Title Study on Long-Distance Electric Mobility on a Multinational Route

1. Introduction

Modern long-distance personal mobility is largely dependent on vehicles with internal combustion engines, which significantly contribute to environmental pollution and climate change.

One of the primary transformations in the mobility sector in recent years has been the significant adoption of electric vehicles (EVs) in everyday life. Data on EV market growth, along with various studies, suggest that public acceptance of these vehicles will continue to increase [15]. EVs represent a promising technology for achieving a sustainable transportation sector due to their extremely low to zero carbon emissions, low noise levels, high efficiency, operational flexibility, and potential for grid integration [3,19]. Promoting EV use is a priority aligned with the European Union’s (EU) goal to become the first climate-neutral continent by 2050. This objective requires ambitious reforms in the transport sector to achieve a 90% reduction in transport-related greenhouse gas emissions by 2050. EV use for short-distance travel in urban areas is already established [10]. However, their deployment for long-distance journeys remains limited, primarily due to factors such as limited range per charge [15], slow charging beyond certain battery levels (80-90%), and a lack of uniformly distributed infrastructure [13]. A further complication lies in the varying state of charging infrastructure across different countries.

The literature discusses numerous studies on long-distance travel with EVs. For instance, in [1] is conducted a simulation along the highway connecting Paris and Lyon (France), enabling a comparative analysis. The analysis of the results from this simulation offers general recommendations for EV designers and stakeholders in the EV sector, such as drivers, vehicle manufacturers, and infrastructure planners responsible for charging points. The challenge of route planning for EVs is examined in [4] as one of the primary research trends. Various studies also carefully address issues related to the perception of electric mobility. In [2], an analysis of changes in human mobility highlights that, beyond technological innovations such as autonomous vehicles and EVs or data connectivity and communication technologies, social behavior often plays a significant role. Other researchers emphasize the need for interdisciplinary studies to tackle the complex challenges associated with EVs. For example, [5] advocates for further research into the social, economic, and political dimensions of EV adoption, including issues of equity and access to charging infrastructure. Evaluating consumer choice regarding EVs represents a dynamic and contemporary area within the literature.

Although relatively new, the field of research on EVs and charging infrastructure includes several highly regarded studies [6,7]. Simultaneously, there are numerous areas with potential for further investigation.

This study aims to identify the primary issues related to the use of EVs for long-distance travel across multiple countries through a real-world experiment. The findings are intended to provide recommendations for future efforts in designing a comprehensive electric mobility system.

2. Materials and Methods

The five main components of the research methodology, along with their sequence, are summarized in Figure 1: Case Definition; Data Collection from various Sources; Selection; Experimental Research; and, finally, Calculation of Average Performance Metrics along the Route.

Case Definition

The case definition process consists of four steps: the first two involve a literature review on EVs and their charging infrastructure; a study of road infrastructure and potential routes from Ruse to Paris; and an examination of the temporal horizon.

Literature Review on EVs

As of March 2024, there are 13,466 registered EVs in Bulgaria, comprising a highly diverse fleet that includes various brands: Tesla, Dacia, BMW, Hyundai, Mercedes, Volvo, Renault, Volkswagen, Opel, Kia, Peugeot, Skoda, and others. EVs are available in a wide range of sizes and configurations, from compact urban cars to larger models, with the range of each model on a single charge varying according to specific attributes. On average, EVs can travel between 150 and 400 km on a full battery charge, as indicated in the literature [21,22]. However, this range is influenced by numerous factors, such as weather conditions [12,16], road conditions and gradient [18], traffic intensity, driving speed, battery degradation, and others [11,14].

Literature Review on EV Charging

Compared to conventional vehicles, EVs still have a significantly lower range - approximately 380 km (average range among 10 passenger EVs currently on the market) and thus require more frequent recharging. Charging time depends on the type and capacity of the vehicle’s battery as well as on the charging point’s capacity (Table 1). While “slow” and “normal” chargers are more suitable for home or office charging cycles, “rapid” and “ultra-fast” chargers are better suited for highways and major road networks. Drivers often express concerns that their EV may not have enough range to reach their destination [15] and that charging may require waiting in line if charging posts are already occupied. These concerns are exacerbated by the range limitations and worries about the availability of charging stations along the route.

