Smart Charging Infrastructures

A. Chitra, PhD is an Associate Professor in the School of Electrical Engineering at the Vellore Institute of Technology with more than 20 years of experience. She has published more than 63 papers in reputed journals and conferences, three patents, and three books. Her research areas include neural networks, induction motor drives, reliability analysis of multilevel inverters, and electric vehicles.W. Razia Sultana, PhD is an Associate Professor in the School of Electrical Engineering at the Vellore Institute of Technology. She has published many papers in reputed journals. Her research interests include model predictive control of power converters, design and control of multilevel inverters, and control of power converters for electric vehicles.V. Indragandhi, PhD is an Associate Professor in the School of Electrical Engineering at the Vellore Institute of Technology with more than 12 years of experience. She has authored one book, published more than 100 research articles in leading peer-reviewed international journals, and filed three patents. Her research focuses on renewable energy and power electronics.

• Preface xv1 Towards Sustainable Mobility: An Autonomous Electric Vehicle Charging Station Powered by Multifaceted Renewable Energy Sources 1K. Kathiravan and P. N. Rajnarayanan1.1 Introduction 21.2 Description of the Proposed Charging Station 41.3 Design and Analysis of the System 51.3.1 PV System 51.3.2 Wind 81.3.3 Fuel Cell 91.3.4 Boost Converter with MPPT 91.3.5 Buck Converter 101.3.6 EV Charge Controller 101.4 System Design Calculations 111.4.1 PV System 111.4.2 Wind Turbine 131.4.3 Fuel Cell 131.4.4 Battery Energy Storage System 141.5 Result Analysis 151.5.1 Case 1: PV BES Setup 151.5.2 Case 2: PV BES Wind Setup 181.5.3 Case 3: PV BES FC Setup 191.5.4 Case 4: BES Wind Setup 211.5.5 Case 5: BES FC Setup 221.5.6 Case 6: BES Wind FC Setup 231.5.7 Case 7: PV BES Wind FC Setup 241.6 Conclusion and Future Outlook 25References 262 Innovating EV Charging Infrastructure: A Hybrid Energy Storage System Approach for Solar Powered-Based dc Microgrid 29Sandeep S. D., Satyajit Mohanty and Shashi Bhushan2.1 Introduction 292.2 System Architecture 302.2.1 Modeling of PV System 302.2.2 Battery Storage System 322.2.3 Supercapacitor 332.3 Power Management System 332.4 Results and Discussion 362.5 Conclusion 39References 393 Design of Intermediate Charging Facilitated Port Configuration of Charging Station with Consideration of Reliability and Cost 41K. Vaishali and D. Rama Prabha3.1 Introduction 423.2 Methodology for Estimating the Reliability Probability of Charging Ports 433.3 Introduced Pattern Identical and Non-Identical Configuration 463.4 Results and Discussions 493.4.1 Identical Port Configuration 493.5 Conclusion 54References 554 AI-Based Smart Charging Infrastructures: Revolutionizing Electric Vehicle Integration 57V. Bagyaveereswaran, S.L. Arun, M. Manimozhi and B. Jaganatha Pandian4.1 Introduction 584.2 Fundamentals of Smart Charging 594.2.1 Benefits of Smart-Charging Infrastructure 614.2.2 Deployment Factors for Smart Charging 624.3 Role of AI in Smart Charging 644.3.1 Understanding Artificial Intelligence in Charging Infrastructures 644.3.2 Machine Learning Algorithms for Predictive Charging 664.3.2.1 Benefits of ML-Powered Predictive Charging 694.3.3 Real-Time Data Analytics and Optimization Techniques 704.3.3.1 Real-Time Data Analytics 714.3.3.2 Optimization Techniques 714.3.4 AI-Based Demand Response Management 724.3.4.1 Understanding Demand Response Management 734.3.4.2 Benefits of AI-Based DRM for Charging Stations 744.4 Components of AI-Based Smart Charging Systems 744.4.1 Sensors and IoT Devices for Data Collection 754.4.2 Cloud Computing and Edge Computing Platforms 774.4.2.1 Cloud Computing Platforms 784.4.2.2 Edge Computing Platforms 784.4.3 Communication Protocols and Network Infrastructure 794.4.4 Control Algorithms for Dynamic Charging Control 814.5 Challenges and Future Directions 834.5.1 Security and Privacy Concerns in AI-Driven Infrastructures 844.5.2 Scalability and Interoperability Issues 844.5.3 Regulatory and Policy Implications 864.5.4 Emerging Technologies and Trends in Smart Charging 86Bibliography 875 EV Smart Charging Using RES—Challenges 91Sowmya Ramachandradurai, Joylin Mary J. and D.F. Jingle