Comprehensive Course Structure

The Auto Electrical program at Government Polytechnic Kaladhungi is structured over eight semesters, with a balanced mix of core subjects, departmental electives, science electives, and laboratory courses. This carefully curated curriculum ensures that students acquire both foundational knowledge and specialized skills needed for professional success.

Semester	Course Code	Course Title	Credit Structure (L-T-P-C)	Pre-requisites
I	ENG101	English for Engineers	3-0-0-3	None
I	MAT101	Engineering Mathematics I	4-0-0-4	None
I	PHY101	Physics for Engineers	3-0-0-3	None
I	CHM101	Chemistry for Engineers	3-0-0-3	None
I	BEE101	Basic Electrical Engineering	3-0-0-3	None
I	CSE101	Computer Programming	2-0-2-3	None
I	ELE101	Electrical Circuit Analysis	3-0-0-3	BEE101
I	LAT101	Lab: Basic Electrical Engineering	0-0-3-1	BEE101
I	LAT102	Lab: Computer Programming	0-0-3-1	CSE101
II	MAT201	Engineering Mathematics II	4-0-0-4	MAT101
II	PHY201	Engineering Physics	3-0-0-3	PHY101
II	MAT202	Probability and Statistics	3-0-0-3	MAT101
II	ELE201	Analog Electronics	3-0-0-3	ELE101
II	ELE202	Digital Electronics	3-0-0-3	ELE101
II	MCH201	Engineering Mechanics	3-0-0-3	None
II	LAT201	Lab: Analog Electronics	0-0-3-1	ELE201
II	LAT202	Lab: Digital Electronics	0-0-3-1	ELE202
III	MAT301	Engineering Mathematics III	4-0-0-4	MAT201
III	ELE301	Power Electronics	3-0-0-3	ELE202
III	ELE302	Microcontroller Applications	3-0-0-3	CSE101
III	ELE303	Embedded Systems	3-0-0-3	ELE202
III	MCH301	Vehicle Dynamics	3-0-0-3	MCH201
III	ELE304	Control Systems	3-0-0-3	MAT201
III	LAT301	Lab: Power Electronics	0-0-3-1	ELE301
III	LAT302	Lab: Microcontroller Applications	0-0-3-1	ELE302
IV	MAT401	Engineering Mathematics IV	4-0-0-4	MAT301
IV	ELE401	Advanced Power Electronics	3-0-0-3	ELE301
IV	ELE402	Vehicle Diagnostics	3-0-0-3	ELE301
IV	ELE403	Sensor Integration	3-0-0-3	ELE202
IV	MCH401	Materials Science	3-0-0-3	None
IV	ELE404	Communication Protocols	3-0-0-3	ELE202
IV	LAT401	Lab: Embedded Systems	0-0-3-1	ELE303
V	ELE501	Electric Vehicle Technologies	3-0-0-3	ELE301
V	ELE502	Smart Transportation Systems	3-0-0-3	ELE401
V	ELE503	Artificial Intelligence in Automotive	3-0-0-3	ELE202
V	ELE504	Renewable Energy Integration	3-0-0-3	ELE301
V	ELE505	Battery Management Systems	3-0-0-3	ELE301
V	LAT501	Lab: Electric Vehicle Technologies	0-0-3-1	ELE501
V	LAT502	Lab: Smart Transportation Systems	0-0-3-1	ELE502
VI	ELE601	Advanced Control Systems	3-0-0-3	ELE404
VI	ELE602	Industrial Automation	3-0-0-3	ELE401
VI	ELE603	Predictive Maintenance	3-0-0-3	ELE402
VI	ELE604	Autonomous Vehicle Systems	3-0-0-3	ELE503
VI	LAT601	Lab: Advanced Control Systems	0-0-3-1	ELE601
VI	LAT602	Lab: Industrial Automation	0-0-3-1	ELE602
VII	ELE701	Capstone Project I	0-0-6-6	All previous semesters
VIII	ELE801	Capstone Project II	0-0-6-6	ELE701

Detailed Course Descriptions

Each departmental elective course in the Auto Electrical program is designed to provide students with advanced knowledge and practical skills necessary for addressing real-world engineering challenges. These courses are taught by experienced faculty members who are actively involved in research and industry projects.

