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El. knyga: Vehicle Power Management: Modeling, Control and Optimization

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  • Formatas: PDF+DRM
  • Serija: Power Systems
  • Išleidimo metai: 12-Aug-2011
  • Leidėjas: Springer London Ltd
  • Kalba: eng
  • ISBN-13: 9780857297365
Kitos knygos pagal šią temą:
Vehicle Power Management: Modeling, Control and OptimizationDidesnis paveikslėlis
  • Formatas: PDF+DRM
  • Serija: Power Systems
  • Išleidimo metai: 12-Aug-2011
  • Leidėjas: Springer London Ltd
  • Kalba: eng
  • ISBN-13: 9780857297365
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Vehicle Power Management addresses the challenge of improving vehicle fuel economy and reducing emissions without sacrificing vehicle performance, reliability and durability. It opens with the definition, objectives, and current research issues of vehicle power management, before moving on to a detailed introduction to the modeling of vehicle devices and components involved in the vehicle power management system, which has been proven to be the most cost-effective and efficient method for initial-phase vehicle research and design.

Specific vehicle power management algorithms and strategies, including the analytical approach, optimal control, intelligent system approaches and wavelet technology, are derived and analyzed for realistic applications. Vehicle Power Management also gives a detailed description of several key technologies in the design phases of hybrid electric vehicles containing battery management systems, component optimization, hardware-in-the-loop and software-in-the-loop.

Vehicle Power Management provides graduate and upper level undergraduate students, engineers, and researchers in both academia and the automotive industry, with a clear understanding of the concepts, methodologies, and prospects of vehicle power management.



