Presentation of 2021 master’s theses from the BFH Energy Storage Research Centre
16.08.2021 Smart charging stations for electric cars, a fuel cell testing rig that can also be used on electrolytic cells, current harmonics in power distribution networks and the development of reliable methods for predicting the lifespan of batteries: on 11 August, students of the Master of Science in Engineering degree programme and staff working in association with the BFH Energy Storage Research Centre presented their theses on these topics at the Switzerland Innovation Park Biel-Bienne (SIPBB).
The master’s theses were produced in association with the BFH Energy Storage Research Centre. Further information can be found here:
To improve the development of hydrogen systems, the fuel cell testing rig at Bern University of Applied Sciences has been upgraded to the latest technical standards. New components have been integrated into the system, enabling electrolytic cells to be tested as well as fuel cells. The platform is based on a Raspberry Pi and programmed in Python. The new system provides a flexible basis for future projects and upgrades.
Supervisor: Prof. Michael Höckel
Non-linear loads are increasingly being connected to the power grid. The current harmonics created by these loads can have a significant impact on voltage and network elements. The long-term measurements recorded by a distribution network operator were analysed in depth to assess current harmonics. Another network operator measured severely distorted current on a medium-voltage power line that was caused by current harmonics. The impact on the low-voltage grid was analysed and the sources of these distortions identified based on three measurement campaigns on medium-voltage and low-voltage grids.
Supervisor: Prof. Michael Höckel
EVs – Smart charging, optimised operational management for EV charging stations | David Zurflüh, PV Lab
With the rapid increase in the number of photovoltaic systems and the fast-growing market for electric vehicles, there is also a great need for intelligently controlled charging stations to avoid overloading the power grid. Three photovoltaic systems and three charging stations were fitted with grid analysers at BHF’s Tiergarten campus in Burgdorf in December 2020 with a view to achieving better alignment between photovoltaic energy production and the charging processes. For this thesis, smart system management concepts were drawn up and the measurement data recorded over six months was analysed.
Supervisor: Prof. Urs Muntwyler
Cloud Based Battery Management Systems using Big Data and Machine Learning (englisch) | Prathyusha Nerella, Battery and Storage Systems Lab
The development of reliable lifetime prediction methods for Battery Management Systems (BMS) requires better State of Health (SOH) estimation methods to understand the degradation mechanism in electric batteries. Data Driven Methods (DDMs) use machine learning models like regression models, Support Vector Machine (SVM) or deep learning models like Recurrent Neural Network (RNN) to estimate the SOH of a battery. Drive cycle data from e-bikes, containing various battery usage features, can be used in DDMs to predict the SOH of batteries and understand the battery behavior to enhance the battery performance.
Supervisor: Prof. Dr. Andrea Vezzini