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요약문 4
Ⅰ. 서론 12
1.1. 연구배경 및 필요성 12
1.2. 연구목적 및 내용 17
Ⅱ. 연구 내용 및 방법 19
2.1. 하이브리드 및 유로-6. 경유차 기술 특성 19
2.2. 시험자동차 23
2.3. 주행경로 설정 24
2.4. 이동식 배출가스 시험장치(PEMS) 26
2.5. PEMS 측정데이터 신뢰성 29
2.6. 실제도로 배출가스 측정데이터 분석방법 30
2.6.1. 이동평균구간(MAW, Moving Averaging Windows) 분석방법 30
2.6.2. 가중 평균 배출량(Weighted Emission) 분석방법 33
2.7. 하이브리드 자동차용 동력시스템 모델 개발 35
2.7.1. 모델 구성에 필요한 input data 39
2.7.2. AVL CRUISE 내 Parallel Hybrid Model 모델 예시 분석 42
2.7.3. 병렬형 하이브리드 차량 동력흐름 분석 44
2.7.4. 차량동역학 기반 Parallel Hybrid 초기 개발 모델 45
2.7.5. 차량동역학 기반 Parallel Hybrid 개발 모델 - Fuzzy logic 기반 개선모델 47
2.8. 휘발유 및 경유차에 대한 기존 모델 개발 57
2.8.1. 고속 영역에서의 lock-up clutch 적용 기술 57
2.8.2. WLTP 제안 연비 보정법 적용을 통한 연비 편차 저감 기술 분석 58
2.9. 주행모드 및 실제 도로 주행 조건에서의 대기오염물질 및 CO₂ 예측 65
Ⅲ. 시험 결과 및 고찰 70
3.1. 차대동력계 시험 결과 70
3.2. 이동평균구간(Moving Averaging Windows) 분석결과 71
3.3. 가중평균 배출량(Weighted Emission Method) 분석결과 77
3.4. RDE-LDV 데이터 분석방법 비교 81
3.5. 주위온도 변화에 따른 실제도로 NOx 배출량 83
3.6. 동력시스템 시뮬레이션 결과 84
3.7. 휘발유 및 경유차에 대한 기존 모델 연구 결과 92
3.8. 주행모드 및 실제도로 주행 조건에서의 대기오염물질 및 CO₂ 예측 연구결과 101
Ⅳ. 결론 105
Ⅴ. 참고문헌 107
Table 1-1. Korean emission standards for light duty vehicles 12
Table 2-1. Specification of test vehicles 23
Table 2-2. Description on PEMS test routes 24
Table 2-3. Specifications of PEMS (Model OBS-2000) 27
Table 2-4. Specifications of PEMS (Model ECO-STAR) 28
Table 2-5. Determining of engine generation power based on Fuzzy logic control 49
Table 2-6. Vehicle specification 59
Table 2-7. Information of NIER and HWFET driving moed 60
Table 2-8. Power system model developed vehicle list of last year and this year 66
Table 2-9. Creating a virtual vehicle dynamics model - Appling SULEV and EURO-6 vehicle emission map data 69
Table 3-1. NOx emissions averaged vehicle speed distribution (Combined 1 route) 72
Table 3-2. NOx emissions averaged vehicle speed distribution (Combined 2 route) 72
Table 3-3. 50% C.P and 90% C.P (MAW) for NOx emissions (Combined 1 route) 74
Table 3-4. 50% C.P and 90% C.P (MAW) for NOx emissions (Combined 2 route) 74
Table 3-5. 50% C.P and 90% C.P (MAW) for RDE emissions (Combined 2 test route) 76
Table 3-6. Weighted emission and 50% C.P for NOx emission (Combined 1 route) 82
Table 3-7. Weighted emission and 50% C.P for NOx emission (Combined 2 route) 83
Table 3-8. Verifying fuel economy prediction accuracy of advanced parallel hybrid model 87
Table 3-9. Advancing fuel economy prediction accuracy by applying lock-up clutch characteristics and effective rolling radius 93
Table 3-10. Simulation cases: generating a vehicle driving patterns in allowable limits error range (+/-3.2km/h) 98
Table 3-11. Simulation cases: generating a inaccurate road load simulation conditions 99
Fig. 1-1. Share of NOx by 2012(Korea) and by 2013(Europe) 13
Fig. 1-2. Atmospheric environment, Seoul weather information by year, 2014 13
Fig. 1-3. Recent Trends in Roadside NOx Concentration in UK (2010) 14
Fig. 1-4. Real-driving NOx emissions for light-duty vehicles (Europe) 15
Fig. 1-5. Real-driving NOx emissions for Korean light-duty vehicles 15
Fig. 1-6. Analysis of Vehicle Simulation 16
Fig. 1-7. Structure to conduct studies 18
Fig. 2-1. stage operating states of hybrid vehicles 19
Fig. 2-2. Functional classification of the hybrid vehicles 20
