Comprehensive Course Listing by Semester
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | AE-101 | Engineering Mathematics I | 3-1-0-4 | - |
| 1 | AE-102 | Applied Physics | 3-1-0-4 | - |
| 1 | AE-103 | Chemistry for Engineers | 3-1-0-4 | - |
| 1 | AE-104 | Engineering Graphics | 2-1-0-3 | - |
| 1 | AE-105 | Basic Electrical Engineering | 3-1-0-4 | - |
| 1 | AE-106 | Computer Programming Fundamentals | 2-1-0-3 | - |
| 2 | AE-201 | Engineering Mathematics II | 3-1-0-4 | AE-101 |
| 2 | AE-202 | Material Science | 3-1-0-4 | - |
| 2 | AE-203 | Mechanics of Solids | 3-1-0-4 | - |
| 2 | AE-204 | Electrical Circuits and Networks | 3-1-0-4 | AE-105 |
| 2 | AE-205 | Electronic Devices and Circuits | 3-1-0-4 | - |
| 2 | AE-206 | Introduction to Automobile Engineering | 2-1-0-3 | - |
| 3 | AE-301 | Control Systems | 3-1-0-4 | AE-201, AE-204 |
| 3 | AE-302 | Power Electronics | 3-1-0-4 | AE-205 |
| 3 | AE-303 | Digital Electronics | 3-1-0-4 | AE-205 |
| 3 | AE-304 | Microcontroller Applications | 3-1-0-4 | - |
| 3 | AE-305 | Vehicle Dynamics and Control | 3-1-0-4 | AE-203 |
| 3 | AE-306 | Automotive Electrical Systems | 3-1-0-4 | AE-204 |
| 4 | AE-401 | Electric Vehicle Technology | 3-1-0-4 | AE-302, AE-306 |
| 4 | AE-402 | Battery Management Systems | 3-1-0-4 | AE-302 |
| 4 | AE-403 | Vehicle Communication Protocols | 3-1-0-4 | AE-303 |
| 4 | AE-404 | Embedded Systems Design | 3-1-0-4 | AE-304 |
| 4 | AE-405 | Smart Grid Integration for EVs | 3-1-0-4 | AE-302 |
| 4 | AE-406 | Advanced Diagnostics and Maintenance | 3-1-0-4 | AE-306 |
| 5 | AE-501 | Autonomous Vehicle Engineering | 3-1-0-4 | AE-403, AE-404 |
| 5 | AE-502 | Cybersecurity in Automotive Systems | 3-1-0-4 | AE-403 |
| 5 | AE-503 | Predictive Maintenance Techniques | 3-1-0-4 | AE-406 |
| 5 | AE-504 | Vehicle Safety and Reliability | 3-1-0-4 | AE-305 |
| 5 | AE-505 | AI in Automotive Applications | 3-1-0-4 | AE-404 |
| 6 | AE-601 | Capstone Project I | 3-0-6-9 | - |
| 6 | AE-602 | Capstone Project II | 3-0-6-9 | AE-601 |
| 7 | AE-701 | Research Methodology | 2-0-0-2 | - |
| 7 | AE-702 | Special Topics in Auto Electrical | 3-1-0-4 | - |
| 8 | AE-801 | Industry Internship | 0-0-0-6 | - |
| 8 | AE-802 | Final Year Project | 3-0-6-9 | AE-602 |
Detailed Descriptions of Advanced Departmental Electives
Advanced departmental elective courses form a crucial part of the curriculum, providing students with specialized knowledge and practical skills in emerging fields. These courses are designed to challenge students intellectually while preparing them for real-world engineering challenges.
One such course is Autonomous Vehicle Engineering, which explores the principles and technologies behind self-driving cars. Students learn about sensor fusion, perception systems, localization algorithms, path planning, and control strategies. The course includes hands-on lab sessions where students work with ROS (Robot Operating System) to simulate autonomous navigation.
The Cybersecurity in Automotive Systems course focuses on securing connected vehicles against cyber threats. It covers topics such as secure communication protocols, intrusion detection systems, and vulnerability assessment techniques. Students gain experience using security tools like Wireshark and Nessus for network analysis and penetration testing.
Predictive Maintenance Techniques teaches students how to use data analytics and machine learning algorithms to predict equipment failures before they occur. Through case studies and lab exercises, students learn to analyze sensor data from vehicles and implement predictive models using Python and scikit-learn libraries.
The Vehicle Safety and Reliability course delves into the design and testing of safety-critical systems in vehicles. It covers crashworthiness analysis, fault tree analysis, reliability modeling, and compliance with international safety standards like ISO 26262.
AI in Automotive Applications introduces students to artificial intelligence techniques applied in automotive engineering. Topics include neural networks, deep learning, computer vision, and natural language processing. Students work on projects involving object detection in traffic scenarios and speech recognition systems for infotainment units.
Project-Based Learning Philosophy
The department's philosophy on project-based learning is centered around experiential education that bridges the gap between theory and practice. Students engage in both individual and group projects throughout their academic journey, culminating in a final-year thesis or capstone project.
Mini-projects are introduced in the third year, allowing students to explore specific aspects of Auto Electrical engineering. These projects typically last 3-4 weeks and involve small teams working on defined problems under faculty supervision. Projects can range from designing a basic electronic circuit for vehicle lighting to developing a simple diagnostic tool.
The final-year thesis or capstone project is a comprehensive endeavor that spans the entire semester. Students select their projects based on personal interest, industry relevance, and available resources. Faculty mentors guide students through the research process, from problem definition to solution implementation and documentation.
Project selection involves a proposal submission phase where students present their ideas to faculty panels. Criteria for selection include innovation potential, feasibility, and alignment with current industry trends. Students are encouraged to collaborate with external organizations or participate in ongoing research initiatives.