Johan Bergelin, Doctoral student

E-mail:
johan.bergelin@mdu.se
Room:
U1-062
Phone:
+46 764 953 692
Division:
Division of Computer Science and Software Engineering
Research group:
Automated Software language and SOftware engineering
Web:
Linkedin page


I am currently a PhD student at Mälardalen University, where I mostly work with Model-based systems engineering and related technologies. There is a particular focus on continuous practices linked with DevOps and Artificial intelligence in various forms to support modeling, such as recommender systems. This work is mainly carried out via industry, with different industrial partners and other Universities. In addition to the research, I am involved in the MDU solar team and the MDU doctoral council. Previously I graduated from Mälardalen University with a degree in dependable aeronautic systems, and before that, I was a sheet metal worker (plåtslagare). In my free time (if any) I enjoy strength training and the card game Magic: The Gathering where I play on a semi-competitive level in local to international events.

I am currently involved in the following courses:

DVA848 - model-based development for dependable systems

DVA206 - Kravhantering (Requirements management)

FLA401 - Project in dependable systems

CEL405 - Projekt in electronics, this is a flexible course and potential projects can be discussed based on interest.

Johan is mainly involved with Model-based systems engineering (MBSE) and works closely with the industry partner Volvo CE. He is also part of the AIDOaRt project working on the Volvo CE use case regarding architecture modeling. In this context, Johan is performing research on early validation.

MBSE

The work with MBSE is performed primarily at the early stages of development, focusing on the early analysis and validation of various models. Topics of particular interest is how black box representations can be analyzed and further evolved in industrial contexts.

DevOps 

DevOps and related aspects are being integrated with various modelling activities. In particular we try to understand how continuous practives can enable modelling, and how modelling can enable continuous practices. We are applying our research in collaboration with industrial partners and also via the solar team project. 

AI

One of our main collaborative partners is working closely with recommender systems, where we are trying to improve modelling capabilities in the context of complex Cyber-Physical systems. 

Battery systems

We have done work related to batteries with a focus on Heterogenous batteries. In short, the aim is to combine various types of battery cell chemistries into one single connected system. In addition, there are research challenges related to architecture definitions and analysis, along with the more technical electrical aspects and control of the management system.