PhD presentations Matthias Kuipers and Christopher Hecht
All presentations online (ZOOM) and CARL, seminar rooms, ground floor, Campus-Boulevard 89, 52074 Aachen, Germany
Registration and dial-in data via firstname.lastname@example.org
Wednesday, September 6, 2023
09:00 a.m. [Time zone Berlin], Matthias Kuipers, M. Sc.
“Development of a Virtual Cell Design Tool for Objective Comparisons between State-of-the-Art Battery Cells and Next Generation Technologies“ (presentation language: German)
Finding the most suitable battery cell for a given application is a difficult task, especially when next-generation battery technologies are considered alongside current battery cells. This challenge necessitates a technically profound approach to objectively compare battery cells, ultimately helping to select the right cell for the right application while quantifying the expected characteristics of each battery technology.
Within the scope of this work, a model-based tool was developed to virtually design and compare battery cells. It describes and calculates a variety of key characteristics from material level over component level to cell level. The model was developed and initially parametrized based on data required from post-mortem analysis of six state-of-the-art battery cells. Other than modeling existing cells, the model can be used to design large quantities of virtual cells. The capabilities of the virtual cell design tool were demonstrated within the scope of this work, by exploring the impact of different anode materials, different cathode materials, and different cell housings, individually. The investigations on different anode and cathode materials incorporated materials only recently released to the market as well as promising next-generation technologies. Based on the results it was possible to quantify to which extent each technology is able to influence the main cell performance parameters.
10:15 a.m. [Time zone Berlin], Christopher Hecht, M. Sc.
"Usage overview, prediction, and siting optimization for electric vehicles public charging infrastructure with machine learning and big data methods" (presentation language: English)
To achieve the European and the national German climate protection goals, emissions must be reduced in the mobility sector. For passenger cars, battery electric vehicles are the most economic and ecological solution available. To make such vehicles attractive for users, a highly available and comfortable to use charging infrastructure is necessary. “Usage overview, prediction, and siting optimization for electric vehicles public charging infrastructure with machine learning and big data methods” employs data-driven methods to address this challenge. The focus lies on the two main aspects of this challenge: where should new stations be placed and how can these stations then be used most efficiently. As a starting point, the currently used terms are defined and the overall market situation described. The first step of analysis to follow is to provide an understanding of how public charging stations are currently used. We do so by analysing a large dataset containing the usage data of 22,000 public charging stations in Germany between 2019 and 2021. Based on these findings, an algorithm is developed, which is able to make a highly accurate prediction for how likely it is that a station is occupied in a given hour. This allows station operators to adapt prices dynamically and navigation system operators to reroute vehicles to less busy stations, thus overall improving utilization of existing stations. For new constructions, a tool is developed that predicts how much energy would be sold in any given point. This way, stations are positioned where they provide power most conveniently for users.