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
1.4 System Design Calculations 11
1.5 Result Analysis 15
1.6 Conclusion and Future Outlook 25
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
2.3 Power Management System 33
2.4 Results and Discussion 36
2.5 Conclusion 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
3.5 Conclusion 54
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
4.3 Role of AI in Smart Charging 64
4.4 Components of AI-Based Smart Charging Systems 74
4.5 Challenges and Future Directions 83
5 EV Smart Charging Using RES-Challenges 91
Sowmya Ramachandradurai, Joylin Mary J. and D.F. Jingle Jabha
5.1 Introduction 92
5.2 System Description 92
5.3 Results and Discussion 98
5.4 Conclusion 99
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
6.4 Deep Learning and Its Implementation 115
6.5 Micro-Grid and Control Mechanism 123
6.6 Results and Discussion 130
6.7 Conclusion 134
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
7.3 Results and Discussion 148
7.4 Conclusion 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
9 IoT-Based Smart Charging Systems 175
Tanmay Sharma, Pramatha S. Vasishtha and Razia Sultana W.
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
9.9 Advantages 192
9.10 Conclusion 193
10 Embedded Control of Power Converters in E-Mobility 195
Yeddula Pedda Obulesu and Pallamkuppam Vinodh Kumar
10.1 Introduction 196
10.2 Evolution of Digital Control in Power Converters 199
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
10.8 Field Programmable Gate Arrays (FPGAs) 208
10.9 Code Composer Studio and JTAG 212
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
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
11.3 Operating Modes 236
11.4 Result and Discussion 237
11.5 Conclusion 240
12 EV Power Train Performance with DC Motor 243
Nithya Chandran and R. Resmi
12.1 Introduction 243
12.2 Methodology 244
12.3 Results and Discussion 249
12.4 Conclusion 251
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
13.2 Literature Review 257
13.3 Methodology 259
13.4 Result and Discussions 262
13.5 Conclusion 263
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
14.4 Results and Discussion 273
14.5 Analysis Comparison 277
14.6 Conclusion 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
15.4 Optimization in Sensor Fault Detection 293
15.5 Advantages and Category of Metaheuristic Algorithm 297
15.6 Result and Discussion 305
15.7 Conclusion 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
16.3 Study Objectives 322
16.4 Scope of Study 326
16.5 Conclusion 329
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
17.3 Control Strategy and Simulation Outcomes 342
17.4 Conclusion 346
References 347
Index 349
1
Towards Sustainable Mobility: An Autonomous Electric Vehicle Charging Station Powered by Multifaceted Renewable Energy Sources
K. Kathiravan1 and P. N. Rajnarayanan2*
1Department of Electrical and Electronics Engineering, Mother Theresa Institute of Engineering & Technology, Palamaner, Andhra Pradesh, India
2Department of Electrical and Electronics Engineering, Theni Kammavar Sangam College of Technology, Koduvilarpatti, Tamil Nadu, India
Abstract
The rapid expansion of electric vehicles (EVs) has resulted in an urgent requirement for an effective, eco-friendly, and long-lasting charging infrastructure. This study investigated the development of an autonomous EV charging station that integrates the advantages of various renewable energy sources, including solar panels, wind turbines, hydrogen fuel cells (HFCs), and battery energy storage (BES) systems. This study was motivated by the fact that electric vehicles (EVs) have a lower environmental impact, require less maintenance, and have higher energy efficiency. The ever-growing fleet of electric vehicles poses a challenge to the current charging infrastructure, placing heavy energy demand on the power system network. To mitigate this, this study suggests integrating several renewable energy sources to power the EVCS, which will lessen its dependence on the traditional grid. The design process is elaborated upon with a comprehensive set of equations for each renewable energy component. Different combinations were examined to determine the potential of these sources of synergy. A detailed model and simulation are created within the Matlab-Simulink environment to gain a thorough understanding of the autonomous EVCS's behavior. The simulation results validated the most efficient use of a blend of renewable energy sources, confirming the practicality and effectiveness of this autonomous charging station. The findings of this study represent a substantial contribution to the field of electric vehicle charging systems because they have the potential to advance sustain-ability and lessen the burden on current infrastructure.
Keywords: Energy optimization, autonomous electric vehicle charging station, hybrid renewable energy configuration, sustainable transportation, photovoltaic, wind, hydrogen fuel cells, battery energy storage system
1.1 Introduction
Undoubtedly, road travel is the dominant mode of transportation for most people worldwide. Conventional vehicles depend on fossil fuels [1]. Fossil fuel consumption in the transportation sector has harmed the total amount of greenhouse gas and as other gaseous and particulate emissions to the environment [2]. Currently, the electric vehicle (EV) market has grown significantly, leading to a dramatic rise in global EV adoption [3]. EVs are essential for sustainable and efficient transportation [4]. EVs produce almost no pollution, are more energy-efficient and quieter, and require less maintenance [5].
