Electric and Plug-in Hybrid Vehicle Networks

Optimization and Control

Emanuele Crisostomi, Robert Shorten, Sonja Stüdli, Fabian Wirth

• Publisher: CRC Press
• Year: 2017
• Print ISBN: 9781498744997
• eBook ISBN: 9781351647915
• Topic: Technology & Engineering
• Subtopic: Electrical Engineering & Telecommunications
• Index: Technology & Engineering

This book explores the behavior of networks of electric and hybrid vehicles. The topics that are covered include: energy management issues for aggregates of plug-in vehicles; the design of sharing systems to support electro-mobility; context awareness in the operation of electric and hybrid vehicles, and the role that this plays in a Smart City context; and tools to test and design massively large-scale networks of such vehicles. The book also introduces new and interesting control problems that are becoming prevalent in the EV-PHEV's context, as well as identifying some open questions. A particular focus of the book is on the opportunities afforded by networked actuation possibilities in electric and hybrid vehicles, and the role that such actuation may play in air-quality and emissions management.

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Contents

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Preface

Acronyms

1 Introduction to Electric Vehicles

1.1 Introduction

1.2 Benefits and Challenges

1.3 Contribution of the Book

2 Disruption in the Automotive Industry

2.1 Introduction

2.2 Causes for Change

I Energy Management for EVs

3 Introduction to Energy Management Issues

3.1 Introduction

3.2 Energy Consumption in Road Networks

3.3 Distribution of Charging Facilities

3.4 Interaction with the Power Grid

4 Traffic Modeling for EVs

4.1 Introduction

4.2 Traffic model

4.2.1 Basic notions of markov chains and graph theory

4.2.2 Basic markovian model of traffic dynamics

4.2.3 Benefits of using markov chain to model mobility dynamics

4.2.4 Energy consumption in a markov chain traffic model of EVs

4.2.5 Dealing with negative entries

4.3 Sample applications

4.3.1 Traffic load control

4.3.1.1 Theoretical approach

4.3.1.2 Decentralized traffic load control

4.4 Concluding remarks

5 Routing Algorithms for EVs

5.1 Introduction

5.2 Examples of selfish routing for EVs

5.3 Collaborative routing

5.3.1 A motivating example

5.3.2 Collaborative routing under feedback

5.4 Concluding remarks

6 Balancing charging loads

6.1 Introduction

6.2 Stochastic balancing for charging

6.3 Basic algorithm

6.3.1 Charging stations

6.3.2 Electric vehicles

6.3.3 Protocol implementation

6.4 Analysis

6.4.1 Quality of service analysis: Balancing behavior

6.4.2 Quality of service analysis: Waiting times

6.5 Simulations

6.6 Concluding remarks

7 Charging EVs

7.1 Introduction

7.2 EV Charging Schemes

7.2.1 Control Architectures

7.2.2 Communication Requirements

7.2.3 Degree of Control Actuation

7.2.4 Supported Services

7.2.5 Control Methods

7.2.6 Measurement and Forecasting Requirements

7.2.7 Operational Time Scales

7.2.8 Charging Policies

7.3 Specific Charging Algorithms for Plug-In EVs

7.3.1 Management Strategies

7.3.2 Binary Automaton Algorithm

7.3.3 AIMD Type Algorithm

7.4 Test Scenarios

7.4.1 Domestic Charging

7.4.2 Workplace Scenario

7.5 Simulations

7.5.1 Binary Algorithm

7.5.2 AIMD in a Domestic Scenario

7.5.3 AIMD in a Workplace Scenario

7.5.4 Binary and AIMD Algorithm Scenario

7.6 Concluding Remarks

8 Vehicle to Grid

8.1 Introduction

8.2 V2G and G2V Management of EVs

8.2.1 Assumptions and constraints

8.2.2 Management of active/reactive power exchange

8.2.3 V2G power flows

8.3 Unintended consequences of V2G operations

8.3.1 Utility functions

8.3.2 Optimization problem

8.3.3 Example

8.3.4 Alternative cost functions

8.4 Concluding remarks

II The Sharing Economy and EVs

9 Sharing Economy and Electric Vehicles

9.1 Introduction and Setting

9.2 Contributions

10 On-Demand Access and Shared Vehicles

10.1 Introduction

10.2 On Types of Range Anxiety

10.3 Problem Statement

10.3.1 Data Analysis and Plausibility of Assumptions

10.3.2 Comments on NTS Dataset

10.4 Mathematical Models

10.4.1 Model 1: Binomial Distribution

10.4.2 Model 2: A Queueing Model

10.4.3 Two Opportunities for Control Theory

10.5 Financial Calculations

10.5.1 Range Anxiety Model (VW Golf vs. Nissan Leaf)

10.5.2 Range Anxiety Model with a Range of Vehicle Sizes

10.5.3 Financial Assumptions and Key Conclusions

10.5.4 Long-Term Simulation

10.6 Reduction of Fleet Emissions

10.6.1 Case Study

10.7 Concluding Remarks

11 Sharing electric charge points and parking spaces

11.1 Introduction

11.2 Setting: Parking spaces

11.3 Dimensioning and statistics

11.3.1 The dimensioning formulae

11.3.2 Parking data and example

11.4 Efficient allocation of premium spaces

11.4.1 Algorithm

11.4.2 Example

11.5 Turning private charge points into public ones

11.6 Concluding Remarks

III EVs and Smart Cities

12 Context-Awareness of EVs in Cities

12.1 Introduction

13 Using PHEVs to Regulate Aggregate Emissions (twinLIN)

13.1 Background

13.2 Cooperative pollution control

13.2.1 The networked car

13.2.2 Pollution modeling and simulation

13.2.3 Mathematical formulation

13.2.4 Integral control

13.3 Simulations

13.3.1 Simulation set-up

13.3.2 Disturbance rejection

13.3.3 Extensions

13.4 Concluding remarks

14 Smart Procurement of Naturally Generated Energy (SPONGE)

14.1 Mathematical formulation

14.2 Practical implementation

14.2.1 SPONGE simulation results

14.3 Specific use case: Sponge for plug-in buses

14.3.1 Sponge bus problem formulation

14.3.2 Construction of the utility functions

14.3.2.1 Electrical energy consumption

14.3.2.2 Saving of CO₂

14.3.2.3 Utility functions f_i

14.4 Optimization problem

14.5 Simulation results

14.6 Concluding remarks

15 An Energy-Efficient speed Advisory System for EVs

15.1 Introduction

15.2 Power Consumption in EVs

15.3 Algorithm

15.4 Simulation

15.4.1 Consensus and Optimality

15.5 Concluding Remarks

IV Platform Analytics and Tools

16 E-Mobility Tools and Analytics

16.1 Introduction

17 A Large-Scale SUMO-Based Emulation Platform

17.1 Introduction

17.2 Prior work

17.3 Description of the Platform

17.4 Sample Application

17.5 Concluding Remarks

18 Scale-free distributed optimization tools for smart city applications

18.1 Introduction

18.2 The AIMD Algorit...

Table of contents

• Cover
• Halftitle Page
• Title Page
• Copyright
• Table of Contents