整合式燃料電池/鋰電池/能源與散熱智能管理系統
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2018
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本研究的目的是開發整合式質子交換膜燃料電池與鋰電池混合電能之智慧能源管理系統及熱管理系統；採用粒子群聚演算法（Particle Swarm Optimization, PSO）進化系統優化，並分為兩個部分：（1）混合電動機車的質子交換膜燃料電池與鋰電池之間的能量管理，以及（2）混合電動機車之中質子交換膜燃料電池與鋰電池的兩種廢熱源來控制各自溫度於最佳工作溫度；質子交換膜燃料電池與鋰電池的管內目標工作溫度分別為55 ˚C和40 ˚C。依據電動機車系統動力學及熱管理系統動力學，利用Matlab /Simulink軟體進行理論建模和性能分析，並整合系統控制單元，透過動態模擬嘗試控制雙電力源在其最佳操作區間，在熱能管理系統中進行PSO與Rule-Based模擬分析比較結果。成功控制經由四輸入(鋰電池SOC、鋰電池館內溫度、燃料電池耗氫量及燃料電池)與五輸出 (雙直流/直流轉換器、風扇、水泵及單一比例閥)，控制能源於最佳化分配及系統溫度至最佳操作點。模擬結果證明PSO更優化能量管理系統及熱能管理系統，其結果顯示能量消耗改善4.708 %，燃料電池溫度穩定度改善45.38%，鋰電池溫度穩定度改善25.46 %，模擬結果證明PSO更優化能量管理系統及熱能管理系統。
The purpose of this study is to develop an intelligent energy management system for a proton exchanged membrane fuel cell (PEMFC)/lithium battery hybrid powertrain with its corresponding thermal management system (TMS). An evolutionary optimization method, Particle Swarm Optimization (PSO), was applied for the system control unit. The control strategy of the control unit was separated into two segments: (1)the energy management among PEMFC/lithium battery of a hybrid-energy electric vehicle (EV), and (2)the temperature control of two waste-heat sources from the PEMFC/lithium battery. For the TMS, the target internal pipe temperatures for the PEMFC and lithium batteries were 55˚C and 40˚C. In order to analyze the energy and thermal systems, we used Matlab /Simulink software to establish the theoretical model. Dynamic modeling was used to maintain the two power sources in their optimal operation range, and results of PSO and rule-based modeling were analyzed in the thermal system. We successfully controlled the optimal distribution of energy and the optimal operating temperature through four inputs (SOC of the lithium battery, temperature within the lithium battery tube, hydrogen consumption of the fuel cell, and the temperature within the fuel cell tube) and five outputs (two DC/DC converters, fan, water pump, and the proportional valve). Simulation results proved that the PSO optimized the energy management system and the thermal management system. The energy consumption improved by 4.708%, the temperature stability of the fuel cell by 45.38 %, and the temperature stability of the lithium batteries by 25.46 %.
The purpose of this study is to develop an intelligent energy management system for a proton exchanged membrane fuel cell (PEMFC)/lithium battery hybrid powertrain with its corresponding thermal management system (TMS). An evolutionary optimization method, Particle Swarm Optimization (PSO), was applied for the system control unit. The control strategy of the control unit was separated into two segments: (1)the energy management among PEMFC/lithium battery of a hybrid-energy electric vehicle (EV), and (2)the temperature control of two waste-heat sources from the PEMFC/lithium battery. For the TMS, the target internal pipe temperatures for the PEMFC and lithium batteries were 55˚C and 40˚C. In order to analyze the energy and thermal systems, we used Matlab /Simulink software to establish the theoretical model. Dynamic modeling was used to maintain the two power sources in their optimal operation range, and results of PSO and rule-based modeling were analyzed in the thermal system. We successfully controlled the optimal distribution of energy and the optimal operating temperature through four inputs (SOC of the lithium battery, temperature within the lithium battery tube, hydrogen consumption of the fuel cell, and the temperature within the fuel cell tube) and five outputs (two DC/DC converters, fan, water pump, and the proportional valve). Simulation results proved that the PSO optimized the energy management system and the thermal management system. The energy consumption improved by 4.708%, the temperature stability of the fuel cell by 45.38 %, and the temperature stability of the lithium batteries by 25.46 %.
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熱管理系統, 能源管理系統, 粒子群聚演算法, 雙電力, 鋰電池, 燃料電池, Thermal Management System, Energy Management System, Particle Swarm Optimization, Two Power, Lithium Battery, Fuel Cell