Automated and Electric Vehicle: Design, Informatics and Sustainability

Contents
1 Energy Efficient Control of Vehicles
1.1 Introduction
1.2 Architecture
1.3 Rule-Based Energy Management Strategy
1.3.1 Energy Management Control Based on Deterministic Rules
1.3.2 Energy Management Control Based on Fuzzy Rules
1.4 Optimization-Based Energy Management Strategy
1.4.1 Global-Optimization-Based Energy Management Strategy
1.4.2 Instantaneous-Optimization-Based Energy Management Strategy
1.5 Conclusion
References
2 Battery Management System of Electric Vehicle
2.1 Introduction
2.2 The Topologies of Battery Management System
2.2.1 Centralized BMS Architecture
2.2.2 Modular BMS Topology
2.2.3 Primary/Subordinate BMS
2.2.4 Distributed BMS Architecture
2.3 The Hardware of Battery Management System
2.3.1 Main Board
2.3.2 The Slave Board (LCU)
2.3.3 Battery Disconnect Unit (BDU)
2.3.4 High-Voltage Control Board
2.4 The Software of Battery Management System
2.4.1 Application Layer
2.4.2 Runtime Environment (RTE)
2.4.3 Basic Software (BSW)
2.5 Principal BMS Functions
2.5.1 Battery Monitoring
2.5.2 Battery Protection
2.5.3 Estimating the Battery’s Operational State
2.5.4 Optimizing Battery Performance
2.5.5 Reporting Operational Status
2.6 The Core Algorithm of BMS
2.6.1 Definition of SOC and SOH
2.6.2 SOC Prediction Methods
2.6.3 SOH Prediction Methods
2.7 Conclusion
References
3 Speed Forecasting Methodology and Introduction
3.1 Introduction
3.2 Model-Driven Approach to Vehicle Speed Prediction
3.2.1 Micro-Model Based Vehicle Speed Prediction
3.2.2 Macro-Model Based Vehicle Speed Prediction
3.2.3 Forecasting Methods Based on Exponential Change
3.3 Data-Driven Approach to Vehicle Speed Prediction
3.3.1 Speed Prediction Algorithm Based on Adaptive Kalman Filtering
3.3.2 A Markov Chain Model-Based Approach to Vehicle Speed Prediction
3.3.3 Speed Prediction Based on Machine Learning
3.3.4 Speed Prediction Based on Deep Learning
3.4 Conclusion
References
4 Eco-Driving Behavior of Automated Vehicle
4.1 Introduction
4.2 Speed Planning of Traffic Signal Lights Based on Internet of Vehicles System
4.3 Speed Planning Method Based on Model Predictive Control
4.4 Conclusion
References
5 Service Planning and Operation for Autonomous Valet Parking
5.1 Introduction
5.1.1 Motivation
5.1.2 Main Contributions
5.1.3 Structural Organization
5.2 Background
5.2.1 Parking Service Model
5.2.2 Parking Resource Management
5.3 Autonomous Valet Parking
5.3.1 Savp
5.3.2 Lavp
5.4 Parking Resource Management Strategies
5.4.1 Users’ Decision Making
5.4.2 Global Controlling
5.4.3 Parking Reserving
5.4.4 Slot Sharing
5.4.5 Dynamic Pricing
5.5 Future Directions
5.5.1 Blockchain Technology
5.5.2 Deep Reinforcement Learning
5.6 Conclusion
References
6 Navigation Service Optimization for Electric Vehicle
6.1 Introduction
6.2 Background
6.2.1 Type of EVs
6.2.2 EV Charging Use Case
6.3 Navigation Service System
6.3.1 Network Communication for Navigation Service
6.3.2 Network Entities
6.3.3 System Cycle
6.4 Navigation Service Optimization Based on Plug-In Charging Mode
6.4.1 Introduction for Plug-In Charging Mode
6.4.2 Optimal Plug-In Charging Strategy
6.4.3 Spatial and Temporal Characteristic Under Plug-In Charging Mode
6.4.4 Navigation Service Optimization in Different Manner Under Plug-In Charging Mode
6.4.5 Optimization Solving Methods for Navigation Service
6.5 Navigation Service Optimization Based on Battery Swapping Mode
6.5.1 Introduction for Battery Swapping Mode
6.5.2 Challenges for Battery Swapping Service
6.5.3 Navigation Service Optimization on Temporal and Spatial Characteristic Under Battery Swapping Mode
6.5.4 Navigation Service Optimization in Different Manner Under Battery Swapping Mode
6.6 New Charging Service Methods for Electric Vehicle
References
7 AI-Based GEVs Mobility Estimation and Battery Aging Quantification Method
7.1 Introduction and Related Works
7.2 V2G Schedulable Capacity Modelling and Prediction
7.2.1 Modelling GEVs Mobility with Statistical Models
7.2.2 Modelling GEVs Mobility with Data-Driven Learning Models
7.2.3 Learning Algorithms for V2G Capacity Prediction
