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Submit a ProposalSmart Charging Infrastructures
One Line 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.
Audience
Researchers and industrialists working with charging systems, artificial intelligence, energy management systems, smart charging systems, IoT solutions for smart charging, and AI solutions for establishing charging infrastructure.
Researchers and industrialists working with charging systems, artificial intelligence, energy management systems, smart charging systems, IoT solutions for smart charging, and AI solutions for establishing charging infrastructure.
Description
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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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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Author / Editor Details
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.
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.
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Table of Contents
Preface
1. Towards Sustainable Mobility: An Autonomous Electric Vehicle Charging Station Powered by Multifaceted Renewable Energy Sources
K. Kathiravan and P. N. Rajnarayanan
1.1 Introduction
1.2 Description of the Proposed Charging Station
1.3 Design and Analysis of the System
1.3.1 PV System
1.3.2 Wind
1.3.3 Fuel Cell
1.3.4 Boost Converter with MPPT
1.3.5 Buck Converter
1.3.6 EV Charge Controller
1.4 System Design Calculations
1.4.1 PV System
1.4.2 Wind Turbine
1.4.3 Fuel Cell
1.4.4 Battery Energy Storage System
1.5 Result Analysis
1.5.1 Case 1: PV BES Setup
1.5.2 Case 2: PV BES Wind Setup
1.5.3 Case 3: PV BES FC Setup
1.5.4 Case 4: BES Wind Setup
1.5.5 Case 5: BES FC Setup
1.5.6 Case 6: BES Wind FC Setup
1.5.7 Case 7: PV BES Wind FC Setup
1.6 Conclusion and Future Outlook
References
2. Innovating EV Charging Infrastructure: A Hybrid Energy Storage System Approach for Solar Powered-Based DC Microgrid
Sandeep S. D., Satyajit Mohanty and Shashi Bhushan
2.1 Introduction
2.2 System Architecture
2.2.1 Modeling of PV System
2.2.2 Battery Storage System
2.2.3 Supercapacitor
2.3 Power Management System
2.4 Results and Discussion
2.5 Conclusion
References
3. Design of Intermediate Charging Facilitated Port Configuration of Charging Station with Consideration of Reliability and Cost
K. Vaishali and D. Rama Prabha
3.1 Introduction
3.2 Methodology for Estimating the Reliability Probability of Charging Ports
3.3 Introduced Pattern Identical and Non-Identical Configuration
3.4 Results and Discussions
3.4.1 Identical Port Configuration
3.5 Conclusion
References
4. AI-Based Smart Charging Infrastructures: Revolutionizing Electric Vehicle Integration
V. Bagyaveereswaran, S.L. Arun, M. Manimozhi and B. Jaganatha Pandian
4.1 Introduction
4.2 Fundamentals of Smart Charging
4.2.1 Benefits of Smart-Charging Infrastructure
4.2.2 Deployment Factors for Smart Charging
4.3 Role of AI in Smart Charging
4.3.1 Understanding Artificial Intelligence in Charging Infrastructures
4.3.2 Machine Learning Algorithms for Predictive Charging
4.3.2.1 Benefits of ML-Powered Predictive Charging
