This book discusses an integrative approach combining Human Factors expertise with Automotive Engineering. It develops an in-depth case study of designing a fuel-efficient driving intervention and offers an examination of an innovative study of feed-forward eco-driving advice.
Assisted Eco-Driving: A Practical Guide to the Design and Testing of an Eco-Driving Assistance System offers an examination of an innovative study of feed-forward eco-driving advice based on current vehicle and road environment status. It presents lessons, insights and utilises a documented scientific and research-led approach to designing novel speed advisory and fuel use minimisation systems suitable for combustion vehicles, hybrids and electric vehicles
The audience consists of system designers and those working with interfaces and interactions, UX, human factors and ergonomics and system engineering. Automotive academics, researchers, and practitioners will also find this book of interest.
Recenzijas
Assisted Eco-Driving addresses one of the most important topics for transportation
in these times of the threats from climate change: how can we reduce energy consumption
from vehicles. The reduction of energy consumption is important for internal
combustion engines, electric vehicles, and hybrids. For electric vehicles, it can help
to reduce range anxiety as well as reduce the demand on the wider energy production
and transmission system. The authors of this book take a truly multidisciplinary
approach, combining automotive engineering, computer science, and human factors to
show that truly novel solutions will only be forthcoming if all these perspectives are
considered together. They demonstrate this via desktop models, driving simulations,
and, ultimately through on-road studies. This book is a must-read for anyone tackling
the energy crisis in transportation and beyond.
Professor Mike Regan, University of New South Wales, Australia
This book tackles the difficult problem of reducing energy consumption in transportation
by focussing on the interlink between eco-driving and automation. Tools such
from the disciplines of engineering, computer science, and human factors are used to
characterise driver interaction with eco-driving assistance systems with the aim of
reducing energy consumption. In simulator studies and a road trial, the authors showcase
eco-driving assistance solutions to overcome the many design challenges. This
makes this book an excellent contribution to, and inspiration for, fruitful research and
design for user-energy interaction from a multidisciplinary perspective. I can recommend
this book to all those involved in designing systems to reducing energy consumption
in transportation and beyond.
Professor Thomas Franke, University of Lübeck, Germany
This book provides a practical, comprehensive, and multidisciplinary approach to the
design, development, implementation, and evaluation of eco-driving systems. A fundamental
challenge is to design vehicle interfaces that provide sufficient feedback to
drivers to reduce fuel (energy) consumption. A range of methods is used to examine
eco-driving including driver-vehicle modelling, driving simulation, and naturalistic
driving. The authors are leading and award-winning scientists from engineering,
computer science, and human factors who have pushed the boundaries of eco-driving
knowledge forward on multiple fronts. I recommend this book to all those engaged in
tackling the problems faced by human contributions to climate change.
Professor Jeff K. Caird, University of Calgary, Canada
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| Preface |
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xix | |
| About the Authors |
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| Acknowledgements |
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Chapter 1 Eco-Driving: Reducing Emissions from Everyday Driving Behaviours |
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Transportation Contribution to GHG |
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2 | (1) |
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3 | (3) |
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6 | (1) |
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7 | (4) |
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Eco-Driving Feedback in Other Sensory Modalities |
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11 | (3) |
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14 | (1) |
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14 | (5) |
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Chapter 2 Applying Cognitive Work Analysis to Understand Fuel-Efficient Driving |
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19 | (30) |
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19 | (1) |
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20 | (2) |
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22 | (1) |
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23 | (1) |
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30 | (3) |
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33 | (2) |
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Social Organisation and Cooperation Analysis |
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35 | (2) |
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Worker Competency Analysis |
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37 | (2) |
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Generating Specifications |
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39 | (5) |
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44 | (1) |
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45 | (4) |
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Chapter 3 Adaptive Driver Modelling in Eco-Driving Assistance Systems |
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49 | (22) |
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49 | (1) |
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ADAS for Safety and Eco-Driving |
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50 | (1) |
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Models of Driver Behaviour |
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51 | (1) |
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51 | (2) |
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53 | (2) |
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55 | (1) |
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55 | (1) |
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Naturalistic Data Collection |
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55 | (1) |
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56 | (1) |
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56 | (3) |
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59 | (4) |
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63 | (1) |
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Comparison of Models with Naturalistic Data |
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63 | (1) |
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Parameters Characterising Driver Behaviour |
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64 | (1) |
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65 | (1) |
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66 | (1) |
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66 | (5) |
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Chapter 4 Taming Design with Intent Using Cognitive Work Analysis |
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71 | (22) |
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71 | (1) |
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72 | (2) |
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74 | (2) |
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76 | (1) |
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76 | (1) |
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76 | (1) |
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76 | (3) |
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79 | (1) |
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Workshop 1 Waiting at Traffic Lights |
