Smart Charging Infrastructures
Description
Drive the future of sustainable mobility with this essential book, which offers a comprehensive, multi-disciplinary guide to the challenges and AI-driven innovations for developing smart, efficient electric vehicle charging solutions.
The shift to electric vehicles supports the global commitment to reduce greenhouse gas emissions and decrease reliance on fossil fuels. However, crucial charging infrastructure is a key component for encouraging the adoption of electric vehicles. As a developing country, India is experiencing rapid urbanization, leading to higher vehicle ownership rates. With more vehicles on the road, the demand for charging infrastructure is growing, making smart chargers essential to efficiently manage and distribute electricity for electric vehicles. This book offers a comprehensive look at the challenges and innovations for electric vehicle charging solutions to expedite the transition to net-zero emissions. It focuses on the convergence of various technologies, including AI and deep and machine learning for smart charging systems. Through a multi-disciplinary approach and real-world case studies, this book will serve as an essential resource for innovators looking towards the future of green transportation.
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Persons
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.
Content
Preface xv
1 Towards Sustainable Mobility: An Autonomous Electric Vehicle Charging Station Powered by Multifaceted Renewable Energy Sources 1
K. Kathiravan and P. N. Rajnarayanan
1.1 Introduction 2
1.2 Description of the Proposed Charging Station 4
1.3 Design and Analysis of the System 5
1.3.1 PV System 5
1.3.2 Wind 8
1.3.3 Fuel Cell 9
1.3.4 Boost Converter with MPPT 9
1.3.5 Buck Converter 10
1.3.6 EV Charge Controller 10
1.4 System Design Calculations 11
1.4.1 PV System 11
1.4.2 Wind Turbine 13
1.4.3 Fuel Cell 13
1.4.4 Battery Energy Storage System 14
1.5 Result Analysis 15
1.5.1 Case 1: PV BES Setup 15
1.5.2 Case 2: PV BES Wind Setup 18
1.5.3 Case 3: PV BES FC Setup 19
1.5.4 Case 4: BES Wind Setup 21
1.5.5 Case 5: BES FC Setup 22
1.5.6 Case 6: BES Wind FC Setup 23
1.5.7 Case 7: PV BES Wind FC Setup 24
1.6 Conclusion and Future Outlook 25
References 26
2 Innovating EV Charging Infrastructure: A Hybrid Energy Storage System Approach for Solar Powered-Based dc Microgrid 29
Sandeep S. D., Satyajit Mohanty and Shashi Bhushan
2.1 Introduction 29
2.2 System Architecture 30
2.2.1 Modeling of PV System 30
2.2.2 Battery Storage System 32
2.2.3 Supercapacitor 33
2.3 Power Management System 33
2.4 Results and Discussion 36
2.5 Conclusion 39
References 39
3 Design of Intermediate Charging Facilitated Port Configuration of Charging Station with Consideration of Reliability and Cost 41
K. Vaishali and D. Rama Prabha
3.1 Introduction 42
3.2 Methodology for Estimating the Reliability Probability of Charging Ports 43
3.3 Introduced Pattern Identical and Non-Identical Configuration 46
3.4 Results and Discussions 49
3.4.1 Identical Port Configuration 49
3.5 Conclusion 54
References 55
4 AI-Based Smart Charging Infrastructures: Revolutionizing Electric Vehicle Integration 57
V. Bagyaveereswaran, S.L. Arun, M. Manimozhi and B. Jaganatha Pandian
4.1 Introduction 58
4.2 Fundamentals of Smart Charging 59
4.2.1 Benefits of Smart-Charging Infrastructure 61
4.2.2 Deployment Factors for Smart Charging 62
4.3 Role of AI in Smart Charging 64
4.3.1 Understanding Artificial Intelligence in Charging Infrastructures 64
