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49,80 €ISBN 978-3-8440-6654-8Softcover238 pages46 figures354 g21 x 14,8 cmEnglishThesis
April 2019
Brita Rohrbeck
Location Planning of Charging Stations for Electric City Buses
Modern urban transportation concepts increasingly involve the usage of electric vehicles, in particular of electric city buses. Many projects on electric private transport are already undertaken, and a lot of investigation is done with respect to advantageously located charging stations for the individual traffic. Examinations on the electrification of bus networks are rare, though.
In this work, mixed-integer linear models are introduced in order to determine a cost-optimal distribution of charging stations along bus routes. A basic model for just one bus line is enhanced by different features: The possibility of various types of batteries is integrated, different charging technologies may be employed, and the option of an additional bus is given. The model is then extended towards a network of lines. Battery ageing has a crucial influence on the feasibility of a solution also in the succeeding years. As a consequence, a multi-period model is presented and complemented with a battery ageing function. Reformulations for shorter run times are discussed.
The models are applied to data sets, which are based on real-world data of the bus network in the city of Mannheim. They are solved with CPLEX. The solutions are compared to the insights into the electrified bus line in Mannheim. By means of this reference, the quality of the solutions is verified and the models validated.
In this work, mixed-integer linear models are introduced in order to determine a cost-optimal distribution of charging stations along bus routes. A basic model for just one bus line is enhanced by different features: The possibility of various types of batteries is integrated, different charging technologies may be employed, and the option of an additional bus is given. The model is then extended towards a network of lines. Battery ageing has a crucial influence on the feasibility of a solution also in the succeeding years. As a consequence, a multi-period model is presented and complemented with a battery ageing function. Reformulations for shorter run times are discussed.
The models are applied to data sets, which are based on real-world data of the bus network in the city of Mannheim. They are solved with CPLEX. The solutions are compared to the insights into the electrified bus line in Mannheim. By means of this reference, the quality of the solutions is verified and the models validated.
Keywords: E-Mobility; Strategic Location Planning; Operations Research; MILP; Optimisation
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