混成綠能系統進階建模驗證與強健控制｜國立清華大學博碩士論文庫

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研究生：	白立文 Ángel Giancarlo Miranda Canales
論文名稱：	混成綠能系統進階建模驗證與強健控制 Advanced Model Validation and Robust Power Management of Hybrid Green Energy Systems
指導教授：	洪哲文 Hong, Che-Wun
口試委員:	黃緒哲 Huang, Shiuh-Jer 陳國聲 Chen, Kuo-Shen 鄭博泰 Cheng, Po-Tai 洪翊軒 Hong, Yi-Xuan 洪哲文 Hong, Che-Wun
學位類別：	博士 Doctor
系所名稱：	工學院 - 動力機械工程學系 Department of Power Mechanical Engineering
論文出版年：	2014
畢業學年度：	102
語文別：	英文
論文頁數：	126
中文關鍵詞：	高電力鋰電池、太陽電池、耗散控制函數、綠能源管理策略、真實轉換器效率預測、熱電晶片空調模組
外文關鍵詞：	High power lithium ion battery model, Solar cells, Passivity-based control, Hybrid energy power management system, Realistic power converter efficiency, Thermoelectric chip air conditioner model
相關次數：	點閱：2 下載：0
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近年，綠色能源與儲能裝置應用於汽車工業、可攜式裝置與固定式設備是個不斷成長的市場且熱門的研究領域。在綠能車輛方面，其能量供應可能為單一來源，或者結合了輔助動力供應系統。在此研究中，主要的動力來源是由化學反應緩慢但高功率輸出的燃料電池，而次要的動力輸出來源是太陽能電池模組。在儲能裝置方面，此研究選擇鋰電池，但也可以是超級電容。本篇論文特別的地方正是此能源管理策略的一般性與普遍性。
論文中的參數確認是經由動態負載與實驗頻譜分析技術而得到的。就染料敏化太陽能電池(Dye sensitized solar cells)而言，文中提出了一個新的參數確認方式:藉由量測無光時阻抗與溫度變化的關係，進而推得整個DSSC模組可以穩定且可靠操作的環境參數。動態負載分析是為了得到鋰電池堆的在高功率運作狀態下，其溫度與電流的關係。這種先進的參數確認技術所衍生出簡潔但精確的模型，大大地提升基於Kalman filter等等方法去估計電池電量的準確性。
為了精準地預測儲能與供能裝置(如鋰電池、太陽能電池等等)轉換器(power converter)在穩定運作時之間的影響，論文中用數值模擬一個考慮到真實實驗狀態下每個裝置都有轉換器的能源系統。要達到上述能耐，文中應用幾個方法:
1. 應用Euler-Lagrange架構而提出了一個新的包括歐姆消耗(conduction losses , CL)與無用信號時間(dead time , DT)效應的兩種轉換器其非理想狀態模型。
2. 藉由包含CL+DT的動態模型，推導出經由非線性數學運算法: 耗散控制函數(passivity based controllers, PBCs)而得到的內電流與外部電壓，這大幅改善了穩態時的誤差信號。
3. 基於CL+DT模型與其個別PBCs，此論文提供了穩態操作解。此解可以應用在混合綠能系統的設計，並預測其電流需求。如果使用只考慮到CL的模型，或是理想模型，即使DT小到只有0.1961微秒，其能量輸出還是遠遜於文中提出包括CL+DT PBCs的模型。
4. 利用非線性控制法則PBCs應用在能源管控系統，確保在運作期間電流可以被精準地追蹤。
5. 賦予此先進模型擁有保護能源裝置的能力。
基於以上五點可以得到一個可以適應各種混合能量裝置又可以保護這些裝置的簡便但精確的模型。
此研究以綠色能源作為熱電晶片空調能量來源，運用以上模型做為模擬驗證與操作設計。熱電晶片的優點為:無動件、無冷媒、模組化且易於更換。這裡提出了無動件空調系統的可行性分析與建議的操作方針。實驗用MATLAB/Simulink來預測這種空調在應用在汽車的空調系統的表現與效能。

Green energy power sources and storage devices are an ever growing market and research area for vehicles, portable and stationary applications. In green energy vehicles, the power may come from an exclusive main power source or combined with a secondary auxiliary supply. In this research, the main and secondary power source may be composed of a slow dynamics high power throughput energy source such as a fuel cell and a “free” energy source solar cell module. The storage device is chosen to be a lithium-ion battery pack, but may as well be a super-capacitor. This generality is the special characteristic of the proposed power management strategy.
