Artificial Intelligent Techniques for Electric and Hybrid Electric Vehicles

Description

Electric vehicles/hybrid electric vehicles (EV/HEV) commercialization is still a challenge in industries in terms of performance and cost. The performance along with cost reduction are two tradeoffs which need to be researched to arrive at an optimal solution. This book focuses on the convergence of various technologies involved in EV/HEV. The book brings together the research that is being carried out in the field of EV/HEV whose leading role is by optimization techniques with artificial intelligence (AI). Other featured research includes green drive schemes which involve the possible renewable energy sources integration to develop eco-friendly green vehicles, as well as Internet of Things (IoT)-based techniques for EV/HEVs. Electric vehicle research involves multi-disciplinary expertise from electrical, electronics, mechanical engineering and computer science. Consequently, this book serves as a point of convergence wherein all these domains are addressed and merged and will serve as a potential resource for industrialists and researchers working in the domain of electric vehicles. Cover Title Page Copyright Page Contents Preface Chapter 1 IoT-Based Battery Management System for Hybrid Electric Vehicle 1.1 Introduction 1.2 Battery Configurations 1.3 Types of Batteries for HEV and EV 1.4 Functional Blocks of BMS 1.4.1 Components of BMS System 1.5 IoT-Based Battery Monitoring System References Chapter 2 A Noble Control Approach for Brushless Direct Current Motor Drive Using Artificial Intelligence for Optimum Operation of the E 2.1 Introduction 2.2 Introduction of Electric Vehicle 2.2.1 Historical Background of Electric Vehicle 2.2.2 Advantages of Electric Vehicle 2.2.2.1 Environmental 2.2.2.2 Mechanical 2.2.2.3 Energy Efficiency 2.2.2.4 Cost of Charging Electric Vehicles 2.2.2.5 The Grid Stabilization 2.2.2.6 Range 2.2.2.7 Heating of EVs 2.2.3 Artificial Intelligence 2.2.4 Basics of Artificial Intelligence 2.2.5 Advantages of Artificial Intelligence in Electric Vehicle 2.3 Brushless DC Motor 2.4 Mathematical Representation Brushless DC Motor 2.5 Closed-Loop Model of BLDC Motor Drive 2.5.1 P-I Controller & I-P Controller 2.6 PID Controller 2.7 Fuzzy Control 2.8 Auto-Tuning Type Fuzzy PID Controller 2.9 Genetic Algorithm 2.10 Artificial Neural Network-Based Controller 2.11 BLDC Motor Speed Controller With ANN-Based PID Controller 2.11.1 PID Controller-Based on Neuro Action 2.11.2 ANN-Based on PID Controller 2.12 Analysis of Different Speed Controllers 2.13 Conclusion References Chapter 3 Optimization Techniques Used in Active Magnetic Bearing System for Electric Vehicles 3.1 Introduction 3.2 Basic Components of an Active Magnetic Bearing (AMB) 3.2.1 Electromagnet Actuator 3.2.2 Rotor 3.2.3 Controller 3.2.3.1 Position Controller 3.2.3.2 Current Controller 3.2.4 Sensors 3.2.4.1 Position Sensor 3.2.4.2 Current Sensor 3.2.5 Power Amplifier 3.3 Active Magnetic Bearing in Electric Vehicles System 3.4 Control Strategies of Active Magnetic Bearing for Electric Vehicles System 3.4.1 Fuzzy Logic Controller (FLC) 3.4.1.1 Designing of Fuzzy Logic Controller (FLC) Using MATLAB 3.4.2 Artificial Neural Network (ANN) 3.4.2.1 Artificial Neural Network Using MATLAB 3.4.3 Particle Swarm Optimization (PSO) 3.4.4 Particle Swarm Optimization (PSO) Algorithm 3.4.4.1 Implementation of Particle Swarm Optimization for Electric Vehicles System 3.5 Conclusion References Chapter 4 Small-Signal Modelling Analysis of Three-Phase Power Converters for EV Applications 4.1 Introduction 4.2 Overall System Modelling 4.2.1 PMSM Dynamic Model 4.2.2 VSI-Fed SPMSM Mathematical Model 4.3 Mathematical Analysis and Derivation of the Small-Signal Model 4.3.1 The Small-Signal Model of the System 4.3.2 Small-Signal Model Transfer Functions 4.3.3 Bode Diagram Verification 4.4 Conclusion References Chapter 5 Energy Management of Hybrid Energy Storage System in PHEV With Various Driving Mode 5.1 Introduction 5.1.1 Architecture of PHEV 5.1.2 Energy Storage System 5.2 Problem Description and Formulation 5.2.1 Problem Description 5.2.2 Objective 5.2.3 Problem Formulation 5.3 Modeling of HESS 5.4 Results and Discussion 5.4.1 Case 1: Gradual Acceleration of Vehicle 5.4.2 Case 