Analysis of electric vehicle charging station usage and profitability in Germany based on empirical data

Hecht, Christopher; Figgener, Jan; Sauer, Dirk Uwe

doi:10.1016/j.isci.2022.105634

Cited by 20 publications

(11 citation statements)

References 27 publications

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“…Battery replacement is expected after six years due to limited battery lifetime. The projected costs for the installation of charging infrastructure are estimated at 600 EUR/kW [48,52,54]. This estimate encompasses the expenses associated with the charging station itself, as well as the upstream network infrastructure required by the transport operator and the associated construction costs.…”

Section: Cost Assumptionsmentioning

confidence: 99%

“…This estimate encompasses the expenses associated with the charging station itself, as well as the upstream network infrastructure required by the transport operator and the associated construction costs. To include the costs for maintenance and repair of the charging stations, costs of 35 EUR/kW/a are assumed, which corresponds to 5.8% of the investment costs [54]. Energy costs are set at 0.25 EUR/kWh for electricity and 1.70 EUR/L for diesel, with a 5% yearly cost increase for both sources.…”

Section: Cost Assumptionsmentioning

confidence: 99%

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Optimizing Fleet Structure for Autonomous Electric Buses: A Route-Based Analysis in Aachen, Germany

Sistig,

Sinhuber,

Rogge

et al. 2024

Sustainability

Self Cite

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Intelligent transportation systems enhance the potential for sustainable, user-friendly, and efficient transport. By eliminating driver costs, autonomous buses facilitate the redesign of networks, timetables, and fleet structure in a cost-effective manner. The electrification of bus fleets offers the opportunity to further improve the environmental sustainability of transportation networks, but requires adjustments to vehicle schedules due to the limited range and charging requirements. This paper examines the intricate relationship between electrification and autonomous buses. To this end, timetables for autonomous electric buses of different sizes were developed for a real bus route in Aachen, Germany. The resulting electric vehicle scheduling problem was then solved using an adaptive large neighborhood search to determine the number of vehicles needed and the total cost of ownership. By eliminating driver costs, vehicles with lower passenger capacity become much more attractive, albeit at a slightly higher cost. In comparison, the incremental costs of electrification are low if the right approach is taken. Fluctuations in typical passenger numbers can be used to modify timetables and vehicle schedules to accommodate the charging needs of autonomous electric buses. In particular, electric bus concepts with fewer charging stations and lower charging power benefit from adapting the timetable to passenger numbers. The results demonstrate that the specific requirements of electric buses should be considered when adapting networks and timetables in order to design a sustainable transport network.

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Section: Cost Assumptionsmentioning

confidence: 99%

Section: Cost Assumptionsmentioning

confidence: 99%

Optimizing Fleet Structure for Autonomous Electric Buses: A Route-Based Analysis in Aachen, Germany

Sistig,

Sinhuber,

Rogge

et al. 2024

Sustainability

Self Cite

View full text Add to dashboard Cite

show abstract

“…Weather also influences the use of public stations [87,107], as do extraordinary events and traffic information [108]. Daytime and weekday public charging occurs mainly at alternating current (AC) charging stations, while direct current (DC) fast-charging stations are more popular on weekends [109]. The temporal distribution of usage also depends on the where the charging stations are located [110].…”

Section: Analysis Of Infrastructure Usagementioning

confidence: 99%

“…The temporal distribution of usage also depends on the where the charging stations are located [110]. In general, fast-charging stations show a higher usage rate than public level 2 charging stations [103,111], making higher profits due to better margins [109]. However, the utilization rate of DC stations appears to decrease as one moves toward rural areas [112].…”

Section: Analysis Of Infrastructure Usagementioning

confidence: 99%

A Literature Review on the Charging Behaviour of Private Electric Vehicles

Andrenacci,

Valentini

2023

Applied Sciences

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Electric mobility is one of the ways of containing greenhouse gas and local pollutants emissions in urban areas. Nevertheless, the massive introduction of battery-powered electric vehicles (EVs) is introducing some concerns related to their energy demand. Modelling vehicle usage and charging behavior is essential for charge demand forecasting and energy consumption estimation. Therefore, it is crucial to understand how the charging decisions of EV owners are influenced by different factors, ranging from the charging infrastructure characteristics to the users’ profiles. This review examines the approaches used to investigate charging behavior and highlights the trends and differences between the results, remarking on any gaps worthy of further investigation.

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“…This analysis should include EV density, locational attributes, and competitor presence. Such insights can enhance profitability prospects, especially during the current phase of modest EV prevalence [13][14][15]. Additionally, for electricity suppliers, understanding potential charging station locations can aid in forecasting electricity demand, crucial for grid development planning [16,17].…”

Section: Introductionmentioning

confidence: 99%

Optimal locating and sizing of charging stations for large‐scale areas based on GIS data and grid partitioning

Prakobkaew,

Sirisumrannukul

2023

IET Generation Trans & Dist

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This research proposes an optimisation model for identifying the optimal locations and sizes of public charging stations for large‐scale areas based on geographic information system (GIS) data and grid partitioning. A grid partitioning technique is utilised to generate subareas and investigate the demand for electric vehicle (EV) chargers calculated based on the number of EVs in each subarea. The optimal locations are formulated as a maximum covering location problem and solved by integer programming to ensure that all vehicle access subareas, according to the GIS data, are covered by charging stations. The sizes of charging stations are formulated as a knapsack problem and solved by the EV‐charge point ratio to guarantee each subarea has the appropriate number of chargers. The Voronoi diagram is integrated to estimate vehicle density and improve visualisation. The model is extended to include potential power loss reduction by adjusting the distribution of chargers in relation to the proximity to electrical substations. The developed methodology has been implemented by the whole country area of Thailand, which covers an area of approximately 510,000 km2. The study results are represented in a map blueprint that is very useful in strategic planning to support the so‐called EV30@30 government policy's ambitious national target of 2,050,000 units of EVs in 2030.

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Analysis of electric vehicle charging station usage and profitability in Germany based on empirical data

Cited by 20 publications

References 27 publications

Optimizing Fleet Structure for Autonomous Electric Buses: A Route-Based Analysis in Aachen, Germany

Optimizing Fleet Structure for Autonomous Electric Buses: A Route-Based Analysis in Aachen, Germany

A Literature Review on the Charging Behaviour of Private Electric Vehicles

Optimal locating and sizing of charging stations for large‐scale areas based on GIS data and grid partitioning

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