Intelligent Control for Modern Transportation Systems

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The book comprehensively discusses concepts of artificial intelligence in green transportation systems. It further covers intelligent techniques for precise modeling of complex transportation infrastructure, forecasting and predicting traffic congestion, and intelligent control techniques for maximizing performance and safety. It further provides MATLAB® programs for artificial intelligence techniques. It discusses artificial intelligence-based approaches and technologies in controlling and operating solar photovoltaic systems to generate power for electric vehicles. Highlights how different technological advancements have revolutionized the transportation system. Presents core concepts and principles of soft computing techniques in the control and management of modern transportation systems. Discusses important topics such as speed control, fuel control challenges, transport infrastructure modeling, and safety analysis. Showcases MATLAB® programs for artificial intelligence techniques. Discusses roles, implementation, and approaches of different intelligent techniques in the field of transportation systems. It will serve as an ideal text for professionals, graduate students, and academicians in the fields of electrical engineering, electronics and communication engineering, civil engineering, and computer engineering.

Author(s): Arunesh Kumar Singh, Bhavnesh Kumar, Ibraheem, Asheesh Kumar Singh, Shahida Khatoon
Publisher: CRC Press
Year: 2023

Language: English
Pages: 203

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
About the Book
About the Editors
Contributors
1 Fundamentals of Modern Transportation Systems
1.1 Introduction
1.2 Key Components
1.2.1 Infrastructure
1.2.2 Vehicles
1.2.3 Fuel
1.2.4 Control and Communication Systems
1.3 Historical Background
1.4 Infrastructure for Road Transport: Emerging Technologies
1.4.1 Early Road Networks
1.4.2 Modern Road Networks
1.4.3 Toll Roads
1.4.3.1 Electronic Toll Collection
1.4.3.2 FASTag
1.5 Vehicles for Road Transport: Current and Future
1.5.1 Conventional Vehicles Driven by Heat Engines
1.5.2 Internal Combustion Engine
1.5.2.1 Technological Advancements in ICE
1.5.3 Battery-Powered Vehicles
1.5.3.1 Electric Motor
1.5.3.2 Battery
1.5.4 Hybrid Vehicles with Both IC Engine and Battery
1.5.5 FCEVs (Fuel-Cell Electric Vehicles)
1.6 Fuel
1.6.1 Alternative Fuel
1.6.2 Battery
1.7 Control and Communication Systems
1.7.1 Between EVSE and CMS
1.8 Conclusion
References
2 Modern Transport System: Various Categories and Transitioning Challenges
Abbreviations
2.1 Introduction
2.2 Classification of EVs
2.2.1 Hybrid Electric Vehicles
2.2.2 Plug-In Hybrid Electric Vehicles
2.2.3 Battery Electric Vehicles
2.3 Charging Infrastructure
2.3.1 Charging Based on Different Levels
2.3.1.1 Level-1 Charging
2.3.1.2 Level-2 Charging
2.3.1.3 Level-3 Charging
2.3.2 Charging Based on Charge Transfer Pattern
2.3.2.1 Inductive/Wireless Charging
2.3.2.2 Conductive Charging
2.3.2.3 Battery Swapping
2.4 Headwinds for Transformation in the Transport Sector
2.4.1 World Population Growth
2.4.2 Demographic and Social Change
2.4.3 Globalisation 2.0
2.4.4 Urbanisation
2.4.5 Climate Change Issues
2.5 Tailwinds in Transformation in the Transport Sector
2.5.1 Range
2.5.2 Technical Immaturity
2.5.3 Cost of Ownership
2.5.4 Infrastructural Challenges
2.5.5 Impact on Electric Grid
2.5.6 Safety
2.6 Impact of EV on Distribution Network and Electricity Market
2.7 Conclusion
References
3 Intelligent Control for PMSM Drive Train
3.1 Introduction
