Economic Evaluation of Vehicle Operation in Road Freight Transport—Case Study of Slovakia

Abstract

The European Union is committed to reducing greenhouse gas emissions across all sectors, including the transportation sector. It is possible to assume that road freight transport will need to undergo technological changes, leading to greater use of alternative powertrains. This article builds on previous research on the energy consumption of battery electric trucks (BETs) and assesses the economic efficiency of electric vehicles in freight transport through a cost calculation. The primary objective was to determine the conditions under which a BET becomes cost-effective for a transport operator. These findings are practically relevant for freight carriers. Unlike other studies, this article does not focus on total cost of ownership (TCO) but rather compares the variable and fixed costs of BETs and conventional internal combustion engine trucks (ICETs). In this article, the operating costs of BETs were calculated and modeled based on real-world measurements of a tested vehicle. The research findings indicate that BETs are economically efficient, primarily when state subsidies are provided, compensating for the significant difference in purchase costs between BETs and conventional diesel trucks. This study found that optimizing operational conditions (daily routes) enables BETs to reach a break-even point at approximately 110,000 km per year, even without subsidies. Another significant finding is that battery capacity degradation leads to a projected annual operating cost increase of approximately 4%.

1. Introduction

The European Union aims to keep global warming below 2 °C, with efforts to limit it to 1.5 °C. The EU plans to reduce emissions by at least 55% by 2030 compared to 1990 levels [1,2,3]. In the transport sector, the target is to reduce greenhouse gas emissions by approximately 20% by 2030 compared to 2008 [4]. This will be achieved by regulatory measures aligned with the European Green Deal and the Fit for 55 package [5,6]. According to [7,8], heavy-duty trucks and buses with a gross vehicle weight above 3.5 t are responsible for 7% of greenhouse gas emissions in the European Union. Many electric vehicles (EVs), particularly those of lower gross weight, are already available on the market [9,10]. However, a portion of freight vehicles with conventional internal combustion engines will soon be replaced by battery electric vehicles (BEVs) or fuel cell electric vehicles (FCEVs), as these have the potential to reduce the share of greenhouse gas emissions from transport [11,12]. FCEVs can achieve greater driving ranges thanks to fuel cells that produce electrical energy through the chemical reaction of oxygen and hydrogen [13]. Studies also confirm that BEVs are particularly competitive for short distances, whereas FCEVs are more suitable for long-haul transport [14]. A disadvantage of this solution is the economic challenge of hydrogen production. The efficiency of various hydrogen production methods is assessed by the authors in [15]. Significant advancements are being made in battery production and technology, which is why BEVs are generally preferred over FCEVs in Europe [16].

One of the problems with BEVs is the insufficient charging infrastructure, which is underdeveloped in some countries. Several studies [17,18,19] agree that, within the European Union, there is virtually no public charging infrastructure available for BETs, which can pose a significant problem for many long-distance freight operators [20]. Even regarding charging points (CPs) in general, the situation in the EU is not ideal. In 2025, as many as 55.32% of all CPs were located in just three countries—The Netherlands (19.90%), Germany (17.87%) and France (17.56%) [21]—Figure 1.

The economics of EVs are also strongly influenced by policy—laws, regulations, and subsidies. Currently, the EV market is dominated by three major regions: the USA, China, and the EU. Each of these regions has adopted unique strategies and differs in terms of regulations, government policies, and market structure [22].

The question, therefore, is whether it is even possible to electrify the fleet of freight vehicles. This question was also addressed by the authors in the study [23], who modeled the possibilities of the current and future infrastructure. Positive prospects are particularly noted for medium-duty trucks with a gross vehicle weight of up to 12 tons. They found that, with current technology, it would be possible to cover up to 75% of vehicle kilometers traveled with BETs in Switzerland (66% in Finland). For heavier vehicles up to 26 tons, only about 30% to 33% of the total distance traveled could be achieved with BETs. The most significant potential for electrification lies mainly in food delivery, postal services, urban logistics, and goods distribution. In contrast, the transportation of heavy industrial cargo (e.g., construction materials and chemicals) appears problematic due to their higher weights and longer transport distances. An additional advantage of BETs is the reduction in noise pollution compared to ICETs, which is particularly beneficial in urban areas and for nighttime deliveries [24]. Even in the case of the Slovak Republic, several fully electric heavy-duty vehicles are currently in use, primarily for local delivery rather than long-distance transport. The concerns of transport operators can be divided into two main categories, which are described below:

• Concerns related to the battery capacity of BETs, charging, and driving range.
• Concerns regarding the economic efficiency of BETs compared to ICETs.

