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E-raamat: Electric and Plug-in Hybrid Vehicle Networks: Optimization and Control

(University College Dublin, Ireland), , (University of Passau, Germany), (University of Pisa, Dept. of Energy, Systems, Territory, and Construction Engineering, Italy)
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Electric and Plug-in Hybrid Vehicle Networks: Optimization and ControlSuurem pilt
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This book explores new approaches towards the optimal switching of PHEVs between the electrical engine and the internal combustion engine (ICE), in order to optimize some utility functions of interest for municipalities (e.g., maintain the level of harmful emissions below some given thresholds in central areas, or close to sensitive points, like hospitals). The text focuses on the interface of cooperative and connected mobility and plug-in vehicles. It introduces new schemes are proposed that can be used to alleviate the inconvenience of the smart grid to charge a possibly massive number of Evs/PHEVS. The book reviews the most interesting control problems becoming prevalent in the EV-PHEV's context and provides innovative and non-conventional solutions that involve stakeholders related with the electric vehicles' business, with the goal of outlining further opportunities to make such vehicles gain favorable momentum.

Arvustused

"The book is a formidable source of sound models able to describe how near future EVs can be employed, in order to overcome their inherent gap with respect to current vehicles with internal combustion engines. The book addresses by means of a comprehensive approach; energy management of EVs, referring to road network and the grid; sharing economy and EVs (on demand access, sharing charge points); and EVs and smart cities, with particular attention to emissions." Giampiero Mastinu, Politecnico di Milano, Italy