4.3.2 Machine Learning Algorithms for Predictive Charging 66
4.3.2.1 Benefits of ML-Powered Predictive Charging 69
4.3.3 Real-Time Data Analytics and Optimization Techniques 70
4.3.3.1 Real-Time Data Analytics 71
4.3.3.2 Optimization Techniques 71
4.3.4 AI-Based Demand Response Management 72
4.3.4.1 Understanding Demand Response Management 73
4.3.4.2 Benefits of AI-Based DRM for Charging Stations 74
4.4 Components of AI-Based Smart Charging Systems 74
4.4.1 Sensors and IoT Devices for Data Collection 75
4.4.2 Cloud Computing and Edge Computing Platforms 77
4.4.2.1 Cloud Computing Platforms 78
4.4.2.2 Edge Computing Platforms 78
4.4.3 Communication Protocols and Network Infrastructure 79
4.4.4 Control Algorithms for Dynamic Charging Control 81
4.5 Challenges and Future Directions 83
4.5.1 Security and Privacy Concerns in AI-Driven Infrastructures 84
4.5.2 Scalability and Interoperability Issues 84
4.5.3 Regulatory and Policy Implications 86
4.5.4 Emerging Technologies and Trends in Smart Charging 86
Bibliography 87
5 EV Smart Charging Using RES-Challenges 91
Sowmya Ramachandradurai, Joylin Mary J. and D.F. Jingle Jabha
Acronyms 91
5.1 Introduction 92
5.2 System Description 92
5.2.1 Description of Photovoltaic (PV) Source 93
5.2.2 Description of Wind Energy 93
5.2.3 Description of EV 94
5.2.4 Objective Function 95
5.2.5 Constraint Conditions 95
5.2.5.1 Equality Constraint 95
5.2.5.2 Generator Constraint 96
5.2.6 Framework of Optimization Algorithm 96
5.3 Results and Discussion 98
5.4 Conclusion 99
References 101
6 Green Energy-Based Active Grid Optimization Using Deep Learning for EV Charging Infrastructure 105
D. Shruthi, R. Raja Singh, S. L. Arun and R. Rengaraj
6.1 Introduction 106
6.2 Active Grid and Edge Computing 107
6.3 Modeling of Standalone Hybrid System 109
6.3.1 Solar PV Cell Model 109
6.3.2 Wind Turbine Model 112
6.3.3 EV Battery Model 114
6.4 Deep Learning and Its Implementation 115
6.4.1 Energy Demand Pattern 117
6.4.2 Wind Speed 120
6.4.3 Solar Irradiation 121
6.5 Micro-Grid and Control Mechanism 123
6.5.1 Microgrid Functioning in Different Modes 124
6.5.1.1 Islanded Mode 125
6.5.1.2 Multiple Microgrid Control with Centralized Energy Storage System 125
6.5.2 Energy Storage System Simulation 126
6.5.3 Wind Energy Storage System Simulation 127
6.5.4 EV Battery Control Mechanism 129
6.6 Results and Discussion 130
6.6.1 Deep Learning 130
6.6.2 Matlab/Simulink Model 132
6.7 Conclusion 134
References 135
7 Bearing Fault Diagnosis in Permanent Magnet Synchronous Motor Using Deep Neural Network 137
Geetha G., Shanthini C., Geethanjali P. and Yokkeshwaran K.
7.1 Introduction 138
7.2 Methodology 141
7.2.1 Discrete Wavelet Transform 142
7.2.2 Kurtogram 144
7.2.3 Deep Neural Network-VGG 146
7.3 Results and Discussion 148
7.3.1 Case 1: Using DWT 148
7.3.2 Case 2: Using Kurtogram 148
7.4 Conclusion 152
References 152
8 Enhancing Efficiency in Bidirectional CLLC Resonant Converters: A Hybrid Control Approach 157
Aryan Chaturvedi, M. Rajalakshmi and Razia Sultana W.
8.1 Introduction 158
8.2 Bidirectional CLLC Resonant Converter 159
8.3 Working by Controlling Conversion of Frequency 160
8.4 How the Inductance Factor (k) Affects Voltage Gain (M) 162
8.5 How the Quality Factor (Q) Influences Voltage Gain (M) 163
8.6 Understanding Frequency-Conversion Control 164
8.7 Combining Frequency Conversion and Phase Shifting with a Hybrid Control Strategy 165
8.8 Simulation Results and Discussion 168
8.9 Conclusion 173
References 173
9 IoT-Based Smart Charging Systems 175
Tanmay Sharma, Pramatha S. Vasishtha and Razia Sultana W.