Concerns related to batteries need to be addressed primarily from a technical perspective. Heavy-duty vehicles have high energy consumption and, therefore, require large battery capacities. At the same time, as battery capacity increases, so does its weight, which in turn, reduces the vehicle’s payload capacity [25]. However, there is still the possibility of using trucks configured as mild or full hybrids [23]. Battery technology also plays a crucial role. Some studies also highlight the importance of proper battery selection regarding capacity [26] and technology. According to [27], most current BEVs use lithium–ion (Li-Ion), nickel–metal hydride (Ni-MH), and lithium–polymer (Li-Polymer) batteries. Lithium–ion is the dominant battery technology due to its high energy density and increased power per unit mass. These batteries offer long lifespans and require minimal maintenance. Another option is nickel–metal hydride batteries, which, however, have increased weight and cost, and decreased energy density compared to lithium batteries, making them unsuitable for modern EVs. Lithium–polymer batteries use a solid-state electrolyte, making them less hazardous in an accident.

The shorter driving range of BETs can be addressed by increasing battery capacity, but also by optimizing the routes of such vehicles. Optimizing routes can also contribute to EVs’ economic sustainability and efficiency. This is applied, for example, in municipal waste collection [28]. The authors of [29] developed an extensive review of vehicle routing problems, including those focused on solid waste collection. These vehicles operate on relatively short routes within cities, making their gradual replacement by environmentally friendly vehicles highly desirable [30,31]. Since city delivery and collection vehicles often accelerate and decelerate, part of the energy can be recovered through regenerative braking [32]. The authors in [33] investigated the economic efficiency of BEVs in regard to driving patterns, vehicle range, and charge strategies. Electromobility can also be supported by photovoltaic panels placed on the roofs of vehicles or by static panels installed at parking locations to assist with recharging [32,34,35]. The right choice of charging strategy is also important. For example, the authors of [14] found that ultra-fast charging is economically advantageous for short distances, while battery swapping is more suitable for medium distances, and fast charging is best for long-haul routes. Furthermore, it is important to examine the specific operational conditions of the vehicle, and the cost of fuel or electricity [36].

Another common concern of transport operators is the economics of operating BETs and the return on investment. One of the frequently discussed approaches in the literature is the so-called total costs of ownership (TCO), for example, in studies [37,38,39]. TCO mainly includes procurement, energy, maintenance, and repair costs. All of these calculations also take into account battery degradation and vehicle depreciation, and also consider other, less significant costs (taxes, fees, insurance, inspections [40], diagnostics [41], etc.). The authors in [37] compared the TCO for heavy-duty trucks, analyzing both BEVs and FCEVs. The results showed that BEVs are currently more economically advantageous, although the cost difference is expected to diminish gradually, thanks to the gradual advancement of hydrogen technology. For a 16-ton vehicle with a range of 500 km, the TCO for FCEVs is 122% higher than that for BEVs, while for a 49-ton vehicle with a range of 1000 km, the difference is 36%.

The calculation of total cost of ownership (TCO) is an interesting method, which, however, is more often used for small passenger vehicles when the costs over a certain period need to be determined. For private passenger cars, a financial return cannot be expected, as these vehicles are not used for business purposes. Therefore, the approach in this article is based on evaluating the return on investment in BETs. Our research in this area is unique, as the testing was conducted under real-world conditions. Monitored parameters included mileage, vehicle weight, temperature (external and internal), and elevation gain along the individual routes. These parameters were subsequently used to create an electric energy consumption model. Charging costs represent the most significant component of the variable costs. This model was then used to determine the costs relative to mileage.

The research on electricity consumption and the subsequent cost modeling was carried out for a specific tested vehicle. However, this represents a certain limitation of this study; as of today, only 19 BETs with a gross vehicle weight over 12 tons are registered in the Slovak Republic.

Table 1. Number of BEVs registered in Slovakia. Source: [42].

Specification	Category	Number
Electric motorcycles	L	2571
Passenger cars	M1	16,593
Busses	M2 + M3	53
Light-duty trucks (up to 3.5 tons)	N1	995
Medium-duty trucks (over 3.5 tons and up to 12 tons)	N2	26
Heavy-duty trucks (over 12 tons)	N3	19
Work machinery and tractors	PS + T	24

shows the total statistics of all-electric vehicles (only BEVs, excluding hybrids) as of 31 March 2025.

Out of the 19 registered BETs (in Slovakia) with a gross vehicle weight over 12 tons, as many as 6 (i.e., 32%) are Mercedes-Benz Actros models. The remaining vehicles are manufactured by companies such as MAN (Munich, Germany), Renault (Bourg-en-Bresse, France), Volvo (Ghent, Belgium), and BYD (Lancaster, CA, USA). Due to the popularity of this vehicle model and its one-third market share among BETs, it was chosen for the measurements aimed at determining the input parameters for the calculation.

The uniqueness of this study lies in the combination of testing a BET under real-world operating conditions of a Slovak transport company and in developing a model of electric energy consumption based on the input data. Subsequently, an economic analysis was conducted, and the results are presented in this article.

2. Materials and Methods

The initial measurement of electric energy consumption and other operational parameters was carried out on a Mercedes Benz eActros 400 6x2 vehicle equipped with a refrigerated body and a hydraulic liftgate.