4.3.3 Real-Time Data Analytics and Optimization Techniques
4.3.3.1 Real-Time Data Analytics
4.3.3.2 Optimization Techniques
4.3.4 AI-Based Demand Response Management
4.3.4.1 Understanding Demand Response Management
4.3.4.2 Benefits of AI-Based DRM for Charging Stations
4.4 Components of AI-Based Smart Charging Systems
4.4.1 Sensors and IoT Devices for Data Collection
4.4.2 Cloud Computing and Edge Computing Platforms
4.4.2.1 Cloud Computing Platforms
4.4.2.2 Edge Computing Platforms
4.4.3 Communication Protocols and Network Infrastructure
4.4.4 Control Algorithms for Dynamic Charging Control
4.5 Challenges and Future Directions
4.5.1 Security and Privacy Concerns in AI-Driven Infrastructures
4.5.2 Scalability and Interoperability Issues
4.5.3 Regulatory and Policy Implications
4.5.4 Emerging Technologies and Trends in Smart Charging
Bibliography
5. EV Smart Charging Using RES—Challenges
Sowmya Ramachandradurai, Joylin Mary J. and D.F. Jingle Jabha
Acronyms
5.1 Introduction
5.2 System Description
5.2.1 Description of Photovoltaic (PV) Source
5.2.2 Description of Wind Energy
5.2.3 Description of EV
5.2.4 Objective Function
5.2.5 Constraint Conditions
5.2.5.1 Equality Constraint
5.2.5.2 Generator Constraint
5.2.6 Framework of Optimization Algorithm
5.3 Results and Discussion
5.4 Conclusion
References
6. Green Energy-Based Active Grid Optimization Using Deep Learning for EV Charging Infrastructure
D. Shruthi, R. Raja Singh, S. L. Arun and R. Rengaraj
6.1 Introduction
6.2 Active Grid and Edge Computing
6.3 Modeling of Standalone Hybrid System
6.3.1 Solar PV Cell Model
6.3.2 Wind Turbine Model
6.3.3 EV Battery Model
6.4 Deep Learning and Its Implementation
6.4.1 Energy Demand Pattern
6.4.2 Wind Speed
6.4.3 Solar Irradiation
6.5 Micro-Grid and Control Mechanism
6.5.1 Microgrid Functioning in Different Modes
6.5.1.1 Islanded Mode
6.5.1.2 Multiple Microgrid Control with Centralized Energy Storage System
6.5.2 Energy Storage System Simulation
6.5.3 Wind Energy Storage System Simulation
6.5.4 EV Battery Control Mechanism
6.6 Results and Discussion
6.6.1 Deep Learning
6.6.2 Matlab/Simulink Model
6.7 Conclusion
References
7. Bearing Fault Diagnosis in Permanent Magnet Synchronous Motor Using Deep Neural Network
Geetha G., Shanthini C., Geethanjali P. and Yokkeshwaran K.
7.1 Introduction
7.2 Methodology
7.2.1 Discrete Wavelet Transform
7.2.2 Kurtogram
7.2.3 Deep Neural Network-VGG
7.3 Results and Discussion
7.3.1 Case 1: Using DWT
7.3.2 Case 2: Using Kurtogram
7.4 Conclusion
References
8. Enhancing Efficiency in Bidirectional CLLC Resonant Converters: A Hybrid Control Approach
Aryan Chaturvedi, M. Rajalakshmi and Razia Sultana W.
8.1 Introduction
8.2 Bidirectional CLLC Resonant Converter
8.3 Working by Controlling Conversion of Frequency
8.4 How the Inductance Factor (k) Affects Voltage Gain (M)
8.5 How the Quality Factor (Q) Influences Voltage Gain (M)
8.6 Understanding Frequency-Conversion Control
8.7 Combining Frequency Conversion and Phase Shifting with a Hybrid Control Strategy
8.8 Simulation Results and Discussion
8.9 Conclusion
References
9. IoT-Based Smart Charging Systems
Tanmay Sharma, Pramatha S. Vasishtha and Razia Sultana W.
