Electric bus fleet size and mix problem with optimization of charging infrastructure

Matthias Rogge, Evelien van der Hurk, Allan Larsen and Dirk Uwe Sauer

Applied Energy, 2018, vol. 211, issue C, 282-295

Abstract: Battery electric buses are seen as a well-suited technology for the electrification of road-based public transport. However, the transition process from conventional diesel to electric buses faces major hurdles caused by range limitations and required charging times of battery buses. This work addresses these constraints and provides a methodology for the cost-optimized planning of depot charging battery bus fleets and their corresponding charging infrastructure. The defined problem covers the scheduling of battery buses, the fleet composition, and the optimization of charging infrastructure in a joint process. Vehicle schedule adjustments are monetized and evaluated together with the investment and operational costs of the bus system. The resulting total cost of ownership enables a comparison of technical alternatives on a system level, which makes this approach especially promising for feasibility studies comprising a wide range of technical concepts. Two scenarios of European cities are analyzed and discussed in a case study, revealing that the cost structure is influenced significantly by the considered bus type and its technical specifications. For example, the total energy consumption of the considered lightweight bus is up to 32% lower than the total consumption of the high range bus, although the deadheading mileage increases. However, the total costs of ownership for operating both bus types are relatively close, due to the increased fleet size and driver expenses required for the lightweight bus system. The case study furthermore reveals that a mixed fleet of different bus types could be advantageous depending on the operational characteristics of the bus route.

Keywords: Electric bus scheduling; Charger scheduling; Transportation system modeling; Infrastructure planning; TCO optimization; Genetic algorithm (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (78)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:211:y:2018:i:c:p:282-295

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DOI: 10.1016/j.apenergy.2017.11.051

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