URL study guide
https://tue.osiris-student.nl/onderwijscatalogus/extern/cursus?cursuscode=5AT020&collegejaar=2026&taal=enDescription
This is a system level course which introduces the aspects of electric drives, batteries and their interaction in different system settings. Topics on advanced control, industrial and environmental aspects will be treated only at high level to familiarize you with the concepts. Follow-up courses on these topics will be recommended in the specific lectures for further in-depth knowledge.In this course , the following will be week based topics. The topics mentioned after “CBL” indicate predominantly the CBL content section:
♦ W1: Introduction to net-zero energy systems, and electric drives (e-drives) in vehicles and high-tech applications
• CBL: Introduction to CBL (Way-of-working, iLO, Group forming), General Introduction to Machine learning, and AI techniques for electrified systems
♦ W2: Electrical Machines for E-drives (types, operation principles, manufacturing and eco-design challenges)
• CBL eDrive: Introduction to Reinforcement Learning (RL) for motor control
♦ W3: Power Electronics for E-drives (types, design, operation principles, vehicle and high-tech system differences)
• CBL eDrive: RL for motor control
♦ W4: Charging concepts (types, operation principles,kW to MW charging systems)
• CBL Contact Moment. and Half-way course reflection
♦ W5: Batteries (design, operation)
• CBL Battery: Digital Twinning of battery systems, ,
♦ W6: Batteries (Functionalities of online battery management systems)l
• CBL Battery: State and parameter estimation techniques
♦ W7: Powertrain control and system simulation concept (From Rule based towards Fuzzy and Data-driven controllers/self learning control (adaptive/predictive controllers)
• CBL Battery: CBL Contact Moment
♦ W8: Sustainability week by expert talk: LCA,CRM,Materials, process, life time estimation methodologies for zero-emission system, operational lifetime challanges
• Final course reflection. The last lecture hour includes in-class self-reflection.
Objectives
After following the course, you are able to• Identify the working principles of the main electrical components (electrical machine, power electronics, charging, battery) in an electrified motion system (vehicles, high-tech applications, robotics, etc. ) for net-zero footprint.
• Explain current and future electrified system solutions in selected mobile (vehicle technologies) and stationary (robotics, health-care) applications considering the component technology and the basic concepts for the component control
• Describe the multi-physical interactions between the electrified system components in a given application and be able to integrate these components on system level
• Make first design estimations of the electrical components to analyze the electrified system performance (via Matlab/Simulink scripts)
• Analyze and compare traditional design and control techniques with data-driven (AI-based) methods (such as reinforcement learning and regression analysis) for the most common challenges encountered during the operational lifetime of the electrified systems with specific focus on identifying their respective strengths and limitation in handling nonlinearities and uncertainties.
• Develop and implement Python-based simulation workflows that integrate AI based approaches with classical Matlab/Simulink models to evaluate performance and robustness of the eDrive and battery system for their design and operation
• Critically assess and justify design and control choices and results for a self-defined e-drive-battery challenge, demonstrating how AI-enhanced control can contribute to energy-efficient and net-zero technology solutions. through a report and open discussion that adheres to academic standards of clarity, structure, logic, objectivity, accuracy, and reproducibility.
• Explain current and future electrified system solutions in selected mobile (vehicle technologies) and stationary (robotics, health-care) applications considering the component technology and the basic concepts for the component control
• Describe the multi-physical interactions between the electrified system components in a given application and be able to integrate these components on system level
• Make first design estimations of the electrical components to analyze the electrified system performance (via Matlab/Simulink scripts)
• Analyze and compare traditional design and control techniques with data-driven (AI-based) methods (such as reinforcement learning and regression analysis) for the most common challenges encountered during the operational lifetime of the electrified systems with specific focus on identifying their respective strengths and limitation in handling nonlinearities and uncertainties.
• Develop and implement Python-based simulation workflows that integrate AI based approaches with classical Matlab/Simulink models to evaluate performance and robustness of the eDrive and battery system for their design and operation
• Critically assess and justify design and control choices and results for a self-defined e-drive-battery challenge, demonstrating how AI-enhanced control can contribute to energy-efficient and net-zero technology solutions. through a report and open discussion that adheres to academic standards of clarity, structure, logic, objectivity, accuracy, and reproducibility.