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79 | (1) |
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79 | (3) |
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Validation of the Display |
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82 | (2) |
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Workshop 2 Accelerating to Overtake |
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84 | (1) |
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84 | (3) |
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Validation of the Display |
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87 | (1) |
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88 | (1) |
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89 | (1) |
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90 | (3) |
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Chapter 5 Applying Design with Intent to Support Creativity in Developing Vehicle Fuel Efficiency Interfaces |
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93 | (24) |
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93 | (1) |
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93 | (2) |
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Design with Intent Toolkit |
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95 | (3) |
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98 | (1) |
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98 | (1) |
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98 | (1) |
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98 | (1) |
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Review of the Ideas and Final Coding |
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99 | (1) |
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Design Results & Discussion |
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100 | (3) |
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103 | (1) |
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103 | (1) |
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103 | (2) |
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105 | (1) |
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105 | (1) |
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Validation Results & Discussion |
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105 | (4) |
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109 | (1) |
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110 | (1) |
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Realising Eco-Driving through Design |
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111 | (1) |
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111 | (1) |
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112 | (5) |
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Chapter 6 Incorporating Driver Preferences into Eco-Driving Optimal Controllers |
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117 | (26) |
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117 | (2) |
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119 | (1) |
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119 | (1) |
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Models of Vehicle Following |
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120 | (2) |
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Models of Cornering Speed |
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122 | (1) |
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The Driver Satisfaction Model |
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123 | (1) |
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Development of Cost Function and Constraints |
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123 | (1) |
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Choice of Weighting Parameters |
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124 | (3) |
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Incorporation of Cornering Constraints |
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127 | (2) |
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Comparison of the Model with IDM |
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129 | (2) |
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131 | (1) |
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131 | (3) |
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134 | (1) |
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135 | (2) |
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137 | (1) |
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138 | (5) |
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Chapter 7 Receding Horizon Eco-Driving Assistance Systems for Electric Vehicles |
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143 | (26) |
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143 | (2) |
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145 | (1) |
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Vehicle Motion and Driver Modelling |
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146 | (1) |
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146 | (1) |
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146 | (1) |
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Electric Powertrain Energy Consumption Model |
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147 | (1) |
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148 | (3) |
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151 | (1) |
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Full-Horizon Optimisation |
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152 | (1) |
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152 | (1) |
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153 | (2) |
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155 | (2) |
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157 | (1) |
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157 | (1) |
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Lead Vehicle Trajectory Prediction |
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157 | (1) |
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Receding Horizon Cost Function |
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158 | (1) |
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159 | (1) |
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MPC without Terminal Cost |
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159 | (1) |
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160 | (3) |
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Test Case Under Real-World Driving Data |
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163 | (2) |
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165 | (4) |
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Chapter 8 In Simulator Assessment of a Feedforward Visual Interface to Reduce Fuel Use |
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169 | (24) |
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169 | (2) |
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171 | (1) |
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171 | (1) |
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171 | (1) |
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171 | (1) |
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Equipment and Driving Scenario |
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171 | (3) |
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174 | (1) |
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175 | (2) |
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177 | (1) |
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177 | (1) |
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178 | (1) |
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178 | (2) |
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180 | (1) |
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181 | (1) |
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182 | (1) |
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Mean Braking Deceleration |
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182 | (1) |
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183 | (1) |