Preface xiii
Acronyms xv
1 Introduction to Electric Vehicles
1(6)
1.1 Introduction
1(1)
1.2 Benefits and Challenges
1(4)
1.3 Contribution of the Book
5(2)
2 Disruption in the Automotive Industry
7(4)
2.1 Introduction
7(1)
2.2 Causes for Change
7(4)
I Energy Management for Electric Vehicles (EVs)
11(80)
3 Introduction to Energy Management Issues
13(4)
3.1 Introduction
13(1)
3.2 Energy Consumption in Road Networks
13(1)
3.3 Distribution of Charging Facilities
14(1)
3.4 Interaction with the Power Grid
15(2)
4 Traffic Modeling for EVs
17(16)
4.1 Introduction
17(1)
4.2 Traffic Model
17(9)
4.2.1 Basic Notions of Markov Chains and Graph Theory
17(2)
4.2.2 Basic Markovian Model of Traffic Dynamics
19(1)
4.2.3 Benefits of Using Markov Chain to Model Mobility Dynamics
20(1)
4.2.4 Energy Consumption in a Markov Chain Traffic Model of EVs
21(3)
4.2.5 Dealing with Negative Entries
24(2)
4.3 Sample Applications
26(3)
4.3.1 Traffic Load Control
26(1)
4.3.1.1 Theoretical Approach
27(1)
4.3.1.2 Decentralized Traffic Load Control
28(1)
4.4 Concluding Remarks
29(4)
5 Routing Algorithms for EVs
33(12)
5.1 Introduction
33(2)
5.2 Examples of Selfish Routing for EVs
35(5)
5.3 Collaborative Routing
40(4)
5.3.1 A Motivating Example
40(1)
5.3.2 Collaborative Routing under Feedback
41(3)
5.4 Concluding Remarks
44(1)
6 Balancing Charging Loads
45(12)
6.1 Introduction
45(1)
6.2 Stochastic Balancing for Charging
46(1)
6.3 Basic Algorithm
47(2)
6.3.1 Charging Stations
47(1)
6.3.2 Electric Vehicles
47(1)
6.3.3 Protocol Implementation
48(1)
6.4 Analysis
49(3)
6.4.1 Quality of Service Analysis: Balancing Behavior
49(1)
6.4.2 Quality of Service Analysis: Waiting Times
50(2)
6.5 Simulations
52(2)
6.6 Concluding Remarks
54(3)
7 Charging EVs
57(24)
7.1 Introduction
57(3)
7.2 EV Charging Schemes
60(5)
7.2.1 Control Architectures
60(2)
7.2.2 Communication Requirements
62(1)
7.2.3 Degree of Control Actuation
62(1)
7.2.4 Supported Services
63(1)
7.2.5 Control Methods
63(1)
7.2.6 Measurement and Forecasting Requirements
64(1)
7.2.7 Operational Time Scales
65(1)
7.2.8 Charging Policies
65(1)
7.3 Specific Charging Algorithms for Plug-In EVs
65(5)
7.3.1 Management Strategies
66(1)
7.3.2 Binary Automaton Algorithm
67(2)
7.3.3 AIMD Type Algorithm
69(1)
7.4 Test Scenarios
70(1)
7.4.1 Domestic Charging
70(1)
7.4.2 Workplace Scenario
70(1)
7.5 Simulations
71(7)
7.5.1 Binary Algorithm
72(1)
7.5.2 AIMD in a Domestic Scenario
72(5)
7.5.3 AIMD in a Workplace Scenario
77(1)
7.5.4 Binary and AIMD Algorithm Scenario
77(1)
7.6 Concluding Remarks
78(3)
8 Vehicle to Grid
81(10)
8.1 Introduction
81(1)
8.2 V2G and G2V Management of EVs
82(4)
8.2.1 Assumptions and Constraints
82(1)
8.2.2 Management of Active/Reactive Power Exchange
83(1)
8.2.3 V2G Power Flows
83(3)
8.3 Unintended Consequences of V2G Operations
86(4)
8.3.1 Utility Functions
86(2)
8.3.2 Optimization Problem
88(1)
8.3.3 Example
89(1)
8.3.4 Alternative Cost Functions
90(1)
8.4 Concluding Remarks
90(1)
II The Sharing Economy and EVs
91(48)
9 Sharing Economy and Electric Vehicles
93(4)
9.1 Introduction and Setting
93(1)
9.2 Contributions
94(3)
10 On-Demand Access and Shared Vehicles
97(22)
10.1 Introduction
97(1)
10.2 On Types of Range Anxiety
98(1)
10.3 Problem Statement
99(5)
10.3.1 Data Analysis and Plausibility of Assumptions
100(3)
10.3.2 Comments on NTS Dataset
103(1)
10.4 Mathematical Models
104(5)
10.4.1 Model 1: Binomial Distribution
105(1)
10.4.2 Model 2: A Queueing Model
106(1)
10.4.3 Two Opportunities for Control Theory
107(2)
10.5 Financial Calculations
109(7)
10.5.1 Range Anxiety Model (VW Golf vs. Nissan Leaf)
111(1)
10.5.2 Range Anxiety Model with a Range of Vehicle Sizes
112(1)
10.5.3 Financial Assumptions and Key Conclusions
113(1)
10.5.4 Long-Term Simulation
114(2)
10.6 Reduction of Fleet Emissions
116(2)
10.6.1 Case Study
116(2)
10.7 Concluding Remarks
118(1)
11 Sharing Electric Charge Points and Parking Spaces
119(20)
11.1 Introduction
119(1)
11.2 Setting: Parking Spaces
120(2)
11.3 Dimensioning and Statistics
122(7)
11.3.1 The Dimensioning Formulae
123(1)
11.3.2 Parking Data and Example
124(5)
11.4 Efficient Allocation of Premium Spaces
129(3)
11.4.1 Algorithm
129(3)
11.4.2 Example
132(1)
11.5 Turning Private Charge Points into Public Ones
132(3)
11.6 Concluding Remarks
135(4)
III EVs and Smart Cities
139(52)
12 Context-Awareness of EVs in Cities
141(2)
12.1 Introduction
141(2)
13 Using PHEVs to Regulate Aggregate Emissions (twinLIN)
143(16)
13.1 Background
145(2)
13.2 Cooperative Pollution Control
147(6)
13.2.1 The Networked Car
148(1)
13.2.2 Pollution Modeling and Simulation
149(2)
13.2.3 Mathematical Formulation
151(1)
13.2.4 Integral Control
152(1)
13.3 Simulations
153(3)
13.3.1 Simulation Set-up
153(1)
13.3.2 Disturbance Rejection
153(2)
13.3.3 Extensions
155(1)
13.4 Concluding Remarks
156(3)
14 Smart Procurement of Naturally Generated Energy (SPONGE)
159(20)
14.1 Mathematical Formulation
161(2)
14.2 Practical Implementation
163(5)
14.2.1 SPONGE Simulation Results
165(3)
14.3 Specific Use Case: SPONGE for Plug-in Buses
168(5)
14.3.1 Sponge Bus Problem Formulation
169(2)
14.3.2 Construction of the Utility Functions
171(1)
14.3.2.1 Electrical Energy Consumption
171(1)
14.3.2.2 Saving of CO2
171(1)
14.3.2.3 Utility Functions fi
172(1)
14.4 Optimization Problem
173(2)
14.5 Simulation Results
175(1)
14.6 Concluding Remarks
176(3)
15 An Energy-Efficient Speed Advisory System for EVs
179(12)
15.1 Introduction
179(1)
15.2 Power Consumption in EVs
180(2)
15.3 Algorithm
182(3)
15.4 Simulation
185(1)
15.4.1 Consensus and Optimality
185(1)
15.5 Concluding Remarks
186(5)
IV Platform Analytics and Tools
191(22)
16 E-Mobility Tools and Analytics
193(2)
16.1 Introduction
193(2)
17 A Large-Scale SUMO-Based Emulation Platform
195(10)
17.1 Introduction
195(1)
17.2 Prior work
196(2)
17.3 Description of the Platform
198(3)
17.4 Sample Application
201(1)
17.5 Concluding Remarks
201(4)
18 Scale-Free Distributed Optimization Tools for Smart City Applications
205(8)
18.1 Introduction
205(1)
18.2 The AIMD Algorithm
205(2)
18.3 Optimal Resource Allocation
207(2)
18.4 Scale-Free Advantages of AIMD
209(1)
18.5 Passivity
210(2)
18.6 Concluding Remarks
212(1)
Postface 213(2)
References 215(20)
Index 235
Emanuele Crisostomi received the Bachelors degree in computer science engineering, the Masters degree in automatic control, and the Ph.D. degree in automatics, robotics, and bioengineering, from the University of Pisa, Italy, in 2002, 2005, and 2009, respectively. He is currently an Assistant Professor of electrical engineering with the Department of Energy, Systems, Territory and Constructions Engineering, University of Pisa. His research interests include control and optimization of large-scale systems, with applications to smart grids and green mobility networks.



