Energy-Efficient Driving of Road Vehicles (eBook)

Toward Cooperative, Connected, and Automated Mobility

Antonio Sciarretta, Ardalan Vahidi (Autoren)

eBook Download: PDF

2019 | 1st ed. 2020
XIX, 294 Seiten
Springer International Publishing (Verlag)
978-3-030-24127-8 (ISBN)

Infos

This book elaborates the science and engineering basis for energy-efficient driving in conventional and autonomous cars. After covering the physics of energy-efficient motion in conventional, hybrid, and electric powertrains, the book chiefly focuses on the energy-saving potential of connected and automated vehicles. It reveals how being connected to other vehicles and the infrastructure enables the anticipation of upcoming driving-relevant factors, e.g. hills, curves, slow traffic, state of traffic signals, and movements of nearby vehicles. In turn, automation allows vehicles to adjust their motion more precisely in anticipation of upcoming events, and to save energy. Lastly, the energy-efficient motion of connected and automated vehicles could have a harmonizing effect on mixed traffic, leading to additional energy savings for neighboring vehicles. Building on classical methods of powertrain modeling, optimization, and optimal control, the book further develops the theory of energy-efficient driving. In addition, it presents numerous theoretical and applied case studies that highlight the real-world implications of the theory developed. The book is chiefly intended for undergraduate and graduate engineering students and industry practitioners with a background in mechanical, electrical, or automotive engineering, computer science or robotics.

Antonio Sciarretta is a professional researcher in mechanical engineering. Since 2006 he has been working at IFPEN in Rueil-Malmaison, France. He also serves as lecturer at ETH Zürich, Switzerland. His research interests focus on thermal machines and control of powertrains, and more spefically on hybrid vehicles. 

He wrote several books, including the textbook 'Vehicle Propulsion Systems' (978-3-642-35912-5) with Lino Guzzella.

Ardalan Vahidi is professor at the Department of Mechanical Engioneering of Clemson University, SC, USA. He joined the Department in 2005 after the completion of his Ph.D. at University of Michigan. His research interests focus on Energy Systems, Vehicular Systems and Autoamtic Control.

