Student (fmd) Machine learning based methodology for spatial distribution of brake wear emissions

Deutsches Zentrum für Luft- und Raumfahrt e.V. · Stuttgart

ThesisGerman needed: basic

Still open on 12 Nov

You apply on the company's own website.

Key facts

Pay: Paid by a fixed pay scale (TVöD-Bund)
Duration: 6 months
Job starts: As soon as possible
Language needed: English and German
Subject you study: Mechanical Engineering, Automotive Engineering, Environmental Engineering, Informatics, Data Science

Literature research on passenger car brake wear, braking energy, brake wear emission models, and the influence of vehicle and braking system technologies
Development of scenarios describing current and potential future passenger car braking systems, including the influence of regenerative braking and vehicle electrification
Review and aggregation of existing models relating braking energy to brake wear emissions and identification of suitable model formulations for different vehicle and braking system scenarios
Collection, processing, and aggregation of input data relevant for estimating braking energy, such as road network characteristics, traffic information, vehicle characteristics, and other spatially resolved data
Development and testing of machine learning models to predict the spatial distribution of braking energy within an urban road network
Integration of braking energy and brake wear models to estimate spatially resolved passenger car brake wear emissions
Development of a scenario-capable methodology for estimating future brake wear emissions under different vehicle fleet and braking system assumptions. Evaluation and validation of the developed methodology using suitable urban case studies and available data

Studies in the field of mechanical engineering, automotive engineering, environmental engineering, informatics, data science, or related courses
Experience or strong interest in machine learning, data analysis, and modeling
Interest in vehicle technology, brake wear, transport emissions, and data science applications in transport research
Programming knowledge in Python, including machine learning and data analysis libraries
Basic knowledge of statistics, numerical modeling, or machine learning
Fluent expression in English and basic knowledge of German

DLR stands for diversity, appreciation and equality for all people.
We promote independent work and the individual development of our employees both personally and professionally.
To this end, we offer numerous training and development opportunities.
Equal opportunities are of particular importance to us, which is why we want to increase the proportion of women in science and management in particular.
Applicants with severe disabilities will be given preference if they are qualified.

Literature research on passenger car brake wear, braking energy, brake wear emission models, and the influence of vehicle and braking system technologies
Development of scenarios describing current and potential future passenger car braking systems, including the influence of regenerative braking and vehicle electrification
Review and aggregation of existing models relating braking energy to brake wear emissions and identification of suitable model formulations for different vehicle and braking system scenarios
Collection, processing, and aggregation of input data relevant for estimating braking energy, such as road network characteristics, traffic information, vehicle characteristics, and other spatially resolved data
Development and testing of machine learning models to predict the spatial distribution of braking energy within an urban road network
Integration of braking energy and brake wear models to estimate spatially resolved passenger car brake wear emissions
Development of a scenario-capable methodology for estimating future brake wear emissions under different vehicle fleet and braking system assumptions. Evaluation and validation of the developed methodology using suitable urban case studies and available data

Studies in the field of mechanical engineering, automotive engineering, environmental engineering, informatics, data science, or related courses
Experience or strong interest in machine learning, data analysis, and modeling
Interest in vehicle technology, brake wear, transport emissions, and data science applications in transport research
Programming knowledge in Python, including machine learning and data analysis libraries
Basic knowledge of statistics, numerical modeling, or machine learning
Fluent expression in English and basic knowledge of German

DLR stands for diversity, appreciation and equality for all people.
We promote independent work and the individual development of our employees both personally and professionally.
To this end, we offer numerous training and development opportunities.
Equal opportunities are of particular importance to us, which is why we want to increase the proportion of women in science and management in particular.
Applicants with severe disabilities will be given preference if they are qualified.