Abbreviation
9.1 Introduction
9.2 Remote Monitoring and Telematics
9.3 Infrastructure Connectivity for Charging
9.4 Autonomous Driving and Advanced Driver Assistance Systems (ADAS)
9.5 Logistics and Fleet Management
9.6 Sustainability and Energy Management
9.7 Services and User Experience
9.8 Algorithms for Shortest Path Finding
9.8.1 Dijkstra’s Algorithm
9.8.2 Bellman–Ford Algorithm
9.8.3 A* Search Algorithm
9.8.4 Floyd–Warshall Algorithm
9.8.5 Bidirectional Search Algorithm
9.8.6 Rapidly Exploring Random Tree Algorithm
9.8.7 Probabilistic Roadmap Algorithm
9.8.8 Hybrid RRT-PRM Model
9.9 Advantages
9.10 Conclusion
References
10. Embedded Control of Power Converters in E-Mobility
Yeddula Pedda Obulesu and Pallamkuppam Vinodh Kumar
10.1 Introduction
10.1.1 Key Components of EV
10.2 Evolution of Digital Control in Power Converters
10.2.1 Key Functions of Embedded Control of Power Converters
10.2.2 Components of Embedded Control Systems
10.2.3 Control Strategies
10.2.4 Challenges and Innovations
10.3 Embedded Systems and Digital Control
10.4 Tools and Technologies for Digital Control Systems
10.5 Implementation of Embedded Digital Control Based on DSPs
10.6 Key Components in Embedded Digital Controllers
10.7 Signal Generation for Power Converter Devices
10.7.1 Operating Frequency and Resolution
10.7.2 Modes of Operation
10.8 Field Programmable Gate Arrays (FPGAs)
10.9 Code Composer Studio and JTAG
10.9.1 Functional Requirements of a Non-Inverting Buck-Boost Converter
10.10 Software Development Environment (SDE): Compiler, Linker, Assembler, and Downloader
10.11 STM-Based Embedded Controllers
10.12 Main Traction Inverter
10.13 On-Board Charger
10.14 Battery Management System (BMS)
Acknowledgement
11. Solar Piezo Hybrid Power Charging System
Vedanth S., Varun Baalaji S., Shairahul Gautam S., Sharan Vikash, Ashwini K. and R. Resmi
11.1 Introduction
11.2 Methodology
11.2.1 Simulation Modelling in MATLAB/Simulink
11.2.2 Brief Description of Various Parts
11.2.3 Block Diagram and Working
11.3 Operating Modes
11.4 Result and Discussion
11.4.1 Simulation Results in MATLAB/Simulink
11.4.2 Hardware Implementation
11.4.3 IoT Integration
11.5 Conclusion
Acknowledgments
References
12. EV Power Train Performance with DC Motor
Nithya Chandran and R. Resmi
12.1 Introduction
12.2 Methodology
12.2.1 Architecture of Battery EV Power Train
12.2.2 Requirements of Electric Traction Motors
12.2.3 Machine Topologies
12.2.4 Vehicle Dynamics and Estimation of Output Parameters
12.3 Results and Discussion
12.3.1 Simulation Results
12.3.2 Cost–Benefit Analysis
12.4 Conclusion
Acknowledgment
References
13. RC Vehicle for Delivery
Vemulapati Dhanunjaya Reddy, Mallireddy Jayanthi Reddy, Manoj Kumar S., R. Resmi and Y. N. V. Ganesh
13.1 Introduction
13.1.1 Description of the RC Vehicle
13.1.1.1 Functioning of L298N Motor Drive
13.1.1.2 The Functioning of ESP32 Camera Module
13.2 Literature Review
13.2.1 Research Gap
13.3 Methodology
13.3.1 Radio-Controlled (RC) Vehicle
13.3.2 Camera System
13.3.3 Pan-Tilt Mechanism
13.3.4 Anti-Theft Locking System
13.3.5 Mobile-Application Interface
13.4 Result and Discussions
13.5 Conclusion
References
14. Aerodynamic Drag Reduction in Heavy Vehicles
Amutha Prabha N., Abhishek Gudipalli, Dyuti Ranjan Acharya, Indragandhi V. and Manee Sangaran Diagarajan
14.1 Introduction
14.2 Literature Survey
14.3 Methodology
14.3.1 Geometry and Meshing
14.3.2 Inlet, Outlet, and Boundary Conditions
14.3.3 Computational Procedure
14.4 Results and Discussion
14.4.1 Pressure Contour Comparison
