| Title |
Charge-Arrival-Time profiles for long EV routes / |
| Authors |
Šaltenis, Simonas ; Barauskas, Andrius ; Brilingaitė, Agnė ; Bukauskas, Linas ; Čeikutė, Vaida ; Čivilis, Alminas |
| DOI |
10.1109/ACCESS.2023.3334392 |
| Full Text |
|
| Is Part of |
IEEE access.. Piscataway : Institute of Electrical and Electronics Engineers (IEEE). 2023, vol. 11, p. 133004-133020.. eISSN 2169-3536 |
| Keywords [eng] |
roads ; data models ; charging stations ; batteries ; uncertainty ; routing ; mathematical models |
| Abstract [eng] |
Electric vehicles (EVs) with increasingly large batteries and the rapid development of charging infrastructure enable convenient long-distance electric mobility. Nevertheless, as charging or waiting for an available charger may still take a significant fraction of the travel time, planning long routes is challenging. We argue that, for the plans to be useful, the main challenge lies not in computing an optimal route under some specific assumptions but in managing the inherent uncertainty. In particular, varying and uncertain traffic results not only in similarly uncertain travel times but also in uncertain energy use and, thus, uncertain required charging times. To model long EV routes, we propose Charge-Arrival-Time (CAT) profiles. A CAT profile of a route captures the expected intervals and associated probabilities of arrival time and the arrival charge level of the battery as well as any interdependence of the two. A rich underlying data model is used as a starting point for a stepwise presentation of the mathematical model of CAT profiles. A heuristic algorithm to select charging stops is presented to exemplify their use. Detailed simulations on the road network of Germany show the benefits of precise modeling compared to alternative models. |
| Published |
Piscataway : Institute of Electrical and Electronics Engineers (IEEE) |
| Type |
Journal article |
| Language |
English |
| Publication date |
2023 |
| CC license |
|