Table 2. Basic technical parameter of tested vehicle, Mercedes Benz eActros 400. Source: [43].

Specification	Value	Units
Max. vehicle length	9600	mm
Width (external/internal)	2600/2460	mm
Body height (external/internal)	2669/2450	mm
Body length (external/internal)	7664/7484	mm
Wheelbase	4900	mm
Gross vehicle weight	27	t
Payload capacity	14	t
Axle load capacity	8.0/11.5/7.5	t
Motor power (max./continuous)	400/330	kW
Max. speed	89	km/h
Battery type	Lithium–ion	–
Number of battery packs	4	–
Installed battery capacity	4 × 112 = 448	kWh
Charging power (max.)	160	kW
Charging time (20–80%)	100	min

provides more detailed technical specifications of the vehicle.

The research procedure described in this article is illustrated in Figure 2. It also includes the outputs that are discussed in the Results section.

The development of the economic model was preceded by research into the energy consumption of BETs under real-world conditions. The model was created based on data collected during delivery runs using the Mercedes-Benz eActros 400 6x2 electric truck. The collected data included the distance traveled (km), elevation gain (m), gross vehicle weight (kg), interior and exterior temperatures (in °C), and electricity consumption (in kWh/100 km). Most of these data—specifically distance, weight, temperatures, and consumption—were obtained directly from the vehicle’s onboard software.

The next step was to analyze the relationships between the individual variables. For this purpose, we used the IBM SPSS Statistics Data Editor software (v29.0.0). The analysis showed that the most significant factor affecting consumption is the external temperature—as the temperature decreases, consumption increases. Vehicle weight also influences consumption, although to a lesser extent. The impact of elevation gain on the route was less apparent in the data analysis.

Calculations were carried out using multiple regression analysis, which allows for the inclusion of all relevant factors influencing consumption as dependent variables. In this case, the dependent variable is energy consumption, while the independent variables include vehicle weight, mileage, elevation gain, and interior and exterior temperatures. The relationships between the variables were described using the parameters of a general multiple regression model.

After modeling the vehicle’s energy consumption, it was possible to determine the vehicle’s range and calculate its operating costs. The cost calculation presented in this article considers dividing costs into variable and fixed categories [44]. The basic relationship between variable and fixed costs is described as (1) and (2) according to [45]. The following paragraphs describe the individual items used to create the final calculation model.

$$C_t=C_v+C_f,$$

(1)

$$C_v=c_v·M,$$

(2)

where C_t are the total costs (EUR/year), C_v are variable costs (EUR/year), C_f are fixed costs (EUR/year), c_v are variable unit costs (EUR/km), and M is the vehicle mileage (EUR/km).

The price of electricity is an important input that significantly affects the level of variable costs. The selected unit price of EUR 0.20/kWh also accounts for a discount for bulk electricity consumption.

The toll rate was determined in accordance with Act No. 497/2013 Coll. [46], which establishes the toll calculation method, the toll rate amount, and the system of discounts on toll rates for the use of designated road sections. The vehicle is subject to the rate for vehicles with a gross weight of 12,000 kg or more and three axles. A 7% discount on the toll rate based on the realized mileage was also considered. According to the current wording of Act No. 497/2013 Coll., the toll rate for EVs is not preferential compared to ICEVs meeting Euro V emission standards.

It is possible to assume slightly lower maintenance and repair costs for EVs [47]. For simplicity, this study assumes that the maintenance costs of an EV will be 30% lower than those of a conventional diesel vehicle. Therefore, the cost value for a diesel vehicle was reduced by 30% for the EV model.

The motor vehicle tax rate is determined per Act No. 361/2014 Coll. on Motor Vehicle Tax and on the Amendment of Certain Acts [48]. According to § 6 and Annex No. 1 of the Act, the carrier determines the amount of the tax based on the engine output specified in the vehicle registration certificate.

Table 3. Operational costs of the tested BET per one year. Source: internal company’s data.

Type	Item	Costs [EUR]
Fixed costs	Motor vehicle tax	0.00
Fixed costs	Annual driver salary	30,000.00
Fixed costs	Depreciation	400,000.00
Fixed costs	Energy (electricity)	9227.13
Variable costs	Toll fees	6733.20
Variable costs	Maintenance and repairs	6225.17

presents the values of individual cost items related to the carrier’s realized mileage, 40,000 km/year.

In modeling the cost levels, an average energy consumption of 109 kWh/100 km was considered.

The total electricity consumption increased by 5%, as energy losses occurred during the charging of the EV. The vehicle manufacturer provided this value.

3. Results

The research output is a model of variable, fixed, and total costs for operating a BET.

3.1. Cost Simulation Based on Actual Mileage

3.1.1. Without Subsidies

Figure 3 shows the trend in unit costs depending on the changing mileage. The figure indicates that variable costs remain constant and represent the lowest share of total costs. In contrast, fixed costs do not fall below the level of variable costs in any modeled scenario. This is due to the high acquisition cost of BETs.