This book addresses the challenge of improving vehicle fuel economy and reducing emissions without sacrificing performance, reliability and durability. It details several key technologies in the design phases of hybrid electric vehicles.
1 Introduction
1(12)
1.1 Energy and Environmental Challenges
1(2)
1.2 Energy Conversion Chain for Vehicle Energy Consumption
3(3)
1.3 Fuel Efficiency
6(1)
1.4 Main Objectives of This Book
7(1)
1.5 Issues in Research on Vehicle Power Management
8(1)
1.6 Book Organization
9(4)
References
11(2)
2 Vehicle Power Management: Basic Concepts
13(36)
2.1 Vehicle Configurations
13(7)
2.1.1 Configuration of Conventional Vehicles
13(1)
2.1.2 Configuration of Electric Vehicles
14(1)
2.1.3 Configuration of Hybrid Electric Vehicles
15(5)
2.2 Vehicle Fuel Consumption and Performance
20(8)
2.2.1 Vehicle Energy Losses
20(2)
2.2.2 Vehicle Emissions
22(2)
2.2.3 Vehicle Performance and Drivability Analysis
24(2)
2.2.4 Vehicle Operation Modes
26(2)
2.3 Power Demand in Drive Cycles
28(5)
2.3.1 Definition and Standards of Drive Cycles
28(2)
2.3.2 Power Demand
30(3)
2.4 Definitions and Objectives of Vehicle Power Management
33(1)
2.5 Power Management in Conventional Vehicles
34(2)
2.6 Power Management of Hybrid Electric Vehicles
36(2)
2.7 Software Tools
38(11)
2.7.1 MATLAB/Simulink
39(1)
2.7.2 ADVISOR
40(2)
2.7.3 PSAT
42(5)
References
47(2)
3 Modeling of Vehicle Propulsion Systems
49(58)
3.1 Internal Combustion Engine
49(5)
3.1.1 Analysis of Normalized Engine Variables
50(1)
3.1.2 Expressions of Engine Efficiency
51(1)
3.1.3 State-Space Representation for ICE
52(2)
3.2 Electric Machines
54(11)
3.2.1 Brushed DC Motor
55(3)
3.2.2 Induction Motor
58(4)
3.2.3 PMSM and BLDCM
62(3)
3.3 Batteries
65(8)
3.3.1 Lead-Acid Battery
65(1)
3.3.2 NiMH Battery
66(2)
3.3.3 Lithium-Ion Battery
68(1)
3.3.4 State of Charge and Battery Capacity
69(1)
3.3.5 Equivalent Circuit
70(1)
3.3.6 Battery Efficiency
71(2)
3.4 Ultracapacitor
73(5)
3.4.1 Equivalent Circuit
74(2)
3.4.2 Ultracapacitor Efficiency
76(2)
3.5 Fuel Cell
78(8)
3.5.1 Relation Between Pressure and Flows
81(1)
3.5.2 Fuel Cell Voltage Expressions
82(2)
3.5.3 Fuel Cell Efficiency
84(2)
3.6 Flywheel
86(3)
3.6.1 Expressions for Flywheel Energy Storage and Release
87(2)
3.6.2 Flywheel Power Losses
89(1)
3.7 Gearbox
89(5)
3.7.1 Expressions for Gear Ratios
90(1)
3.7.2 Analysis of Gearbox Losses
91(1)
3.7.3 Windage Losses
91(1)
3.7.4 Oil Churning Loss
92(1)
3.7.5 Sliding Friction Losses
92(1)
3.7.6 Rolling Friction Losses
93(1)
3.8 Continuously Variable Transmission (CVT)
94(4)
3.8.1 CVT Representations
95(1)
3.8.2 CVT Power Losses
96(2)
3.9 Planetary Gears
98(9)
3.9.1 Speed Relationships
98(2)
3.9.2 Efficiency of Planetary Gear Train
100(2)
3.9.3 Optimized Control of the Planetary Based HEV
102(2)
References
104(3)
4 Analytical Approach for the Power Management of Blended Mode PHEV
107(34)
4.1 Simplified Analytical Solution
108(14)
4.1.1 Vehicle Model
108(3)
4.1.2 Control Strategy
111(4)
4.1.3 Determining the Thresholds Using Constant Speed Driving
115(1)
4.1.4 Validation of Control Parameter Table Using PSAT
116(1)
4.1.5 Implementation of the Control Strategy in Standard Driving Cycles
117(5)
4.2 Unified Analytical Solution
122(19)
4.2.1 The Total Fuel Consumption and Total Battery Energy
124(2)
4.2.2 Optimization Strategy
126(2)
4.2.3 Model Setup for the Powertrain Components
128(4)
4.2.4 Results and Discussion
132(6)
References
138(3)
5 Wavelet Technology in Vehicle Power Management
141(38)
5.1 Fundamentals of Wavelets and Filter Banks
141(14)
5.1.1 Continuous Wavelet Analysis
141(5)
5.1.2 Discrete Wavelet Transform
146(2)
5.1.3 Filter Banks
148(7)
5.2 Feasibility Analysis of Wavelets Applied to Vehicle Power Management
155(5)
5.2.1 Adverse Effects of Certain Transient Power Demand on Power Sources
155(3)
5.2.2 Applications and Advantages of Wavelets on Analyzing Transient Processes in Electrical Power Systems
158(1)
5.2.3 Power Source Combinations Available for Wavelet Applications in Vehicles
159(1)
5.3 Wavelet-Based Power Split Strategy
160(15)
5.3.1 Wavelet-Based Power Split Structure
160(5)
5.3.2 Mathematical Expressions for Wavelet-Based Power Split Algorithm
165(10)
5.4 Demonstration of Wavelet Application for Vehicle Real-Time Environment
175(4)
References
176(3)
6 Dynamic Programming and Quadratic Programming for Vehicle Power Management
179(30)
6.1 Principle of Dynamic Programming
180(3)
6.2 Hybrid Electric Vehicle Powertrain Analysis and DP Realization
183(14)
6.2.1 Dynamic Programming Realization for Series HEV
184(8)
6.2.2 Dynamic Programming Realization for Parallel HEV
192(1)
6.2.3 Dynamic Programming Realization for Series-Parallel HEV
192(5)
