منبع مقاله درباره سلسله مراتب، tradition

0.908 0.897 0.88 0.86 0.84 0.84
0.2 0.894 0.91 0.904 0.88 0.86 0.84 0.84 0.84
0.2 0.893 0.904 0.89 0.86 0.84 0.84 0.84 0.84
0.2 0.901 0.9 0.87 0.84 0.84 0.84 0.84 0.84
0.2 0.902 0.887 0.85 0.84 0.84 0.84 0.84 0.84
0.2 0.891 0.87 0.84 0.84 0.84 0.84 0.84 0.84
0.2 0.883 0.86 0.84 0.84 0.84 0.84 0.84 0.84
0.2 0.881 0.84 0.84 0.84 0.84 0.84 0.84 0.84
0.2 0.878 0.84 0.84 0.84 0.84 0.84 0.84 0.84
0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2];
2) منحنی بازده موتور احتراقی[5] :
شکل(4-ض3) منحنی بازده موتور احتراقی
fc_map_spd=[104.5 149.2 220.9 292.5 364.1 435.7 507.4 552.2 596.9]*30/? (rpm);
fc_max_trq=[61 67.6 73.7 78.5 80.9 77.3 76.2 73.3 68.7]; (N.m)
3) مشخصات خودرو :
جدول (3-ض3)مشخصات خودرو
0.2
بهره کاهشی دیفرانسیل (Gfd)
0.9Kg.m2
ممان اینرسی محور (Jax)
0.9Kg.m2
ممان ایترسی سیستم انتقال (Jtr)
1000Kg
جرم خودرو (Mv)
0.001
ثابت (Kr)
0.256m
شعاع چرخ (rw)
1.75m2
سطح جلویی خودرو(Av)
1.2Kg/m3
چگالی هوا(ro)
0.315
ضریب اصکاک غلطشی (Cd)
3)مشخصات باتری :
جدول (4-ض3)مشخصات خودرو
25Ah
ماکزیمم ظرفیت باتری (Qmax)
0.7
حالت شارژ اولیه (SOC0)
مقالات فارسی و انگلیسی تهیه شده :
[1] S.M.T. Bathaee, S.R.Emami, A.Hajizadeh,” Dynamic Modeling and Intelligent
Control of Hybrid Electric Vehicle”
– پذیرفته شده در کنفرانس IASTED MIC2005 (Modelling,Identification and Control ) در کشور اتریش (به علت عدم حمایت مالی از سوی دانشگاه، ارائه نگردید. )
– پذیرفته شده در کنفرانس
The 9th World Multi-Confernce on Systematic, Cybernetics and Informatics (WMCI2005) July10-13,2005 Orlando,Florida, USA
– پذیرفته شده در کنفرانس سیستم های هوشمند (CIS2004) دانشگاه کرمان
[2] S.M.T. Bathaee, A.Hajizadeh, S.R.Emami, “A Fuzzy-based Supervisory Robust
Control For Parallel Hybrid Electric Vehicles”
– ارسال برای کنفرانس IASTED
(Automation, Control and Application ~ ACIT-ACA 2005~)
[3] سید روح اله امامی میبدی، امین حاجی زاده گستج ، سید محمد تقی بطحایی ” آشنایی با گروه تحقیقاتی خودرو های هایبرید برقی دانشگاه صنعتی خواجه نصیر الدین طوسی و مدلسازی و کنترل سلسله مراتبی خودرو هایبرید برقی”
– ارسال برای کنفرانس بین المللی مهندسی برق – دانشگاه زنجان
[4] S.M.T. Bathaee, A.Hajizadeh, S.R.Emami, “Control Strategy for Parallel Hybrid
Electric Vehicles by Fuzzy Clustering and Neuro-Fuzzy Model”
– آماده سازی برای ارسال به کنفرانسها و مجلّات معتبر
Abstract
Control strategies for hybrid electric vehicles are usually aimed at several simultaneous objectives. The primary one is usually the minimization of the vehicle fuel consumption, while also attempting to minimize engine emissions and maintaining or enhancing driveability. Regardless of the topology of the vehicle, the essence of the HEV control problem is the instantaneous management of the power flows from more devices to achieve the overall control objectives. One important characteristic of this generic problem is that the control objectives are mostly integral in nature (fuel consumption and emission per mile of travel), or semi-local in time like driveability, while the control actions are local in time. Furthermore, the control objectives are often subject to integral constraints, such as nominally maintaining the battery state-of-charge (SOC). The global nature of both objectives and constraints do not lend itself to traditional global optimization technique, because the main problem with global optimization index is whole of driving cycle should be predetermined and real time control strategy is not implemented simply. A common method to control of the complex dynamic systems with many uncertainties is designing some different of local controllers each for a specific operating area or determined objects and then designing of a switching strategy through the subsystems to achieve the global objectives of the system. In this thesis, the hierarchical control structure has been investigated due to the complexity of hybrid electric vehicle powertrain. From the view point of hierarchy, the switching strategy relates to upper hierarchy and plays the key role in systems operating. Then for each subsystems of hybrid electric vehicle, itself local controller has been designed and after that in order to achieve the operating objectives, switching strategy through subsystems for the real time control strategy has been designed.
Keywords: Hybrid Electric Vehicles, Real Time Control Strategy, Hierarchical Structure, Hybrid System, Switching Strategy.
K.N.TOOSI UNIVERSITY OF TECHNOLOGY
ELECTRICAL ENGENEERING COLLEGE
Submitted in partial fulfillment of the requirements
For the Master degree of science in Electrical Engineering
Title:
Hierarchical Real Time Control Strategy for Hybrid Electric Vehicles
BY:
Amin Hajizadeh Gastaj
Under the supervision of
Dr. S.M.T.Bathaee
February 2005
1-Continuos Variable Transmission
2 State Of Charge (SOC)
3 Rule-Base
4 Logic
5 global
6 local
7 US EPA HighWay Fuel Economy certification Test (HWFET)
8 Offline
9 Online
10 latching electrical energy
11 Real time
12 Driveline
13 EPA Urban Dynamometer Driving Schedule for Heavy-Duty Vehicles
14 Multi-mode
15 Sub-optimal
16 Driving Pattern Recegnition (DPR)
17 Adaptive Dynamic Programming (ADP)
18 Forward
19 throttle
20 Parallel Hybrid Electric Vehicle(PHEV)
21 Driver power command
22 scaling factor
23 Finite State Machine
24 Hierarchical Structure
25 reset
26 Jumping
27 Supervisory Control
28 Fuel Economy
29 Vehicle System Controller(VSC)
30 benchmark
31 manifold
32 throttle
33 Benchmark
34 Adaptive Neuro-Fuzzy Inference System
35 New York City Cycle(NYCC)
36 Current Conditions
37 Hybrid Dynamical Control
38 Absolute
39 Nonholonomic
40 Rule base
41 Linguistic Variables
42 Inference
43 Difuzzification
44 Center of gravity
45 Weighted Average
46 Adaptive Neuro Fuzzy Inference System
47 Back propagation
48 Sugeno
49 Weight Average
50 Linear
51 constant
52 Grid partitioning
53 Subtractive Clustering
54 Simulink
55 Finite State Machine
56 Event Driven System
57 States
58 Conditions
59 Events
60 Decision Points
61 Default transition
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مطلب مرتبط :   مقاله رایگان درموردکسب و کار، عوامل موثر، عملکرد سازمان

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