混合儲能系統(hybrid energy storage system, HESS)是當今新興能源轉型、提升能源使用效率的發展方向。因此，本研究結合高比能量、低比功率的鋰離子電池與高比功率、低比能量的超級電容等兩種性能相反的儲能元件，建構鋰離子電池組與超級電容組等效電路模型與熱動態效應模型，分別透過充放電實驗進行模型驗證，發現電壓與溫度響應受參數影響而有不同表現，與單顆響應趨勢相同。而後將鋰離子電池組與超級電容組並聯連接，完成被動式混合儲能系統。超級電容在暫態期間提供能源輔助，鋰離子電池在穩態期間供應主要能源，透過能源分配可保護鋰離子電池，並以最大限度使用超級電容，來提升電池續航力。另外，採用無跡卡爾曼濾波器(unscented Kalman filter, UKF)為基礎之適應性估測策略，以不同參數組合搭配遞迴最小平方法(recursive least squares method, RLS)來更新參數，對被動式混合儲能系統之多顆鋰離子電池與超級電容進行參數、荷電狀態(state of charge, SOC)、健康狀態(state of health, SOH) 及溫度等進行估測。研究結果顯示，在自然散熱狀態下，使用不同充放電電流行程，鋰離子電池電壓最大絕對誤差小於0.014 V，電壓平均絕對誤差小於0.001 V，溫度最大絕對誤差小於0.009 ℃，溫度平均絕對誤差小於0.001 ℃。超級電容電壓最大絕對誤差小於0.007 V，電壓平均絕對誤差小於0.001 V，溫度最大絕對誤差小於0.009 ℃，溫度平均絕對誤差小於0.001 ℃，且皆在參數估測上具有良好的準確性，預期未來可應用在不同混合儲能系統架構，為能源發展開啟新的思路。

Hybrid energy storage system (HESS) is the emerging development direction of energy transformation and improving energy efficiency. This study is to combine two energy storage elements with opposite performance, such as lithium-ion battery with high specific energy, low specific power and ultracapacitor with high specific power, low specific energy, and construct the equivalent circuit model and thermal model of the lithium-ion battery pack and the ultracapacitor pack. The models are verified through charge and discharge experiments respectively. The voltage and temperature responses are affected by the parameters and have different performances, which is the same as the trend of a single cell. Then the lithium-ion battery pack is connected in parallel with the ultracapacitor pack to complete the passive hybrid energy storage system. The ultracapacitors provide energy assistance during the transient time, and the lithium-ion batteries supply the main energy during the steady-state time. Through energy distribution, the lithium-ion battery can be protected, and the ultracapacitor can be used to the maximum to improve battery life. In addition, adaptive estimation strategies based on the unscented Kalman filter (UKF) are used to update the parameters with different parameter combinations by using the recursive least squares method (RLS). Estimate the parameters, state of charge (SOC), state of health (SOH), and temperature of lithium-ion batteries and ultracapacitors in the energy storage system. The study result detect that in natural heat dissipation different current cycles. In the lithium-ion battery, the maximum absolute error of voltage is less than 0.014 V, and the mean absolute error of voltage is less than 0.001 V. The maximum absolute error of temperature is less than 0.009 ℃, and the mean absolute error of temperature is less than 0.001 ℃. In the ultracapacitor, the maximum absolute error of voltage is less than 0.007 V, and the mean absolute error of voltage is less than 0.001 V. The maximum absolute error of temperature is less than 0.009 ℃, and the mean absolute error of temperature is less than 0.001 ℃. It has good accuracy in parameter estimation and is expected to be applied to different hybrid energy storage system architectures in the future, to spark off new ideas for energy development.

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