EVs must be connected to the electrical distribution network for charging. However, a significant increase in EVs can impair the distribution network by causing power losses and affecting voltage profiles and overloads, which can compromise the quality of the electric supply [6]. Consequently, the distribution network must be ready for EV-specific demands and requirements. Enhancing the electrical system is one solution to these issues, although it is costly. A preferable alternative is to use battery energy storage (BES) between electric vehicle charging stations (EVCS) and distribution networks [7]. However, the use of BES will only minimally reduce the strain on the distribution network, although accommodating a high volume of EVCS remains difficult [8]. The environmental benefits of electric vehicles (EVs) depend heavily on the sources of electricity-powered charging stations. Integrating renewable energy sources into EVCS is crucial to maximize their positive impact [9].
Photovoltaic (PV) systems are typically employed as renewable energy sources in EVCS. It is the most extensively researched renewable energy source because of its affordability, accessibility, and easy maintenance [10]. Several studies [11-13] have been conducted on PV-based EVCS. Solar irradiation is intermittent; hence, the power injected into the grid relies on the amount of solar irradiation available [14]. Wind energy is another common form of renewable energy. Wind energy is a limitless, cost-free, and sustainable resource. Wind turbines are a low-carbon method for harnessing the kinetic energy of wind. Wind-energy-powered EVCS have been reported in the literature [15-17].
The weather affects the amount of energy produced by renewable energy sources. The efficiency of PV depends on solar radiation, and the wind energy depends on the direction of the wind and its velocity. As a result, different sources of energy must be identified to supply electrical energy, because the same amount of energy cannot be delivered continuously. Electrical energy may be produced using fuel cells, regardless of weather conditions. In a fuel cell system, hydrogen is employed as the fuel and can be produced through electrolysis [18]. The integration of PV and fuel-cell systems was reviewed in [19].
Most earlier publications have concentrated on integrating two renewable energy sources: solar and fuel cells [18], and solar and wind [20, 21]. This study is inspired by the development of an autonomous EVCS that integrates various renewable energy resources.
The uniqueness of this study can be summarized as follows:
- Establishment of novel and cutting-edge autonomous charging infrastructure for EVCS.
- The focus is on combining various renewable energy sources, such as wind, solar, and hydrogen fuel cells, to develop an all-encompassing Battery Energy Storage (BES) system for EVCS.
- Deployment of a BES unit array in parallel to guarantee EVCS operation even in the event of primary energy source failure.
- Utilization of a BES equipped with a unidirectional buck converter as a single EVCS terminal; this arrangement may be expanded to support multiple terminal ports at different voltage levels.
- To improve the primary energy supply, a PI-based unidirectional boost converter for wind and photovoltaic energy sources was proposed.
- Integrating wind and photovoltaic energy as EVCS's main energy sources.
- Integrating a fuel cell with a unidirectional variable DC voltage regulator to supply backup or reserve.
1.2 Description of the Proposed Charging Station
Figure 1.1 illustrates the potential structure of the autonomous EVCS system. An autonomous EVCS system is an off-grid design that uses renewable energy as its source of power. The primary renewable energy sources, PV and wind, supply energy to the EVCS. The terminals of the PV and the wind were connected to the boost converter. This system optimizes the efficiency of the solar panel and wind turbine by ensuring their maximum power point tracking (MPPT) conditions are met. This is achieved by matching the voltage levels from these primary sources with the DC bus voltage. The generated energy varies according to the solar irradiation level and wind velocity, and it is volatile. To overcome this, the BES bank and hydrogen-powered fuel cell are connected to the DC bus through a DC-DC converter. The charge controller is a one-quadrant converter, whereas the DC-DC converter has two quadrants.
Upon closing switch 1, the autonomous EVCS is propelled by the primary renewable energy sources. When switch 2 is engaged, the BES bank becomes fully connected to the DC bus, thereby becoming available to supplement the power supply from the primary sources. If the primary renewable energy sources are either unavailable or inadequate to satisfy the energy demand, the autonomous EVCS seamlessly switches to draw power from BES 3, which is connected to an auxiliary renewable source in the form of a hydrogen-powered fuel cell. In this mode, the fuel cell acts as a reserve power source for the EVCS.
Figure 1.1 Proposed autonomous system.
To facilitate the energization of the fuel cell by BES 3 of the BES bank, switch 3 is closed. However, this requires that switches 1 and 2 be opened to disconnect the primary sources, BES 1 and BES 2, of the BES bank. Consequently, under such circumstances, energization is not possible for the BES bank. because all switches are open. However, it is noteworthy that BES 3 of the BES bank can still supply energy to the EVCS to the extent of its capacity. In the alternate scenario, when switch 2 is engaged, the entire BES bank becomes available to supply the required energy to the EVCS up to its full capacity.
The proposed off-grid design does not depend on a single charging energy source. Hence, the proposed design acts as an autonomous system for charging the EVCS. If required, the EVCS port can be extended to multiple ports.
The operational configurations of the autonomous EVCS system described above, along with the corresponding energy-supply flexibility, testify to the innovative layout of this state-of-the-art technology. This system's skillful integration and exploitation of primary and auxiliary renewable energy sources is extremely amazing, and serves as a model for sustainable energy solutions.
1.3 Design and Analysis of the System
1.3.1 PV System
The electrical design of the system includes the...
