AI TECHNIQUES FOR ELECTRIC AND HYBRID ELECTRIC VEHICLES (18EE743)

AI TECHNIQUES FOR ELECTRIC AND HYBRID ELECTRIC VEHICLES

Course Code 18EE743
CIE Marks 40
Teaching Hours/Week (L: T: P) (3:0:0)
SEE Marks 60
Credits 03 
Exam Hours 03

Course Learning Objectives:

• To explain IoT Based Battery Management System (BMS) and types of batteries for Hybrid Electric Vehicles (HEV)
• To explain the advantages of AI, the use of brushless DC motor and its control in the electric vehicle.
• To explain the optimization techniques and control strategies for active magnetic bearing (AMB) systems for electric vehicles.
• To explain the modelling and analysis of power converters and hybrid energy storage system foe electric vehicles.

Module-1

IoT-Based Battery Management System for Hybrid Electric Vehicle: IoT Based Battery Management
System (BMS) for Hybrid Electric Vehicles (HEV) : Introduction, Battery configuration, Types of batteries for HEV and Electric Vehicles (EV), Functional Blocks of Battery Management Systems, IoT-based BMS.

Module-2

Brushless Direct Current Motor Drive Using Artificial Intelligence for Optimum Operation of the Electric Vehicle: Basics of Artificial Intelligence, Advantages of Artificial Intelligence in EV, Brushless DC Motor, Mathematical Representation Brushless DC Motor, Closed-Loop Model of BLDC Motor Drive, PID Controller, Fuzzy Control, Auto-Tuning Type Fuzzy PID Controller, Genetic Algorithm, Artificial Neural Network-Based Controller, BLDC Motor Speed Controller with ANN Based PID Controller, Analysis of Different Speed Controllers.

Module-3

Optimization Techniques Used in Active Magnetic Bearing System for Electric Vehicles : Basic
Components of an Active Magnetic Bearing (AMB), Active Magnetic Bearing in Electric Vehicles System, Control Strategies for AMB in EVs.

Module-4

Small-Signal Modeling Analysis of Three-Phase Power Converters for EV Applications : Introduction,
Overall System Modeling, Mathematical Modeling and Analysis of Small Signal Modeling.

Module-5

Energy Management of Hybrid Energy Storage System (HESS) in PHEV With Various Driving Mode:
Introduction, Problem Description, and Formulation, Modeling of HESS and its Analysis.

Course Outcomes: At the end of the course the student will be able to:

• Discuss IoT Based Battery Management System and type of batteries for EV and HEV.
• Explain AI-Based BLDC drive for optimum operation of EV.
• Explain the Active Magnetic Bearing system for EVs.
• Model and analyze three-phase converters for EV applications.
• Model and analyze Energy Management of HESS in PHEV.

Question paper pattern:

• The question paper will have ten full questions carrying equal marks.
• Each full question will be for 20 marks.
• There will be two full questions (with a maximum of four sub-questions) from each module.
• Each full question will have a sub- questions covering all the topics under a module.
• The students will have to answer five full questions, selecting one full question from each module.

Textbook

1 Artificial Intelligent Techniques for Electric and Hybrid Electric Vehicles Chitra A, P. Sanjeevikumar, and S. Himavathi Wiley 2020