Betreuer/in: Seiwerth
Electromobility is playing a central role in the energy transition. Battery Electric Vehicles (BEVs) can subsantially reduce greenhouse gas emissions in the mobility sector, particulary as electricity generation becomes increasingly decarbonized. For individual households and BEV users, a key practical question remains: which charging strategy actually reduces their personal carbon footprint and by how much?
Answering this question is not straight forward as there is currently no simple, simulation-based decision support tool which combines household and mobility profiles with multiple charging strategies and CO2 accounting methods, and presents the resulting CO2 impacts in a transparent form that can be understood by end users.
This bachelor’s thesis aims to address this gap by developing a Python-based simulation tool for CO2-efficient household charging of BEVs. By analysing unidirectional charging, smart charging and V2G for selected household archetypes, using both real and synthetic simulated profiles.
The decision support tool quantifies household-level CO2 emissions under different CO2 accounting approaches and derives CO2 balances as well as saving potentials for characteristic profile types.
Raum 0.4.137, Martensstr. 3, Erlangen
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Zoom-Meeting beitreten:
https://fau.zoom-x.de/j/68350702053?pwd=UkF3aXY0QUdjeSsyR0tyRWtLQ0hYUT09
Meeting-ID: 683 5070 2053
Kenncode: 647333