Event

Course Syllabus: Data-Driven Algorithms in Vehicle Technology
Motivation for the Course: Nowadays, engineers often develop technical systems using a
combination of hard- and software. This is true especially for modern passenger vehicle
development. In a digitalized world, such developments are built on knowledge gained from relevant
data sources, e.g. the vehicle sensors. Therefore, engineers in automobile technology need
qualifications from data science to successfully create new functionalities in the cars. To prevent
remaining purely theoretical, the algorithms in this course are explained using a real-world problem
of “EV Routing”. Students have the opportunity to test methods in Python with frequent exercises
presented.
Goal of the Course: Students have a basic understanding of data-driven algorithms such as Markov
Models, Machine Learning or Monte-Carlo Methods. The approach for building data-driven models in
automobile technology are known to students and they are able to test algorithms in the
programming language “Python”. Furthermore, students have learnt how to analyse the algorithm
performance.
Content:
1. Introduction to function development as well as the prerequisites for the course (e.g.
Fundamentals for running Python code)
2. Fundamentals for EV Routing and relevant data sources
3. Parameter estimation and state classification algorithms to determine the current situation
of the vehicle
4. Learning methods for driver behaviour
5. Forecast algorithms to predict future energy consumption of an electric vehicle