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Comprehensive Course List Across 8 Semesters

Detailed Description of Advanced Departmental Electives

Advanced Battery Management Systems: This course delves into the intricate design and optimization of battery management systems (BMS) for electric vehicles. Students explore advanced topics such as state-of-charge estimation, thermal management, fault diagnosis, and battery health monitoring. The curriculum includes theoretical foundations, practical simulations using MATLAB/Simulink, and real-world case studies from leading automakers.

Microcontroller Applications in Vehicles: This elective focuses on the application of microcontrollers in automotive systems. Students gain hands-on experience with ARM Cortex-M series processors, develop embedded software for vehicle control units (ECUs), and integrate sensors and actuators into real-time systems. The course emphasizes design methodologies, debugging techniques, and performance optimization.

Autonomous Driving Technologies: This advanced course explores the principles of autonomous driving, including perception systems, motion planning, control algorithms, and decision-making frameworks. Students work with simulation environments like CARLA and ROS to develop autonomous vehicle prototypes and participate in competitions such as the IEEE Autonomous Vehicle Challenge.

Vehicle Diagnostics and Maintenance: This course covers modern diagnostic techniques for automotive electrical systems, including OBD-II protocols, fault code analysis, and predictive maintenance strategies. Students learn to use advanced diagnostic tools, interpret system data, and perform preventive maintenance on complex vehicle networks.

Smart Transportation Systems: This course addresses the integration of digital technologies into transportation networks, focusing on traffic management systems, connected vehicle communications, intelligent infrastructure, and urban mobility planning. Students study smart city initiatives, implement communication protocols, and evaluate system performance using real-world datasets.

Renewable Energy Integration: This elective explores how renewable energy sources such as solar and wind can be integrated into automotive applications. Students examine hybrid power generation systems, energy storage technologies, and grid integration strategies for sustainable transportation solutions.

Advanced Materials in Automotive: This course investigates the role of advanced materials in automotive engineering, including composites, nanomaterials, smart materials, and their applications in vehicle design and manufacturing. Students study material selection criteria, manufacturing processes, and performance testing methods.

Power Electronic Converters: This course focuses on power electronic converters used in automotive systems, including DC-DC converters, inverters, rectifiers, and motor drives. Students learn to design, simulate, and analyze converter circuits using industry-standard tools and apply them to real-world applications such as EV charging and motor control.

Intelligent Transportation Systems: This course explores the application of AI, IoT, and data analytics in transportation networks. Students study smart traffic lights, vehicle-to-infrastructure (V2I) communication, route optimization algorithms, and predictive maintenance systems for urban mobility.

Vehicle Control Systems: This advanced elective covers control system design for automotive applications, including engine control, chassis control, powertrain management, and safety systems. Students develop control algorithms using MATLAB/Simulink, implement them on real-time platforms, and test their performance in simulated environments.

Data Analytics for Automotive: This course introduces students to data analytics techniques applied in automotive engineering. Topics include predictive modeling, machine learning algorithms, big data processing, and visualization tools used in vehicle diagnostics, fleet management, and customer insights.

IoT in Transportation: This elective explores the implementation of Internet of Things (IoT) technologies in transportation systems. Students study sensor networks, wireless communication protocols, cloud computing platforms, and edge computing solutions for real-time data collection and analysis in vehicles and infrastructure.

Advanced Control Systems: This course delves into advanced control system design methodologies, including adaptive control, robust control, nonlinear control, and optimal control. Students apply these concepts to complex automotive systems such as autonomous vehicles, hybrid powertrains, and intelligent transportation networks.

Research Methodology: This course provides students with the foundational knowledge required for conducting research in automotive engineering. Topics include literature review, hypothesis formulation, experimental design, data analysis, and scientific writing. Students learn to plan and execute small-scale research projects under faculty supervision.

Technical Writing: This course enhances students' technical communication skills through writing reports, research papers, project documentation, and presentation materials. Emphasis is placed on clarity, structure, and adherence to academic and professional standards in technical writing.

Project-Based Learning Philosophy

Our department believes that project-based learning is central to developing competent engineers who can bridge the gap between theory and practice. The program incorporates mandatory mini-projects throughout the curriculum, culminating in a comprehensive final-year thesis or capstone project.

The structure of these projects follows a multi-stage approach designed to foster innovation, teamwork, and problem-solving skills:

• Mini Projects (Semesters 7): Students work on two mini-projects during their seventh semester. Each project lasts approximately 12 weeks and involves selecting a domain-specific topic under faculty guidance. Projects are evaluated based on technical depth, innovation, feasibility, and presentation quality.
• Final Year Thesis/Capstone Project (Semester 8): In the eighth semester, students undertake a substantial project that spans the entire semester. The project must address a real-world problem in automotive engineering or related fields. Students are paired with faculty mentors based on their interests and career goals.

The evaluation criteria for projects include:

• Technical execution and innovation
• Problem-solving approach
• Team collaboration and communication
• Documentation quality
• Presentation skills
• Impact on industry or society

Students have the freedom to choose their projects from a pool of suggested topics or propose their own ideas. Faculty mentors provide guidance, resources, and feedback throughout the project lifecycle. Regular progress meetings, milestone reviews, and peer evaluations ensure that students stay on track and meet expectations.