The previous graph expressed the costs per kilometer traveled by the vehicle. However, it is more common to express costs over a monitored period (one year) depending on the total distance traveled. Figure 4 shows the costs expressed in this way. The figure indicates that variable costs again represent the lowest component of total costs, and their amount increases linearly with the mileage achieved during the monitored period, while fixed costs remain constant in this case.

The result of the cost analysis of ICET and BET is a comparison of the unit costs for both types of vehicles. In this comparison, the development of unit variable costs (c_v), unit fixed costs (c_f), and total costs (c_t)—representing the sum of fixed and variable costs—was evaluated. Figure 5 shows that the BET’s variable costs represent the lowest component across all modeled mileage scenarios. The EV’s fixed costs fall below the diesel vehicle’s total costs at a mileage between 40,000 km and 50,000 km. BET’s total costs reach the level of the diesel vehicle’s total costs at a mileage of 100,000 km. At this mileage, BET’s fixed costs reach the level of ICET’s variable costs. It can be concluded that the BET is still economically inefficient at the realized mileage (46,511 km), mainly due to its high acquisition cost. However, the EV’s variable costs are significantly lower across all simulated mileage scenarios.

Figure 6 shows the opposite representation, i.e., it expresses the total costs over the observed period. The graph in the figure indicates that the variable costs of the BET represent the lowest cost component. The total costs of the ICET reach the level of the BET’s variable costs at a realized mileage of between 40,000 km and 50,000 km, with the increase in the ICET’s total costs approaching the total costs of the BET at a realized mileage exceeding 100,000 km. This is because the variable cost components of ICET (maintenance, fuel price) grow faster with mileage compared to the variable costs of the BET.

3.1.2. With Subsidies

Due to the high acquisition price of the BET, a comparison of the costs of electric and diesel trucks was carried out, assuming the application of business subsidies. Since the Slovak Republic currently does not have a support scheme for purchasing electric trucks, the support plan applied in Austria (ENIN) was used. The ENIN program supports the purchase of electric trucks by covering 80% of the additional investment costs, where the additional costs represent the difference between the purchase price of a zero-emission vehicle and the average purchase price of a conventional reference vehicle meeting the Euro VI standard. The amount of the subsidy is calculated according to Formula (3):

$$P_{sub}=0.8·(P_{BET}-P_{ICET}),$$

(3)

where P_sub is the subsidy amount, P_BET is the price of the electric truck, and P_ICET is the price of the truck with a conventional combustion engine. After carrying out the calculations for several available truck brands, the average purchase prices of BETs and ICETs, as well as the subsidy amount, can be determined. The values are shown in

Table 4. Purchase price of the BET assuming state subsidies. Source: [43].

Item	Price (EUR)
Average purchase price of ICET	112,312.37
Amount of subsidy	230,150.10
Adjusted final purchase price of BET	169,849.90

.

After applying the aforementioned subsidy, a 40% reduction in fixed costs would be achieved, resulting in a 30% reduction in total costs. The changes in fixed and total costs after the subsidy application are presented in

Table 5. Changes in fixed and total costs due to government subsidies.

Mileage	Without Subsidies		With Subsidies
	Fixed Costs (EUR/km)	Total Costs (EUR/km)	Fixed Costs (EUR/km)	Total Costs (EUR/km)
100,000	0.97	1.53	0.58	1.14
90,000	1.07	1.63	0.65	1.21
80,000	1.21	1.77	0.73	1.29
70,000	1.38	1.94	0.83	1.39
60,000	1.61	2.17	0.97	1.53
50,000	1.93	2.50	1.17	1.73
46,512	2.08	2.64	1.25	1.82
40,000	2.42	2.98	1.46	2.02
30,000	3.22	3.79	1.94	2.51
20,000	4.83	5.40	2.92	3.49
10,000	9.67	10.24	5.83	6.41

.

A graphical representation of the development of variable and fixed costs depending on the vehicle mileage is more illustrative than a numerical one. Figure 7 indicates that, after applying the subsidy, the total operating costs of the ICET are higher than those of the BET. If the Slovak Republic were to implement a support system for the purchase of zero-emission vehicles, the unit fixed costs of the EV would reach nearly the same level as the unit fixed costs of the diesel vehicle. The figure also shows that the variable costs are significantly lower, meaning that the deployment of this vehicle is more cost-effective than that of ICET.

If the Slovak Republic were to introduce support for purchasing an electric freight vehicle, the total operating costs of such a vehicle would be fundamentally affected. Figure 8 shows that, in this case, the fixed costs for ICET and BET reach the same level, while the variable and total costs of the BET are significantly lower than those of the ICET.

The comparisons of individual cost components reveal that the share of variable costs in the BET is almost 20% lower than in the ICET, indicating that the ICET is considerably more expensive to operate. Conversely, the fixed costs are 21% higher for the BET than for the conventional diesel vehicle. The efficiency of the BET is thus based on its lower maintenance costs and its significantly lower fuel cost (electricity). Figure 9 displays the variable and fixed costs ratio to the total costs for both types of vehicles.

