Using tracking data and an electro-mobility simulator to establish the energy requirements of electric minibus taxis in Tshwane
| dc.contributor.author | Abdelgadir S | |
| dc.contributor.author | Giliomee S | |
| dc.contributor.author | Venter C | |
| dc.contributor.author | Booysen M J | |
| dc.date.accessioned | 2023-07-26T08:22:46Z | |
| dc.date.available | 2023-07-26T08:22:46Z | |
| dc.date.issued | 2023-07-10 | |
| dc.description | The original publication is available at: https://www.satc.org.za/conference-papers.html | en |
| dc.description | 41st Southern African Transport Conference 10-13 July 2023 Pretoria, South Africa Conference Proceedings | |
| dc.description.abstract | The minibus taxi (MBT) is the dominant form of public transport across Sub-Saharan Africa (SSA). With a growing global call for greener transport, MBTs are seen as a key sector of implementation. The electrification of MBTs entails many challenges, including limited electricity resources and the lack of understanding of MBTs’ operational behaviour. In this paper, we estimate the electricity demand for future electric MBTs in the City of Tshwane, South Africa. We use existing origin and destination mobility data, which originated from vehicle-based tracking, and a micro-mobility simulation tool with an embedded electric vehicle model, called EV-Fleet-Sim. This simulation tool uses various SUMO packages to simulate mobility and calculate energy expenditure. The mobility dataset consists of various stop locations from a MBT fleet’s daily operation. The simulator uses a routing model, a virtual map, and a virtual driver model to convert the origin and destination data to high-fidelity mobility traces. The results are used in the electro-kinetic model to estimate the vehicles’ energy needs, from which charging opportunities can be derived. To illustrate this process and outputs, eight exemplar taxis with different operational patterns are selected for analysis. The results show a minimum and maximum median daily energy usage of 56 kWh and 215 kWh respectively, based on the mean observed daily distances travelled of 94 km to 330 km. While the energy demand varies significantly according to trip length and type of operation of the sub-fleet of 8 vehicles, clear morning and afternoon peaks are identified, along with charging opportunities during midday and at night. | |
| dc.description.uri | https://www.satc.org.za/conference-papers.html | |
| dc.format.extent | 13 pages | en |
| dc.identifier.citation | Abdelgadir et al. 2023. Using tracking data and an electro-mobility simulator to establish the energy requirements of electric minibus taxis in Tshwane. | |
| dc.identifier.isbn | 978-0-6397-8659-9 | |
| dc.identifier.uri | https://scholar.sun.ac.za/handle/10019.1/127464 | |
| dc.language.iso | en | |
| dc.subject.name | Tshwane | en |
| dc.subject.name | Minibus taxis | en |
| dc.title | Using tracking data and an electro-mobility simulator to establish the energy requirements of electric minibus taxis in Tshwane | |
| dc.type | Article |