14.4.2 Velocity Contour Comparison
14.4.3 Streamline Profile
14.4.4 VelocityVector Profile
14.5 Analysis Comparison
14.5.1 Streamline Comparison at Rear to Understand Flow Characteristics
14.5.2 Drag Force Comparison
14.6 Conclusion
References
15. Review of Optimization-Based Sensor Fault Detection for Lithium-Ion Batteries in Electric Vehicles
Mohana Devi S. and V. Bagyaveereswaran
15.1 Introduction
15.2 Gestalt of Battery Sensors
15.3 Utilization of Battery Sensors in Electric Vehicles
15.3.1 Significance of Sensor Fault Identification in Li-Ion Batteries
15.3.2 Sensor Fault Modeling
15.4 Optimization in Sensor Fault Detection
15.5 Advantages and Category of Metaheuristic Algorithm
15.5.1 Applications of Metaheuristic Approach for Sensor Fault Detection in Lithium-Ion Batteries
15.5.2 Challenges in Fault Detection
15.6 Result and Discussion
15.7 Conclusion
References
16. Development of a Hybrid Foot‑Stamping Bicycle with Dynamic Electric Support: A Sustainable Alternative to Traditional Pedal and Electric Bicycles
Sumant Shyam, Jahnavi Gayatri D., Anushka and Abhishek Gudpalli
16.1 Introduction
16.2 Background and Motivation
16.2.1 Limitations of Traditional Pedal-Based Bicycles
16.2.2 The Rise of Electric Bicycles (E-Bikes)
16.2.3 The Need for a Hybrid Solution
16.2.4 Innovative Foot-Powered System
16.2.5 Electric Dynamic Support
16.2.6 Motivation for the Proposed Design
16.2.7 Design Concepts
16.3 Study Objectives
16.3.1 Design and Development of the Foot-Stamping Mechanism
16.3.2 Integration of Dynamic Electric Support
16.3.3 Performance Evaluation and Efficiency Analysis
16.3.4 Sustainability and Environmental Impact
16.3.5 User Experience and Accessibility
16.3.6 Prototype Development and Testing
16.4 Scope of Study
16.4.1 Design and Engineering Focus
16.4.2 Prototyping and System Testing
16.4.3 Energy Efficiency and Sustainability Assessment
16.4.4 User Experience and Practical Application
16.4.5 Technical and Financial Feasibility
16.4.6 Limitations and Constraints
16.5 Conclusion
References
17. A Novel Multilevel Inverter with Reduced Switch for Electric Vehicle Applications
Vijaya Sambhavi Y. and Vijayapriya R.
17.1 Introduction
17.2 Proposed MLI
17.2.1 Description and Analysis of Proposed MLI Circuit
17.3 Control Strategy and Simulation Outcomes
17.4 Conclusion
References
Index
Back to Top
Preface
1. Towards Sustainable Mobility: An Autonomous Electric Vehicle Charging Station Powered by Multifaceted Renewable Energy Sources
K. Kathiravan and P. N. Rajnarayanan
1.1 Introduction
1.2 Description of the Proposed Charging Station
1.3 Design and Analysis of the System
1.3.1 PV System
1.3.2 Wind
1.3.3 Fuel Cell
1.3.4 Boost Converter with MPPT
1.3.5 Buck Converter
1.3.6 EV Charge Controller
1.4 System Design Calculations
1.4.1 PV System
1.4.2 Wind Turbine
1.4.3 Fuel Cell
1.4.4 Battery Energy Storage System
1.5 Result Analysis
1.5.1 Case 1: PV BES Setup
1.5.2 Case 2: PV BES Wind Setup
1.5.3 Case 3: PV BES FC Setup
1.5.4 Case 4: BES Wind Setup
1.5.5 Case 5: BES FC Setup
1.5.6 Case 6: BES Wind FC Setup
1.5.7 Case 7: PV BES Wind FC Setup
1.6 Conclusion and Future Outlook
References
2. Innovating EV Charging Infrastructure: A Hybrid Energy Storage System Approach for Solar Powered-Based DC Microgrid
Sandeep S. D., Satyajit Mohanty and Shashi Bhushan
2.1 Introduction
2.2 System Architecture
2.2.1 Modeling of PV System
2.2.2 Battery Storage System
2.2.3 Supercapacitor
2.3 Power Management System
2.4 Results and Discussion
2.5 Conclusion
References