Advanced Power Electronics

This course delves into the design and analysis of power electronic converters, including DC-DC converters, AC-DC rectifiers, inverters, and motor drives. Students learn to model and simulate power systems using tools like MATLAB/Simulink, analyze efficiency losses, and optimize component selection for specific applications. The course also covers emerging trends in wide bandgap semiconductors (SiC, GaN) and their impact on power electronics design.

Learning Objectives:

1. Understand the principles of power conversion circuits
2. Analyze and design converters for various load conditions
3. Simulate and evaluate power electronic systems using software tools
4. Apply knowledge to industrial applications such as renewable energy systems and electric vehicles

Vehicle Diagnostics

This course explores the latest diagnostic techniques used in modern vehicles. Students study fault detection algorithms, data interpretation methods, and integration of diagnostic tools with vehicle control systems. The curriculum includes hands-on experience with OBD-II scanners, CAN bus analyzers, and real-time diagnostic software.

Learning Objectives:

1. Identify common vehicle faults using diagnostic tools
2. Interpret vehicle telemetry data for predictive maintenance
3. Develop troubleshooting strategies for complex systems
4. Apply diagnostic principles to improve service quality and reduce downtime

Sensor Integration

This course focuses on integrating various types of sensors into automotive systems. Students learn about sensor characteristics, signal conditioning techniques, data fusion algorithms, and wireless communication protocols. Practical sessions involve building sensor networks for vehicle monitoring and control applications.

Learning Objectives:

1. Select appropriate sensors for specific automotive applications
2. Design and implement sensor integration systems
3. Analyze sensor data for performance optimization
4. Apply sensor technologies in real-time control systems

Smart Transportation Systems

This course introduces students to the architecture of intelligent transportation systems (ITS). Topics include traffic management, vehicle-to-everything (V2X) communication, GPS navigation, and smart city integration. Students engage in simulation exercises using tools like SUMO (Simulation of Urban Mobility) and CARLA (Car Learning Architecture).

Learning Objectives:

1. Understand the components of ITS infrastructure
2. Design communication protocols for connected vehicles
3. Analyze traffic flow patterns and optimize routing strategies
4. Integrate ITS with existing urban planning frameworks

Artificial Intelligence in Automotive

This course explores the application of AI and machine learning techniques in automotive systems. Students study neural networks, deep learning architectures, computer vision, and natural language processing as applied to autonomous vehicles, driver assistance systems, and predictive maintenance. The course includes projects involving image recognition for obstacle detection and decision-making algorithms for navigation.

Learning Objectives:

1. Apply machine learning models to automotive data analysis
2. Develop AI-based solutions for vehicle automation tasks
3. Understand neural network architectures for real-time systems
4. Implement AI tools in autonomous driving applications

Renewable Energy Integration

This course addresses the integration of renewable energy sources into automotive and transportation systems. Students study solar panels, wind turbines, energy storage technologies, and grid stability issues related to clean transportation. Practical components include designing hybrid power systems and evaluating environmental impacts.

Learning Objectives:

1. Design renewable energy systems for transportation applications
2. Analyze efficiency and cost-effectiveness of different energy sources
3. Evaluate environmental impact and sustainability factors
4. Integrate renewable technologies with existing automotive infrastructure

Battery Management Systems

This course focuses on the design, implementation, and optimization of battery management systems (BMS) for electric vehicles. Students study battery chemistry, cell balancing techniques, thermal management, state-of-charge estimation, and safety protocols. Projects involve designing BMS for specific vehicle types and conducting performance testing.

Learning Objectives:

1. Understand battery chemistry and electrochemical processes
2. Design BMS architectures for various applications
3. Implement algorithms for state-of-charge estimation
4. Evaluate safety mechanisms and regulatory compliance

Industrial Automation

This course provides an overview of industrial automation principles and their application in manufacturing and control systems. Topics include programmable logic controllers (PLCs), SCADA systems, industrial communication networks, and process control strategies. Students gain hands-on experience using industrial simulation software and hardware platforms.

