本研究針對掛載於電動車(Electric Vehicle, EV)之飛輪儲能系統(Flywheel Energy Storage System, FESS)，提出一轉速追蹤及減少能耗的控制策略。 控制架構以無感測(Sensorless)之直接磁場導向控制(Direct Field Oriented Control, DFOC)為基礎，將梯度下降法(Gradient Descent, GD)以及遞迴最小平方法(Recursive Least Sqaure Method, RLS)與擴展型卡爾曼濾波器(Extended Kalman Filter, EKF)進行結合，構成一可即時(Real-time)估測的新型估測器: 適應擴展型卡爾曼濾波器(Adaptive Extended Kalman Filter, AEKF)，成功改善EKF因感應馬達(Induction Motor, IM)之系統參數變化而產生估測誤差過大的問題。 此外，本研究對弱磁(Field Weakening)進行延伸，將FOC(Field Oriented Control, FOC)一般經由離線所建立的弱磁曲線，改為對轉子磁通(Rotor Magnetic Flux)的直接控制，並基於馬達/發電機單元(Motor/Generator Unit, MGU)掛載不同轉動慣量(Moment of Inertia)之飛輪時，給予合適的線上(On-line)磁通控制策略，使其可達到弱磁運轉以及磁能損失最小化的目的，包含了適用於低慣量的滑動模式損失最小化策略(Loss Minimization Sliding Mode Control, LMSMC)以及適用於高慣量的模糊弱磁策略(Field Weakening Fuzzy Logic Control, FWFLC)。
為了驗證完整控制架構可達到即時控制之效果，本研究將控制策略寫入數位訊號處理器(Digital Signal Processor, DSP)，並配合變頻器(Inverter)驅動無載及掛載高慣量鋁合金飛輪之MGU。 實驗結果說明了在狀態估測、轉速追蹤以及損失最小化之控制結果與電腦模擬時一致，證實了本控制策略在理論與實務層面均有良好的成效。 而為了初步驗證車載飛輪之可行性，本研究架設了硬體迴路(Hardware-in-the-Loop, HIL)實驗平台，模擬實體飛輪儲能系統在虛擬車輛中對於能量充放之動態，藉此驗證電動車內之飛輪能量交換單元之能量可控性，並探討動能與電能間的轉換效率。

This thesis is aimed at motor speed tracking and power loss minimization strategy based on sensorless Field Oriented Control (FOC) for Flywheel Energy Storage System (FESS) embedded in Electric Vehicles (EVs). A new observer named as Adaptive Extended Kalman Filter (AEKF), integrated with Gradient Descent (GD), Recursive Least Square (RLS), and Extended Kalman Filter (EKF), is proposed. AEKF possesses an adaptive attribute able to tolerate the parameter uncertainties at Induction Motors (IMs). Unlike the conventional approach by constructing field weakening curve off-line, rotor flux is controlled directly by the magnetic flux controller via closed loop. By generalized field weakening scheme, two on-line field weakening and loss minimization strategies, i.e., Loss Minimization Sliding Mode Control (LMSMC) and Field Weakening Fuzzy Logic Control (FWFLC), are designed with respect to Motor/Generator Unit (MGU) equipped with flywheels in low/high moment of inertia. To evaluate the performance of the proposed control strategies in capacity-limited computational environment, the control strategies are lodged into a Digital Signal Processor (DSP). In addition, to examine the feasibility of FESS embedded in EVs, the Hardware-in-the -Loop (HIL) experimental test rig is constructed as well. According to the experimental results, the state estimation, motor speed tracking, and power loss minimization are fairly similar to computer simulations which are undertaken earlier. To sum up, the proposed control strategies can be realizable in the real world.

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