Publisher Description

Electric vehicles are changing transportation dramatically and this unique book merges the many disciplines that contribute research to make EV possible, so the reader is informed about all the underlying science and technologies driving the change. An emission-free mobility system is the only way to save the world from the greenhouse effect and other ecological issues. This belief has led to a tremendous growth in the demand for electric vehicles (EV) and hybrid electric vehicles (HEV), which are predicted to have a promising future based on the goals fixed by the European Commission's Horizon 2020 program. This book brings together the research that has been carried out in the EV/HEV sector and the leading role of advanced optimization techniques with artificial intelligence (AI). This is achieved by compiling the findings of various studies in the electrical, electronics, computer, and mechanical domains for the EV/HEV system. In addition to acting as a hub for information on these research findings, the book also addresses the challenges in the EV/HEV sector and provides proven solutions that involve the most promising AI techniques. Since the commercialization of EVs/HEVs still remains a challenge in industries in terms of performance and cost, these are the two tradeoffs which need to be researched in order to arrive at an optimal solution. Therefore, this book focuses on the convergence of various technologies involved in EVs/HEVs. Since all countries will gradually shift from conventional internal combustion (IC) engine-based vehicles to EVs/HEVs in the near future, it also serves as a useful reliable resource for multidisciplinary researchers and industry teams.

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Electric vehicles are changing transportation dramatically and this unique book merges the many disciplines that contribute research to make EV possible, so the reader is informed about all the underlying science and technologies driving the change. An emission-free mobility system is the only way to save the world from the greenhouse effect and other ecological issues. This belief has led to a tremendous growth in the demand for electric vehicles (EV) and hybrid electric vehicles (HEV), which are predicted to have a promising future based on the goals fixed by the European Commission's Horizon 2020 program. This book brings together the research that has been carried out in the EV/HEV sector and the leading role of advanced optimization techniques with artificial intelligence (AI). This is achieved by compiling the findings of various studies in the electrical, electronics, computer, and mechanical domains for the EV/HEV system. In addition to acting as a hub for information on these research findings, the book also addresses the challenges in the EV/HEV sector and provides proven solutions that involve the most promising AI techniques. Since the commercialization of EVs/HEVs still remains a challenge in industries in terms of performance and cost, these are the two tradeoffs which need to be researched in order to arrive at an optimal solution. Therefore, this book focuses on the convergence of various technologies involved in EVs/HEVs. Since all countries will gradually shift from conventional internal combustion (IC) engine-based vehicles to EVs/HEVs in the near future, it also serves as a useful reliable resource for multidisciplinary researchers and industry teams. Audience The core audience is researchers, industry engineers and post-graduate students working in artificial intelligent techniques, motor control schemes, battery management systems (BMS), IoT- based systems for EV/HEVs, front end display schemes for EV/HEV, parameter estimation using AI-based methods, renewable energy systems, and maximum power tracking.

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Electric vehicles are changing transportation dramatically and this unique book merges the many disciplines that contribute research to make EV possible, so the reader is informed about all the underlying science and technologies driving the change. An emission-free mobility system is the only way to save the world from the greenhouse effect and other ecological issues. This belief has led to a tremendous growth in the demand for electric vehicles (EV) and hybrid electric vehicles (HEV), which are predicted to have a promising future based on the goals fixed by the European Commission's Horizon 2020 program. This book brings together the research that has been carried out in the EV/HEV sector and the leading role of advanced optimization techniques with artificial intelligence (AI). This is achieved by compiling the findings of various studies in the electrical, electronics, computer, and mechanical domains for the EV/HEV system. In addition to acting as a hub for information on these research findings, the book also addresses the challenges in the EV/HEV sector and provides proven solutions that involve the most promising AI techniques. Since the commercialization of EVs/HEVs still remains a challenge in industries in terms of performance and cost, these are the two tradeoffs which need to be researched in order to arrive at an optimal solution. Therefore, this book focuses on the convergence of various technologies involved in EVs/HEVs. Since all countries will gradually shift from conventional internal combustion (IC) engine-based vehicles to EVs/HEVs in the near future, it also serves as a useful reliable resource for multidisciplinary researchers and industry teams. Audience The core audience is researchers, industry engineers and post-graduate students working in artificial intelligent techniques, motor control schemes, battery management systems (BMS), IoT- based systems for EV/HEVs, front end display schemes for EV/HEV, parameter estimation using AI-based methods, renewable energy systems, and maximum power tracking.

