BME Electromobility Research Group
Transportation, Logistics, Supply Chain And Storage
Hu
6 employees
- Employees
- 6
BME Electromobility Research Group Overview
- Headquarters
- Hu
- Website
- competence.bme.hu
- Industry
- Transportation, Logistics, Supply Chain And Storage
- Employees
- 6
- Founded
- 2022
- NAICS
-
Transportation and WarehousingSupport Activities for TransportationSupport Activities for Air Transportation
About BME Electromobility Research Group
Research Group at the Department of Transport Technology and Economy, Faculty of Transportation Engineering and Vehicle Engineering in the Budapest University of Technogy and Economics. Members are: Csaba CSISZÁR DSc, Bálint CSONKA PhD, Dávid FÖLDES PhD, Simon NAGY MSc, Péter SZILASSY MSc, Patrik HEGYI BSc We investigate factors affecting the spread and use of electric vehicles and develop planning and operational methods to support electromobility. We integrate expertise in transport and electrical engineering, energetics, and social sciences with a holistic view. We conduct interdisciplinary quantitative and qualitative studies. Our research group has competence in multi-criteria evaluation, system development, mathematical modeling, and multi-objective optimization. We investigate the social acceptance of electric cars, develop information system to support electric car use, elaborated a decision supporting tool based on the total cost of ownership model of new vehicles, a charging station locating method and a charging management method . Our aim is to reduce the negative impact of charging on the power grid using local power generation and energy storage systems. Therefore, we model the charging demand and supply of the power network and elaborate centralized and decentralized charging management solutions. We investigate the life-cycle environmental impact of e-cars and calculated the total cost of ownership. We elaborate on methods to optimize the public electric bus service in urban areas. Accordingly, we model the service and identify the factors affecting the energy consumption. Stochastic factors are considered with probability functions. The optimal service is determined with a confidence level. We investigate the shared services and micromobility using electric vehicles.
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