Keywords

1 Introduction

Global warming is largely the result of carbon dioxide emissions, so minimizing emissions of this gas is a widely recognized goal. Transportation in particular is responsible for a large proportion of these emissions, and great effort is being put into reducing emissions further. Replacing internal combustion engine vehicles with electric vehicles (EVs) is currently being pursued as a way to reduce emissions. EVs do not produce local emissions and, depending on the production of electrical energy, lower global emissions in terms of CO2 equivalents. However, they also present new challenges [1]. Conventional EVs typically store energy in batteries that need to be recharged. This can be done either at a private charging point, such as a wallbox, or at public charging stations. The use of a private wallbox is not a viable option in densely populated areas, nor are other forms of private charging points. Therefore, there are plans to expand the public charging infrastructure throughout Germany. The German government has set a target of one million public charging points by 2030 [2]. To implement a strategically appropriate expansion of the charging infrastructure, it is essential to consider the actual traffic flows. To realize that during the planning process, an approach is presented here that integrates the charging infrastructure into a traffic flow simulation. This way, the impact on traffic flows can be determined and optimized in advance when planning a new charging station. However, before the simulated planning of new charging stations can begin, there must be a way to implement charging stations into the simulation environment. Therefore, this research paper presents the implementation of all existing public charging infrastructure of a given area into a traffic flow simulation. The traffic flow simulation software used is SUMO (Simulation of Urban MObility), developed by the German Aerospace Center (DLR), an open-source simulation software for large-scale microscopic traffic flow simulation [3]. To consider charging stations in the simulation scenario, an algorithm was developed to automatically import and position the charging stations within the simulation as close as possible to their real geographic coordinates. The fundamental principles of this import algorithm can also be used to import planned future charging stations, which are to be analyzed in a simulation environment first. This analysis allows urban and transport planners to assess the impact of the location of charging stations on traffic flow and their future use during the planning process for public charging infrastructure, thereby identifying the optimal location.

2 Database

Charging stations in public places in Germany can be found in the so-called charging station register (CSR) [4]. The charging stations of various operators are listed there, but this is not mandatory, so completeness is not always guaranteed. The register is freely available and serves as a database within this research paper. The data for each charging station includes the name of the charging station operator, the street, and house number where the charging station is located, as well as the postcode, city, and district. The longitude and latitude of the location, which are crucial for accurate placement within the simulation environment, are also provided for precise localization. Additionally, supplementary information regarding the charging capabilities is included. Furthermore, map data from OpenStreetMap (OSM) is used to describe the road network [5]. The map contains data on roads, railways, waterways, forests and buildings. These two databases are used to create the foundation of the traffic flow simulation. To complete the simulation, a description of the traffic demand is required, e.g. based on origin-destination (OD) matrices. Among others, a vehicle model for EVs is available that shows characteristic driving and charging behavior of common EV. Figure 1 shows a part of the German city of Essen, which is used here and serves as an exemplary region in the following. The area under consideration is the postcode area 45145 within the district of Frohnhausen with a size of around 3.6 km2 and a population of around 33,200 [6]. The area under consideration in the figure has already been realized in SUMO and integrated into the appropriate image section of Google Maps [7] as an orientation.

Fig. 1.
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Area under consideration (45145, Germany), in sumo-gui and Google Maps©

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3 Methodology

Currently, charging stations in SUMO are implemented as inductive loops embedded in road segments [8]. These inductive charging areas do not sufficiently reflect common charging options. The charging infrastructure mainly consists of charging stations with multiple charging points. In reality, public charging stations are typically located in parking lots specifically designed for charging EVs. This behavior has to be mapped in SUMO. To represent this, a combination of inductive charging areas and parking lots can be used. An inductive charging area is placed on a lane, which is specified by the framework conditions in SUMO, and a parking lot is provided next to it on the roadside. In the simulation, the parking lot and the charging area are linked so that vehicles parked in the parking area can charge even though they are not placed directly on the inductive charging area. Even if the representation of the charging points deviates somewhat from reality due to this adjustment, the deviation between simulation and reality will be minimized when positioning the existing public charging stations correctly.