The parameter identification is performed by dynamic load conditions and experimental spectroscopy analysis techniques. For the dye sensitized solar cells, a new identification is performed by obtaining the dark-current impedance under different temperatures and then extending this model to the solar cell mode impedance for a reliable representation. The dynamic load method is employed to obtain a high power integrated battery model for the lithium-ion battery pack. This advanced parameter identification technique results in a lightweight but accurate model which can further enhance state of charge Kalman filter based estimators or other advanced techniques.
A systematic analysis of an active topology power system with numerical simulations that take into account realistic experimental conditions will also be performed to accurately predict the steady-state effects of two types of power converters on the energy storage and energy source devices; fuel cells, solar cells, olivine batteries, etc. In order to achieve this, it is chosen to (i) employ the Euler-Lagrange (EL) framework to propose new non-ideal models comprising conduction losses (CL) and dead-time (DT) effects of two types of power converters. (ii) Demonstrate the vast improvement of the steady state error reduction for the derived inner current and outer voltage nonlinear passivity based controllers (PBCs) based on the CL+DT models with dynamic simulations. (iii) Provide analytic steady state operation solutions that result from the CL+DT models and their respective PBC controllers which can be employed to predict the current required from the energy source to aid in the design and sizing of hybrid green energy power systems. Without the new CL+DT PBC controllers, the output power is seen to be vastly reduced with respect to the demanded power even with dead-time periods as small as 0.1961 μs if the CL model and ideal model (IM) PBCs are employed. (iv) Modularly unify the system under passivity based nonlinear control laws for a seamless design of the power management system ensuring accurate inner current control tracking during operation. (v) Embed the ability to include power source protection instructions based on advanced model characterizations. With these initiatives, a convenient modularity to adapt to different hybrid power arrangements is proposed and thus, can easily be designed to protect each one of them.
The proposed management strategy in this research could be applied to a green power air conditioner, whose load is composed of thermoelectric chips. However, because of the high computational demands of the power converters, the power system cannot be fully simulated alongside. In addition, the time scales of the cabin, DSSCs and LiBS are in the seconds scales, while the power converters time scales are in the microsencond range. Therefore, a novel idea to derive the steady state efficiency of the power converters using fundamental theory from the PBCs will be proposed. The relationship between the cabin dynamics and the environment, as well as the DSSCs and LiBs will be fully simulated with realistic dynamics. A feasibility study will be performed in order to determine scenarios where the solid-state air conditioner may be used and will include guidelines for its success. A MATLAB/Simulink environment will predict the efficiency and performance of the air conditioner in the cabin of a vehicle. Finally, by importing the efficiency relationships from fundamental theory, the electrical efficiency of the system will be designed for the first time.