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184 | (1) |
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185 | (1) |
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186 | (1) |
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Effects on Fuel Consumption |
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187 | (1) |
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Effects on Cognitive Workload |
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187 | (1) |
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188 | (1) |
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Opportunities for Future Work |
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188 | (1) |
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188 | (1) |
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189 | (4) |
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Chapter 9 Assisted versus Unassisted Eco-Driving for Electrified Powertrains |
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193 | (12) |
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193 | (1) |
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194 | (1) |
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195 | (1) |
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Parallel Hybrid Powertrain |
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196 | (1) |
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Battery Electric Powertrain |
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197 | (1) |
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197 | (1) |
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197 | (1) |
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198 | (1) |
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198 | (1) |
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199 | (2) |
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201 | (1) |
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202 | (1) |
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202 | (1) |
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203 | (2) |
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Chapter 10 Predictive Eco-Driving Assistance on the Road |
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205 | (22) |
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205 | (2) |
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207 | (1) |
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208 | (1) |
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208 | (1) |
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209 | (1) |
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209 | (1) |
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210 | (1) |
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210 | (1) |
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211 | (2) |
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Predictive Optimisation of Vehicle Speed |
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213 | (1) |
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214 | (1) |
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214 | (1) |
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215 | (1) |
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215 | (2) |
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217 | (1) |
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218 | (1) |
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218 | (2) |
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220 | (2) |
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222 | (1) |
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223 | (1) |
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224 | (1) |
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224 | (3) |
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Chapter 11 Designing for Eco-Driving: Guidelines for a More Fuel-Efficient Vehicle and Driver |
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227 | (8) |
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227 | (1) |
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227 | (3) |
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230 | (1) |
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Summary of Guidelines, by
Chapter |
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231 | (1) |
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231 | (1) |
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231 | (1) |
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232 | (1) |
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232 | (1) |
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233 | (1) |
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233 | (1) |
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233 | (1) |
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233 | (2) |
| Author Index |
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235 | (4) |
| Subject Index |
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239 | |
Dr. Craig K. Allison earned his PhD in Web Science (Psychology) from the University of Southampton in 2016. Craig received his M.Sc in Web Science from the University of Southampton in 2011, and his B.Sc in Psychology in 2009, also from the University of Southampton. Craigs research background originated within spatial psychology, before transitioning to Human Factors research. Craig has worked on numerous topics, primarily related to the aviation and automotive industries. With expertise in both qualitative and quantitative analysis, Craig has extensive experience running research trials and working in multidisciplinary teams. Craigs currently Lecturer in Psychology at Solent University, Southampton.
Dr. James M. Fleming earned the MEng and DPhil degrees in Engineering Science from the University of Oxford in 2012 and 2016 respectively, following which he spent three years as a Research Fellow at the University of Southampton before joining the Wolfson School of Mechanical, Electrical and Manufacturing Engineering at Loughborough University in September 2019. He has research interests in the theory and practice of optimal control and model predictive control, with applications to fuel- and energy- efficient driving, motorcycle stability and renewable energy.
Dr Xingda Yan earned the B.Eng. degree in automation from the Harbin Institute of Technology, Harbin, China, in 2012, and the Ph.D. degree in electrical engineering from the University of Southampton, Southampton, U.K., in 2017. He was a Research Fellow with the Mechanical Engineering Department at the University of Southampton. Xindga is currently an automotive power engineer at Compound Semiconductor Applications Catapult, Newport, UK and a visiting researcher with the Mechatronics Engineering Group, University of Southampton. His research interests include power electronics, hybrid system modelling and control, model predictive control, hybrid electric vehicle modelling, and energy management.
Dr Roberto Lot was Professor of Automotive Engineering at the University of Southampton (UK) from 2014 to August 2019 and has recently moved to the University of Padova (Italy). He earned a PhD in Mechanics of Machines in 1998 and a Master Degree cum laude in Mechanical Engineering in 1994 from the University of Padua (Italy). His research interests include both road and race vehicles, in particular dynamics and control. He has directed several national and international research projects and published more than 100 scientific papers and contributing to make our vehicles safer, faster, and more eco-friendly.
Professor Neville A. Stanton PhD, DSc, is a Chartered Psychologist, Chartered Ergonomist and Chartered Engineer. He holds the chair in Human Factors Engineering in the Faculty of Engineering and the Environment at the University of Southampton in the UK. He has degrees in Occupational Psychology, Applied Psychology and Human Factors Engineering and has worked at the Universities of Aston, Brunel, Cornell and MIT. His research interests include modelling, predicting, analysing and evaluating human performance in systems as well as designing the interfaces and interaction between humans and technology.