3.2. Cost Simulation Based on Optimized Mileage

The research in this section focused on evaluating the cost progression at an optimized mileage for the tested BET. The calculation of the optimal mileage was carried out using Formula (4) according to [45]:

$$t_t=t_d+t_{lu}+t_i,$$

(4)

where:

t_t is the vehicle turnaround time;

t_d is the vehicle driving time;

t_lu is the loading and unloading time;

t_i is the idle time.

A statistical dataset provided by the carrier served as the basis for the calculation. It was based on the most frequently recurring delivery routes, where the driving time does not exceed 4.5 h. The calculation considered the different energy consumptions (kWh/100 km) for each month due to varying average temperatures. It also considered different charging times resulting from temperature differences. A working day of 12 h was assumed. Accounting for these factors created a model in which the vehicle performs two turnarounds (from the distribution center to the delivery point and back). As a result of optimizing the daily mileage, the optimal annual mileage can be calculated [45] using the relationship (5), where WD_Y is the number of working days per year, and $\overline{l_d}$ is the average optimal daily mileage.

$$L_{opt.}={WD}_Y·\overline{l_d}=352.00·380.00=\mathrm{133,971.20} (\mathrm{k}\mathrm{m}/\mathrm{y}\mathrm{e}\mathrm{a}\mathrm{r})$$

(5)

From the above, it can be concluded that the optimal annual mileage of the carrier could reach approximately 133,971.2 km per year, assuming that the vehicle operates for one 12 h shift per day.

3.2.1. Without Subsidies

When comparing the total costs, the EV also appears more efficient when achieving annual mileage above 110,000 km. Although the fixed costs of the EV are considerably higher, they are offset by its lower variable costs and the effective utilization of the EV. The comparison of the total costs is shown in Figure 10, which indicates that the variable costs increase more slowly with mileage compared to those of the diesel vehicle. This is also because the diesel vehicle’s consumption is not as significantly affected by ambient temperature, it does not achieve savings in consumption on longer routes through recuperation, and it is considerably more expensive to maintain.

3.2.2. With Subsidies

The provision of support for carriers (state subsidies) would profoundly impact the decision-making of businesses and transport companies when procuring an electric heavy-duty vehicle. By granting this subsidy, the fixed costs of BETs would be reduced to the same level as those of ICETs. In contrast, the BET retains a substantially lower variable-cost component. In this scenario, the BET emerges as the more economically efficient option since its variable costs increase more slowly with mileage than the ICET.

When comparing total costs, the electric heavy-duty vehicle likewise proves significantly more cost-effective than its diesel counterpart. Figure 11 illustrates the trajectory of total costs for the electric and diesel trucks; in this simulation, the impact of efficient vehicle utilization on total costs is most apparent.

3.3. Cost Simulation Considering Battery Capacity Degradation

The anticipated calendar and cycle losses arising from battery operation and aging will substantially influence the operating costs of a BET.

Table 6. Adjustment of actual mileage considering battery capacity loss.

Time of Ownership (Years)	1	2	3	4	5	6
Annual capacity reduction (%)	12.4	16.0	19.2	22.0	24.7	7.4
Reduced batt. capacity (kWh)	392.4	376.5	362.1	349.5	337.3	414.9
Reduced mileage (150,000 km)	131,385	126,060	121,245	117,030	112,950	138,930

displays the percentage loss of battery capacity, where each percentage refers to the reduction relative to the original capacity. “Reduced battery capacity” denotes the remaining usable capacity (in kWh), and “Reduced mileage” indicates the attainable distance (out of an initial 150,000 km) after accounting for both calendar and cycle losses over the ownership period.

After applying the mileage reduction, the variable and fixed cost components were calculated using the standard cost formulas. The time-series characteristics method was used to identify cost changes between successive periods. This approach allows both the analysis of the development of a single time series and the comparison of multiple series.

The growth coefficient of the time series and its relative growth rate were computed as shown in Formula (6), according to [49]:

$$k_t={\displaystyle \frac{y_t}{y_{t-1}}},$$

(6)

where:

t = 2, 3, …, n.

The average growth rate of the series is given by (7):

$$\overline{k_t}=\sqrt[n-1]{k_2·\dots ·k_n}=\sqrt[n-1]{{\displaystyle \frac{y_n}{y_t}}},$$

(7)

where:

n is the number of observations in the time series;

y₁ is the first observation;

y_n is the n-th observation.

From the calculations presented in

Table 7. Relative cost increases due to battery capacity loss. Source: authors.

Time of Ownership (Years)	1	2	3	4	5	6	Average
Relative increase (–)	1.06	1.04	1.03	1.03	1.03	1.03	1.04
Relative increase (%)	6	4	3	3	3	3	4

, it can be concluded that the year-on-year cost development—driven by the loss in battery capacity—is negative. On average, a 4% annual increase in operating costs due to capacity degradation can be expected.