Learning Objectives:

1. Design and implement industrial control systems
2. Program PLCs for automation tasks
3. Understand SCADA architecture and data visualization
4. Apply control theory to industrial processes

Predictive Maintenance

This course focuses on using data analytics and machine learning for predictive maintenance in automotive systems. Students study failure analysis techniques, condition monitoring methods, sensor-based diagnostics, and optimization strategies. The curriculum includes case studies from real-world applications and practical exercises involving data processing and model validation.

Learning Objectives:

1. Identify maintenance needs using predictive models
2. Apply statistical methods for failure prediction
3. Design condition monitoring systems for vehicles
4. Evaluate maintenance strategies based on cost-benefit analysis

Autonomous Vehicle Systems

This advanced course explores the technical aspects of autonomous vehicle development, including perception systems, localization algorithms, path planning, and control strategies. Students engage in projects involving sensor fusion, decision-making frameworks, and simulation environments for testing autonomous driving capabilities.

Learning Objectives:

1. Understand perception systems for autonomous vehicles
2. Design navigation and control algorithms
3. Implement sensor fusion techniques for vehicle autonomy
4. Evaluate autonomous systems in simulation and real-world environments

Advanced Control Systems

This course builds upon foundational control theory to cover advanced topics such as robust control, optimal control, nonlinear control, and adaptive control. Students study mathematical modeling of complex systems and design controllers for industrial applications including automotive systems.

Learning Objectives:

1. Apply advanced control techniques to real-world systems
2. Model complex systems using mathematical frameworks
3. Design robust controllers for uncertain environments
4. Evaluate controller performance using simulation tools

Project-Based Learning Philosophy

The Auto Electrical program places significant emphasis on project-based learning as a means of fostering innovation, teamwork, and practical problem-solving skills. The curriculum integrates mandatory mini-projects throughout the academic journey, culminating in a comprehensive capstone project in the final year.

Mini-Projects Structure

Mini-projects are introduced in the second semester and continue through the sixth semester. Each project is designed to reinforce key concepts learned in lectures and laboratories while providing exposure to real-world engineering challenges. Projects typically last 8-12 weeks and involve teams of 3-5 students working under faculty supervision.

Examples of mini-project topics include:

• Designing a basic electric vehicle controller using microcontrollers
• Developing an embedded system for traffic light control in smart cities
• Building a sensor network for monitoring engine performance
• Creating a predictive maintenance model for commercial vehicles
• Implementing an autonomous navigation algorithm for small robots

Mini-projects are evaluated based on technical merit, innovation, presentation quality, and teamwork. Students receive feedback from faculty mentors and peers, which helps refine their engineering thinking and communication skills.

Final-Year Thesis/Capstone Project

The final-year capstone project is a significant undertaking that allows students to apply all knowledge gained during the program to address an industry-relevant problem. Students select projects from a list provided by faculty or propose their own ideas after consultation with advisors.

Capstone projects are typically completed over two semesters and involve extensive research, design, implementation, testing, and documentation. The final output includes a detailed report, a working prototype or simulation model, and a presentation to faculty and industry experts.

Evaluation criteria for the capstone project include:

• Problem identification and solution approach
• Technical depth and innovation
• Practical applicability and scalability
• Quality of documentation and presentation
• Performance evaluation and testing results

Faculty mentors are assigned based on the student's interests, project scope, and available expertise. Regular meetings with mentors ensure progress tracking and timely resolution of challenges.

Project Selection Process

The process for selecting capstone projects begins in the seventh semester when students submit preferences for potential topics. Faculty members propose a limited number of research-oriented projects aligned with their current work or industry collaborations. Students also have the option to propose original ideas that are feasible within the program's resources and timeframe.

Project selection involves a review by a committee comprising faculty from relevant disciplines. The final decision considers factors such as academic relevance, technical feasibility, resource availability, and alignment with student interests and career goals.

Once selected, projects undergo detailed planning sessions where students define objectives, milestones, deliverables, and timelines. This structured approach ensures that students remain focused on achieving meaningful outcomes while developing professional project management skills.