Fig. 2-3. Functional Principle of Urea-SCR 21
Fig. 2-4. Functional Principle of LNT 22
Fig. 2-5. NOx storage reduction (LNT) aftertreatment reaction 22
Fig. 2-6. PEMS test routes for real driving emission measurement 24
Fig. 2-7. Relative positive acceleration of short trip in real-road PEMS test routes, NEDC, CVS-75 and WLTC 25
Fig. 2-8. Typical speed distributions of PEMS test routes 26
Fig. 2-9. Schematics of PEMS for real driving emission measurement 27
Fig. 2-10. Photographs of PEMS installation to test vehicles 28
Fig. 2-11. Correlation of emission results between PEMS and CVS equipment 29
Fig. 2-12. Comparison of real time NOx and CO2 emission rate between PEMS and CVS 30
Fig. 2-13. Concept of moving averaging window method 31
Fig. 2-14. Example of CO2 characteristics curve with vehicle speed for moving averaging window analysis 32
Fig. 2-15. Example of cumulative NOx emissions expressed as deviation ratios for 50% C.P and 90% C.P during normal driving conditions (MAW) 33
Fig. 2-16. Concept of weighted emissions method (power bin) proposed by EU 34
Fig. 2-17. Normalization process of the vehicle power pattern(Pe) into a normalized power(Pnorm) frequency 34
Fig. 2-18. Normalized standard power frequencies proposed by EC-JRC 35
Fig. 2-19. AVL CRUISE program GUI 36
Fig. 2-20. Main forces acting on vehicle 36
Fig. 2-21. AVL CRUISE program calculation process 38
Fig. 2-22. Structure of a parallel-type hybrid vehicle 39
Fig. 2-23. Example of fuel consuption map & THC emission map data 40
Fig. 2-24. Example of engine maximum torque curve characteristic data 40
Fig. 2-25. Principles of Smallest error square method 40
Fig. 2-26. Example of CVT and gear box data 42
Fig. 2-27. Example of AVL CRUISE Parallel Hybrid vehicle model configuration 43
Fig. 2-28. Main data connection of Hybrid vehicle model 43
Fig. 2-29. Power train structure of parallel Hybrid vehicle model 44
Fig. 2-30. Operating conditions of parallel hybrid vehicle 45
Fig. 2-31. The initial development model of parallel hybrid vehicle 46
Fig. 2-32. Fuzzy logic based Parallel Hybrid vehicle model calculation flow 48
Fig. 2-33. Example of optimum engine and motor operation condition region 49
Fig. 2-34. Determination of vehicle driving force condition based on Fuzzy logic (Pdrive) 50
Fig. 2-35. Determination of battery SOC condition based on Fuzzy logic 51
Fig. 2-36. Determination of motor speed condition based on Fuzzy logic 52
Fig. 2-37. Determining battery charging quantity by using extra engine power 53
Fig. 2-38. Main calculation modules composing parallel hybrid model 54
Fig. 2-39. Extra engine power for specific driving point 55
Fig. 2-40. Generation of matlab simulink DLL file 56
Fig. 2-41. Application torque converter lock-up clutch characteristic data 57
Fig. 2-42. Expansion of lock-up clutch application area [3] 58
Fig. 2-43. Velocity profile of NIER and HWFET driving moed 59
Fig. 2-44. Velocity deviation within speed tolerance (+/-3.2km/h) 61
Fig. 2-45. Actual road load conditions of test vehicles and generation of inaccurate road load condition 63
Fig. 2-46. Velocity profile of WLTP mode 64
Fig. 2-47. Willans line Generation by using WLTP mode test data 65