3.3.1. Without Subsidies

The research further focused on a cost simulation considering battery capacity degradation. It is important to note that the calculation was carried out based on the change in costs relative to the change in mileage. The effect of changes in charging time due to cyclic losses was not included in the calculation due to a lack of relevant data. The calculation also assumes that the BET can be used in a 12 h operating shift; however, due to the loss of battery capacity, it can be assumed that the vehicle may no longer be able to achieve the same mileage within a single shift. Several factors contribute to cost increases resulting from capacity degradation, including faster battery depletion, longer charging times, reduced range, greater losses during loading, and vehicle downtime. It is also important to note that this simulation assumes that the fixed cost component remains unchanged while only the variable costs are affected.

Figure 12 shows the development of unit costs, assuming a 4% increase compared to the original condition. From this, the BET still appears more cost-effective at a realized mileage above 140,000 km.

3.3.2. With Subsidies

The final part of this study compared costs under the assumption of state support for businesses purchasing BETs, accounting for annual battery capacity losses. The comparison of unit costs (Figure 13) shows that the BET appears more economically efficient than the conventional ICET across the entire range of simulated mileage.

When comparing total costs, a similar scenario can be assumed. Since the applied subsidy would significantly reduce fixed costs, the vehicle is expected to also demonstrate substantial savings in this case.

3.4. Model Validation

One of the additional tasks of the research on the economic efficiency of operating BETs in the Slovak Republic was to verify the accuracy of the model. For this reason, a short survey was conducted among transport operators who operate BETs. The survey aimed to verify whether the modeled variable and fixed costs correspond to the actual costs incurred by transport operators.

The survey focused on five transport companies, of which three operate a single BET, one company operates two BETs, and one company operates four BETs. The survey targeted only vehicles in category N3, i.e., those with a gross vehicle weight over 12 tons. From the responses, we found that the carriers mainly use semi-trailer trucks and box trucks.

The results of the model validation are as follows:

• The operators identified variable unit costs for BETs in the range of EUR 0.50 to EUR 0.65 per kilometer. In the cost model presented in this article, variable unit costs of approximately EUR 0.56 to EUR 0.58 per kilometer were considered. This indicates that the model corresponds to the practical cost range.
• The operators identified absolute fixed costs per BET in the range of EUR 90,000 to EUR 120,000 per year. In the economic model, a value of EUR 96,667 per year was used. This indicates that the modeled fixed costs fall within the range observed in practice.

In this way, we verified the modeled values in real-world conditions, and we can, therefore, assume that the model provides economically accurate results.

4. Discussion

This article presents the economic part of the research on a tested BET. The data were collected during real-world operation using a test vehicle, a Mercedes-Benz eActros 400 6x2, in April 2023. Measurements were carried out using the vehicle’s internal diagnostics and data recorded by the onboard computer. Additional data were obtained from external sources (e.g., elevation profile of the route).

The first part of the research focused on determining the vehicle’s characteristic energy consumption. Given the regenerative breaking of the BET, the influence of route elevation as well as ambient temperature was also examined.

The results of this part of the research showed that the most significant factor affecting consumption is the ambient temperature, with a difference of nearly 40% between the minimum and maximum modeled temperatures. Another factor influencing vehicle consumption is the total elevation gain along the route. The research showed that using the full battery capacity (BET used on longer routes) considerably reduces energy consumption. A surprising finding was that vehicle weight does not significantly affect consumption; the difference in consumption between an empty and fully loaded vehicle was approximately 5%. These factors considerably affect the variable component of operating costs.

Based on the first phase of research, several conclusions could be stated:

• The most significant factor affecting the energy consumption of electric vehicles is ambient temperature.
• The efficiency of electric vehicles decreases with increasing road gradient, making them less effective for use on routes with steep inclines.
• It was also found that vehicle weight does not significantly impact energy consumption.
• Charging times are significantly longer in winter months compared to optimal temperatures.

The second part of the research focused on the economic assessment of the operation and the modeling of various scenarios (with or without subsidies, including the loss of battery capacity, etc.). The main objective of the economic assessment and cost modeling of the BET was to compare the development of individual cost components for BETs and ICETs. Although many authors assess electric vehicles, including BETs, using the TCO approach [50,51,52], this study applied a dynamic cost calculation method that separates costs into variable and fixed components. This type of calculation is familiar to transport operators; therefore, the results presented in this article can be directly applied in practice.