JabhaAcronyms 915.1 Introduction 925.2 System Description 925.2.1 Description of Photovoltaic (PV) Source 935.2.2 Description of Wind Energy 935.2.3 Description of EV 945.2.4 Objective Function 955.2.5 Constraint Conditions 955.2.5.1 Equality Constraint 955.2.5.2 Generator Constraint 965.2.6 Framework of Optimization Algorithm 965.3 Results and Discussion 985.4 Conclusion 99References 1016 Green Energy-Based Active Grid Optimization Using Deep Learning for EV Charging Infrastructure 105D. Shruthi, R. Raja Singh, S. L. Arun and R. Rengaraj6.1 Introduction 1066.2 Active Grid and Edge Computing 1076.3 Modeling of Standalone Hybrid System 1096.3.1 Solar PV Cell Model 1096.3.2 Wind Turbine Model 1126.3.3 EV Battery Model 1146.4 Deep Learning and Its Implementation 1156.4.1 Energy Demand Pattern 1176.4.2 Wind Speed 1206.4.3 Solar Irradiation 1216.5 Micro-Grid and Control Mechanism 1236.5.1 Microgrid Functioning in Different Modes 1246.5.1.1 Islanded Mode 1256.5.1.2 Multiple Microgrid Control with Centralized Energy Storage System 1256.5.2 Energy Storage System Simulation 1266.5.3 Wind Energy Storage System Simulation 1276.5.4 EV Battery Control Mechanism 1296.6 Results and Discussion 1306.6.1 Deep Learning 1306.6.2 Matlab/Simulink Model 1326.7 Conclusion 134References 1357 Bearing Fault Diagnosis in Permanent Magnet Synchronous Motor Using Deep Neural Network 137Geetha G., Shanthini C., Geethanjali P. and Yokkeshwaran K.7.1 Introduction 1387.2 Methodology 1417.2.1 Discrete Wavelet Transform 1427.2.2 Kurtogram 1447.2.3 Deep Neural Network-VGG 1467.3 Results and Discussion 1487.3.1 Case 1: Using DWT 1487.3.2 Case 2: Using Kurtogram 1487.4 Conclusion 152References 1528 Enhancing Efficiency in Bidirectional CLLC Resonant Converters: A Hybrid Control Approach 157Aryan Chaturvedi, M. Rajalakshmi and Razia Sultana W.8.1 Introduction 1588.2 Bidirectional CLLC Resonant Converter 1598.3 Working by Controlling Conversion of Frequency 1608.4 How the Inductance Factor (k) Affects Voltage Gain (M) 1628.5 How the Quality Factor (Q) Influences Voltage Gain (M) 1638.6 Understanding Frequency-Conversion Control 1648.7 Combining Frequency Conversion and Phase Shifting with a Hybrid Control Strategy 1658.8 Simulation Results and Discussion 1688.9 Conclusion 173References 1739 IoT-Based Smart Charging Systems 175Tanmay Sharma, Pramatha S. Vasishtha and Razia Sultana W.Abbreviation 1759.1 Introduction 1769.2 Remote Monitoring and Telematics 1769.3 Infrastructure Connectivity for Charging 1779.4 Autonomous Driving and Advanced Driver Assistance Systems (ADAS) 1789.5 Logistics and Fleet Management 1789.6 Sustainability and Energy Management 1799.7 Services and User Experience 1809.8 Algorithms for Shortest Path Finding 1809.8.1 Dijkstra’s Algorithm 1809.8.2 Bellman–Ford Algorithm 1829.8.3 A* Search Algorithm 1829.8.4 Floyd–Warshall Algorithm 1839.8.5 Bidirectional Search Algorithm 1849.8.6 Rapidly Exploring Random Tree Algorithm 1859.8.7 Probabilistic Roadmap Algorithm 1879.8.8 Hybrid RRT-PRM Model 1899.9 Advantages 1929.10 Conclusion 193References 19310 Embedded Control of Power Converters in E-Mobility 195Yeddula Pedda Obulesu and Pallamkuppam Vinodh Kumar10.1 Introduction 19610.1.1 Key Components of EV 19810.2 Evolution of Digital Control in Power Converters 19910.2.1 Key Functions of Embedded Control of Power Converters 20010.2.2 Components of Embedded Control Systems 20110.2.3 Control Strategies 20110.2.4 Challenges and Innovations 20110.3 Embedded Systems and Digital Control 20210.4 Tools and Technologies for Digital Control Systems 20210.5 Implementation of Embedded Digital Control Based on DSPs 20310.6 Key Components in Embedded Digital Controllers 20510.7 Signal Generation for Power Converter Devices 20710.7.1 Operating Frequency and Resolution 20710.7.2 Modes of Operation 20710.8 Field Programmable Gate Arrays (FPGAs) 20810.9 Code Composer Studio and JTag 21210.9.1 Functional Requirements of a Non-Inverting Buck-Boost Converter 21710.10 Software Development Environment (SDE): Compiler, Linker, Assembler, and Downloader 21910.11 STM-Based Embedded Controllers 22610.12 Main Traction Inverter 22710.13 On-Board Charger 22810.14 Battery Management System (BMS) 229Acknowledgement 23011 Solar Piezo Hybrid Power Charging System 231Vedanth S., Varun Baalaji S., Shairahul Gautam S., Sharan