3. Design of Intermediate Charging Facilitated Port Configuration of Charging Station with Consideration of Reliability and Cost
K. Vaishali and D. Rama Prabha
3.1 Introduction
3.2 Methodology for Estimating the Reliability Probability of Charging Ports
3.3 Introduced Pattern Identical and Non-Identical Configuration
3.4 Results and Discussions
3.4.1 Identical Port Configuration
3.5 Conclusion
References
4. AI-Based Smart Charging Infrastructures: Revolutionizing Electric Vehicle Integration
V. Bagyaveereswaran, S.L. Arun, M. Manimozhi and B. Jaganatha Pandian
4.1 Introduction
4.2 Fundamentals of Smart Charging
4.2.1 Benefits of Smart-Charging Infrastructure
4.2.2 Deployment Factors for Smart Charging
4.3 Role of AI in Smart Charging
4.3.1 Understanding Artificial Intelligence in Charging Infrastructures
4.3.2 Machine Learning Algorithms for Predictive Charging
4.3.2.1 Benefits of ML-Powered Predictive Charging
4.3.3 Real-Time Data Analytics and Optimization Techniques
4.3.3.1 Real-Time Data Analytics
4.3.3.2 Optimization Techniques
4.3.4 AI-Based Demand Response Management
4.3.4.1 Understanding Demand Response Management
4.3.4.2 Benefits of AI-Based DRM for Charging Stations
4.4 Components of AI-Based Smart Charging Systems
4.4.1 Sensors and IoT Devices for Data Collection
4.4.2 Cloud Computing and Edge Computing Platforms
4.4.2.1 Cloud Computing Platforms
4.4.2.2 Edge Computing Platforms
4.4.3 Communication Protocols and Network Infrastructure
4.4.4 Control Algorithms for Dynamic Charging Control
4.5 Challenges and Future Directions
4.5.1 Security and Privacy Concerns in AI-Driven Infrastructures
4.5.2 Scalability and Interoperability Issues
4.5.3 Regulatory and Policy Implications
4.5.4 Emerging Technologies and Trends in Smart Charging
Bibliography
5. EV Smart Charging Using RES—Challenges
Sowmya Ramachandradurai, Joylin Mary J. and D.F. Jingle Jabha
Acronyms
5.1 Introduction
5.2 System Description
5.2.1 Description of Photovoltaic (PV) Source
5.2.2 Description of Wind Energy
5.2.3 Description of EV
5.2.4 Objective Function
5.2.5 Constraint Conditions
5.2.5.1 Equality Constraint
5.2.5.2 Generator Constraint
5.2.6 Framework of Optimization Algorithm
5.3 Results and Discussion
5.4 Conclusion
References
6. Green Energy-Based Active Grid Optimization Using Deep Learning for EV Charging Infrastructure
D. Shruthi, R. Raja Singh, S. L. Arun and R. Rengaraj
6.1 Introduction
6.2 Active Grid and Edge Computing
6.3 Modeling of Standalone Hybrid System
6.3.1 Solar PV Cell Model
6.3.2 Wind Turbine Model
6.3.3 EV Battery Model
6.4 Deep Learning and Its Implementation
6.4.1 Energy Demand Pattern
6.4.2 Wind Speed
6.4.3 Solar Irradiation
6.5 Micro-Grid and Control Mechanism
6.5.1 Microgrid Functioning in Different Modes
6.5.1.1 Islanded Mode
6.5.1.2 Multiple Microgrid Control with Centralized Energy Storage System
6.5.2 Energy Storage System Simulation
6.5.3 Wind Energy Storage System Simulation
6.5.4 EV Battery Control Mechanism
6.6 Results and Discussion
6.6.1 Deep Learning
6.6.2 Matlab/Simulink Model
6.7 Conclusion
References
7. Bearing Fault Diagnosis in Permanent Magnet Synchronous Motor Using Deep Neural Network
Geetha G., Shanthini C., Geethanjali P. and Yokkeshwaran K.
7.1 Introduction
7.2 Methodology
7.2.1 Discrete Wavelet Transform
7.2.2 Kurtogram
7.2.3 Deep Neural Network-VGG
7.3 Results and Discussion
7.3.1 Case 1: Using DWT
7.3.2 Case 2: Using Kurtogram
7.4 Conclusion
References
8. Enhancing Efficiency in Bidirectional CLLC Resonant Converters: A Hybrid Control Approach
Aryan Chaturvedi, M. Rajalakshmi and Razia Sultana W.