Fig. 2-48. Analysis of average emission results at various engine operation condition 66
Fig. 2-49. NOx emission map of gasoline and diesel vehicle 67
Fig. 2-50. Velocity profile of with 6 types of real driving conditions 68
Fig. 3-1. Chassis dynamometer test results with NEDC, WLTC and CVS-75 driving cycles 70
Fig. 3-2. on-road NOx emissions averaged vehicle speed for combined 1 and combined 2 test routes 71
Fig. 3-3. on-road NOx emissions and deviation ratio with PEMS test routes 73
Fig. 3-4. on-road NOx emissions and cumulative frequency of NOx emissions expressed as deviation ratio on combined 1 and combined 2 test routes 73
Fig. 3-5. on-road RDE emissions and cumulative frequency expressed as deviation ratio on combined 2 test routes 75
Fig. 3-6. Characteristics of Prius-pi(PHEV) MAW analysis 76
Fig. 3-7. Normalization and denormalization of power frequencies with Euro-6 vehicle 77
Fig. 3-8. Weighted NOx emission rate as normalized power bin and target power bin on Combined 1 route 78
Fig. 3-9. Weighted NOx emission rate as normalized power bin and target power bin on Combined 2 route 79
Fig. 3-10. Weighted RDE emission rate as normalized power bin and target power bin on Combined 1 test route 80
Fig. 3-11. Willans line calculated test at a diesel vehicles 81
Fig. 3-12. Comparison of weighted emissions(power bin) results with 50% C.P(MAW) results on combined 1 and combined 2 test routes 82
Fig. 3-13. On-road NOx emissions with ambient temp.(Combined 1 route) 84
Fig. 3-14. Verifying prediction accuracy of engine operating points by using initial hybrid model simulation results (NIER07 mode) 84
Fig. 3-15. Predictions of vehicle driving conditions and battery SOC by using initial hybrid model simulation (NIER07 mode) 85
Fig. 3-16. Verifying prediction accuracy of driving point and SOC by using Fuzzy logic based hybrid model simulation (advanced) 86
Fig. 3-17. hold on control logic generation based on Matlab simulink program 88
Fig. 3-18. Application of hold on control logic 89
Fig. 3-19. Advancing prediction accuracy of vehicle operating condition applying hold on 89
Fig. 3-20. Example of power flow at regenerative braking condition 90
Fig. 3-21. Example of power flow at low load condition (only using motor) 91
Fig. 3-22. Example of power flow at high load condition 91
Fig. 3-23. Gear box shifting map - lock-up clutch area 92
Fig. 3-24. Advancing prediction accuracy of engine operating conditions by applying lock-up clutch characteristics and effective rolling radius 93
Fig. 3-25. Verifying prediction accuracy of road load force acting on vehicle 94
Fig. 3-26. Verifying prediction accuracy of engine operating conditions 95
Fig. 3-27. Verifying prediction accuracy of engine operating conditions 96
FIg. 3-28. CO₂ prediction results - deviation in target speed and actual vehicle test speed 97
Fig. 3-29. Detailed vehicle performance characteristic prediction results at NIER03 and 14 mode - Diesel1 99
Fig. 3-30. CO₂ prediction results - deviation in target road condition and actual road load condition 100
Fig. 3-31. Generating a NOx emission map by using REAP program and chassis dynamometer test data 102
Fig. 3-32. CO₂ and NOx prediction results based on real driving condition 103
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