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

白立文(Ángel Giancarlo Miranda Canales)

指導教授 ： 洪哲文

國立清華大學/工學院/動力機械工程學系/博士(2014年)

https://doi.org/10.6843/NTHU.2014.00087

摘要

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。

關鍵字

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

並列摘要

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.

並列關鍵字

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

參考文獻

[1] M. S. Whittingham, "Electrical Energy Storage and Intercalation Chemistry," Science, vol. 192, pp. 1126-1127, 1976.

[3] L. Cai and R. E. White, "Mathematical modeling of a lithium ion battery with thermal effects in COMSOL Inc. Multiphysics (MP) software," Journal of Power Sources, vol. 196, pp. 5985-5989, 2011.

[4] V. Boovaragavan, S. Harinipriya, and V. R. Subramanian, "Towards real-time (milliseconds) parameter estimation of lithium-ion batteries using reformulated physics-based models," Journal of Power Sources, vol. 183, pp. 361-365, 2008.

[5] A. Zenati, P. Desprez, and H. Razik, "Estimation of the SOC and the SOH of Li-ion Batteries, by combining Impedance Measurements with the Fuzzy Logic Inference," 36th Annual Conference on IEEE Industrial Electronics Society, 2010.

[6] M. Chen and G. A. Rincon-Mora, "Accurate electrical battery model capable of predicting, runtime and I-V performance," IEEE Transactions on Energy Conversion, vol. 21, pp. 504-511, 2006.