混合儲能系統透過結合不同儲能元件，提升系統整體的能源使用效率及循環壽命，逐漸成為電能產業中的主要趨勢。本研究結合高比能量的鋰離子電池、高比功率的超級電容及單向升壓轉換器，建構鋰離子電池半主動混合儲能系統。根據所建立的鋰離子電池、超級電容及單向升壓轉換器數學模型，設計了一套基於無跡卡爾曼濾波器與積分終端滑動模式控制的能量管理策略。在策略中，透過一階數位低通濾波器對負載功率進行濾波，以得到較為平緩的輸出功率。
基於無跡卡爾曼濾波器和遞迴最小平方法，建立適應性估測策略，用於估測鋰離子電池與超級電容的狀態（荷電狀態、溫度、健康狀態和電壓值）及參數（等效串聯電阻、擴散電阻和擴散電容）。根據所估測的鋰離子電池荷電狀態和超級電容荷電狀態，建立了一套規則導向策略，調整鋰離子電池的目標輸出功率，使其能以較平滑功率輸出，避免瞬時大功率輸出，從而延長電池壽命，同時確保直流母線端電壓穩定。最終，使用積分終端滑動模式控制器控制鋰離子電池的輸出電流。
後續以不同工作狀態下的實驗及模擬驗證能量管理策略的有效性。實驗結果顯示，能量管理策略在不同工作情況下皆能達成精準控制及準確估測。在控制性能方面，系統可在0.5秒內收斂至追跡目標，且均方根誤差皆小於0.0185 A；在估測方面，鋰離子電池及超級電容的端電壓估測均方根誤差分別小於0.0286 V與0.029 V，溫度估測均方根誤差分別小於0.0408 °C和0.0937 °C，等效串聯電阻準確度分別達到80 %和74 %以上，擴散電阻準確度分別達到77 %和78 %以上，擴散電容準確度分別達到99 %和93 %以上。

Hybrid Energy Storage Systems (HESS), which combine different energy storage components, are becoming a major trend in the energy industry for their ability to enhance overall energy efficiency and cycle life. This study integrates high-energy-density lithium-ion batteries, high-power-density ultracapacitors, and a unidirectional boost converter to construct a semi-active HESS. An energy management strategy incorporating an Unscented Kalman Filter (UKF) and Integral Terminal Sliding Mode Control (ITSMC) is designed. A first-order low-pass filter (LPF) filters the load power to achieve a smoother output power.
An adaptive estimation strategy using UKF and Recursive Least Squares (RLS) estimates the state (state of charge, temperature, state of health, and voltage) and parameters (equivalent series resistance, diffusion resistance, and diffusion capacitance) of the lithium-ion battery and ultracapacitors. A rule-based strategy adjusts the target output power of the lithium-ion battery to ensure smoother power output, prevent instantaneous high-power output, and extend battery lifespan while maintaining stable DC bus voltage. ITSMC is used to control the output current of the lithium-ion battery.
Experiments and simulations under different operating conditions verify the strategy's effectiveness. Results show the system can converge to the tracking target within 0.5 seconds, with a root mean square error (RMSE) of less than 0.0185 A. The voltage estimation RMSE for the lithium-ion battery and ultracapacitors is less than 0.0286 V and 0.029 V, respectively. The temperature estimation RMSE is less than 0.0408 °C and 0.0937 °C. The accuracy for equivalent series resistance estimation is above 80 % and 74 %, for diffusion resistance it is above 77 % and 78 %, and for diffusion capacitance, it is above 99 % and 93 %.

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