8.1 Introduction
8.2 Bidirectional CLLC Resonant Converter
8.3 Working by Controlling Conversion of Frequency
8.4 How the Inductance Factor (k) Affects Voltage Gain (M)
8.5 How the Quality Factor (Q) Influences Voltage Gain (M)
8.6 Understanding Frequency-Conversion Control
8.7 Combining Frequency Conversion and Phase Shifting with a Hybrid Control Strategy
8.8 Simulation Results and Discussion
8.9 Conclusion
References
9. IoT-Based Smart Charging Systems
Tanmay Sharma, Pramatha S. Vasishtha and Razia Sultana W.
Abbreviation
9.1 Introduction
9.2 Remote Monitoring and Telematics
9.3 Infrastructure Connectivity for Charging
9.4 Autonomous Driving and Advanced Driver Assistance Systems (ADAS)
9.5 Logistics and Fleet Management
9.6 Sustainability and Energy Management
9.7 Services and User Experience
9.8 Algorithms for Shortest Path Finding
9.8.1 Dijkstra’s Algorithm
9.8.2 Bellman–Ford Algorithm
9.8.3 A* Search Algorithm
9.8.4 Floyd–Warshall Algorithm
9.8.5 Bidirectional Search Algorithm
9.8.6 Rapidly Exploring Random Tree Algorithm
9.8.7 Probabilistic Roadmap Algorithm
9.8.8 Hybrid RRT-PRM Model
9.9 Advantages
9.10 Conclusion
References
10. Embedded Control of Power Converters in E-Mobility
Yeddula Pedda Obulesu and Pallamkuppam Vinodh Kumar
10.1 Introduction
10.1.1 Key Components of EV
10.2 Evolution of Digital Control in Power Converters
10.2.1 Key Functions of Embedded Control of Power Converters
10.2.2 Components of Embedded Control Systems
10.2.3 Control Strategies
10.2.4 Challenges and Innovations
10.3 Embedded Systems and Digital Control
10.4 Tools and Technologies for Digital Control Systems
10.5 Implementation of Embedded Digital Control Based on DSPs
10.6 Key Components in Embedded Digital Controllers
10.7 Signal Generation for Power Converter Devices
10.7.1 Operating Frequency and Resolution
10.7.2 Modes of Operation
10.8 Field Programmable Gate Arrays (FPGAs)
10.9 Code Composer Studio and JTAG
10.9.1 Functional Requirements of a Non-Inverting Buck-Boost Converter
10.10 Software Development Environment (SDE): Compiler, Linker, Assembler, and Downloader
10.11 STM-Based Embedded Controllers
10.12 Main Traction Inverter
10.13 On-Board Charger
10.14 Battery Management System (BMS)
Acknowledgement
11. Solar Piezo Hybrid Power Charging System
Vedanth S., Varun Baalaji S., Shairahul Gautam S., Sharan Vikash, Ashwini K. and R. Resmi
11.1 Introduction
11.2 Methodology
11.2.1 Simulation Modelling in MATLAB/Simulink
11.2.2 Brief Description of Various Parts
11.2.3 Block Diagram and Working
11.3 Operating Modes
11.4 Result and Discussion
11.4.1 Simulation Results in MATLAB/Simulink
11.4.2 Hardware Implementation
11.4.3 IoT Integration
11.5 Conclusion
Acknowledgments
References
12. EV Power Train Performance with DC Motor
Nithya Chandran and R. Resmi
12.1 Introduction
12.2 Methodology
12.2.1 Architecture of Battery EV Power Train
12.2.2 Requirements of Electric Traction Motors
12.2.3 Machine Topologies
12.2.4 Vehicle Dynamics and Estimation of Output Parameters
12.3 Results and Discussion
12.3.1 Simulation Results
12.3.2 Cost–Benefit Analysis
12.4 Conclusion
Acknowledgment
References
13. RC Vehicle for Delivery
Vemulapati Dhanunjaya Reddy, Mallireddy Jayanthi Reddy, Manoj Kumar S., R. Resmi and Y. N. V. Ganesh