3.2 Conventional Control Strategy
3.2.1 Traditional PI-RES Controller Design
3.3 Proposed Controller Design
3.3.1 ANFIS Architecture
3.3.2 ANFIS-RES Algorithm
3.4 Implementation of ANFIS-RES Controller
3.5 Results and Discussion
3.5.1 Performance Analysis Using the PI-RES Controller
3.5.2 Performance Analysis Using the ANFIS-RES Controller
3.5.3 Comparative Analysis of the Proposed Controller and the PI-RES Controller
3.6 Conclusion
References
4 IoT-Based Vehicle Tracking and Accident Alert System
4.1 Introduction
4.2 Internet of Things
4.3 Methodology
4.4 Result and Discussion
4.5 Conclusion
References
5 Intelligent Control for Passenger Safety: Overview, Dynamic Modeling and Control of Active Suspension Systems
Abbreviations
5.1 Introduction: Overview and Requirement of Suspension System in Vehicle
5.2 Challenges in Designing the Vehicle Suspension Control System
5.3 Characterization of Suspension Systems
5.3.1 Passive Suspension System
5.3.2 Semi-Active Suspension System
5.3.3 Active Suspension System
5.4 Two-Degree of Freedom (2-DOF) Quarter Car Active Suspension System Model
5.4.1 Mathematical Modeling
5.4.2 State Space Model of an Active Suspension System
5.4.2.1 Uncertain Modelling
5.5 Intelligent Control Techniques for Active Suspension System
5.5.1 H[sub(∞)] (i.e., ''H-Infinity) Controller
5.5.2 μ-Synthesis Controller
5.5.2.1 Disturbance Rejection Response Using H-Infinity and μ-Synthesis Controller
5.6 Conclusion
References
6 Electric Vehicle Energy Management: An Intelligent Approach
6.1 Introduction
6.2 Smart Charging Strategies for EVs
6.3 Uncertainty Handling in DR Algorithms
6.4 Decentralized Energy Management for EVs
6.5 Conclusions
References
7 Driver Activity Tracking for Vehicle Data Recorder System
7.1 Introduction
7.1.1 Existing and Proposed System
7.1.2 Challenges and Issues
7.1.3 Applications
7.2 Literature Review
7.2.1 Near Field Communication
7.2.2 The Smart Tachograph
7.2.3 An In-Vehicle Data Recorder (IVDR) For Evaluation of Driving Behavior and Safety
7.2.4 Automated Tachograph Chart Analysis System
7.3 Methodology
7.3.1 Core Technologies
7.3.1.1 Near Field Communication
7.3.1.2 RFID Technology
7.3.2 Vehicle Data Recorder System Design
7.3.2.1 Context Diagram
7.3.2.2 Vehicle Data Recorder Offers the Below Interfaces to Connect to the External Environment
7.3.2.3 System Requirements
7.3.3 Vehicle Data Recorder System Implementation
7.4 Results Analysis
7.4.1 View of Driver Activities in the System
7.4.2 Security Aspects of The NFC Card-Based Digital Tachograph System
7.5 Conclusion and Future Work
References
8 Robust Control of Hybrid Maglev-Based Transportation System
8.1 Introduction
8.2 Dynamical Modelling of HMTS
8.2.1 Nonlinear State-Space Model of HMTS
8.2.2 Nonlinear Model of HMTS in Normal Form
8.2.3 Control Objective
8.3 Design of Robust Controller for HMTS
8.4 Robust Control Using AGS Based on Neural Network
8.5 Simulation Study
8.5.1 Simulation Results
8.6 Conclusion
References
9 Reinforcement Learning-Based Intelligent Energy Management System for Electric Vehicle
9.1 Introduction
9.2 Electric Vehicle and Energy Management System
9.2.1 Electric Vehicle
9.2.2 Energy Management System for Electric Vehicles
9.3 Reinforcement Learning Basics
9.4 Reinforcement Learning-Based Energy Management System for Electric Vehicle
9.5 Conclusion
Appendix-1
References
10 Soft-Computing-Based Discharge Parameters Estimator for Li-ion Batteries
10.1 Introduction
10.2 Performance of Li-Ion Batteries: Technical Review
10.3 Discharge Parameter Estimation Methodology
10.4 Results and Observations
10.5 Conclusion
References
Index