The availability of charging infrastructure varies significantly across countries, with payment systems lacking standardized minimum requirements and insufficient user information. It is worth marking that national fuel station chains have integrated charging posts at many of their locations along highways.

Within Europe, and particularly the EU, there is a unified standard for EV charging plugs (Type 2 and Combo 2 Combined Charging Systems).

According to Annex II of the EU’s Alternative Fuels Infrastructure Directive (AFID) [9], alternating current (AC) charging points for EVs must be equipped, for interoperability purposes, with at least open Type 2 sockets or connectors for vehicles, as specified in standard EN 62196-2.

The AFID Directive (Annex II) mandates that high-power direct current (DC) charging points for EVs be equipped, for interoperability purposes, with at least Combo 2 Combined Charging Systems, as outlined in standard EN 62196-3.

The distribution of charging stations remains uneven, as there are no clear and consistent minimum infrastructure requirements to ensure EV mobility across regions. This is largely driven by private initiatives and investments rather than cohesive strategy and standards. The lack of harmonized payment systems with minimum requirements, along with real-time information on station availability and service billing, further complicates long-distance EV travel.

Preliminary destination selection and study of road infrastructure and potential routes.

The initial criteria for destination selection are as follows:

- A route exceeding 2,000 km in length;

- Passing through multiple countries;

- The team should have no prior EV charging experience before the experiment.

Following these conditions, the chosen destination was Ruse, Bulgaria, to Paris, France.

For this destination, two main route options were considered:

-Traveling exclusively within the EU (Bulgaria, Romania, Hungary, Austria, Germany, France), as shown in Figure 2.

-Traveling through both EU and non-EU countries (Bulgaria, Serbia, Croatia, Slovenia, Austria, Germany, Belgium, France*), as shown in Figure 3.

*An alternative route, shorter than the one selected exists.

Figure 3. Route from Ruse to Paris through EU and Non-EU Countries.

Figure 3. Route from Ruse to Paris through EU and Non-EU Countries.

Study of temporal horizon

The energy consumption of an EV depends on weather conditions, with literature indicating that in some cases, winter energy consumption increases by more than 20%, while in hot summer conditions it rises by more than 10% [21,22].

Data collection from various sources

During the research development, both existing and new data were collected. The research team developed a data collection plan, specifying sources, scope, methods, and timelines relevant to the study. The data collection process is illustrated in Figure 4. To track trends and provide reliable information for analysis, the appropriate study period was established, ensuring access to historical data.

The collected data are substantial in volume and are characterized by exceptional heterogeneity in terms of format, presentation method, temporal scope, territorial coverage, complexity, and volume.

Data from institutions were obtained through an exchange of letters requesting information provision. Following an examination of public sources, the necessary data for the research were extracted. The organization of data acquisition from institutions and public sources serves as a guarantee of their quality.

Through independent investigations, data essential for the research were secured, as well as additional data for analytical purposes.

To overcome the challenges in evaluating the collected data, they were systematized according to their applicability, sources, and scope. Data evaluation was conducted through an analysis of their quality, availability, and timeliness of receipt. The applicability of the data for performing the necessary analyses is contingent upon their quality.

Through independent investigations, the data essential for the research were secured, as well as additional data for analytical purposes.

To overcome the challenges in evaluating the collected data, they were systematized according to their applicability, sources, and scope. Data evaluation was conducted through an analysis of their quality, availability, and timeliness of receipt. The applicability of the data for performing the necessary analyses is contingent upon their quality.

Figure 5 illustrates the block diagram of the processes involved in data collection.

Selection

This stage is related to the selection of the EV for the experiment; the route; the temporal horizon and the development of a schedule for movement along the route.

Selection of EV: a car with a mileage of 330 km and a battery capacity of 54 kWh was chosen, which is close to the average for Europe at the moment. The car is traveling with two passengers and luggage, i.e. a load of around 240 kg.

Route selection: The route was chosen to pass through countries outside the EU and through several EU member states. The requirement to assess the degree of compatibility between the charging infrastructure conditions in the two groups of countries (EU and non-EU), and to guarantee a wider sample size for the study, prompted the decision.

The team’s selected rest stops also influenced the decision to pass through specific cities

Route: Ruse, Bulgaria – Vidin, Bulgaria – Belgrade, Serbia – Zagreb, Croatia – Maribor, Slovenia – Salzburg, Austria – Munich, Germany – Wuppertal, Germany – Liège, Belgium – Paris, France.Figure 3 illustrates this route.