13.1 Introduction
13.1.1 Description of the RC Vehicle
13.1.1.1 Functioning of L298N Motor Drive
13.1.1.2 The Functioning of ESP32 Camera Module
13.2 Literature Review
13.2.1 Research Gap
13.3 Methodology
13.3.1 Radio-Controlled (RC) Vehicle
13.3.2 Camera System
13.3.3 Pan-Tilt Mechanism
13.3.4 Anti-Theft Locking System
13.3.5 Mobile-Application Interface
13.4 Result and Discussions
13.5 Conclusion
References
14. Aerodynamic Drag Reduction in Heavy Vehicles
Amutha Prabha N., Abhishek Gudipalli, Dyuti Ranjan Acharya, Indragandhi V. and Manee Sangaran Diagarajan
14.1 Introduction
14.2 Literature Survey
14.3 Methodology
14.3.1 Geometry and Meshing
14.3.2 Inlet, Outlet, and Boundary Conditions
14.3.3 Computational Procedure
14.4 Results and Discussion
14.4.1 Pressure Contour Comparison
14.4.2 Velocity Contour Comparison
14.4.3 Streamline Profile
14.4.4 VelocityVector Profile
14.5 Analysis Comparison
14.5.1 Streamline Comparison at Rear to Understand Flow Characteristics
14.5.2 Drag Force Comparison
14.6 Conclusion
References
15. Review of Optimization-Based Sensor Fault Detection for Lithium-Ion Batteries in Electric Vehicles
Mohana Devi S. and V. Bagyaveereswaran
15.1 Introduction
15.2 Gestalt of Battery Sensors
15.3 Utilization of Battery Sensors in Electric Vehicles
15.3.1 Significance of Sensor Fault Identification in Li-Ion Batteries
15.3.2 Sensor Fault Modeling
15.4 Optimization in Sensor Fault Detection
15.5 Advantages and Category of Metaheuristic Algorithm
15.5.1 Applications of Metaheuristic Approach for Sensor Fault Detection in Lithium-Ion Batteries
15.5.2 Challenges in Fault Detection
15.6 Result and Discussion
15.7 Conclusion
References
16. Development of a Hybrid Foot‑Stamping Bicycle with Dynamic Electric Support: A Sustainable Alternative to Traditional Pedal and Electric Bicycles
Sumant Shyam, Jahnavi Gayatri D., Anushka and Abhishek Gudpalli
16.1 Introduction
16.2 Background and Motivation
16.2.1 Limitations of Traditional Pedal-Based Bicycles
16.2.2 The Rise of Electric Bicycles (E-Bikes)
16.2.3 The Need for a Hybrid Solution
16.2.4 Innovative Foot-Powered System
16.2.5 Electric Dynamic Support
16.2.6 Motivation for the Proposed Design
16.2.7 Design Concepts
16.3 Study Objectives
16.3.1 Design and Development of the Foot-Stamping Mechanism
16.3.2 Integration of Dynamic Electric Support
16.3.3 Performance Evaluation and Efficiency Analysis
16.3.4 Sustainability and Environmental Impact
16.3.5 User Experience and Accessibility
16.3.6 Prototype Development and Testing
16.4 Scope of Study
16.4.1 Design and Engineering Focus
16.4.2 Prototyping and System Testing
16.4.3 Energy Efficiency and Sustainability Assessment
16.4.4 User Experience and Practical Application
16.4.5 Technical and Financial Feasibility
16.4.6 Limitations and Constraints
16.5 Conclusion
References
17. A Novel Multilevel Inverter with Reduced Switch for Electric Vehicle Applications
Vijaya Sambhavi Y. and Vijayapriya R.
17.1 Introduction
17.2 Proposed MLI
17.2.1 Description and Analysis of Proposed MLI Circuit
17.3 Control Strategy and Simulation Outcomes
17.4 Conclusion
References
Index
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