors.

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