Abbreviation 175
9.1 Introduction 176
9.2 Remote Monitoring and Telematics 176
9.3 Infrastructure Connectivity for Charging 177
9.4 Autonomous Driving and Advanced Driver Assistance Systems (ADAS) 178
9.5 Logistics and Fleet Management 178
9.6 Sustainability and Energy Management 179
9.7 Services and User Experience 180
9.8 Algorithms for Shortest Path Finding 180
9.8.1 Dijkstra's Algorithm 180
9.8.2 Bellman-Ford Algorithm 182
9.8.3 A* Search Algorithm 182
9.8.4 Floyd-Warshall Algorithm 183
9.8.5 Bidirectional Search Algorithm 184
9.8.6 Rapidly Exploring Random Tree Algorithm 185
9.8.7 Probabilistic Roadmap Algorithm 187
9.8.8 Hybrid RRT-PRM Model 189
9.9 Advantages 192
9.10 Conclusion 193
References 193
10 Embedded Control of Power Converters in E-Mobility 195
Yeddula Pedda Obulesu and Pallamkuppam Vinodh Kumar
10.1 Introduction 196
10.1.1 Key Components of EV 198
10.2 Evolution of Digital Control in Power Converters 199
10.2.1 Key Functions of Embedded Control of Power Converters 200
10.2.2 Components of Embedded Control Systems 201
10.2.3 Control Strategies 201
10.2.4 Challenges and Innovations 201
10.3 Embedded Systems and Digital Control 202
10.4 Tools and Technologies for Digital Control Systems 202
10.5 Implementation of Embedded Digital Control Based on DSPs 203
10.6 Key Components in Embedded Digital Controllers 205
10.7 Signal Generation for Power Converter Devices 207
10.7.1 Operating Frequency and Resolution 207
10.7.2 Modes of Operation 207
10.8 Field Programmable Gate Arrays (FPGAs) 208
10.9 Code Composer Studio and JTag 212
10.9.1 Functional Requirements of a Non-Inverting Buck-Boost Converter 217
10.10 Software Development Environment (SDE): Compiler, Linker, Assembler, and Downloader 219
10.11 STM-Based Embedded Controllers 226
10.12 Main Traction Inverter 227
10.13 On-Board Charger 228
10.14 Battery Management System (BMS) 229
Acknowledgement 230
11 Solar Piezo Hybrid Power Charging System 231
Vedanth S., Varun Baalaji S., Shairahul Gautam S., Sharan Vikash, Ashwini K. and R. Resmi
11.1 Introduction 231
11.2 Methodology 233
11.2.1 Simulation Modelling in MATLAB/Simulink 233
11.2.2 Brief Description of Various Parts 234
11.2.3 Block Diagram and Working 235
11.3 Operating Modes 236
11.4 Result and Discussion 237
11.4.1 Simulation Results in MATLAB/Simulink 237
11.4.2 Hardware Implementation 238
11.4.3 IoT Integration 239
11.5 Conclusion 240
Acknowledgments 240
References 240
12 EV Power Train Performance with DC Motor 243
Nithya Chandran and R. Resmi
12.1 Introduction 243
12.2 Methodology 244
12.2.1 Architecture of Battery EV Power Train 244
12.2.2 Requirements of Electric Traction Motors 245
12.2.3 Machine Topologies 246
12.2.4 Vehicle Dynamics and Estimation of Output Parameters 247
12.3 Results and Discussion 249
12.3.1 Simulation Results 249
12.3.2 Cost-Benefit Analysis 250
12.4 Conclusion 251
Acknowledgment 251
References 252
13 RC Vehicle for Delivery 255
Vemulapati Dhanunjaya Reddy, Mallireddy Jayanthi Reddy, Manoj Kumar S., R. Resmi and Y. N. V. Ganesh
13.1 Introduction 256
13.1.1 Description of the RC Vehicle 256
13.1.1.1 Functioning of L298N Motor Driver 256
13.1.1.2 The Functioning of ESP32 Camera Module 256
13.2 Literature Review 257
13.2.1 Research Gap 259
