Automotive: Technology Landscape
Description
Comprehensive understanding of the automotive technology ecosystem including traditional automotive engineering, emerging digital technologies, software platforms, connectivity solutions, and the integration of AI, IoT, and advanced manufacturing technologies.
Learning Objectives
Learners will be able to analyze traditional automotive engineering technologies including powertrain systems and manufacturing processes, evaluate emerging digital technologies including AI, IoT, and machine learning applications in automotive, assess software-defined vehicle architectures and over-the-air update mechanisms, understand connectivity technologies including 5G, V2X communication, and cloud platforms, and design technology integration strategies that combine hardware and software solutions to meet automotive industry requirements and regulatory standards.
Topics (10)
Comprehensive overview of traditional automotive technologies including internal combustion engines, transmission systems, suspension and chassis technologies, braking systems, steering systems, and mechanical component integration that form the foundation of vehicle engineering.
Detailed examination of EV technologies including lithium-ion battery technologies, electric motor designs, power electronics, charging systems (AC/DC, fast charging), battery management systems, thermal management, and energy recovery systems.
Analysis of automotive connectivity technologies including 5G networks for automotive applications, vehicle-to-everything (V2X) communication, vehicle-to-infrastructure (V2I) systems, vehicle-to-vehicle (V2V) communication, cellular connectivity, and satellite communication systems.
Comprehensive study of automotive cloud computing including cloud platform selection, data analytics for vehicle performance, predictive maintenance analytics, fleet management platforms, real-time data processing, and big data applications in automotive.
Analysis of automotive cybersecurity including threat assessment, security frameworks, encryption technologies, secure over-the-air updates, functional safety standards (ISO 26262), cybersecurity standards (ISO 21434), and risk management approaches for connected vehicles.
Examination of Industry 4.0 in automotive including industrial robotics, automation systems, digital twin technology for manufacturing, predictive maintenance in production, quality control systems, and smart factory implementations with IoT integration.
Analysis of emerging technologies including quantum computing for optimization, advanced materials (carbon fiber, lightweight metals), hydrogen fuel cell technology, autonomous vehicle AI advancements, augmented reality applications, and next-generation human-machine interfaces.
Analysis of AI applications including computer vision for autonomous driving, machine learning for predictive maintenance, natural language processing for voice interfaces, deep learning for sensor fusion, AI-powered manufacturing optimization, and quality control systems.
Comprehensive study of automotive IoT including sensor technologies (lidar, radar, cameras, ultrasonic), sensor fusion systems, real-time data processing, edge computing implementations, telemetry systems, and IoT platform integration for connected vehicles.
Examination of software-defined vehicle architecture including centralized domain controllers, software platform design, over-the-air update mechanisms, software lifecycle management, microservices architecture, and the transition from distributed ECUs to centralized computing platforms.