Introduction to Artificial Intelligence in Automotive Engineering

Understanding Theory and Practical Applications

This book serves as a practical and timely guide to understanding how artificial intelligence can be applied and leveraged in automotive engineering. It demystifies complex methods of applying artificial intelligence and provides readers with the tools to creatively and immediately apply them to their own engineering challenges.

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.

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