Author Biography

Chitra A. received her PhD from Pondicherry University and is now an associate professor in the School of Electrical Engineering, at Vellore Institute of Technology, Vellore, India. She has published many papers in SCI journals and her research areas include PV-based systems, neural networks, induction motor drives, reliability analysis of multilevel inverters, and electrical vehicles. Sanjeevikumar Padmanaban obtained his PhD from the University of Bologna, Italy, in 2012, and since 2018, he has been a faculty member in the Department of Energy Technology, Aalborg University, Esbjerg, Denmark. He has authored more than 300 scientific papers. Jens Bo Holm-Nielsen currently works at the Department of Energy Technology, Aalborg University and is Head of the Esbjerg Energy Section. He has executed many large-scale European Union and United Nations projects in research aspects of bioenergy, biorefinery processes, the full chain of biogas and green engineering. He has authored more than 100 scientific papers. S. Himavathi received her PhD degree in the area of fuzzy modelling from Anna University, Chennai, India in 2003. Currently, she is a professor in the Department of Electrical and Electronics Engineering, Pondicherry Engineering College, Pondicherry, India.

Table of Contents

Preface xiii 1 IoT-Based Battery Management System for Hybrid Electric Vehicle 1
Sivaraman Palanisamy and Sharmeela Chenniappan 1.1 Introduction 1 1.2 Battery Configurations 3 1.3 Types of Batteries for HEV and EV 5 1.4 Functional Blocks of BMS 6 1.4.1 Components of BMS System 7 1.5 IoT-Based Battery Monitoring System 11 References 14 2 A Noble Control Approach for Brushless Direct Current Motor Drive Using Artificial Intelligence for Optimum Operation of the Electric Vehicle 17
Upama Das, Pabitra Kumar Biswas and Chiranjit Sain 2.1 Introduction 18 2.2 Introduction of Electric Vehicle 19 2.2.1 Historical Background of Electric Vehicle 19 2.2.2 Advantages of Electric Vehicle 20 2.2.2.1 Environmental 20 2.2.2.2 Mechanical 20 2.2.2.3 Energy Efficiency 20 2.2.2.4 Cost of Charging Electric Vehicles 21 2.2.2.5 The Grid Stabilization 21 2.2.2.6 Range 21 2.2.2.7 Heating of EVs 22 2.2.3 Artificial Intelligence 22 2.2.4 Basics of Artificial Intelligence 23 2.2.5 Advantages of Artificial Intelligence in Electric Vehicle 24 2.3 Brushless DC Motor 24 2.4 Mathematical Representation Brushless DC Motor 25 2.5 Closed-Loop Model of BLDC Motor Drive 30 2.5.1 P-I Controller & I-P Controller 31 2.6 PID Controller 32 2.7 Fuzzy Control 33 2.8 Auto-Tuning Type Fuzzy PID Controller 34 2.9 Genetic Algorithm 35 2.10 Artificial Neural Network-Based Controller 36 2.11 BLDC Motor Speed Controller With ANN-Based PID Controller 37 2.11.1 PID Controller-Based on Neuro Action 38 2.11.2 ANN-Based on PID Controller 38 2.12 Analysis of Different Speed Controllers 39 2.13 Conclusion 41 References 42 3 Optimization Techniques Used in Active Magnetic Bearing System for Electric Vehicles 49
