Electric Vehicle Integration via Smart Charging: Technology, Standards, Implementation, and Applications

Preface
Contents
Editors and Contributors
About the Editors
Contributors
1 Standardised Domestic EV Smart Charging for Interoperable Demand Side Response: PAS 1878 and 1879
1.1 Introduction
1.1.1 Purpose of Demand-Side Response
1.1.2 Status Quo, Challenges and Outlook
1.1.3 Assumptions of the Standardised Framework
1.1.4 Overview of Operation
1.1.5 Underpinning Principles
1.1.6 Scope
1.2 System Architecture
1.2.1 Functional Architecture
1.2.1.1 Compatibility with International Standards
1.2.1.2 Key Requirements
1.2.2 Descriptions of Functional Devices and Entities
1.2.2.1 DSR Service Provider (DSRSP)
1.2.2.2 Customer Energy Manager (CEM)
1.2.2.3 Home Energy Management System (HEMS)
1.2.2.4 Chargepoint (The ESA Functionality)
1.2.2.5 Chargepoint Manufacturer
1.2.2.6 Electric Vehicle (EV)
1.2.2.7 System Operators and Market Participants (SOMPs)
1.2.2.8 Electricity Supplier
1.2.2.9 National Electricity Regulator
1.2.3 Descriptions of Interfaces
1.2.3.1 Interface A
1.2.3.2 Interface B
1.2.3.3 Manufacturer Interface
1.2.3.4 Interface C
1.2.3.5 Interface M
1.2.3.6 External System Interface
1.2.3.7 Chargepoint and EV Interface
1.3 Operation Framework
1.3.1 Operation Process and DSR Modes
1.3.1.1 (a) Consumer Registration with the DSRSP
1.3.1.2 (b) Discovery, Authentication and Device Registration
1.3.1.3 (c) Initialisation
1.3.1.4 (d) Normal Operation
1.3.1.5 (e) De-registration
1.3.2 Power Profiles for DSR
1.3.2.1 Flexibility Offers as Power Profiles
1.3.2.2 Frequency Response Indicator
1.3.2.3 Information Required for Power Profiles
1.3.2.4 Power Reporting
1.3.3 Cyber Security Approach
1.4 EV Smart Charging for DSR Services
1.4.1 Mapping to IEC/ISO Standards for EVs
1.4.2 Example Use Case: EV Implementation for DSR Services
1.4.2.1 Registration
1.4.2.2 Normal Operation
1.4.2.3 De-registration
Bibliography
2 The Concept of Li-Ion Battery Control Strategies to Improve Reliability in Electric Vehicle (EV) Applications
2.1 Introduction
2.2 Battery Management System (BMS)
2.3 Battery Fault Detection
2.4 Battery State-of-Function Estimation
2.4.1 Battery SoH Estimation
2.4.2 Battery SoC Estimation
2.5 Conclusions
References
3 Recognition of Electric Vehicles Charging Patterns with Machine Learning Techniques
3.1 Introduction
3.1.1 Electric Vehicles
3.1.1.1 Taxonomy of EVs
3.1.1.2 EV Integration's Benefits
3.1.1.3 Challenges and Problems of EVs High Penetration
3.1.2 Data Challenges of the High Penetration of the EVs
3.1.3 Energy Management of the EVs' Smart Charging
3.1.3.1 Concepts and Applications
3.1.3.2 Challenges and Opportunities
3.1.4 Literature Review on EV Integration
3.2 Identification of EV Charging Patterns
3.2.1 Clustering Concept and Principles
3.2.1.1 Concept of the Clustering
3.2.1.2 Principles of the Clustering
3.2.2 Clustering of the Charging Patterns
3.2.3 Utilization of ML Algorithms for Clustering the Charging Patterns
3.2.3.1 Unsupervised Learning
3.2.3.2 Supervised Learning
3.2.4 ML-Based Approach to Cluster the EV Charging Behaviors
3.2.4.1 Preprocessing
3.2.4.2 EV's Charging Behavior Clustering Using K-Means Algorithm
3.2.4.3 K-NN Classification for EV Charging Behavior
3.2.5 A Toy Example
3.2.6 Application of Charging Pattern Recognition in Smart Charging
3.3 Status Quo, Challenges, and Outlook
3.4 Concluding Remarks
References
4 Cybersecurity and Data Privacy Issues of Electric Vehicles Smart Charging in Smart Microgrids
4.1 Introduction
4.2 Cyberattacks and Security Issues of EVs
4.2.1 Various Attacks on EVs
4.2.1.1 Attacks on Control Systems
4.2.1.2 Attacks on Driving System Parts
4.2.1.3 Attacks on V2X Communication
4.2.2 The Vulnerability of EV Charging Stations to Cyberattacks
4.2.2.1 Web-Based Vulnerabilities
4.2.2.2 Human-Machine Interface Vulnerabilities and Physical Access Points
4.2.2.3 The Vulnerability of Servers
4.2.2.4 The Vulnerability of Smartphones
4.2.2.5 The Vulnerability of Building Energy Management System and Grid Interface
4.2.2.6 The Vulnerability of Original Equipment Manufacturers/Vendors
4.2.3 Cybersecurity Challenges in EV Communication
