基於交叉耦合電壓下降法之最佳化鋰電-超級電容混合電能管理系統

Abstract

本論文發展鋰電-超級電容混合電力系統，為了提高系統之輸出功率，並聯三台直流-直流轉換器組成一套三進一出的系統，然而轉換器因元件匹配、外部雜訊等影響產生負載分配不均之問題，將降低轉換器使用效率甚至燒毀電路元件導致系統癱瘓，轉換器必須透過均流技術來確保模組間的輸出電流相同並提高系統可靠度。本論文提出一種均流架構，此架構結合了既有的改良型電壓下降法與交叉耦合控制，調整模組之下降斜率參數，使模組間電流平均分配，實驗比較傳統電壓下降法、自動主僕法與交叉耦合電壓下降法，實驗結果可知交叉耦合電壓下降法之均流誤差最大值、平均值及標準差分別為[7.2776, 0.9094, 0.5774]，確實優化均流效果。 本論文針對鋰電-超級電容混合電力系統提出最佳化能量管理策略，應用電動機車行車型態作為系統負載，策略因應不同需求功率與超級電容殘電量計算出功率分配比，達到能源消耗最小化與續航力提升之目的。本論文發展基本規則庫控制、最小等效能耗策略與生物地理演算法設計能量管理策略。本論文以非線性遷移模型與粒子群移動演算法提出改良型生物地理演算法提高分配效率。實驗結果以基本規則庫控制策略為能耗比較基準，生物地理演算法、改良型生物地理演算法與最小等效能耗策略之能耗改善率在WMTC行車型態分別為[7%, 8.06%, 9.26%]，在NEDC行車型態分別為[6.69%, 9.41%, 12.39%]，實驗結果可知本論文提出之改良型生物地理演算法相較於生物地理演算法擁有高可行性與環保效率。This paper developed a battery-supercapacitor hybrid power system(HPS), in order to increase the output power of this system, three DC-DC converters had been connected in parallel to form a three-in-one-out system. However, the converters had the problem of uneven load distribution due to mismatched components or external noise, which will reduce the efficiency of the converters, even burn out the circuit components and cause the system to crash. Therefore, the converters must use the current sharing technology to ensure that the output current between the modules can be the same and improve the system reliability. This paper proposed a current sharing architecture which combines the existing improved droop control method and cross-coupled control, by adjusting the droop parameters of the modules, the currents between the modules can be evenly distributed. The experiment compares traditional droop control method, automatic master-slave method and cross-coupled droop control method, the experimental results of cross-coupled droop control method show that the maximum, average and standard deviation of the current sharing error are [7.2776, 0.9094, 0.5774], the proposed cross-coupled droop control method really optimize the current sharing effect. This paper proposed an optimal energy management strategies for battery-supercapacitor hybrid power system, the optimal energy management strategies were developed with two inputs, namely demanded power, and supercapacitor state-of-charge and one output, namely power-split ratio, to achieve the goal of minimizing energy consumption and improving endurance. This study proposed optimal energy management strategys (EMS) for a battery-supercapacitor hybrid power system by using rule-based control (RBC), equivalent consumption minimization strategy (ECMS), biogeographic-based optimization (BBO). In addition, an improved biogeographic-based optimization (IBBO) had been proposed to improve the power-split efficiency, based on the nonlinear migration model and particle swarm algorithm. To evaluate the effectiveness of the proposed EMS, RBC method was developed as the baseline for the comparison, the BBO/IBBO/ECMS strategies greatly reduced the energy consumptions by [7%, 8.06%, 9.26%] during the WMTC driving cycle and [6.69%, 9.41%, 12.39%] during the NEDC driving cycle, the results clearly revealed that the IBBO-based control strategy can regulate the power distribution more efficiently than the conventional BBO-based control does. Therefore, the HPS with the proposed power control system can obtain high feasibility and eco-friendly efficiency for the practical applications.