Suraj Gupta, Pabitra Kumar Biswas, Sukanta Debnath and Jonathan Laldingliana 3.1 Introduction 50 3.2 Basic Components of an Active Magnetic Bearing (AMB) 54 3.2.1 Electromagnet Actuator 54 3.2.2 Rotor 54 3.2.3 Controller 55 3.2.3.1 Position Controller 56 3.2.3.2 Current Controller 56 3.2.4 Sensors 56 3.2.4.1 Position Sensor 56 3.2.4.2 Current Sensor 57 3.2.5 Power Amplifier 57 3.3 Active Magnetic Bearing in Electric Vehicles System 58 3.4 Control Strategies of Active Magnetic Bearing for Electric Vehicles System 59 3.4.1 Fuzzy Logic Controller (FLC) 59 3.4.1.1 Designing of Fuzzy Logic Controller (FLC) Using MATLAB 60 3.4.2 Artificial Neural Network (ANN) 63 3.4.2.1 Artificial Neural Network Using MATLAB 63 3.4.3 Particle Swarm Optimization (PSO) 67 3.4.4 Particle Swarm Optimization (PSO) Algorithm 68 3.4.4.1 Implementation of Particle Swarm Optimization for Electric Vehicles System 70 3.5 Conclusion 71 References 72 4 Small-Signal Modelling Analysis of Three-Phase Power Converters for EV Applications 77
Mohamed G. Hussien, Sanjeevikumar Padmanaban, Abd El-Wahab Hassan and Jens Bo Holm-Nielsen 4.1 Introduction 77 4.2 Overall System Modelling 79 4.2.1 PMSM Dynamic Model 79 4.2.2 VSI-Fed SPMSM Mathematical Model 80 4.3 Mathematical Analysis and Derivation of the Small-Signal Model 86 4.3.1 The Small-Signal Model of the System 86 4.3.2 Small-Signal Model Transfer Functions 87 4.3.3 Bode Diagram Verification 96 4.4 Conclusion 100 References 100 5 Energy Management of Hybrid Energy Storage System in PHEV With Various Driving Mode 103
S. Arun Mozhi, S. Charles Raja, M. Saravanan and J. Jeslin Drusila Nesamalar 5.1 Introduction 104 5.1.1 Architecture of PHEV 104 5.1.2 Energy Storage System 105 5.2 Problem Description and Formulation 106 5.2.1 Problem Description 106 5.2.2 Objective 106 5.2.3 Problem Formulation 106 5.3 Modeling of HESS 107 5.4 Results and Discussion 108 5.4.1 Case 1: Gradual Acceleration of Vehicle 108 5.4.2 Case 2: Gradual Deceleration of Vehicle 109 5.4.3 Case 3: Unsystematic Acceleration and Deceleration of Vehicle 110 5.5 Conclusion 111 References 112 6 Reliability Approach for the Power Semiconductor Devices in EV Applications 115
Krishnachaitanya, D., Chitra, A. and Biswas, S.S. 6.1 Introduction 115 6.2 Conventional Methods for Prediction of Reliability for Power Converters 116 6.3 Calculation Process of the Electronic Component 118 6.4 Reliability Prediction for MOSFETs 119 6.5 Example: Reliability Prediction for Power Semiconductor Device 121 6.6 Example: Reliability Prediction for Resistor 122 6.7 Conclusions 123 References 123 7 Modeling, Simulation and Analysis of Drive Cycles for PMSM-Based HEV With Optimal Battery Type 125
Chitra, A., Srivastava, Shivam, Gupta, Anish, Sinha, Rishu, Biswas, S.S. and Vanishree, J. 7.1 Introduction 126 7.2 Modeling of Hybrid Electric Vehicle 127 7.2.1 Architectures Available for HEV 128 7.3 Series—Parallel Hybrid Architecture 129 7.4 Analysis With Different Drive Cycles 129 7.4.1 Acceleration Drive Cycle 130 7.4.1.1 For 30% State of Charge 130 7.4.1.2 For 60% State of Charge 131 7.4.1.3 For 90% State of Charge 131 7.5 Cruising Drive Cycle 132 7.6 Deceleration Drive Cycle 132 7.6.1 For 30% State of Charge 134 7.6.2 For 60% State of Charge 136 7.6.3 For 90% State of Charge 137 7.7 Analysis of Battery Types 139 7.8 Conclusion 140 References 141 8 Modified Firefly-Based Maximum Power Point Tracking Algorithm for PV Systems Under Partial Shading Conditions 143
Chitra, A., Yogitha, G., Karthik Sivaramakrishnan, Razia Sultana, W. and Padmanaban, S. 8.1 Introduction 143 8.2 System Block Diagram Specifications 146 8.3 Photovoltaic System Modeling 148 8.4 Boost Converter Design 150 8.5 Incremental Conductance Algorithm 152 8.6 Under Partial Shading Conditions 153 8.7 Firefly Algorithm 154 8.8 Implementation Procedure 156 8.9 Modified Firefly Logic 157 8.10 Results and Discussions 159 8.11 Conclusion 162 References 162 9 Induction Motor Control Schemes for Hybrid Electric Vehicles/Electric Vehicles 165
