Artificial Intelligent Techniques for Electric and Hybrid 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