6.3 Efficiency Optimization of PHEV Using Quadratic Programming
197(9)
6.3.1 Architecture of the PHEV
197(2)
6.3.2 Power Flow Analysis
199(2)
6.3.3 Power Management Using QP
201(2)
6.3.4 Optimization Results and Discussion
203(3)
6.4 Summary
206(3)
References
207(2)
7 Intelligent System Approaches for Vehicle Power Management
209(50)
7.1 Fundamentals of Fuzzy Logic
209(13)
7.1.1 Fuzzy Sets
210(2)
7.1.2 Fuzzy Relations
212(1)
7.1.3 Membership Functions
213(2)
7.1.4 Defuzzification
215(2)
7.1.5 Fuzzy Rules
217(2)
7.1.6 Fuzzy Decision Making
219(2)
7.1.7 Fuzzy Inference System
221(1)
7.2 Neural Networks
222(8)
7.2.1 Neuron
223(1)
7.2.2 Feedforward Neural Network
224(1)
7.2.3 Recurrent (Feedback) Neural Network
225(1)
7.2.4 Radial Basis Function (RBF) Neural Network
226(2)
7.2.5 Supervised Learning
228(1)
7.2.6 Unsupervised Learning
228(1)
7.2.7 Properties of Neural Networks
229(1)
7.3 Application of Fuzzy Logic and Neural Network in Vehicle Power Management
230(4)
7.4 A Fuzzy Logic Controller Based on DP Results for a Parallel HEV
234(1)
7.5 Sliding Mode and Fuzzy Logic Based Powertrain Controller for a Series HEV [ 30]
234(11)
7.5.1 Introduction
235(4)
7.5.2 System Configuration and Drive Cycle Selection
239(1)
7.5.3 Fuzzy Logic Control Algorithm
240(1)
7.5.4 Establishment of Sliding Mode Control
241(2)
7.5.5 Simulation Results
243(1)
7.5.6 Discussion
244(1)
7.6 Fuzzy Logic and Sliding Mode Based Regenerative Braking Control in HEV
245(14)
7.6.1 Principle of Braking in PHEV with EMB and Regenerative Braking
245(1)
7.6.2 Distribution of Braking Force Between Regenerative Braking and EMB with Fuzzy Logic Control
246(1)
7.6.3 Antilock Braking Control
247(7)
7.6.4 Simulation Results
254(1)
7.6.5 Discussion
255(1)
References
255(4)
8 Management of Energy Storage Systems in EV, HEV and PHEV
259(28)
8.1 Introduction
259(1)
8.2 Design and Sizing of ESS
260(4)
8.3 Battery Cell Balancing
264(5)
8.4 Battery Management
269(9)
8.4.1 Parameter Monitoring
269(5)
8.4.2 Calculation of SOC
274(1)
8.4.3 Fault and Safety Protection
275(1)
8.4.4 Charge Management
276(2)
8.5 Integrated ESS
278(3)
8.6 Management of Vehicle to Grid (V2G)
281(2)
8.7 Thermal Management
283(4)
References
284(3)
9 HEV Component Design and Optimization for Fuel Economy
287(16)
9.1 Multi-Objective Evolutionary Algorithm for the Optimization of a Series HEV
288(7)
9.1.1 Control Framework of a SHEV Powertrain
290(1)
9.1.2 SHEV Parameter Optimization
291(1)
9.1.3 Optimization Results
292(3)
9.1.4 Discussion
295(1)
9.2 Parallel HEV Design Optimization Example
295(8)
References
300(3)
10 Hardware-in-the-loop and Software-in-the-loop Testing for Vehicle Power Management
303(28)
10.1 Fundamentals of HIL and SIL
303(5)
10.1.1 Components in HIL and SIL
304(3)
10.1.2 Advantages of HIL and SIL
307(1)
10.2 Data Acquisition, Monitoring and Control Units
308(12)
10.2.1 Power Control Units
308(1)
10.2.2 Parameter Measurement and Monitoring
309(2)
10.2.3 Typical Tools Available for Data Acquisition and Processing
311(4)
10.2.4 Electronic Load Applied for Simulating Load Profile
315(2)
10.2.5 Power Converter Setup for Power Split
317(3)
10.3 Global Description and Analysis for a Vehicle Power Management System
320(11)
10.3.1 System Configuration
320(1)
10.3.2 Drive Cycle Selection
321(1)
10.3.3 Control Concepts
321(1)
10.3.4 Analysis of Simulation and Experimental Results
322(5)
10.3.5 Experimental Results
327(2)
References
329(2)
11 Future Trends in Vehicle Power Management
331(12)
11.1 Existing Problems in Present Vehicle Power Management
332(2)
11.2 Future Energy Sources and Energy Storage Systems
334(2)
11.2.1 Hydrogen Internal Combustion Engine
334(1)
11.2.2 Internally Radiating Impulse Structure (IRIS) Engine
335(1)
11.2.3 Lithium Iron Phosphate Battery
335(1)
11.2.4 Nanotechnology in Batteries
336(1)
11.3 Plug-In Hybrid Electric Vehicle
336(2)
11.4 Thoughts of Future Vehicle Power Management
338(5)
References
340(3)
Index 343
Chris Mi is Associate Professor of Electrical and Computer Engineering at the University of Michigan-Dearborn. His research interests are in power electronics, motor drives, electric and hybrid vehicles, and renewable energy systems. Dr. Mi holds a BSc and an MSc degree from Northwestern Polytechnical University, Xian, China, and a PhD degree from the University of Toronto, Toronto, Canada.

Xi Zhang received BSc, MSc and PhD degrees in Electrical Engineering from Shanghai Jiaotong University, Shanghai, China, in 2002, 2004 and 2007 respectively. He joined the University of Michigan-Dearborn in September 2007 as a post-doctoral researcher. His research interests are in the power management of hybrid electric vehicles and power electronics.
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