Vikash, Ashwini K. and R. Resmi11.1 Introduction 23111.2 Methodology 23311.2.1 Simulation Modelling in MATLAB/Simulink 23311.2.2 Brief Description of Various Parts 23411.2.3 Block Diagram and Working 23511.3 Operating Modes 23611.4 Result and Discussion 23711.4.1 Simulation Results in MATLAB/Simulink 23711.4.2 Hardware Implementation 23811.4.3 IoT Integration 23911.5 Conclusion 240Acknowledgments 240References 24012 EV Power Train Performance with DC Motor 243Nithya Chandran and R. Resmi12.1 Introduction 24312.2 Methodology 24412.2.1 Architecture of Battery EV Power Train 24412.2.2 Requirements of Electric Traction Motors 24512.2.3 Machine Topologies 24612.2.4 Vehicle Dynamics and Estimation of Output Parameters 24712.3 Results and Discussion 24912.3.1 Simulation Results 24912.3.2 Cost–Benefit Analysis 25012.4 Conclusion 251Acknowledgment 251References 25213 RC Vehicle for Delivery 255Vemulapati Dhanunjaya Reddy, Mallireddy Jayanthi Reddy, Manoj Kumar S., R. Resmi and Y. N. V. Ganesh13.1 Introduction 25613.1.1 Description of the RC Vehicle 25613.1.1.1 Functioning of L298N Motor Driver 25613.1.1.2 The Functioning of ESP32 Camera Module 25613.2 Literature Review 25713.2.1 Research Gap 25913.3 Methodology 25913.3.1 Radio-Controlled (RC) Vehicle 25913.3.2 Camera System 26013.3.3 Pan-Tilt Mechanism 26013.3.4 Anti-Theft Locking System 26013.3.5 Mobile-Application Interface 26113.4 Result and Discussions 26213.5 Conclusion 263References 26414 Aerodynamic Drag Reduction in Heavy Vehicles 267Amutha Prabha N., Abhishek Gudipalli, Dyuti Ranjan Acharya, Indragandhi V. and Manee Sangaran Diagarajan14.1 Introduction 26714.2 Literature Survey 26814.3 Methodology 26914.3.1 Geometry and Meshing 27014.3.2 Inlet, Outlet, and Boundary Conditions 27214.3.3 Computational Procedure 27214.4 Results and Discussion 27314.4.1 Pressure Contour Comparison 27414.4.2 Velocity Contour Comparison 27514.4.3 Streamline Profile 27614.4.4 VelocityVector Profile 27714.5 Analysis Comparison 27714.5.1 Streamline Comparison at Rear to Understand Flow Characteristics 27714.5.2 Drag Force Comparison 27814.6 Conclusion 279References 27915 Review of Optimization-Based Sensor Fault Detection for Lithium-Ion Batteries in Electric Vehicles 281Mohana Devi S. and V. Bagyaveereswaran15.1 Introduction 28215.2 Gestalt of Battery Sensors 28415.3 Utilization of Battery Sensors in Electric Vehicles 28715.3.1 Significance of Sensor Fault Identification in Li-Ion Batteries 29015.3.2 Sensor Fault Modeling 29315.4 Optimization in Sensor Fault Detection 29315.5 Advantages and Category of Metaheuristic Algorithm 29715.5.1 Applications of Metaheuristic Approach for Sensor Fault Detection in Lithium-Ion Batteries 29815.5.2 Challenges in Fault Detection 30315.6 Result and Discussion 30515.7 Conclusion 306References 30616 Development of a Hybrid Foot‐Stamping Bicycle with Dynamic Electric Support: A Sustainable Alternative to Traditional Pedal and Electric Bicycles 313Sumant Shyam, Jahnavi Gayatri D., Anushka and Abhishek Gudpalli16.1 Introduction 31416.2 Background and Motivation 31416.2.1 Limitations of Traditional Pedal-Based Bicycles 31516.2.2 The Rise of Electric Bicycles (E-Bikes) 31516.2.3 The Need for a Hybrid Solution 31616.2.4 Innovative Foot-Powered System 31716.2.5 Electric Dynamic Support 31716.2.6 Motivation for the Proposed Design 31816.2.7 Design Concepts 31816.3 Study Objectives 32216.3.1 Design and Development of the Foot-Stamping Mechanism 32316.3.2 Integration of Dynamic Electric Support 32316.3.3 Performance Evaluation and Efficiency Analysis 32416.3.4 Sustainability and Environmental Impact 32416.3.5 User Experience and Accessibility 32516.3.6 Prototype Development and Testing 32516.4 Scope of Study 32616.4.1 Design and Engineering Focus 32616.4.2 Prototyping and System Testing 32716.4.3 Energy Efficiency and Sustainability Assessment 32716.4.4 User Experience and Practical Application 32816.4.5 Technical and Financial Feasibility 32816.4.6 Limitations and Constraints 32916.5 Conclusion 329References 33017 A Novel Multilevel Inverter with Reduced Switch for Electric Vehicle Applications 337Vijaya Sambhavi Y. and Vijayapriya R.17.1 Introduction 33717.2 Proposed mli 34017.2.1 Description and Analysis of Proposed MLI Circuit 34117.3 Control Strategy and Simulation Outcomes 34217.4 Conclusion 346References 347Index 349