Sarin, M.V., Chitra, A., Padmanaban, S. and Venkadesan, A. 9.1 Introduction 166 9.2 Control Schemes of IM 167 9.2.1 Scalar Control 167 9.3 Vector Control 168 9.4 Modeling of Induction Machine 169 9.5 Controller Design 174 9.6 Simulations and Results 175 9.7 Conclusions 176 References 177 10 Intelligent Hybrid Battery Management System for Electric Vehicle 179
Rajalakshmi, M. and Razia Sultana, W. 10.1 Introduction 179 10.2 Energy Storage System (ESS) 181 10.2.1 Lithium-Ion Batteries 183 10.2.1.1 Lithium Battery Challenges 183 10.2.2 Lithium–Ion Cell Modeling 184 10.2.3 Nickel-Metal Hydride Batteries 186 10.2.4 Lead-Acid Batteries 187 10.2.5 Ultracapacitors (UC) 187 10.2.5.1 Ultracapacitor Equivalent Circuit 187 10.2.6 Other Battery Technologies 189 10.3 Battery Management System 190 10.3.1 Need for BMS 191 10.3.2 BMS Components 192 10.3.3 BMS Architecture/Topology 193 10.3.4 SOC/SOH Determination 193 10.3.5 Cell Balancing Algorithms 197 10.3.6 Data Communication 197 10.3.7 The Logic and Safety Control 198 10.3.7.1 Power Up/Down Control 198 10.3.7.2 Charging and Discharging Control 199 10.4 Intelligent Battery Management System 199 10.4.1 Rule-Based Control 201 10.4.2 Optimization-Based Control 201 10.4.3 AI-Based Control 202 10.4.4 Traffic (Look Ahead Method)-Based Control 203 10.5 Conclusion 203 References 203 11 A Comprehensive Study on Various Topologies of Permanent Magnet Motor Drives for Electric Vehicles Application 207
Chiranjit Sain, Atanu Banerjee and Pabitra Kumar Biswas 11.1 Introduction 208 11.2 Proposed Design Considerations of PMSM for Electric Vehicle 209 11.3 Impact of Digital Controllers 211 11.3.1 DSP-Based Digital Controller 212 11.3.2 FPGA-Based Digital Controller 212 11.4 Electric Vehicles Smart Infrastructure 212 11.5 Conclusion 214 References 215 12 A New Approach for Flux Computation Using Intelligent Technique for Direct Flux Oriented Control of Asynchronous Motor 219
A. Venkadesan, K. Sedhuraman, S. Himavathi and A. Chitra 12.1 Introduction 220 12.2 Direct Field-Oriented Control of IM Drive 221 12.3 Conventional Flux Estimator 222 12.4 Rotor Flux Estimator Using CFBP-NN 223 12.5 Comparison of Proposed CFBP-NN With Existing CFBP-NN for Flux Estimation 224 12.6 Performance Study of Proposed CFBP-NN Using MATLAB/SIMULINK 225 12.7 Practical Implementation Aspects of CFBP-NN-Based Flux Estimator 229 12.8 Conclusion 231 References 231 13 A Review on Isolated DC–DC Converters Used in Renewable Power Generation Applications 233
Ingilala Jagadeesh and V. Indragandhi 13.1 Introduction 233 13.2 Isolated DC–DC Converter for Electric Vehicle Applications 234 13.3 Three-Phase DC–DC Converter 238 13.4 Conclusion 238 References 239 14 Basics of Vector Control of Asynchronous Induction Motor and Introduction to Fuzzy Controller 241
S.S. Biswas 14.1 Introduction 241 14.2 Dynamics of Separately Excited DC Machine 243 14.3 Clarke and Park Transforms 244 14.4 Model Explanation 251 14.5 Motor Parameters 252 14.6 PI Regulators Tuning 254 14.7 Future Scope to Include Fuzzy Control in Place of PI Controller 256 14.8 Conclusion 257 References 258 Index 259

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