Figure 5. Flow chart of the processes for evaluating the collected data.

Figure 5. Flow chart of the processes for evaluating the collected data.

Selection of Time Horizon for the Experiment: A variant for the experiment during the summer period was chosen, as it is a lighter period in terms of energy consumption (including auxiliary load restrictions if necessary).

Experimental Study

Study Route: It coincides with the one chosen in part 3.

Charging points: They are selected based on the publicly available data about public charging stations.

Execution of the movement schedule: A comparison is made with the planned schedule.

Calculation of average performance indicators for the route

Travel time: This is the total travel time - $t_{move}$ and the time for charging breaks - $t_{{charge}_{all}}$, and for other breaks (for recreation) - $t_{stay}$:

$$T=t_{move}+t_{{charge}_{all}}+t_{stay},\mathrm{m}\mathrm{i}\mathrm{n}.$$

(1)

The charging breaks time is the sum of the charging preparation times $t_{preparation}$ and charging time $t_{charge}$:

$$t_{charg{e}_{all}}=t_{preparation}+t_{charge},\mathrm{m}\mathrm{i}\mathrm{n}.$$

(2)

Time for EV charging preparation, $t_{preparation}$: This is the time required to initiate charging, which includes familiarizing oneself with the terms of use of the respective charging station provider, ensuring compliance with the necessary requirements, and connecting the station’s plug to the EV.

It is important to note that the research team engaged with the charging stations for the first time, resulting in extended setup durations. As a result, the preparation for charging during the initial meeting required more time than with a known and previously utilized charging infrastructure.

EV Charging time,$t_{charge}$: The actual time during which the EV battery draws electricity from the charging post and for which the service is billed.

Energy Consumption of the EV: This refers to the charged energy at each of the $n$ stations during the study, denoted as $E_{{charged}_i}$​, for $(1\le i\le n)$. The specific energy consumption for each trip between two charging sessions is represented as $E_{SEC}$​, as well as the total charged energy consumption $E_{total}$, without considering the amounts of regenerated energy during movement.

The total energy utilized by the EV, $E_{total}$ is the sum of the charged amounts at each charging post:

$$E_{total}={\sum }_{i=0}^n{E}_{{charged}_i},\mathrm{k}\mathrm{W}\mathrm{h}.$$

(3)

The specific consumption refers to the expenditure of electricity per kilometer traveled under certain conditions.

$$E_{SEC}={\displaystyle \frac{E_{{charged}_i}}{S_i}},\mathrm{k}\mathrm{W}\mathrm{h}/\mathrm{k}\mathrm{m},$$

(4)

where $S_i, \left(\mathrm{k}\mathrm{m}\right)$ represents the actual distance traveled by the EV to the respective charging point $i$ from charging point $(i-1)$.

Utilization of Charging Infrastructure for EVs: The primary criterion for the utilization of charging infrastructure is, of course, its actual condition - operational or non-operational. Additionally, its functionality is assessed based on multiple criteria that determine the factors influencing its use, such as location, information availability, amenities, payment options, and others. Possible external factors that could hinder its utilization are also identified.

3. Results

During the course of the study, a distance of 2984 km was covered under various speed regimes and ambient temperatures, along with diverse road conditions and varying degrees of auxiliary load utilization.

The time allocated for charging breaks, calculated using (2), is:

t_(charge_all )=296+813=1109 min ≈ 18.48 h.

The total travel time, calculated using (1), is:

T=2444+1109+2822=6375 min ≈ 106.25 h.

The total energy consumed by the EV, calculated using (3), is:

E_total=506.86 kWh.

Following the methodology, (4) has been applied under various driving modes and conditions. The results are summarized in Table 2 and graphically represented in Figure 6 and Figure 7.

From the information presented in Figure 6, it can be concluded that, during travel without auxiliary loads, losses due to discrepancies in mileage are primarily influenced by the speed modes, while specific consumption varies depending on both the speed modes and ambient temperature in a timely manner.

Figure 7 indicates that when traveling with a relatively high average speed and utilizing auxiliary loads, specific consumption remains fairly consistent regardless of the variations in ambient temperature. In terms of discrepancies, these losses fluctuate in relation to ambient temperature and instantaneous changes in speed.

