Curriculum Overview

The Auto Electrical program at Government Polytechnic Bash Bagarh is meticulously structured to ensure a progressive and comprehensive learning experience. The curriculum spans three academic years, divided into six semesters. Each semester includes core courses, departmental electives, science electives, and laboratory practices designed to build both theoretical knowledge and practical skills.

Semester-wise Course Structure

The following table outlines the course structure for each of the six semesters:

Advanced Departmental Electives

The department offers several advanced elective courses that provide students with specialized knowledge and skills in emerging areas of Auto Electrical:

1. Advanced Battery Technologies

This course delves into the latest developments in lithium-ion batteries, solid-state batteries, and alternative energy storage solutions. Students explore electrode materials, electrolyte formulations, battery management systems (BMS), and lifecycle analysis.

Learning Objectives:

• Understand the chemistry and physics behind various battery technologies
• Design and optimize battery pack configurations for different applications
• Analyze battery performance under varying environmental conditions
• Evaluate safety protocols and regulatory compliance in battery manufacturing

2. Motor Drive Control Systems

This elective focuses on the design and implementation of motor drives used in electric vehicles and industrial applications. Topics include DC motor control, induction motor drives, brushless DC motors, and variable frequency drives.

Learning Objectives:

• Design control algorithms for various types of electric motors
• Implement power electronic converters for motor drive applications
• Analyze efficiency and performance characteristics of different motor drive systems
• Understand the integration of motor drives with vehicle control systems

3. Electromagnetic Compatibility in Vehicles

This course explores EMI/EMC issues in automotive environments, including interference sources, propagation paths, and mitigation techniques. Students learn to design electromagnetic shielding and compliance testing strategies for vehicle components.

Learning Objectives:

• Identify and classify electromagnetic interference sources in vehicles
• Design effective shielding and filtering solutions for automotive electronics
• Conduct electromagnetic compatibility testing and analysis
• Ensure compliance with international EMI standards and regulations

4. Embedded Systems Programming

This elective teaches students how to program microcontrollers, real-time operating systems (RTOS), and embedded software architectures for automotive applications. It includes hands-on labs using ARM Cortex-M series processors and development tools like Keil MDK and STM32CubeIDE.

Learning Objectives:

• Program microcontrollers for automotive control systems
• Develop real-time applications using RTOS environments
• Implement sensor interfaces and communication protocols in embedded systems
• Debug and optimize embedded software performance

5. Vehicle Diagnostics and Maintenance

This course introduces diagnostic tools, fault detection algorithms, predictive maintenance strategies, and service automation techniques used in modern vehicles. Students learn to use OBD-II scanners, data loggers, and advanced diagnostic software.

Learning Objectives:

• Utilize diagnostic tools for vehicle troubleshooting
• Develop predictive maintenance schedules based on sensor data
• Implement service automation in vehicle repair processes
• Evaluate diagnostic accuracy and system reliability

6. Smart Mobility Research

This course focuses on research methodologies, innovation trends, and future mobility concepts such as autonomous vehicles, smart traffic management, and urban transportation optimization. Students work on research projects involving simulation tools and real-world data analysis.

Learning Objectives:

• Conduct literature reviews and identify research gaps in smart mobility
• Design and execute research experiments using simulation software
• Analyze real-world data to derive insights on transportation efficiency
• Present research findings and propose innovative solutions for mobility challenges

7. Advanced Driver Assistance Systems (ADAS)

This elective covers the design, development, and implementation of ADAS features like lane departure warning, adaptive cruise control, automatic emergency braking, and pedestrian detection systems. It includes exposure to computer vision, machine learning, and sensor fusion techniques.

Learning Objectives:

• Design algorithms for perception and decision-making in ADAS
• Integrate multiple sensors for robust system performance
• Implement machine learning models for object recognition and tracking
• Evaluate safety and reliability of ADAS systems through simulations and testing

8. Sustainable Transportation Technologies

This course explores sustainable alternatives to conventional vehicles, including hydrogen fuel cells, solar-powered transportation, biofuels, and energy-efficient vehicle designs. Students examine lifecycle assessments and environmental impact analysis of various technologies.

