Curriculum Overview

The Auto Electrical program is structured across 6 semesters over a duration of three years. The curriculum balances foundational knowledge with specialized technical skills to prepare students for professional success in the automotive electronics industry.

Semester	Course Code	Course Title	Credit Structure (L-T-P-C)	Prerequisites
1	AE101	Basic Electrical Engineering	3-0-0-3	-
1	AE102	Workshop Practice	0-0-6-3	-
1	AE103	Introduction to Auto Electronics	3-0-0-3	-
1	AE104	Basic Electronics	3-0-0-3	-
1	AE105	Mathematics I	3-0-0-3	-
2	AE201	Electronic Devices and Circuits	3-0-0-3	AE104
2	AE202	Digital Logic and Microprocessors	3-0-0-3	AE104
2	AE203	Automotive Wiring Systems	3-0-0-3	AE103
2	AE204	Mathematics II	3-0-0-3	AE105
2	AE205	Physics	3-0-0-3	-
3	AE301	Embedded Systems Design	3-0-0-3	AE202
3	AE302	Control Systems	3-0-0-3	AE201
3	AE303	Power Electronics for Vehicles	3-0-0-3	AE201
3	AE304	Simulation Software Lab	0-0-6-3	AE202
3	AE305	Chemistry	3-0-0-3	-
4	AE401	Electric Vehicle Technology	3-0-0-3	AE303
4	AE402	Advanced Diagnostics & Troubleshooting	3-0-0-3	AE203
4	AE403	Renewable Energy Integration	3-0-0-3	AE303
4	AE404	Research Methodology	2-0-0-2	-
4	AE405	Professional Communication	2-0-0-2	-
5	AE501	Smart Transportation Systems	3-0-0-3	AE401
5	AE502	Autonomous Vehicle Technologies	3-0-0-3	AE401
5	AE503	Project Management	2-0-0-2	-
5	AE504	Industrial Training I	0-0-6-3	-
6	AE601	Final Year Project/Thesis	0-0-12-6	All previous semesters
6	AE602	Internship	0-0-6-3	-
6	AE603	Elective Course I	3-0-0-3	-
6	AE604	Elective Course II	3-0-0-3	-

Advanced Departmental Electives

Departmental electives are offered to give students advanced knowledge and specialized skills in emerging areas of automotive electronics:

1. AI for Automotive Applications: This course explores the application of artificial intelligence algorithms in vehicle systems, including machine learning models for predictive maintenance, autonomous driving, and smart traffic management. Students will gain hands-on experience with AI frameworks like TensorFlow and PyTorch, applying them to real-world automotive challenges.
2. IoT in Vehicles: Focuses on the integration of Internet of Things (IoT) technologies into vehicle systems for enhanced connectivity, monitoring, and control. Topics include sensor networks, cloud computing platforms, data analytics, and security protocols relevant to connected cars.
3. Advanced Battery Technologies: Delves into the latest advancements in battery chemistry, design optimization, and performance evaluation. Students will explore lithium-ion, solid-state, and alternative energy storage systems used in electric vehicles.
4. Vehicle Dynamics and Control: Covers theoretical and practical aspects of vehicle dynamics, including suspension systems, steering mechanisms, braking systems, and stability control. The course integrates computational modeling with physical experimentation to understand dynamic behavior under various conditions.
5. Automotive Data Analytics: Emphasizes data-driven decision-making in automotive industries using statistical methods, big data analytics, and visualization tools. Students learn to extract insights from vehicle telemetry data, customer feedback, and market trends.
6. Sustainable Mobility Solutions: Examines sustainable transportation options including electric vehicles, hydrogen fuel cells, and alternative propulsion systems. The course discusses policy frameworks, environmental impact assessments, and innovation strategies for green mobility.
7. Smart Grid Integration: Explores how smart grids support electric vehicle charging infrastructure, energy management, and demand response systems. Students will study grid stability, power quality issues, and integration of renewable energy sources with transportation networks.
8. Autonomous Vehicle Perception Systems: Focuses on sensor fusion techniques, computer vision algorithms, and perception systems used in self-driving vehicles. The course covers lidar, radar, camera-based object detection, and neural network architectures for autonomous navigation.
9. Embedded Software Engineering: Teaches software development practices specific to embedded systems in automotive applications. Topics include real-time operating systems, microcontroller programming, code optimization, and software testing methodologies tailored for vehicle electronics.
10. Cybersecurity in Automotive Systems: Addresses security threats and vulnerabilities in connected vehicles and smart transportation networks. Students will learn about threat modeling, secure communication protocols, encryption techniques, and incident response strategies for automotive cybersecurity.

Project-Based Learning Philosophy

The department believes in experiential learning as a cornerstone of technical education. Project-based learning is integrated throughout the curriculum to ensure that students develop both theoretical understanding and practical application skills. Mini-projects are assigned during semesters 3 and 4, while the final-year thesis/capstone project provides an opportunity for comprehensive exploration of individual interests within the field.

Mini-projects involve small groups of students working on predefined tasks related to automotive electrical systems. These projects focus on developing problem-solving abilities, teamwork, and technical communication skills. Evaluation criteria include project execution quality, presentation, documentation, and peer assessment.

The final-year capstone project is a significant component that requires students to demonstrate mastery in their chosen area of specialization. Students select topics in consultation with faculty mentors based on current industry trends and personal interests. The project involves research, design, implementation, testing, and documentation phases, culminating in a public presentation and report submission.

Faculty members guide students throughout the project lifecycle, providing mentorship, feedback, and access to resources. Regular meetings are scheduled to track progress, address challenges, and ensure alignment with academic standards and industry expectations.