Inhalt

Preface 7
Acknowledgements 9
Contents 10
Acronyms 15
1 Energy Saving Potentials of CAVs 18
1.1 Introduction 18
1.2 Minimal-Energy Route Navigation 20
1.3 Anticipation in CAV Driving 22
1.3.1 Anticipating the State of the Road 23
1.3.2 Anticipating Signal Phase and Timing 24
1.3.3 Anticipative Car Following 27
1.3.4 Anticipative Lane Selection and Merging 30
1.4 Increased Opportunities for Cooperative Driving 32
1.4.1 Cooperative Car Following 33
1.4.2 Cooperative Lane Change and Merge 35
1.4.3 Cooperative Intersection Control 37
1.4.4 Indirect Benefits Through Traffic Harmonization 39
References 40
2 Fundamentals of Vehicle Modeling 49
2.1 Road Load 51
2.1.1 Forces Acting on Road Vehicles 51
2.1.2 Energy Requirement at the Wheels 53
2.1.3 Energy Required from the Powertrain 55
2.2 Internal Combustion Engine Vehicles 56
2.2.1 Drivetrain (Gearbox) 57
2.2.2 Engine 58
2.2.3 Fuel Energy Consumption of ICEVs 61
2.3 Electric Vehicles 62
2.3.1 Drivetrain 63
2.3.2 Motor and Inverter 63
2.3.3 Power Link 66
2.3.4 Battery 66
2.3.5 Electric Energy Consumption of EVs 68
2.4 Hybrid-Electric Vehicles 69
2.4.1 Drivetrain and Power Link 69
2.4.2 Energy Management Strategy 71
2.4.3 Energy Consumption of HEVs 73
2.5 Human-Powered Vehicles (Bicycles) 74
2.5.1 Drivetrain 74
2.5.2 Cyclist 75
2.5.3 Cycling Profiles 77
References 77
3 Perception and Control for Connected and Automated Vehicles 79
3.1 V2X Communication 79
3.2 Localization and Perception for Automated Driving 82
3.2.1 Sensors for Perception and Localization 82
3.2.2 Algorithms for Perception and Localization 85
3.2.3 Web Services 88
3.3 Planning and Control 88
3.3.1 Mission Planning 89
3.3.2 Mode Planning 89
3.3.3 Motion Planning 90
3.3.4 Motion Control 90
3.3.5 Powertrain Control 93
3.3.6 Algorithms for Planning and Control 93
References 96
4 Route and Traffic Description 99
4.1 Road Network Modeling 99
4.1.1 Road Network Topology 101
4.1.2 Road Network Attributes 102
4.1.3 Intersections 104
4.1.4 Recharge Stations 106
4.2 Microscopic Modeling of Traffic 107
4.2.1 Car-Following Models 108
4.2.2 Advanced Cruise Control Functions 111
4.2.3 Lane-Changing Models 112
4.3 Macroscopic Modeling of Traffic Flows 114
4.3.1 Fundamental Diagrams 114
4.3.2 Kinematic Models 116
4.4 Prediction of Energy Consumption on Road Networks 117
4.4.1 Operating Speed Models 118
4.4.2 Synthetic Speed Profiles 119
4.4.3 Energy Consumption for Traction 120
4.4.4 Energy Consumption for Thermal Comfort 123
References 124
5 Energy-Efficient Route Navigation (Eco-Routing) 127
5.1 Eco-Routing as a Shortest-Path Problem 127
5.1.1 Problem Formulation 127
5.1.2 Routing Algorithms 133
5.1.3 Numerical Solutions 138
5.2 Energy-Optimal Driving Range Estimation 140
5.2.1 Problem Formulation 141
5.2.2 Solution Method 141
5.2.3 Numerical Solutions 142
5.3 Practical Implementation 142
References 144
6 Energy-Efficient Speed Profiles (Eco-Driving) 146
6.1 Eco-Driving Techniques 146
6.1.1 Eco-Driving Scenarios 147
6.1.2 Eco-Driving Rules 147
6.1.3 Eco-Driving Systems 150
6.2 Eco-Driving as an Optimal Control Problem 151
6.2.1 Problem Formulation 151
6.2.2 Solution Methods 157
6.3 Maximizing Wheel-to-Distance Efficiency 163
6.3.1 Problem Formulation 163
6.3.2 Numerical Solutions 164
6.3.3 Analytical Solutions 165
6.4 Maximizing Overall Efficiency of Combustion Engine Vehicles 168
6.4.1 Problem Formulation 169
6.4.2 Numerical Solutions 170
6.4.3 Analytical Solutions 173
6.5 Maximizing Overall Efficiency of Electric Vehicles 177
6.5.1 Problem Formulation 177
6.5.2 Numerical Solutions 179
6.5.3 Analytical Solutions 180
6.6 Maximizing Overall Efficiency of Hybrid Vehicles 185
6.6.1 Problem Formulation 185
6.6.2 Numerical Solutions 186
6.6.3 Analytical Solutions 189
References 192
7 Specific Scenarios and Applications 194
7.1 Acceleration 194
7.1.1 Numerical Analysis 195
7.1.2 Analytical Approach 196
7.2 Deceleration 199
7.2.1 Numerical Analysis 199
7.2.2 Analytical Approach 200
7.3 Road Slopes 202
7.3.1 Numerical Analysis 203
7.3.2 Analytical Approach 204
7.4 Constrained Eco-Driving 205
7.5 Speed Limit 207
7.5.1 Numerical Analysis 207
7.5.2 Analytical Approach 208
7.6 Intersection 213
7.6.1 Numerical Analysis 213
7.6.2 Analytical Approach 215
7.7 Traffic Light 218
7.7.1 Numerical Analysis 219
7.7.2 Analytical Approach 221
7.8 Car Following 222
7.8.1 Numerical Analysis 223
7.8.2 Analytical Approach 225
8 Eco-Driving Practical Implementation 230
8.1 Implementation of Eco-Driving Concepts 230
8.1.1 Eco-Coaching 230
8.1.2 Predictive Cruise Control 232
8.1.3 Eco-ACC 233
8.1.4 Predictive Eco-Driving 235
8.2 Practical Issues 236
8.2.1 Speed and Path Recording 237
8.2.2 Breakpoint Detection 238
8.2.3 Leader Position Prediction 239
8.2.4 Probabilistic Traffic Light Prediction 241
8.2.5 MPC Schemes 242
8.2.6 Setting of Boundary Conditions 245
8.3 On-Board Implementation 247
8.3.1 Human-Machine Interfaces 247
8.3.2 Human Response 250
8.3.3 Automated Drive 251
References 252
9 Detailed Case Studies 255
9.1 Eco-Approach to Signalized Intersections 255
9.1.1 Numerical Approach 255
9.1.2 Simulation Results 259
9.2 Cooperative Intersection Control 263
9.2.1 Formulation as an Optimization Problem 264
9.2.2 Numerical Solution 265
9.2.3 Simulation Results 266
9.2.4 Experimental Results 269
9.3 Anticipative Car Following 270
9.3.1 Formulation as an Optimization Problem 270
9.3.2 Numerical Solution 273
9.3.3 Simulation Results 274
9.4 Anticipative Lane Selection 275
9.4.1 Formulation as an Optimization Problem 275
9.4.2 Numerical Solution 277
9.4.3 Simulation Results 278
9.5 Eco-Routing and Eco-Coaching 278
9.5.1 Experimental Setup 279
9.5.2 Experimental Results 280
References 286
A Parametric Optimization Method for Eco-Driving of ICEVs 288
B Domain of Feasibility of the Analytical Optimal Speed Profiles for EVs 292
B.1 Unconstrained Case 292
B.2 Constrained Case 296
Index 301

Produkt-Details

• Erscheint lt. Verlag: 1.8.2019
• Reihe/Serie: Lecture Notes in Intelligent Transportation and Infrastructure
• Reihe/Serie: Lecture Notes in Intelligent Transportation and Infrastructure
• Zusatzinfo: XIX, 294 p. 122 illus., 64 illus. in color.
• Sprache: englisch
• Themenwelt: Technik ► Bauwesen
• Themenwelt: Technik ► Fahrzeugbau / Schiffbau
• Themenwelt: Technik ► Nachrichtentechnik
• Schlagworte: Connected and Automated Vehicles • Connected Driving • Cooperative Driving • Eco-coaching • Eco-Driving • Eco-routing • odometry • optimal control • predictive EED • Road modeling • Satellite navigation systems • wheel-to-distance efficiency
• ISBN-10: 3-030-24127-0 / 3030241270
• ISBN-13: 978-3-030-24127-8 / 9783030241278