Figure 1 Contributions of this thesis    1
Figure 2 Schematic of a Li-ion battery [2]    4
Figure 3 Parallel active topology for an efficient power management    10
Figure 4 Setup schematic for the experimental (a) procedures and (b) experimental testing profiles    12
Figure 5 Suggested overview for the experimental extraction of power storage devices    14
Figure 6 Proposed equivalent electrical and thermal integrated model for high power batteries    16
Figure 7 Experimental results for the (a) Vocv and Rp, (b) Vocv rate and Cp and (c) enlarged Vocv rate with respect to SoCG. Note: The discharge rate is 1.5 C-rate    21
Figure 8 Fitted relationships for (a) Rp, (b) the Rs, Rp and Cp temperature gains, (c) Cp, and (d) the Rs, Rp and Cp current gains. For the results in (a) and (c), the discharge rate is 1.5 C    24
Figure 9 (a) EEM and (b) ETM validation during charging and at free convection    26
Figure 10 (a) EEM and (b) ETM validation during discharge and at free convection    28
Figure 11 (a) Voltage and (b) temperature RRE between experiments and simulations for both charging and discharging conditions    29
Figure 12 Proposed index of performance radar plot for battery comparisons    31
Figure 13 Equivalent photo electrochemical circuit of a DSSC    33
Figure 14 Proposed procedure to extract a functional solar cell model employing EIS    34
Figure 15 The Nyquist diagram of the EIS test result of the DSSC at (a) 288 K and different bias voltages; (b) the 0.5 and 0.6 V bias response. (c) 0.5 V bias voltage and different operating temperature    35
Figure 16 Measured open circuit voltage/ short circuit current relationship with θ for a DSSC.    38
Figure 17 Comparison of the simulated and experimentally measured V-I curves of the DSSC under an AM1.5 condition at a 300 K operating temperature.    39
Figure 18 Equivalent circuit diagram of the TEC air conditioner system and cabin model for the cooling case. The a and e subscripts are interchangeable depending on the operation mode.    41
Figure 19 DC-DC a) buck, b) boost and c) four quadrant bidirectional buck/boost schematics with inductor/capacitor internal resistances (MOSFET internal resistances not shown)    49
Figure 20 Dynamic PWM binary waveforms and their resulting duty cycles with TDT of a) 0.9804 μs (μ=0.6) and b) 0.1961 μs (μ=0.4) of dead-time, respectively    51
Figure 21 Passivity-based current and voltage regulator performance comparison for the buck converter under the effects of 0.9804 μs (upper row) and 0.1961 μs (lower row) of dead-time, respectively    65
Figure 22 Passivity-based current and voltage regulator performance comparison for the boost converter under the effects of 0.9804 μs (upper row) and 0.1961 μs (lower row) of dead-time, respectively    67
Figure 23 Calculated efficiency of non-ideal power converters    75
Figure 24 Modularity validation of the three types of power converters under three cooperative scenarios for a single hybrid power system    80
Figure 25 Relationship between a solar cell module, the MPPT, an inner current controller block, and the bus voltage    82
Figure 26 MOSFET Driver with high and low side MOSFETs    83
Figure 27 PH955L N-channel MOSFET schematic    83
Figure 28 a) MOSFET Driver schematic and b) prototype with connected PH955L N-channel MOSFETs    85
Figure 29 Allegro® ACS758 current sensors    88
Figure 30 Impedance spectroscopy result for two same-build inductors a) and b) and c) a test for three inductors connected in parallel    89
Figure 31 100uH and 6A inductors    90
Figure 32 TEC air conditioner results for Case 1: (a) temperature and heat and (b) COP and electric power for the TEC air conditioner    98
Figure 33 TEC air conditioner results for Case 2: (a) temperature and heat and (b) COP and electric power for the TEC air conditioner    99
Figure 34 TEC air conditioner results for Case 3: (a) temperature and heat and (b) COP and electric power for the TEC air conditioner    101
Figure 35 Possible TEC interface connections a) with buck converters and b) with boost converters    103
Figure 36 Temperature and heat transfer profiles for a TEC array interfaced to a) buck and b) boost converters    107
Figure 37 Electrical performance of a TEC air conditioner interfaced to a) buck and b) boost converters    108
Figure 38 Temperature and heat transfer profiles for a TEC array interfaced to a) buck and b) boost converters    110
Figure 39 Current waveforms for a power source connected to a buck or boost converter    116
Figure 40 Switching losses in a high-side DC-DC power switch [76]    117
Figure 41 Efficiency prediction comparison of CL+DT versus CL+DT+SwL    119
                                

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