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Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| AUTOMOTIVE ENGINEERING (PhD) | |
| Code | OM602 |
| Name | Artificial Intelligence Applications in Automotive Engineering |
| Term | 2026-2027 Academic Year |
| Term | Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Doktora Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Doç. Dr. ERDİ TOSUN |
| Course Instructor |
The current term course schedule has not been prepared yet.
|
Course Goal / Objective
The aim of this course is to provide students with knowledge of fundamental principles of artificial intelligence methods in automotive engineering, including data-driven modeling approaches and their integration into vehicle systems.
Course Content
This course provides a comprehensive approach to artificial intelligence applications in automotive engineering; fundamental concepts of artificial intelligence and data science, digital transformation and data-driven decision-making in the automotive sector are explained, followed by supervised and unsupervised learning approaches; basic methods such as regression, classification and clustering are analyzed using automotive datasets; data acquisition, preprocessing, feature extraction and model development processes are covered, along with time-series and sensor-based data structures; big data analytics and data management concepts are introduced; artificial intelligence applications in autonomous driving, advanced driver assistance systems and in-vehicle sensor systems are examined; data-driven approaches in battery management, energy systems and fault diagnosis are evaluated; optimization problems, decision support systems and AI-based control approaches are discussed; simulation-based modeling and digital twin concepts are introduced; model performance evaluation, validation and comparison methods are explained, and ethical considerations, safety and engineering limitations are discussed; current industrial applications and future trends are evaluated, enabling students to develop the knowledge and skills required to analyze AI applications in automotive engineering from both theoretical and practical perspectives.
Course Precondition
There are no prerequisites for this course.
Resources
Rao S, AI and Generative AI for Automobile Engineering: Revolutionizing the Road and Future of Driving.
Notes
Lecture Notes
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Explains fundamental AI concepts in automotive engineering |
| LO02 | Analyzes data-driven modeling processes. |
| LO03 | Compares different learning approaches |
| LO04 | Interprets sensor and time-series data |
| LO05 | Evaluates AI applications in automotive systems. |
| LO06 | Assesses model performance and validation methods. |
| LO07 | Explains ethical and engineering constraints. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Gains comprehensive knowledge about current techniques, methods, and their limitations applied in Automotive Engineering. | 4 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Gains knowledge on entrepreneurship, sustainable development and innovation. | |
| PLO03 | Beceriler - Bilişsel, Uygulamalı | Extends the knowledge gained at undergraduate and graduate level and applies it in the field of automotive engineering. | |
| PLO04 | Beceriler - Bilişsel, Uygulamalı | Recognizes the techniques required for applications in the field of automotive technology and uses modern tools. | 5 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Determines alternative solutions by making project planning and time management. | |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Develops new and/or novel ideas and methods related to Automotive Engineering. | |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Determines solution methods by integrating information from different fields with its own field by taking part in multi-disciplinary teams. | |
| PLO08 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Collects, evaluates and interprets data using scientific methods. | |
| PLO09 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Solves complex problems independently, improving the abilities of analysis, synthesis, critical evaluation. | |
| PLO10 | Yetkinlikler - Öğrenme Yetkinliği | Designs a process or a system using modern design methods and tools. | |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Develops new and/or original ideas and methods in the field of Automotive Engineering; develops innovative solutions in system, part or process designs. | |
| PLO12 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Communicates in at least one foreign language by effectively expressing her/his scientific knowledge in written and oral form. | |
| PLO13 | Yetkinlikler - Alana Özgü Yetkinlik | Considers social, environmental, scientific and ethical values in the scientific research process. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to Artificial Intelligence and Digitalization in the Automotive Industry | Reference books and lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 2 | Data Structures and Automotive Data | Reference books and lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 3 | Heat Generation in Batteries | Reference books and lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 4 | Regression and Classification Approaches | Reference books and lecture notes | Öğretim Yöntemleri: Anlatım |
| 5 | Clustering and Data Analysis | Reference books and lecture notes | Öğretim Yöntemleri: Anlatım |
| 6 | Modeling and Prediction Approaches | Reference books and lecture notes | Öğretim Yöntemleri: Anlatım |
| 7 | Time Series Analysis | Reference books and lecture notes | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Reference books and lecture notes | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Image and Sensor Data Analysis | Reference books and lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 10 | Sequential Data and Dynamic Systems | Reference books and lecture notes | Öğretim Yöntemleri: Anlatım |
| 11 | Autonomous Vehicle Systems | Reference books and lecture notes | Öğretim Yöntemleri: Anlatım |
| 12 | Data Analysis in Energy and Battery Systems | Reference books and lecture notes | Öğretim Yöntemleri: Anlatım |
| 13 | Fault Diagnosis and Predictive Maintenance | Reference books and lecture notes | Öğretim Yöntemleri: Anlatım |
| 14 | Simulation and Digital Twin | Reference books and lecture notes | Öğretim Yöntemleri: Anlatım |
| 15 | Current Practices and Future Trends | Reference books and lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 16 | Term Exams | Reference books and lecture notes | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Reference books and lecture notes | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 3 | 42 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 10 | 10 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 20 | 20 |
| Final Exam | 1 | 40 | 40 |
| Total Workload (Hour) | 154 | ||
| Total Workload / 25 (h) | 6,16 | ||
| ECTS | 6 ECTS | ||