The vehicle included in the research had an annual mileage of 46,511 km. Based on the analysis of the current operations of a specific carrier, it can be concluded that at this annual mileage, the tested BET is not economically efficient, primarily due to its high acquisition cost. On the other hand, the variable cost component shows a slower increase with rising mileage compared to the conventional vehicle type. After evaluating the actual costs at the real-world operating mileage, the research focused on assessing the costs at the maximum possible mileage of vehicles during a 12 h working shift. Of course, maintaining this mileage would be challenging with decreasing battery capacity and current infrastructure. The calculations showed that the estimated maximum annual mileage is approximately 130,000 km. A subsequent cost simulation revealed that the total costs of EVs drop below that of conventional vehicles at around 110,000 km per year. This suggests that, despite the high acquisition cost, BETs can become more economically efficient than ICETs. However, this scenario requires extremely high mileage, which carriers can only achieve through efficient vehicle utilization. BETs can significantly reduce variable costs through optimal usage, such as energy recuperation to lower average consumption.

Although the cost model itself does not include subsidies as a separate variable, their impact is thoroughly reflected in the subsequent simulations and result analysis. In the sections “With Subsidies”, both fixed and total costs are recalculated under the assumption of 80% support for vehicle acquisition based on the Austrian ENIN model. The results show that such support significantly reduces fixed costs, allowing the break-even point to be reached at a lower annual mileage.

From the perspective of stakeholders, especially transport operators and policymakers, subsidies have a profound impact on investment decisions. For operators, they improve the return on investment and reduce the risks associated with adopting new technologies. For the state, they represent an effective tool to support the decarbonization of road freight transport. Therefore, although subsidies are not explicitly included in the core equations, their impact is accounted for in the calculations and thoroughly analyzed in this study.

In the final section, costs were simulated and compared under the assumption of an annual decline in battery capacity. The calculations reflected cost changes in response to variations in mileage. The simulation indicated an approximately 4% increase in the variable cost component, as delivering the same mileage would require more work. In this scenario, the BET becomes economically efficient at an annual mileage of approximately 140,000 km (without state subsidies for BET purchases). However, this mileage cannot be achieved with work shifts of 12 h. Achieving it would require extending the vehicle’s operational time and increasing the fixed cost component. Additionally, the calculations did not account for changes in charging and discharging times or their impact on the maximum achievable mileage.

The specific type of cost calculation, or rather the differences between different cost items for the BET and ICET, mean that the results presented in this article are difficult to compare with other studies. For this reason, it was possible to directly compare the two different vehicle propulsion systems and identify the annual mileage at which the BET becomes more cost-effective. The approaches to evaluating the economic efficiency of EVs and their key findings are presented in

Table 8. Comparative matrix of relevant studies. Source: [50,53,54,55,56].

Study	Focus of Simulation	Real-World Measurement	Considering Battery Degradation
Poliak et al., 2025 [this study]	Differences in variable and fixed costs	Yes	Yes
Vijayagopal and Rousseau, 2021 [50]	TCO	No	No
Nykvist and Olsson, 2021 [53]	Energy consumption, battery capacity and charging	No	No
Link and Plötz, 2022 [54]	Energy consumption, battery capacity and charging	Yes	No
Bhardwaj and Mostofi, 2022 [55]	TCO, review of existing literature	No	No
Sen et al., 2017 [56]	Lifecycle assessment	No	No

.

As shown in Table 8, most relevant studies comparing ICETs and BETs did not collect data from real-world vehicle operations. Of course, the table does not include every existing study on this topic. However, it can be assumed that only a small percentage of studies take battery degradation into account—as in the simulation included in this article.

Moreover, other studies rarely perform a direct comparison of the economic efficiency of BETs and ICETs in a way that yields results applicable to freight operators who monitor both costs and vehicle performance on an annual basis. The outcomes of this study are highly valuable for transport companies, as they enable operators to make better-informed decisions about acquiring EVs.

It is essential to acknowledge that this study has several limitations, which are outlined in the following paragraphs. First and foremost, the study is based on data collected from the testing of only one specific type of battery electric truck (Mercedes-Benz eActros 400). Therefore, the results may not be fully representative of the entire BET market. Research involving other types of vehicles will continue in the future once additional partners are found who are willing to provide their vehicles for operational performance measurements.

In connection with this limitation, the overall number of registered BETs over 12 tons in Slovakia is very low. With only 19 registered vehicles, it is difficult to validate the findings on a broader sample and to make meaningful comparisons across different models.

Furthermore, this study focuses primarily on economic indicators of operation, such as fixed and variable costs. It does not include a detailed analysis of environmental impacts or external costs associated with different drivetrain types.

Some of the inputs to the economic model, such as the use of a uniform electricity price or estimated maintenance costs, are simplified and may not fully reflect market price fluctuations or specific contractual conditions of transport companies.

Although the study accounts for battery degradation in the form of reduced capacity and increased variable costs, it does not consider changes in charging times, the impact of a shorter driving range on route planning, or potential vehicle downtimes.

Another limitation of the study is the assumption of continuous and uninterrupted access to charging infrastructure. The cost models do not account for real-world downtimes caused by charging or potential waiting times at occupied charging stations. In the case of insufficient infrastructure development, unproductive operation time may increase, which negatively affects daily mileage and, in turn, the ability to reach the economic break-even point. This aspect can be particularly critical in practice for high-performance vehicles that require fast and readily available charging to maintain an optimized operational regime.

