Curriculum Overview
The Auto Electrical program at Government Polytechnic Tanakpur is structured to provide a comprehensive understanding of automotive electrical systems and their integration with modern technologies. The curriculum spans four years and includes core subjects, departmental electives, science electives, and laboratory sessions designed to enhance practical skills.
Course Structure Across Semesters
| Semester | Course Code | Full Course Title | Credit (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | AE101 | Engineering Mathematics I | 3-1-0-4 | None |
| 1 | AE102 | Basic Electrical Engineering | 3-1-0-4 | None |
| 1 | AE103 | Engineering Mechanics | 3-1-0-4 | None |
| 1 | AE104 | Computer Programming | 2-0-2-3 | None |
| 1 | AE105 | Engineering Drawing | 2-0-2-3 | None |
| 1 | AE106 | Communication Skills | 2-0-0-2 | None |
| 1 | AE107 | Laboratory: Basic Electrical Circuits | 0-0-3-1.5 | AE102 |
| 2 | AE201 | Engineering Mathematics II | 3-1-0-4 | AE101 |
| 2 | AE202 | Electronics Devices and Circuits | 3-1-0-4 | AE102 |
| 2 | AE203 | Digital Electronics | 3-1-0-4 | AE202 |
| 2 | AE204 | Control Systems | 3-1-0-4 | AE101 |
| 2 | AE205 | Mechanics of Materials | 3-1-0-4 | AE103 |
| 2 | AE206 | Laboratory: Electronics Circuits | 0-0-3-1.5 | AE202 |
| 3 | AE301 | Signals and Systems | 3-1-0-4 | AE201 |
| 3 | AE302 | Microprocessor Architecture | 3-1-0-4 | AE203 |
| 3 | AE303 | Embedded Systems Programming | 3-1-0-4 | AE204 |
| 3 | AE304 | Automotive Electronics | 3-1-0-4 | AE202 |
| 3 | AE305 | Sensor Technology | 3-1-0-4 | AE202 |
| 3 | AE306 | Laboratory: Embedded Systems | 0-0-3-1.5 | AE303 |
| 4 | AE401 | Power Electronics | 3-1-0-4 | AE202 |
| 4 | AE402 | Vehicle Dynamics | 3-1-0-4 | AE205 |
| 4 | AE403 | Electronic Control Units (ECUs) | 3-1-0-4 | AE304 |
| 4 | AE404 | Battery Management Systems | 3-1-0-4 | AE401 |
| 4 | AE405 | Automotive Communication Protocols | 3-1-0-4 | AE204 |
| 4 | AE406 | Laboratory: Automotive Electronics | 0-0-3-1.5 | AE404 |
| 5 | AE501 | Advanced Control Systems | 3-1-0-4 | AE301 |
| 5 | AE502 | Autonomous Vehicle Navigation | 3-1-0-4 | AE402 |
| 5 | AE503 | Machine Learning in Robotics | 3-1-0-4 | AE301 |
| 5 | AE504 | Smart Mobility Solutions | 3-1-0-4 | AE405 |
| 5 | AE505 | Automotive Cybersecurity | 3-1-0-4 | AE204 |
| 5 | AE506 | Laboratory: Advanced Projects | 0-0-3-1.5 | AE504 |
| 6 | AE601 | Final Year Project | 0-0-6-6 | AE501, AE502 |
| 6 | AE602 | Project Management | 2-0-0-2 | None |
| 6 | AE603 | Technical Writing | 2-0-0-2 | None |
| 6 | AE604 | Viva Voce Preparation | 1-0-0-1 | None |
| 6 | AE605 | Professional Ethics | 2-0-0-2 | None |
Advanced Departmental Electives
The department offers several advanced departmental electives that allow students to specialize in specific areas of interest. These courses are designed to provide deeper insights into emerging technologies and practical applications within the automotive domain.
1. Electric Vehicle Powertrain Design
This course delves into the design and optimization of powertrains for electric vehicles, covering topics such as motor drives, battery systems, power electronics converters, and energy management strategies. Students gain hands-on experience in designing and simulating EV powertrain components using industry-standard tools.
2. Autonomous Vehicle Navigation Systems
This elective explores the development of navigation algorithms for autonomous vehicles, including path planning, localization, mapping, and decision-making systems. The course combines theoretical knowledge with practical implementation using real-time simulation environments and sensor fusion techniques.
3. Machine Learning in Robotics
This course focuses on applying machine learning algorithms to robotic systems, particularly in the context of autonomous vehicles. Students learn about neural networks, reinforcement learning, computer vision, and deep learning frameworks tailored for automotive applications.
4. Smart Mobility Solutions
This elective introduces students to innovative solutions for urban mobility challenges using IoT, data analytics, and communication technologies. Topics include smart parking systems, traffic optimization algorithms, ride-sharing platforms, and sustainable transportation models.
5. Automotive Cybersecurity Frameworks
This course addresses the growing importance of cybersecurity in connected vehicles. Students learn about secure communication protocols, vulnerability assessments, penetration testing, encryption techniques, and blockchain-based identity management systems for vehicles.
6. Advanced Control Theory
This elective builds upon foundational control systems knowledge by introducing advanced concepts such as nonlinear control, adaptive control, robust control, and optimal control theory. The course emphasizes practical implementation through simulations and real-world case studies.
7. Sensor Fusion Algorithms
This course focuses on integrating data from multiple sensors to improve vehicle perception capabilities. Students explore Kalman filtering, particle filtering, sensor calibration techniques, and multi-sensor fusion architectures used in autonomous driving systems.
8. Embedded System Design for Automotive Applications
This elective covers the design and implementation of embedded systems specifically tailored for automotive applications. Topics include microcontroller architecture, real-time operating systems, firmware development, hardware-software co-design, and debugging techniques.
9. Battery Management Systems
This course provides in-depth knowledge of battery technologies, including lithium-ion, nickel-metal hydride, and solid-state batteries. Students learn about state-of-charge estimation, thermal management, fault detection, and system integration for EV applications.
10. V2X Communication Protocols
This elective explores vehicle-to-everything (V2X) communication technologies that enable safe and efficient interaction between vehicles, infrastructure, and pedestrians. Students study IEEE 802.11p, DSRC, C-V2X standards, and their applications in smart transportation systems.
Project-Based Learning Philosophy
The department believes in experiential learning as a cornerstone of education. Project-based learning is integrated throughout the curriculum to ensure students develop critical thinking, problem-solving, and teamwork skills. The approach emphasizes real-world relevance, interdisciplinary collaboration, and innovation.
Mini-Projects (Semesters 3-5)
Mini-projects are assigned during semesters 3 through 5 to reinforce theoretical concepts and encourage practical application. These projects typically involve small teams of 3-5 students working on specific challenges related to automotive electrical systems. Projects are guided by faculty mentors who provide technical support and evaluation feedback.
Final-Year Thesis/Capstone Project
The final-year capstone project is a comprehensive endeavor that integrates all the knowledge and skills acquired during the program. Students select a topic relevant to their specialization, conduct independent research or development work, and present their findings to a panel of faculty members and industry experts.
Project Selection Process
Students can propose topics based on their interests, faculty recommendations, or industry collaboration projects. The selection process involves submitting project proposals, undergoing review by the departmental committee, and securing approval from faculty mentors. Projects are typically aligned with current industry trends and research needs.
Evaluation Criteria
Projects are evaluated based on multiple criteria including technical depth, innovation, presentation quality, peer feedback, and final deliverables. Each project is assigned a weightage of 30% for the final assessment in the respective semester.