Data has been collected from 20 charging stations, with successful charging of the EV occurring at 17 of them. For rapid and ultra-fast charging stations within the EU, the service cost ranges from €0.46 to €0.73 per kWh, while outside the EU it ranges from €0.91 to €0.98 per kWh. The total distance from the main road/highway to the charging station and back, following the route plan, varies from 0.4 to 17 km. Table 3 presents the distribution of charging stations according to the range they fall into.

Figure 6. The correlation between ambient temperature and speed mode concerning losses due to discrepancies in mileage and specific consumption during travel without auxiliary loads.

Figure 6. The correlation between ambient temperature and speed mode concerning losses due to discrepancies in mileage and specific consumption during travel without auxiliary loads.

Figure 7. The correlation between ambient temperature and speed mode concerning losses due to discrepancies and specific consumption during travel with auxiliary loads. *For clearer presentation of the results, the values of specific resistance have been multiplied by 10.

Figure 7. The correlation between ambient temperature and speed mode concerning losses due to discrepancies and specific consumption during travel with auxiliary loads. *For clearer presentation of the results, the values of specific resistance have been multiplied by 10.

The table indicates that along the planned route, significant number of charging stations (9) are conveniently located within 2 km of a main road/highway, followed by those situated at a nearby distance of 2 to 4 km (6). The difference in the number of stations located in each of the remaining two ranges is minimal, but their combined percentage of the total remains relatively high - 25%. Three of the visited stations are located outside the EU, with the total distance to and from the main road ranging between 0.4 and 0.6 km. This distance is associated with the fact that these charging stations are situated within populated areas.

Table 3. Distribution of charging stations according to the total distance from and back to the main road.

Range (km)	0,4-0,85	1-1,6	2,3-4	4,1-10	> 10
Number of charging stations	5	4	6	3	2

Table 3. Distribution of charging stations according to the total distance from and back to the main road.

Range (km)	0,4-0,85	1-1,6	2,3-4	4,1-10	> 10
Number of charging stations	5	4	6	3	2

Additionally, each charging station was assessed based on the following factors: the presence of a roof over the charging area; availability of a resting area (benches); access to free internet; the option for cash payments; online information about the station’s functionality; the status of station in the internet; information regarding station occupancy on the internet; and the actual condition of the station. The summarized data is presented in Table 4.

5. Conclusions

The study aims to highlight the main problems and provide recommendations for enhancing the potential of electric mobility for long-distance travel across multiple countries. This case was chosen because it frequently serves as a barrier to the adoption of EVs by consumers. The analysis of the results allowed for the identification of key issues and the formulation of essential recommendations. The study lacks a comprehensive sensitivity analysis that could strengthen the results, as the research focused only on several key parameters and the uncertainty analysis for some of them, such as energy consumption, the availability and conditions of charging stations, and others.

The presented methodology for researching electric mobility over long distances, starting from Bulgaria and traversing multiple countries, allows for the determination of the necessary amounts of energy required for charging EVs as well as the main challenges associated with traveling in them.

The research found that electricity consumption and battery usage are significant factors impacting long-distance mobility, alongside the availability of rapid charging points. When traveling without auxiliary loads, losses due to mileage discrepancies are primarily influenced by the speed modes, while specific energy consumption depends on both speed modes and ambient temperature. In the case of travel with auxiliary loads at a relatively high average speed, specific consumption remains nearly constant regardless of variations in ambient temperature. As for discrepancies, these losses vary according to ambient temperature and instantaneous changes in speed.

Regarding the conditions observed at the charging stations, a predominant lack of protective coverings and resting areas was noted, as well as a complete absence of free internet access at all stations. Only one station offered the option for cash payments. Information about the station’s functionality was unavailable online for 25% of the stations, while data regarding their status online was lacking for 40% of the stations. In 50% of cases, there was no information online about station occupancy.

Electric mobility in urban settings has been well researched and demonstrates proven potential. However, when the same EVs are used for long-distance travel and when crossing various countries, numerous problems arise that require solutions. Otherwise, there may be a necessity to own two vehicles: one for city travel and another for long-distance travel, which would further increase the global vehicle fleet. Current applications of long-distance electric mobility may require reassessment depending on future technological and managerial decisions.