Energy Management in Electric Vehicles (BEE657A)

Energy Management in Electric Vehicles

Course Code BEE657A 
CIE Marks 50
Teaching Hours/Week (L:T:P: S) 1:0:0:0 
SEE Marks 50
Total Hours of Pedagogy 15 
Total Marks 100
Credits 01 
Exam Hours 01
Examination type (SEE) MCQ

Module-1

Introduction to Electric Vehicles and Energy Management Overview of electric vehicles (EVs) - Types of EVs (Battery Electric Vehicles, Plug-in Hybrid Electric Vehicles); Advantages and challenges of EVs. Introduction to energy management in EVs - Importance of energy management; Key objectives of energy management in EVs. Electric vehicle components and systems- Battery systems; Power electronics and motor drive systems; Regenerative braking systems; Energy storage and management systems

Module-2

Fundamentals of Energy Management Energy storage technologies for EVs - Lithium-ion batteries; Solid-state batteries; Supercapacitors; Fuel cells. Battery charging and discharging techniques - Charging infrastructure for EVs; Charging modes (AC and DC charging); Fast charging vs. slow charging; Battery management systems (BMS). Energy efficiency and energy loss analysis - Losses in power electronics and motor drive systems; Losses in battery systems; Factors affecting energy efficiency in EVs.

Module-3

Advanced Energy Management Strategies State-of-charge (SoC) estimation and management - SoC estimation techniques (Coulomb counting, Kalman filtering, etc.); SoC balancing techniques; Impact of SoC on battery life and performance. Power management strategies - Optimal power allocation between different vehicle systems; Dynamic power allocation based on driving conditions; Power flow control in EVs. Regenerative braking and energy recovery - Principles of regenerative braking; Control strategies for regenerative braking; Energy recovery and utilization.

Module-4

Optimization Techniques for Energy Management Optimization models for energy management - Linear programming and nonlinear optimization; Model predictive control (MPC) for energy management; Genetic algorithms and other heuristic optimization techniques. Intelligent energy management systems - Artificial intelligence (AI) and machine learning techniques for energy management; Reinforcement learning-based energy management; Datadriven approaches for energy optimization. Realtime energy management algorithms - Real-time optimization algorithms for energy allocation; Adaptive control strategies for energy management; Integration of energy management with navigation systems.

Module-5

Case Studies and Applications Energy management in electric buses and fleet management - Challenges and strategies for energy management in public transportation; Fleet management and scheduling optimization. Energy management in electric vehicles charging infrastructure - Smart charging stations and grid integration; Demand-side management and load balancing. Emerging trends and future directions in energy management - Wireless charging technologies; Vehicle-to-vehicle (V2V) communication for energy optimization; Advanced energy storage and conversion technologies.

Suggested Learning Resources:

Books

1. "Electric Vehicle Technology" by H. C. Rai

2. "Electric Vehicle Energy Management System for Efficiency Optimization" by Jingang Han, Linlin Tan, and Xinbo Ruan

3. "Advanced Electric Drive Vehicles" edited by Ali Emadi

4. "Electric Vehicle Technology Explained" by James Larminie and John Lowry