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研究生:施伯霖
研究生(外文):Shih, Po-Lin
論文名稱:細菌覓食演算法應用於三動力複合動力車系統之最佳能量管理與變速策略
論文名稱(外文):Integrated Optimal Energy Management/Gear Shifting Strategy Using Bacterial Foraging Algorithm for a Three-Power-Source Hybrid Powertrain
指導教授:洪翊軒洪翊軒引用關係
指導教授(外文):Hung, Yi-Hsuan
學位類別:碩士
校院名稱:國立臺灣師範大學
系所名稱:工業教育學系
學門:教育學門
學類:專業科目教育學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:中文
論文頁數:78
中文關鍵詞:細菌覓食演算法規則庫管理最小等效油耗策略混合動力
外文關鍵詞:bacterial foraging algorithmrule-based managementequivalent consumption minimization strategyhybrid power
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本研究旨在開發細菌覓食演算法(Bacterial Foraging Algorithm, BFA)用於三動力複合動力車的能量管理/變速策略系統,並且真正應用硬體嵌入式系統(Hardware-In-The-Loop, HIL)進行即時(Real-Time)驗證驗算法之可行性。本研究中,使用HIL進行評估使用細菌覓食演算法(BFA)之三動力源複合動力車系統能量管理與變速策略控制。車輛子系統包括43 kW內燃機引擎、30 kW馬達、15 kW一體式啟動馬達和1.872 kW-h鋰電池,車重1368 kg。在能量管理系統,BFA能量管理控制,主要有三個步驟(1)趨化、(2)複製、(3)驅散。總疊代次數為30次,共有80個細菌進行最佳能量管理。
BFA與兩種控制策略進行NEDC行車型態之油耗比較:(1)規則庫管理(Rule base):有五種控制模式(系統準備、充電模式、電動模式、複合動力模式及延距模式),根據工程師經驗設定模式切換時機;(2)最小等效油耗策略(Equivalent Consumption Minimization Strategy, ECMS):搭配全域搜尋(Global Search Algorithm, GSA)將範圍內所有的可能解進行尋找,找出最小油耗時之動力分配比與變速策略。最後透過HIL模擬BFA於車輛控制單元(Vehicle Control Unit, VCU)Real-time之可行性與油耗效益驗證。基本規則庫、ECMS、BFA、Real-time,這四種情狀況在NEDC下的等效油耗:[538.9 g、209.6 g、248.9 g、253.6 g],FTP-72等效油耗:[579.2 g、291 g、316.3 g、320.38 g],再來是ECMS、BFA、Real-time三種狀況與基本規則庫相比在NEDC的能耗改善百分比是[61 %、53.8 %、52.9 %],FTP-72下運行之能耗改善百分比是[49.7 %、45.3 %、44.6 %]。其中BFA與Real-time兩者在兩個行車型態中等效油耗改善度有高達98%的相似度,皆僅次於ECMS最佳解。未來將會實施於真實之三動力源e-CVT複合動力車輛。
The purpose of this study is to develop the bacterial foraging algorithm (BFA) by applying it to the energy management/gear shifting strategy system of a three-power-source hybrid powertrain. Furthermore, this study was practical in nature, as it used the real-time simulation Hardware-in-the-Loop (HIL) to verify the algorithm’s feasibility. This study employs HIL to assess the influence that using BFA will have on the energy management and gear shifting strategy control of a three-power-source hybrid powertrain. The vehicle weighs 1,368 kilograms and its subsystems include a 43kW internal combustion engine, 30kW motor, 15kW integrated starter generator, and a 1.872kW-h Ah lithium battery. There are three primary steps for the energy management system and BFA energy management control: 1) chemotaxis, 2) reproduction, and 3) elimination-dispersal. The overall number of iterations was 30, and 80 bacteria were used carry out optimal energy management.
BFA and two control strategies were used to carry out a comparison of fuel consumption with the NEDC (New European Driving Cycle) driving pattern. 1) Rule-based management: There are five control modes, which are system preparation, battery charging mode, electric mode, hybrid power mode, and extended range mode; the engineer used his experience to determine when to set and change modes. 2) Equivalent consumption minimization strategy (ECMS): By incorporating the global search algorithm (GSA), we searched for all the scope’s possibilities in order to find the most minimal fuel consumption for power distribution ratio and gear shifting strategy. At the end of the study, we used HIL to simulate the feasibility and verify fuel consumption benefits of BFA on vehicle control units (VCU) in real time. A basic rule base, ECMS, BFA, and real-time were the four conditions for the equivalent consumption with the NEDC driving pattern: 538.9g, 209.6g, 248.9g, and 253.6g were their respective values. The equivalent consumption values with a FTP-72 driving cycle were 579.2g, 291g, 316.3g, and 320.38g. ECMS, BFA, and real-time were compared with a basic rule base when using a NEDC driving pattern to determine percentage values for improvement in energy consumption: 61%, 53.8%, and 52.9%. Percentage values for improvement in energy consumption for a FTP-72 driving cycle were 49.7%, 45.3%, and 44.6%. The improvement in equivalent consumption values for BFA and real-time for the NEDC driving pattern and FTP-72 driving cycle were 98% similar, and they were only outperformed by ECMS, which was the optimal solution. In the future, this experiment will be used to test a three-power-source e-CVT hybrid-powered vehicle.
摘要 i
Abstract ii
目 次 iv
表 次 vi
圖 次 vii
第 一 章 緒論 1
1.1 引言 1
1.2 研究動機 2
1.3 研究目的 5
1.4 研究方法 6
1.5 文獻回顧 8
1.6 論文架構 12
第 二 章 系統架構與動態模型 13
2.1 系統架構 13
2.1.1 整車架構圖 14
2.2 行車型態模塊 15
2.3 駕駛者模塊 17
2.4 縱向整車動態 18
2.5 內燃機引擎 18
2.6 高功率電動馬達 19
2.7 一體式啟動發電機 20
2.8 儲能鋰電池 21
2.9 變速系統 22
2.10 硬體嵌入式系統架構 24
2.10.1 Real -time模型 24
2.10.2 快速雛型控制器 26
2.10.3 硬體嵌入式系統(HIL) 27
第 三 章 能量管理策略 29
3.1 混合動力車模式介紹 29
3.2 基本規則庫控制策略 31
3.3 最小等效油耗法控制策略 33
3.4 細菌覓食演算法之多變數控制 36
3.4.1 優化控制之控制變數和動力分配比關係 36
3.4.2 細菌覓食演算法步驟介紹 37
3.4.3 細菌覓食演算法各項參數 40
3.4.4 細菌覓食演算法流程圖 41
第 四 章 模擬結果與討論 43
4.1 基本性能結果 43
4.2 各策略車速追蹤結果 44
4.3 控制策略動力分配比結果 47
4.4 各控制策略動力輸出結果 55
4.5 能耗比較結果 61
第 五 章 結論與未來工作 67
5.1 結論 67
5.2 未來工作與建議 69
參考文獻 71
符號彙整 77
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