Robert Shorten is Professor of Control Engineering and Decision Science at University College Dublin. He has held positions in industry at Daimler-Benz Research and IBM Research (where he led the optimization and control activities at the Smart Cities Research Lab), as well as a number of positions in academia. He is a co-founder of the Hamilton Institute at Maynooth University, Ireland and has also held a Visiting Professorship at TU Berlin. Prof. Shortens research spans a number of areas. He has been active in computer networking, automotive research, collaborative mobility (including smart transportation and electric vehicles), as well as basic control theory and linear algebra. His main field of theoretical research has been the study of hybrid dynamical systems. He is currently the EUCA representative for Ireland, and has held a number of editorial roles. He is a co-author of the recently published book: AIMD Dynamics and Distributed Resource Allocation (Corless, King, Shorten, Wirth), SIAM 2016.



Sonja Stüdli received her Bachelors degree in electrical engineering and her Masters degree in mechanical engineering from the ETH Zurich, Switzerland in 2008 and 2011, respectively. She received her Ph.D. degree in electrical engineering from the University of Newcastle, Australia, in 2016. She is currently working at the University of Newcastle as a research assistant in the School of Electrical Engineering and Computing. Her research interests include load management and smart grid operations, networked systems, including vehicle platooning, and distributed control.



Fabian Wirth received the Ph.D. degree in mathematics from the Institute of Dynamical Systems, University of Bremen, Bremen, Germany, in 1995. He has since held positions in Bremen, at the Centre Automatique et Systémes of Ecole des Mines Fontainebleau, France, and was Visiting Professor at the University of Frankfurt. From 2004 to 2006, he was with the Hamilton Institute at NUI Maynooth, Ireland. He is currently Professor of Dynamical Systems at the Faculty of Computer Science and Mathematics, University of Passau, Germany. Besides the modeling and analysis of communication networks his current interests include stability theory, switched systems, queueing theory and largescale systems with applications in communications and logistics.
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