13.3 Methodology 259
13.3.1 Radio-Controlled (RC) Vehicle 259
13.3.2 Camera System 260
13.3.3 Pan-Tilt Mechanism 260
13.3.4 Anti-Theft Locking System 260
13.3.5 Mobile-Application Interface 261
13.4 Result and Discussions 262
13.5 Conclusion 263
References 264
14 Aerodynamic Drag Reduction in Heavy Vehicles 267
Amutha Prabha N., Abhishek Gudipalli, Dyuti Ranjan Acharya, Indragandhi V. and Manee Sangaran Diagarajan
14.1 Introduction 267
14.2 Literature Survey 268
14.3 Methodology 269
14.3.1 Geometry and Meshing 270
14.3.2 Inlet, Outlet, and Boundary Conditions 272
14.3.3 Computational Procedure 272
14.4 Results and Discussion 273
14.4.1 Pressure Contour Comparison 274
14.4.2 Velocity Contour Comparison 275
14.4.3 Streamline Profile 276
14.4.4 VelocityVector Profile 277
14.5 Analysis Comparison 277
14.5.1 Streamline Comparison at Rear to Understand Flow Characteristics 277
14.5.2 Drag Force Comparison 278
14.6 Conclusion 279
References 279
15 Review of Optimization-Based Sensor Fault Detection for Lithium-Ion Batteries in Electric Vehicles 281
Mohana Devi S. and V. Bagyaveereswaran
15.1 Introduction 282
15.2 Gestalt of Battery Sensors 284
15.3 Utilization of Battery Sensors in Electric Vehicles 287
15.3.1 Significance of Sensor Fault Identification in Li-Ion Batteries 290
15.3.2 Sensor Fault Modeling 293
15.4 Optimization in Sensor Fault Detection 293
15.5 Advantages and Category of Metaheuristic Algorithm 297
15.5.1 Applications of Metaheuristic Approach for Sensor Fault Detection in Lithium-Ion Batteries 298
15.5.2 Challenges in Fault Detection 303
15.6 Result and Discussion 305
15.7 Conclusion 306
References 306
16 Development of a Hybrid Foot¿Stamping Bicycle with Dynamic Electric Support: A Sustainable Alternative to Traditional Pedal and Electric Bicycles 313
Sumant Shyam, Jahnavi Gayatri D., Anushka and Abhishek Gudpalli
16.1 Introduction 314
16.2 Background and Motivation 314
16.2.1 Limitations of Traditional Pedal-Based Bicycles 315
16.2.2 The Rise of Electric Bicycles (E-Bikes) 315
16.2.3 The Need for a Hybrid Solution 316
16.2.4 Innovative Foot-Powered System 317
16.2.5 Electric Dynamic Support 317
16.2.6 Motivation for the Proposed Design 318
16.2.7 Design Concepts 318
16.3 Study Objectives 322
16.3.1 Design and Development of the Foot-Stamping Mechanism 323
16.3.2 Integration of Dynamic Electric Support 323
16.3.3 Performance Evaluation and Efficiency Analysis 324
16.3.4 Sustainability and Environmental Impact 324
16.3.5 User Experience and Accessibility 325
16.3.6 Prototype Development and Testing 325
16.4 Scope of Study 326
16.4.1 Design and Engineering Focus 326
16.4.2 Prototyping and System Testing 327
16.4.3 Energy Efficiency and Sustainability Assessment 327
16.4.4 User Experience and Practical Application 328
16.4.5 Technical and Financial Feasibility 328
16.4.6 Limitations and Constraints 329
16.5 Conclusion 329
References 330
17 A Novel Multilevel Inverter with Reduced Switch for Electric Vehicle Applications 337
Vijaya Sambhavi Y. and Vijayapriya R.
17.1 Introduction 337
17.2 Proposed mli 340
17.2.1 Description and Analysis of Proposed MLI Circuit 341
17.3 Control Strategy and Simulation Outcomes 342
17.4 Conclusion 346
References 347
Index 349