2: Gradual Deceleration of Vehicle 5.4.3 Case 3: Unsystematic Acceleration and Deceleration of Vehicle 5.5 Conclusion References Chapter 6 Reliability Approach for the Power Semiconductor Devices in EV Applications 6.1 Introduction 6.2 Conventional Methods for Prediction of Reliability for Power Converters 6.3 Calculation Process of the Electronic Component 6.4 Reliability Prediction for MOSFETs 6.5 Example: Reliability Prediction for Power Semiconductor Device 6.6 Example: Reliability Prediction for Resistor 6.7 Conclusions References Chapter 7 Modeling, Simulation and Analysis of Drive Cycles for PMSM-Based HEV With Optimal Battery Type 7.1 Introduction 7.2 Modeling of Hybrid Electric Vehicle 7.2.1 Architectures Available for HEV 7.3 Series—Parallel Hybrid Architecture 7.4 Analysis With Different Drive Cycles 7.4.1 Acceleration Drive Cycle 7.4.1.1 For 30% State of Charge 7.4.1.2 For 60% State of Charge 7.4.1.3 For 90% State of Charge 7.5 Cruising Drive Cycle 7.6 Deceleration Drive Cycle 7.6.1 For 30% State of Charge 7.6.2 For 60% State of Charge 7.6.3 For 90% State of Charge 7.7 Analysis of Battery Types 7.8 Conclusion References Chapter 8 Modified Firefly-Based Maximum Power Point Tracking Algorithm for PV Systems Under Partial Shading Conditions 8.1 Introduction 8.2 System Block Diagram Specifications 8.3 Photovoltaic System Modeling 8.4 Boost Converter Design 8.5 Incremental Conductance Algorithm 8.6 Under Partial Shading Conditions 8.7 Firefly Algorithm 8.8 Implementation Procedure 8.9 Modified Firefly Logic 8.10 Results and Discussions 8.11 Conclusion References Chapter 9 Induction Motor Control Schemes for Hybrid Electric Vehicles/Electric Vehicles 9.1 Introduction 9.2 Control Schemes of IM 9.2.1 Scalar Control 9.3 Vector Control 9.4 Modeling of Induction Machine 9.5 Controller Design 9.6 Simulations and Results 9.7 Conclusions References Chapter 10 Intelligent Hybrid Battery Management System for Electric Vehicle 10.1 Introduction 10.2 Energy Storage System (ESS) 10.2.1 Lithium-Ion Batteries 10.2.1.1 Lithium Battery Challenges 10.2.2 Lithium–Ion Cell Modeling 10.2.3 Nickel-Metal Hydride Batteries 10.2.4 Lead-Acid Batteries 10.2.5 Ultracapacitors (UC) 10.2.5.1 Ultracapacitor Equivalent Circuit 10.2.6 Other Battery Technologies 10.3 Battery Management System 10.3.1 Need for BMS 10.3.2 BMS Components 10.3.3 BMS Architecture/Topology 10.3.4 SOC/SOH Determination 10.3.5 Cell Balancing Algorithms 10.3.6 Data Communication 10.3.7 The Logic and Safety Control 10.3.7.1 Power Up/Down Control 10.3.7.2 Charging and Discharging Control 10.4 Intelligent Battery Management System 10.4.1 Rule-Based Control 10.4.2 Optimization-Based Control 10.4.3 AI-Based Control 10.4.4 Traffic (Look Ahead Method)-Based Control 10.5 Conclusion References Chapter 11 A Comprehensive Study on Various Topologies of Permanent Magnet Motor Drives for Electric Vehicles Application 11.1 Introduction 11.2 Proposed Design Considerations of PMSM for Electric Vehicle 11.3 Impact of Digital Controllers 11.3.1 DSP-Based Digital Controller 11.3.2 FPGA-Based Digital Controller 11.4 Electric Vehicles Smart Infrastructure 11.5 Conclusion References Chapter 12 A New Approach for Flux Computation Using Intelligent Technique for Direct Flux Oriented Control of Asynchronous Motor 12.1 Introduction 12.2 Direct Field-Oriented Control of IM Drive 12.3 Conventional Flux Estimator 12.4 Rotor Flux Estimator Using CFBP-NN 12.5 Comparison of Proposed CFBP-NN With Existing CFBP-NN for Flux Estimation 12.6 Performance Study of Proposed CFBP-NN Using MATLAB/SIMULINK 12.7 Practical Implementation Aspects of CFBP-NNBased Flux Estimator 12.8 Conclusion References Chapter 13 A Review on Isolated DC–DC Converters Used in Renewable Power Generation Applications 13.1 Introduction 13.2 Isolated DC–DC Converter for Electric Vehicle Applications 13.3 Three-Phase DC–DC Converter 13.4 Conclusion References Chapter 14 Basics of Vector Control of Asynchronous Induction Motor and Introduction to Fuzzy Controller 14.1 Introduction 14.2 Dynamics of Separately Excited DC Machine 14.3 Clarke and Park Transforms 14.4 Model Explanation 14.5 Motor Parameters 14.6 PI Regulators Tuning 14.7 Future Scope to Include Fuzzy Control in Place of PI Controller 14.8 Conclusion References Index EULA