Introduction to Artificial Intelligence in Automotive Engineering

Understanding Theory and Practical Applications
1. Auflage 2026
ISBN: 978-1-041-23722-8
Verlag: Taylor & Francis Ltd

Buch, Englisch, 306 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 435 g

This book serves as a timely and practical guide to understanding how artificial intelligence (AI) can be harnessed in automotive engineering. It not only demystifies complex AI methods but also equips readers with the tools to creatively apply them to their own engineering challenges. Drawing from the authors' extensive teaching experience, this book adopts a hands-on approach to applying AI tools – such as regression, classification, clustering, and deep learning (DL) – to real-world engineering challenges. Through clear explanations and step-by-step algorithmic structures, readers are introduced to the foundational AI concepts and guided through practical applications using real automotive data. Examples include predicting electric vehicle energy consumption under varying conditions, assessing battery degradation, analyzing brake system wear, and implementing intelligent control for thermal management (HVAC systems). This book demonstrates how AI can optimize performance, enhance safety and drive innovation. Each chapter builds essential skills, including setting up machine learning (ML) environments, implementing regression and classification models, constructing decision trees, applying clustering techniques, and designing neural networks (NNs). This guide is essential for postgraduate students interested in exploring how AI can be applied in engineering.

Zielgruppe

Postgraduate, Professional Reference, and Undergraduate Advanced

Autoren/Hrsg.

Mariasiu, Florin Emil

Buidin, Thomas Imre Cyrille

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Technische Wissenschaften Verkehrstechnik | Transportgewerbe Fahrzeugtechnik

Inhaltsverzeichnis

Preface. Introduction: AI and Automotive Engineering. About the Authors 1. Introduction to Artificial Intelligence 2. Regression Methods 3. Classification Tools 4. Decision Trees Method 5. K-means and Hierarchical Clustering 6. Support Vector Machine Method 7. Neural Networks 8. Reinforcement Learning. Index.

Über Autor(innen)

Thomas Imre Cyrille Buidin is assistant professor in the Department of Automotive Engineering and Transports at the Technical University of Cluj-Napoca (UTCN), and a member of the Electric Mobility Applied Research Center (EMARC) research group. He earned his PhD in Mechanical Engineering from UTCN (2024) with a summa cum laude distinction. His thesis analyzed the efficiency of battery thermal management systems, and he was the first within the UTCN to incorporate AI methods into a doctoral thesis from the Mechanical Engineering domain. His domains of research and interest are hybrid and electric vehicles and lithium-ion batteries, with a focus on the energy efficiency optimization of innovative thermal management solutions and systems. The results of his research activity can be summarized as one patent, seven published scientific articles, 200 citations and ten prizes and awards for research and innovation in the fields of Mechanical and Automotive Engineering.

Florin Emil Mariasiu is a full professor at the Technical University of Cluj-Napoca (UTCN), specializing in Automotive and Transport Engineering. He earned his PhD in Mechanical Engineering from the UTCN (2003), as well as his Habilitation diploma in Mechanical Engineering (2015) from the UTCN. His areas of research interest are sustainable energy solutions for mechanical and thermal systems, along with recent concerns related to the application of AI tools in engineering and hybrid education in the automotive field. His research activity can be summarized as four patents, 15 books/chapters, over 100 published scientific articles, over 1,500 citations and more than 20 national and international awards and distinctions for research and innovation in the fields of Mechanical and Automotive Engineering.