4.2.3.1 Limited Connectivity
4.2.3.2 Limited Computational Performance
4.2.3.3 The Scenarios and Threats of Unpredictable Attacks
4.2.3.4 Critical Hazard to the Life of Drivers and Passengers
4.2.4 Data Privacy Challenges in Smart EV Networks
4.2.5 Classifying the Cybersecurity Threats of On-Board Charging
4.2.5.1 Modification
4.2.5.2 Interference
4.2.5.3 Interruption
4.2.5.4 Interception
4.2.6 Risk Assessment
4.2.7 The Review of Attacker-Defender Models
4.2.8 Cybersecurity Requirements
4.2.8.1 The Security Goals for EV Ecosystem
4.2.8.2 Security Requirements Based on NISTIR 7628
4.3 Status Quo, Challenges, and Outlook
4.4 Learned Lessons and Concluding Remarks
References
5 Evaluation of Cyberattacks in Distribution Network with Electric Vehicle Charging Infrastructure
5.1 Introduction
5.2 Status Quo, Challenges, and Outlook
5.2.1 EV2EVSE
5.2.2 EVSE2EVSE
5.2.3 EV2EV
5.3 Related Work
5.4 Cyberattack Model
5.4.1 Response Model
5.5 Experimental Results
5.6 Conclusion
References
6 Electric Vehicle Services to Support the Power Grid
6.1 Introduction
6.2 Classification of EV Services Presentable to the Power Grid
6.2.1 EV's Active and Reactive Power Support Services
6.2.1.1 Frequency Control
6.2.1.2 Load Variance Minimization, Peak Shaving, and Valley Filling
6.2.1.3 Loads Restoration
6.2.1.4 Loss Minimization
6.2.1.5 Voltage Control
6.2.2 Support Services for Renewable Energy Sources Integration
6.3 Combination Capability of EVs' Different Services
6.4 Mathematical Modeling of EVs' Charging and Discharging Optimization Problem in the Power System
6.4.1 Constraints on EVs' Charging and Discharging Optimization Problem
6.4.1.1 EV Constraints
6.4.1.2 Network Constraints
6.4.2 Mathematical Models and Problem-Solving Methods for Optimizing Charge and Discharge of EVs
6.5 Current Status, Challenges, and Outlook
6.6 Conclusion
References
7 Smart Charging of EVs to Harvest Flexibility for PVs
7.1 Status Quo, Challenges and Outlook
7.2 Introduction
7.2.1 Background and Literature Review
7.2.2 Contributions
7.2.3 Chapter Organization
7.3 Determination of Optimal EV Demand Profile
7.3.1 Assumptions
7.3.2 Mathematical Formulation
7.4 Numerical Studies
7.4.1 Data
7.4.2 Case-I: EVs Profile Optimization, Without Considering PVs
7.4.3 Case-II: EVs Profile Optimization, Considering PVs
7.4.4 Comparative Analysis of Cases
7.5 Conclusion
Bibliography
8 A Robust Optimization-Based Model for Smart Charging of PEV Under Multiple Uncertainties
8.1 Introduction
8.2 Mathematical Representation of the Deterministic PEV Smart Charging
8.2.1 Constraints
8.3 The Proposed IGDT-Based Model for Robust Smart PEV Charging
8.3.1 The Information Gap Decision Theory (IGDT)
8.3.2 The Proposed IGDT-Based PEV Smart Charging
8.3.3 Multi-objective Particle Swarm Optimization (MOPSO)
8.3.3.1 Concise Review of PSO Algorithm
8.3.3.2 The Concept of Dominance in a Multi-objective Problem
8.3.3.3 The MOPSO Step-by-Step Implementation
8.3.4 Fuzzy Satisfaction Method
8.4 Numerical Results
8.4.1 Input Data
8.4.2 The SOC and Power Analysis
8.4.3 Robustness Assessment
8.5 Conclusion
References
9 The Role of Smart Electric Vehicle Charging in Optimal Decision-making of the Active Distribution Network
Nomenclature
Sets and Indices
Parameters
Variables
Binary Variables
9.1 Introduction
9.2 Status Quo, Challenges, and Outlook
9.3 Formulation
9.3.1 Hybrid Stochastic Programming/Robust Optimization Model
9.3.2 Electric Vehicles
9.3.3 Combined Heat and Power Unit
9.3.4 Solar Distributed Generations
9.3.5 Distribution System
9.3.6 The Objective Function
9.4 Results and Discussions
9.5 Conclusion
References
10 Operational Challenges of Electric Vehicle Smart Charging
10.1 Status Quo, Challenges, and Outlook
10.2 Definition
10.3 Electric Vehicle Technology
10.4 Electric Vehicles Charging
10.4.1 Charging Standards for Electric Vehicles
10.4.2 Charging Speed and Duration
10.4.3 Electric Vehicle Smart Charging (EVSC)
10.5 Control of EVSC: Centralized and Decentralized Control Approaches
10.6 Benefits of EVSC
10.7 Main Challenges of Using EVSCs
10.7.1 Connectivity and Infrastructure in EVSC
10.7.2 The Minimum Requirements for EVSC
10.8 Conclusion
References
Index