Although this study primarily focuses on the economic aspects of BET operation, future research should also address environmental impacts through lifecycle assessments (LCA). LCA enables a comprehensive evaluation of emissions and resource use throughout the entire lifecycle of a vehicle—from production and use to end-of-life disposal. It is relevant when comparing BETs and ICETs, as electric vehicles typically have higher emissions during production (mainly due to battery manufacturing) but lower operational emissions. The authors in [57] stated that overall climate impact can be reduced by integrating the effects of changes in electricity production over time, battery efficiency fade, refurbished EV batteries, and battery recycling. Together, these parameters resulted in an 18% reduction in the lifecycle climate impacts of a present-day BEV.

This article shows that the pace of BET adoption in Slovakia is very slow. This is caused by several factors. One of the most significant is the underdeveloped charging infrastructure, particularly for heavy-duty vehicles, which limits the practical use of BETs on longer routes. Another factor is regulatory uncertainty—the absence of a stable and long-term framework for government support, as well as unclear or changing tax and financial incentives (for example, the zero-tax rate for BETs will be abolished in Slovakia as of 1 January 2025), both increase investment risk for transport operators. Cultural factors also play a role, as many freight operators in Central and Eastern Europe tend to adopt a conservative approach to innovation, preferring technologies with proven reliability and long-term operational experience. In general, the rollout of electric vehicles in countries with lower living standards will pose a major challenge, as the population is likely to perceive it as an additional financial burden.

5. Conclusions

The novelty of this study lies in the unique practical testing of a BET with a gross vehicle weight exceeding 12 tons and fully electric propulsion. In the initial testing phase, not only was electricity consumption recorded, but also the elevation gains along the route, external and internal air temperatures, and vehicle weight. Thanks to this, it was possible to create a consumption model using multiple regression analysis.

Subsequently, an economic model was developed to assess the economic efficiency of the BET under various conditions. The results of the economic evaluation of the BET from an operational perspective can be summarized as follows:

• The primary cost burden of BETs lies in fixed costs, which remain higher than variable costs across all modeled scenarios due to their high acquisition prices.
• Without subsidies, a BET becomes economically competitive with an ICET only at mileage levels above approximately 100,000 km/year.
• The application of a subsidy program, modeled after the Austrian ENIN scheme, reduces BET fixed costs by 40% and total costs by 30%, making BETs economically favorable even at lower annual mileages.
• At a realized mileage of 46,511 km, the BET remains economically less efficient than ICETs without subsidies; however, variable costs for BETs are consistently lower in all scenarios.
• Optimizing operational conditions (e.g., daily mileage, routing) enables BETs to reach a break-even point at approximately 110,000 km per year, even without subsidies.
• With subsidies, BETs become the more cost-effective option across all modeled distances due to lower variable and total costs, mainly when utilized optimally.
• Battery capacity degradation leads to a projected annual operating cost increase of approximately 4%. Nevertheless, BETs remain economically favorable over ICETs when operating above 140,000 km/year, even accounting for battery aging.
• The variable cost component of BETs is up to 20% lower than that of ICETs, mainly due to lower maintenance and energy costs. Conversely, the fixed costs of BETs remain around 21% higher than those of diesel trucks without subsidies.
• If a national subsidy scheme similar to Austria’s were implemented in the Slovak Republic, BETs would become economically attractive for fleet operators, even under typical Central European operating conditions.

Future research in this area could focus on several key directions. This article mentions that, as of now, there are 20,281 BEVs registered in the Slovak Republic, of which BETs account for only 19 vehicles, i.e., 0.09%. Future research should focus on a broader range of BET types, brands, and configurations, thereby enhancing the generalizability of the findings. Additionally, conducting measurements over a longer period and under various seasonal and operational conditions would provide a more comprehensive understanding of vehicle performance and TCO.

Further studies could also explore regional differences in infrastructure availability, energy prices, and policy support, as well as how these factors influence the economic viability of BET adoption. The economic model must also focus on various alternative scenarios for the distribution of subsidies (different percentage variants or a fixed amount). Another relevant direction would be the integration of lifecycle assessments and environmental impact analyses to capture a more comprehensive sustainability profile of electric trucks in freight transport.

This article confirms that the market uptake of BETs in the freight transport sector in Slovakia is still in its early stages. To accelerate BET adoption, targeted policy measures are necessary. We particularly recommend implementing a long-term and predictable subsidy scheme for vehicle acquisition, expanding publicly accessible high-power charging infrastructure, and introducing tax incentives for environmentally friendly freight transport. Another important tool is the support of pilot projects that allow transport operators to gain hands-on experience with BETs and reduce concerns related to technological and operational risks. Such measures can significantly contribute to the decarbonization of the sector and to strengthening the innovative environment in the field of road freight transport.