Comprehensive Course Structure
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Pre-requisite |
|---|---|---|---|---|
| 1 | AE-101 | Fundamentals of Electronics | 3-1-0-4 | - |
| 1 | AE-102 | Basic Electrical Circuits | 3-1-0-4 | - |
| 1 | AE-103 | Mathematics for Engineering | 4-0-0-4 | - |
| 1 | AE-104 | Physics for Electronics | 3-1-0-4 | - |
| 1 | AE-105 | Introduction to Automotive Systems | 3-1-0-4 | - |
| 1 | AE-106 | Computer Applications in Engineering | 2-1-0-3 | - |
| 1 | AE-107 | Lab: Basic Electronics | 0-0-2-2 | - |
| 2 | AE-201 | Digital Electronics | 3-1-0-4 | AE-101 |
| 2 | AE-202 | Microcontroller Applications | 3-1-0-4 | AE-101 |
| 2 | AE-203 | Automotive Electrical Systems | 3-1-0-4 | AE-102 |
| 2 | AE-204 | Control Systems | 3-1-0-4 | AE-103 |
| 2 | AE-205 | Instrumentation and Measurement | 3-1-0-4 | AE-102 |
| 2 | AE-206 | Lab: Microcontroller Applications | 0-0-2-2 | AE-101 |
| 3 | AE-301 | Sensors and Transducers | 3-1-0-4 | AE-202 |
| 3 | AE-302 | Embedded Systems | 3-1-0-4 | AE-202 |
| 3 | AE-303 | Vehicle Control Systems | 3-1-0-4 | AE-204 |
| 3 | AE-304 | Automotive Electronics | 3-1-0-4 | AE-203 |
| 3 | AE-305 | Power Electronics | 3-1-0-4 | AE-201 |
| 3 | AE-306 | Lab: Embedded Systems | 0-0-2-2 | AE-202 |
| 4 | AE-401 | Electric Vehicle Technology | 3-1-0-4 | AE-303 |
| 4 | AE-402 | Smart Transportation Systems | 3-1-0-4 | AE-301 |
| 4 | AE-403 | Automotive Cybersecurity | 3-1-0-4 | AE-302 |
| 4 | AE-404 | Advanced Driver Assistance Systems (ADAS) | 3-1-0-4 | AE-301 |
| 4 | AE-405 | Project Management | 2-1-0-3 | - |
| 4 | AE-406 | Lab: Smart Transportation Systems | 0-0-2-2 | AE-302 |
| 5 | AE-501 | Renewable Energy Integration in Automotive Systems | 3-1-0-4 | AE-305 |
| 5 | AE-502 | Vehicle Diagnostics and Repair Techniques | 3-1-0-4 | AE-304 |
| 5 | AE-503 | Introduction to Artificial Intelligence for Transportation | 3-1-0-4 | AE-302 |
| 5 | AE-504 | Capstone Project I | 0-0-6-6 | - |
| 5 | AE-505 | Research Methodology | 2-1-0-3 | - |
| 6 | AE-601 | Capstone Project II | 0-0-6-6 | - |
| 6 | AE-602 | Internship | 0-0-6-6 | - |
Advanced Departmental Elective Courses
Each elective course in the Auto Electrical program is designed to provide specialized knowledge and skills that align with current industry trends and future technological developments. These courses are typically offered in the later semesters of the program.
Electric Vehicle Technology: This course delves into the architecture, components, and operational principles of electric vehicles. Students learn about battery technologies, charging systems, motor controllers, and energy management strategies. The curriculum includes hands-on laboratory sessions where students build and test prototypes of EV subsystems.
Smart Transportation Systems: Focusing on vehicle-to-everything (V2X) communication, this course explores how connected vehicles can enhance traffic flow, reduce accidents, and improve urban mobility. Students study wireless communication protocols, data analytics, and simulation tools used in smart city infrastructure.
Automotive Cybersecurity: With increasing connectivity in vehicles, cybersecurity becomes paramount. This course covers threats to automotive networks, security frameworks, encryption techniques, and secure coding practices for embedded systems.
Advanced Driver Assistance Systems (ADAS): Students examine the sensor fusion, perception algorithms, and control mechanisms that enable ADAS functionalities such as lane departure warnings, adaptive cruise control, and automatic emergency braking.
Renewable Energy Integration in Automotive Systems: This elective explores how renewable energy sources like solar and wind can be integrated into automotive applications. It covers energy storage solutions, hybrid systems, and sustainable transportation models.
Vehicle Diagnostics and Repair Techniques: Designed for practical application, this course teaches students diagnostic procedures, fault analysis, and repair methodologies using modern diagnostic tools and equipment found in automotive workshops.
Introduction to Artificial Intelligence for Transportation: This course introduces machine learning techniques and neural networks applied to transportation systems. Students learn how AI can be used for predictive maintenance, route optimization, and autonomous navigation.
Embedded Systems for Vehicles: This advanced course focuses on designing and implementing real-time embedded systems in vehicles. It covers microcontroller architectures, programming languages like C/C++, real-time operating systems, and hardware-software co-design principles.
Control Systems for Automotive Applications: Emphasizing the role of control theory in automotive systems, this course explores PID controllers, state-space models, and feedback mechanisms used in vehicle dynamics, engine management, and braking systems.
Power Electronics for Automotive Applications: This course examines power conversion circuits, DC-DC converters, inverters, and rectifiers used in modern vehicles. Students learn about efficiency optimization, thermal management, and integration with other vehicle subsystems.
IoT in Vehicles: Exploring the Internet of Things (IoT) in automotive contexts, this course covers sensor networks, data transmission protocols, cloud computing integration, and smart vehicle ecosystems.
Vehicle Simulation and Modeling: Using industry-standard simulation software like MATLAB/Simulink, students model and simulate complex automotive systems to predict performance under various conditions.
Automotive Design and Development: This course provides an overview of the design process in automotive engineering. It includes CAD modeling, prototyping techniques, testing methodologies, and regulatory compliance requirements.
Advanced Sensors and Actuators: Focused on modern sensor technologies such as LiDAR, radar, and ultrasonic sensors, this course explores their integration into vehicle systems and real-world applications in ADAS and autonomous driving.
Energy Storage Systems for Vehicles: This course covers battery chemistry, performance characteristics, charging strategies, and lifecycle management of energy storage units in electric vehicles.
Automotive Testing and Quality Assurance: Students learn testing methodologies, quality standards, failure analysis techniques, and regulatory frameworks that ensure vehicle safety and reliability.
Project-Based Learning Philosophy
The department's philosophy on project-based learning is centered around real-world relevance and student engagement. The approach emphasizes collaborative problem-solving, innovation, and practical implementation of theoretical concepts.
Mini-projects are assigned in the third year to expose students to early-stage research and development activities. These projects typically span 4-6 weeks and involve small teams working under faculty supervision. Evaluation criteria include project presentation, documentation quality, technical execution, and peer collaboration.
The final-year thesis/capstone project is a comprehensive endeavor that requires students to demonstrate mastery of their chosen domain. Projects are selected based on industry relevance, feasibility, and alignment with student interests. Faculty mentors guide students through the research process, ensuring they meet academic standards while pushing boundaries of innovation.
Student selection for projects involves discussions between faculty members and interested students. Criteria include academic performance, interest in specific domains, availability of resources, and mentor-student compatibility. The department also encourages interdisciplinary collaborations to broaden perspectives and enhance outcomes.