Learning Objectives:

• Evaluate the feasibility and sustainability of alternative propulsion systems
• Design efficient energy conversion and storage systems for transportation
• Analyze environmental impact and regulatory compliance of green transportation solutions
• Develop business models for sustainable mobility startups

9. IoT and Connected Vehicles

This elective explores the integration of Internet of Things (IoT) technologies in vehicles, focusing on connectivity, data exchange, cloud computing, and user experience design. Students work with wireless protocols, edge computing, and vehicle-to-everything (V2X) communication systems.

Learning Objectives:

• Design IoT architectures for connected vehicle ecosystems
• Implement secure communication protocols for V2X applications
• Develop cloud-based platforms for vehicle data analytics and visualization
• Create user-friendly interfaces for infotainment and navigation systems

10. Data Analytics for Vehicle Systems

This course introduces students to big data analytics, machine learning algorithms, predictive modeling, and real-time decision-making in automotive applications. It includes exposure to tools like Python, TensorFlow, and data visualization platforms.

Learning Objectives:

• Apply statistical methods for analyzing vehicle performance data
• Develop machine learning models for predictive maintenance and fault diagnosis
• Visualize large datasets to extract actionable insights for fleet management
• Integrate analytics into vehicle control systems for real-time optimization

Project-Based Learning Philosophy

Our department strongly believes in project-based learning as a core component of technical education. This approach not only reinforces theoretical knowledge but also develops practical skills, teamwork, and innovation capabilities essential for professional success.

Mini-Projects

Mini-projects are conducted during the third and fourth semesters to allow students to apply concepts learned in class to real-world scenarios. These projects typically last 4-6 weeks and involve small groups of 3-5 students working under faculty supervision.

Structure and Scope

• Project Selection: Students choose projects from a predefined list provided by faculty mentors or propose their own ideas after consultation with supervisors.
• Team Formation: Groups are formed based on interest, skill set, and project requirements. Faculty members act as advisors throughout the process.
• Timeline: Projects begin in the middle of the semester and culminate in a presentation session at the end.
• Evaluation Criteria: Assessment includes progress reports, demonstration of working prototype, final report, and oral defense.

Sample Mini-Project Topics

• Design of a Smart Parking System Using IoT Sensors
• Development of an Electric Scooter with Integrated Battery Monitoring
• Implementation of Lane Departure Warning System for Commercial Vehicles
• Analysis and Optimization of Vehicle Energy Consumption Patterns
• Creation of a Mobile App for Vehicle Diagnostics and Maintenance Reminders

Final Year Thesis/Capstone Project

The final year project is the most significant component of our program, requiring students to complete an in-depth research or development project that demonstrates mastery of the subject matter.

Project Selection Process

• Proposal Submission: Students submit a detailed proposal outlining objectives, methodology, expected outcomes, and timeline.
• Mentor Assignment: Faculty mentors are assigned based on expertise areas aligned with the student's project interests.
• Regular Reviews: Mid-term and final reviews ensure progress adherence to goals.
• Final Presentation: Students present their findings to a panel of faculty members and industry experts.

Project Evaluation Criteria

• Technical Depth: Demonstration of advanced technical knowledge and problem-solving capabilities
• Innovation: Originality and creativity in approach or solution design
• Documentation: Quality of research report, code documentation, and project presentation
• Implementation: Functionality and performance of the final product or system developed

Sample Capstone Project Topics

• Development of a Fully Autonomous Electric Vehicle with AI Navigation Capabilities
• Design and Testing of a Hybrid Powertrain for Urban Delivery Vehicles
• Smart Grid Integration for EV Charging Stations in Residential Communities
• Real-Time Monitoring System for Fleet Management Using IoT Technologies
• Advanced Battery Management Algorithm for Long-Distance Electric Trucks

The project-based learning model at Government Polytechnic Bash Bagarh ensures that students graduate not only with theoretical knowledge but also with practical experience that makes them highly employable and ready to contribute to the evolving automotive industry.