In order to import the public charging infrastructure into the simulation environment, an algorithm has been developed, which is visualized in Fig. 2. The first step is the user input. This includes OSM data of the road network to be simulated. The OSM-data serves as a fundamental component of the simulation, providing the road network (net-file) through an import process. The data required also includes the CSR. In order to import the charging stations from the CSR that are located in the area under consideration, it is necessary to specify the area to be simulated. This can be done either by the city name or by the postcode area. The input acts as a filter to identify the relevant charging stations. After all user inputs have been defined, the required information is extracted from the net-file and the CSR. Each individual edge, which represents a road section of the road network, is identified. The edges and their associated parameters are then stored. Of particular importance is the shape attribute of each edge, from which longitude and latitude of the edges can be determined.

The charging stations in the specified area are then selected from the CSR. Various information about the charging stations is taken into account, such as the geocoordinates, the provider, the charging power, and the number of charging points at the charging station. The power of the charging station and the number of charging points are used to calculate the power of each charging point. Once all the necessary information has been retrieved, the positioning of the charging stations begins. To do this, the nearest edge allowing car traffic (road edge) in the net-file must be found for each of the charging stations located in this area. For each station, the distance to all road edges is determined using geodetic distance. The charging station is placed at the edge with the shortest distance to it. It is important to note that only one corner of the edge is described by geocoordinates in the shape attribute of the edge. Therefore, the exact geocoordinate of the charging station cannot be found, so the charging station is always placed at the beginning of the edge, resulting in a deviation between the positions from the CSR and the charging station in the simulation. A parking lot is placed next to each charging point as described above. All relevant parameters are determined for each charging point, such as the ID, the ID of the lane where the charging station is located, the available power, and the efficiency.

Fig. 2.
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Algorithm for the automated import of charging stations

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4 Results

Once the algorithm has been executed, the charging stations are imported into the road network. An individual charging point always consists of an inductive charging area and a parking lot. The simulation software extracts all information from the data file to graphically represent the charging point and parking lot in the simulation environment, as shown in Fig. 3. As can be seen here, the orange-colored inductive charging area only affects the connected parking lot. The green outlined parking lot with the EV indicates that this parking lot is occupied and the charging process is currently in progress. To validate the algorithm, the area mentioned above was selected. The algorithm identifies six charging stations in this area, each with two charging points that need to be placed within the road network.

Fig. 3.
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Charging EV in a parking lot

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The placement follows the procedure described above and is evaluated here. For the evaluation, the algorithm calculates the deviation between the real geocoordinates of the charging station taken from the CSR and the geocoordinates at which the charging station is to be placed in the simulation. The deviation is given in meters and is shown in Table 1 for all six charging stations. The table shows that the maximum deviation is 90.71 m. The average deviation is 35.11 m. This deviation is inherent to the placement method used. Based on the available edge data, the charging stations are always placed at the start of the edge.

Table 1. Difference in location between real infrastructure and placement in simulation
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In reality, the charging station can be placed anywhere along the road. This can be seen in Fig. 4. In the simulation, the center of the road is the given geocoordinate. Therefore, the enlarged view in Fig. 4 shows, that the charging station was to be positioned in the center of the road.

Fig. 4.
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Comparison of the actual geocoordinates and the geocoordinates in the simulation environment SUMO in the area under consideration of all six documented charging stations

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The location of the charging station within the simulation differs from that of the intersection. Rather, it is situated at the beginning of the edge, with the parking lot situated adjacent to the edge. This configuration results in a smaller deviation of the final charging stations’ locations than calculated above.

5 Conclusion

The algorithm for automatically importing charging stations into a traffic flow simulation in the SUMO simulation environment facilitates the configuration of a simulation with electric vehicles (EVs). The manual effort of placing charging points in the right locations is no longer necessary. In addition, the accuracy of the automated placement method is so high that no traffic impact is to be expected due to inaccuracy. To further increase the placement accuracy of the charging infrastructure, a logical next step could be to determine the geocoordinates over the entire length of the edges to enable more differentiated placement. In further steps the automated import routine can also be used to place additional charging points into a traffic flow simulation. The desired geocoordinates can be specified in a way that the algorithm can place the charging stations at desired locations. The placement could be derived from an analysis of the traffic flow in a simulation, for example on busy roads or avoiding them respectively. It would then be possible to evaluate how the additional charging points affect the traffic flow in the simulated area. Conclusively, the goal is to support urban planning by using simulative methods to place charging stations more efficiently for future construction projects and to reduce inner-city traffic overall.