Curriculum Overview
The Auto Electrical Engineering curriculum at Government Polytechnic Bachalikhal is meticulously designed to provide students with a comprehensive understanding of both theoretical and practical aspects of automotive electrical systems. The program spans eight semesters, each building upon the previous one to ensure progressive learning and skill development.
Course Structure Across Eight Semesters
| Semester | Course Code | Course Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | AE101 | Engineering Mathematics I | 3-1-0-4 | None |
| 1 | AE102 | Physics for Engineers | 3-1-0-4 | None |
| 1 | AE103 | Basic Electrical Circuits | 3-1-0-4 | None |
| 1 | AE104 | Introduction to Computer Programming | 3-1-0-4 | None |
| 2 | AE201 | Engineering Mathematics II | 3-1-0-4 | AE101 |
| 2 | AE202 | Digital Electronics | 3-1-0-4 | AE103 |
| 2 | AE203 | Microprocessors and Microcontrollers | 3-1-0-4 | AE104 |
| 2 | AE204 | Control Systems | 3-1-0-4 | AE201 |
| 3 | AE301 | Electrical Machines | 3-1-0-4 | AE203 |
| 3 | AE302 | Electric Vehicle Technology | 3-1-0-4 | AE201 |
| 3 | AE303 | Embedded Systems Design | 3-1-0-4 | AE203 |
| 3 | AE304 | Sensor Networks and IoT | 3-1-0-4 | AE202 |
| 4 | AE401 | Advanced Control Systems | 3-1-0-4 | AE301 |
| 4 | AE402 | Automotive Communication Protocols | 3-1-0-4 | AE304 |
| 4 | AE403 | Automotive Cybersecurity | 3-1-0-4 | AE204 |
| 4 | AE404 | Smart Grid Integration | 3-1-0-4 | AE302 |
| 5 | AE501 | Autonomous Driving Systems | 3-1-0-4 | AE401 |
| 5 | AE502 | Battery Management Systems | 3-1-0-4 | AE402 |
| 5 | AE503 | Vehicle Diagnostics and Maintenance | 3-1-0-4 | AE403 |
| 5 | AE504 | Sustainable Power Management | 3-1-0-4 | AE404 |
| 6 | AE601 | Advanced Microcontroller Programming | 3-1-0-4 | AE501 |
| 6 | AE602 | Energy Storage Technologies | 3-1-0-4 | AE502 |
| 6 | AE603 | Predictive Maintenance in Automotive Systems | 3-1-0-4 | AE503 |
| 6 | AE604 | Wireless Sensor Networks | 3-1-0-4 | AE504 |
| 7 | AE701 | Research Methodology | 3-1-0-4 | AE601 |
| 7 | AE702 | Mini Project I | 3-1-0-4 | AE602 |
| 7 | AE703 | Mini Project II | 3-1-0-4 | AE603 |
| 7 | AE704 | Advanced Electives I | 3-1-0-4 | AE604 |
| 8 | AE801 | Final Year Thesis/Capstone Project | 3-1-0-4 | AE701 |
| 8 | AE802 | Advanced Electives II | 3-1-0-4 | AE702 |
| 8 | AE803 | Internship | 3-1-0-4 | AE703 |
| 8 | AE804 | Capstone Project Presentation | 3-1-0-4 | AE704 |
Detailed Course Descriptions
The department offers a range of advanced departmental elective courses designed to deepen students' expertise in specialized areas:
- Battery Management Systems: This course explores the design and implementation of battery management systems for electric vehicles, covering topics like cell balancing, thermal management, and safety protocols. Students gain hands-on experience with industry-standard tools and software.
- Autonomous Navigation Algorithms: Focuses on developing algorithms for autonomous vehicle navigation, including path planning, obstacle detection, and decision-making under uncertainty. Practical sessions involve simulation software and real-world datasets.
- Cybersecurity for Connected Cars: Addresses the growing need for cybersecurity in connected vehicles, covering vulnerabilities, threat models, and secure communication protocols. Students engage in ethical hacking exercises and penetration testing.
- Solar Charging Systems for Vehicles: Examines the integration of solar energy into vehicle charging systems, including photovoltaic panel design, energy conversion efficiency, and system optimization techniques.
- Wireless Sensor Networks: Covers design principles, protocols, and applications of wireless sensor networks in automotive environments. Students work on practical projects involving network deployment and data analysis.
- Advanced Microcontroller Programming: Delivers advanced programming concepts for microcontrollers used in automotive systems, emphasizing real-time processing, interrupt handling, and communication protocols.
- Energy Storage Technologies: Explores various energy storage technologies including lithium-ion batteries, supercapacitors, and fuel cells. The course includes laboratory sessions on testing and performance evaluation.
- Predictive Maintenance in Automotive Systems: Introduces predictive maintenance strategies using machine learning algorithms and data analytics. Students learn to build models that predict component failures and optimize maintenance schedules.
- Vehicle Diagnostics and Troubleshooting: Provides comprehensive training in diagnosing and resolving vehicle issues using advanced diagnostic tools and techniques. Includes hands-on sessions with actual vehicles.
- Smart Grid Integration: Focuses on integrating renewable energy sources into automotive systems, covering grid stability, energy management, and regulatory frameworks.
Project-Based Learning Philosophy
The department's philosophy centers around project-based learning to ensure students acquire practical skills and apply theoretical knowledge in real-world scenarios. The approach emphasizes:
- Collaborative Learning: Students work in teams to tackle complex engineering problems, fostering communication and leadership skills.
- Industry Alignment: Projects are often sourced from industry partners, ensuring relevance and applicability to current market demands.
- Mentorship and Guidance: Faculty members serve as mentors throughout the project lifecycle, providing guidance and feedback.
Mini-Projects Structure
Students undertake two mandatory mini-projects during their third and fourth years:
- Mini Project I (Year 3): Focuses on foundational engineering concepts with emphasis on design and prototyping. Projects are evaluated based on innovation, technical execution, and presentation quality.
- Mini Project II (Year 4): Builds upon previous projects, integrating advanced technologies and methodologies. Emphasis is placed on scalability, impact, and potential commercialization.
Final-Year Thesis/Capstone Project
The final year capstone project is a comprehensive endeavor that synthesizes all knowledge gained throughout the program:
- Project Selection Process: Students choose projects in consultation with faculty advisors, considering personal interests, industry trends, and resource availability.
- Mentorship: Each student is assigned a faculty mentor who guides them through the research and development process.
- Evaluation Criteria: Projects are assessed based on originality, technical depth, documentation quality, and presentation skills.
- Presentation and Defense: Students present their work to a panel of faculty members and industry experts, followed by a rigorous questioning session.