Bibliographische Detailangaben

Format:	Buch
Sprache:	English
Veröffentlicht:	Beverly, MA Scrivener Publishing 2020 Hoboken, NJ Wiley
Schlagworte:	Künstliche Intelligenz Elektrofahrzeug Hybridfahrzeug
Beschreibung:	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 &amp -- 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.
Beschreibung:	xvi, 261 Seiten Illustrationen, Diagramme
ISBN:	9781119681908 1119681901