7.2.4 Improving Prediction Accuracy with Rolling Prediction Technology
7.2.5 Performance Illustration of Different Methods
7.3 Quantification of Battery Aging Cost in V2G Management
7.3.1 Quantification of Battery Life Loss in V2G Services
7.3.2 Quantification of Battery Aging Cost in V2G Services
7.4 Conclusion
References
8 Multi-objective Bi-directional V2G Behavior Optimization and Strategy Deployment
8.1 Introduction
8.2 Intelligent V2G Scheduling Systems
8.3 Multi-objective V2G Behaviour Management
8.4 Improving V2G Scheduling Performance with Advanced Intelligent Algorithms
8.5 Utilization of GEVs and Grid State Prediction Information in V2G Scheduling
8.6 Strategy Real-Time Deployment: Fuzzy Logic and Machine Learning Method
8.7 Case Study
8.8 Conclusion
References
9 Local Energy Trading with EV Flexibility
9.1 Introduction
9.2 The Flexibility of Electric Vehicles
9.2.1 Electricity System Flexibility
9.2.2 EV Flexibility
9.3 Local Flexibility Market
9.3.1 Central Markets
9.3.2 Decentralized Markets
9.3.3 P2P Markets
9.4 Trading Mechanisms with EV Flexibility
9.4.1 Blockchain
9.4.2 Game Theory
9.4.3 Machine Learning
9.5 Demonstration
9.5.1 Impact of RERs on the Electricity Market
9.5.2 EV Flexibility Trading in the Balancing Market
9.6 Further Discussions
9.6.1 Carbon in Flexibility Markets
9.6.2 Flexibility Facilitation in the Multi-Energy System (MES)
9.6.3 Balancing Market Design
9.7 Conclusion
References
10 A Review of the Trends in Smart Charging, Vehicle-to-Grid
10.1 Introduction
10.2 Survey Motivations and Structure
10.3 Survey on Economic Feasibility of V2G
10.4 Survey of the Impact of Smart Charging and V2G on Distribution Networks
10.5 Transportation Compatibility for V2G Implementation
10.6 Conclusions
References
11 Communication and Networking Technologies in Internet of Vehicles
11.1 Introduction
11.2 Background
11.2.1 V2V Communication Models
11.2.2 V2I Communication Models
11.2.3 V2P Communication Models
11.2.4 V2N/C Communication Models
11.2.5 Trajectory-Based Communication Models
11.2.6 Technical Standard for V2X
11.3 Use Cases
11.3.1 V2V Communication Use Cases
11.3.2 V2I Communication Use Cases
11.3.3 V2P Communication Use Cases
11.3.4 V2N Communication Use Cases
11.3.5 Trajectory Based Communication Use Cases
11.4 Conclusion
References
12 The Overview of Non-orthogonal Multiple Access in Vehicle-to-Vehicle Communication
12.1 What is NOMA
12.2 Advantages of NOMA
12.2.1 Improved Spectral Efficiency
12.2.2 Massive Connectivity
12.3 Low Density Signature (LDS)
12.3.1 LDS-CDMA
12.3.2 LDS-OFDM
12.4 Sparse Code Multiple Access (SCMA)
12.4.1 System Model
12.4.2 Multi-Stage Optimization Approach of Codebook Design
12.5 Multiuser Shared Access (MUSA)
12.6 Pattern Division Multiple Access (PDMA)
12.7 Comparison
References
13 Decentralized Trust Management System for VANETs
13.1 Introduction
13.2 Basic Concepts of Trust in VANETs
13.2.1 Characteristics of Trust
13.2.2 Common Malicious Attacks in Trust Evaluation for VANETs
13.2.3 Trust Value Calculation
13.2.4 Trust Management
13.2.5 Trust Management Process
13.2.6 Trust Management Architecture
13.3 Classification of Trust Management in VANETs and Current Solutions
13.3.1 Entity-Based Trust Model
13.3.2 Data-Based Trust Model
13.3.3 Combined Trust Model
13.3.4 Blockchain-Based Trust Model
13.4 Existing Problems and Future Research Focus
13.5 Conclusion
References
14 Intrusion Detection System for Connected Automobiles Security
14.1 Introduction
14.1.1 External Communication In-Automobile
14.1.2 Internal Communication In-Automobile
14.1.3 Controller Area Network (CAN)
14.2 Related Work on Automobiles Security
14.3 Typical Attacks in CAN
14.3.1 Sniffing Attack on CAN Bus
14.3.2 Fuzzing Attack on CAN Bus
14.3.3 Falsifying Attack on CAN Bus
14.3.4 Injection Attack on CAN Bus
14.3.5 CAN Bus DoS Attack
14.4 Defense of Connected Automobiles
14.4.1 IDS in the Automobile Industry
14.4.2 Deployment Plan for IDS
14.5 IDS Attacks Detection Approach
14.5.1 Signature Based IDS
14.5.2 Anomaly Based IDS
14.5.3 Comparing Both IDS
14.6 Limitation with Intrusion Detection System
14.7 Future and Research Direction
14.8 Conclusion
References