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- Liao, Yuan, 1991, et al.
(författare)
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Impacts of charging behavior on BEV charging infrastructure needs and energy use
- 2023
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Ingår i: Transportation Research Part D: Transport and Environment. - : Elsevier BV. - 1361-9209. ; 116
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Tidskriftsartikel (refereegranskat)abstract
- Battery electric vehicles (BEVs) are vital in the sustainable future of transport systems. Increased BEV adoption makes the realistic assessment of charging infrastructure demand critical. The current literature on charging infrastructure often uses outdated charging behavior assumptions such as universal access to home chargers and the "Liquid-fuel" mental model. We simulate charging infrastructure needs using a large-scale agent-based simulation of Sweden with detailed individual characteristics, including dwelling types and activity patterns. The two state-of-art archetypes of charging behaviors, "Plan-ahead" and "Event-triggered," mirror the current infrastructure built-up, suggesting 2.3-4.5 times more public chargers per BEV than the "Liquid-fuel" mental model. We also estimate roughly 30-150 BEVs served by a slow charger may be needed for non-home residential overnight charging.
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| 2. |
- Tozluoglu, Çaglar, 1988, et al.
(författare)
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A synthetic population of Sweden: datasets of agents, households, and activity-travel patterns
- 2023
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Ingår i: Data in Brief. - 2352-3409. ; 48
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Tidskriftsartikel (refereegranskat)abstract
- A synthetic population is a simplified microscopic representation of an actual population. Statistically representative at the population level, it provides valuable inputs to simulation models (especially agent-based models) in research areas such as transportation, land use, economics, and epidemiology. This article describes the datasets from the Synthetic Sweden Mobility (SySMo) model using the state-of-art methodology, including machine learning (ML), iterative proportional fitting (IPF), and probabilistic sampling. The model provides a synthetic replica of over 10 million Swedish individuals (i.e., agents), their household characteristics, and activity-travel plans. This paper briefly explains the methodology for the three datasets: Person, Households, and Activity-travel patterns. Each agent contains socio-demographic attributes, such as age, gender, civil status, residential zone, personal income, car ownership, employment, etc. Each agent also has a household and corresponding attributes such as household size, number of children ≤ 6 years old, etc. These characteristics are the basis for the agents’ daily activity-travel schedule, including type of activity, start-end time, duration, sequence, the location of each activity, and the travel mode between activities.
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