Real-world insights on public charging demand and infrastructure use from electric vehicles

Abstract

The city of Berlin has significantly expanded public charging infrastructure for electric vehicles. As a result of this investment, real-world charging data for the city of Berlin are available for the first time. In addition to other metrics, this dataset contains specific information about carsharing vehicles. This research letter offers numerous insights into public charging demand and infrastructure. The results are only now available due to a sufficient fleet size of electric vehicles. The analysis shows that the distribution of charging stations is very unequal in Berlin. The data also show that the infrastructure network is much denser in the city center. While there is an unequal distribution of infrastructure, we see that the utilization of infrastructure is relatively equal. This reflects unequal charging demand, as can be expected based on the location of the infrastructure. We also determine that the majority of public charging events come from free-floating carsharing vehicles. The analysis of infrastructure use shows that the edge of the city center has the highest rates of stations occupied by vehicles after completing charging. Carsharing users occupy infrastructure after charging significantly more than individual private and commercial users. However, if the pricing scheme allows, individual users also occupy infrastructure after completing charging. The research letter provides several policy recommendations for the build-up and operation of charging infrastructure. These focus on charging demand from individual users, infrastructure efficiency, and carsharing operators and their business models. The results are timely as decisions on public charging infrastructure must be made now to meet electric vehicle demand.

Spatial and temporal patterns of electric vehicle charging station utilization: a nationwide case study of Switzerland

“…The distributions in figures 5(a) and (b) show utilization times ranging from a few minutes to more than 48 h. One reason for this range of utilization times could be the use of charging stations as short-and even long-term parking places. The phenomenon of charging stations being used in this way has also been documented in other studies (Hardinghaus et al 2020, Hecht et al 2020. The ANOVA shows a statistically significant relationship between utilization time and population density (F-value = 2.01, p-value = 0.0363).…”

“…Indirect data is often used instead of real-world data from charging stations to determine the demand for charging infrastructure. Hardinghaus et al (2020) identified four data sources for such studies: (1.) GPS (global position system) data from vehicles, (2.)…”

“…Yun et al (2019) analyzed real-time data from 90% of charging stations in Shanghai for charging infrastructure planning. Hardinghaus et al (2020) studied the spatial distribution of charging stations and their demand for the city of Berlin. For the usage analysis, the authors used data from 221 charging stations with 50 491 charging events.…”

Comparison and Analysis of GPS Measured Electric Vehicle Charging Demand: The Case of Western Sweden and Seattle

“…An alternative to NHTS datasets for estimating charging is datasets on EV charging from public and private charging stations (Hardinghaus et al, 2020;Zachary et al, 2019;Kezunovic et al, 2020;Huber et al, 2020;Almaghrebi et al, 2020;Noussan and Neirotti, 2020). Using data from public charging points in Berlin, Hardinghaus et al (2020) found the utilization of charging points to be similar even though the charging points were distributed unequally in the city. Investigating emissions from EVs and using data from public and private chargers in Germany, Noussan and Neirotti (2020) found that the influence of charging strategy had a limited impact on emissions.…”

An overview and performance evaluation of open charge point protocol from an electromobility concept perspective

“…The main purpose was to minimize costs of the CS and to provide an efficient model. A research 42 presents the CSs for EVs in Berlin. Their results show that the CSs' distribution is unequal because of the uneven charging demand.…”