Analysis of the Logistics Impact for the Freight Transportation Sector Using Electric Trucks

Abstract

The transition to sustainable transport in the logistics sector requires innovative strategies, yet companies still face uncertainty regarding the operational, economic, and environmental feasibility of replacing diesel trucks with electric ones. Electric trucks represent a sustainable alternative, contributing to the reduction in pollutant gas emissions, noise reduction in traffic, and lower operational costs, in addition to building sustainable logistics through recharges from renewable energy sources. Although electric trucks offer sustainability benefits, existing research often lacks analyses based on real-world delivery conditions. In this context, the objective of this paper is to analyze the logistical impact of introducing electric trucks for beverage transportation. This study includes assessments of planned route profiles, economic evaluation during operation, emission mitigation costs, and charging analyses under different pricing models in consumer units. These elements were selected to reflect the actual challenges companies face. The results demonstrate that electric trucks can reduce fuel costs by 83.90% and significantly lower carbon emissions, confirming their viability for last-mile freight transport operations. Therefore, the results demonstrate that the process of replacing diesel trucks with electric ones is a viable alternative for companies due to the savings generated during operation and the reduction in pollutant emissions.

1. Introduction

The transportation system plays a fundamental role in a region’s economy and the quality of life for its population. However, the increase in demand for the combustion vehicle fleet tends to be ever greater, increasing the consumption of petroleum derivatives, which contributes to the environmental impact due to pollutant emissions [1,2].

Last-mile freight transport is one of the logistics chain’s most polluting and costly areas. Consequently, innovative strategies and new technologies in freight transport should be easily implemented in the short term [3,4].

Freight transportation aims to distribute the goods in the right quantity, place, and time at the lowest possible cost. Beyond its logistical function, transportation adds value to products by enhancing their accessibility and affordability. As transportation becomes more efficient and cost-effective, it supports economies of scale, reduces overall logistics expenses, fosters market competitiveness, and contributes to lower product prices. Consequently, the business environment increasingly recognizes logistics as a strategic tool for achieving service excellence. In this context, logistical planning develops through the time horizon, seeking to distribute beverages effectively and efficiently [5,6].

The main elements analyzed in the transportation service decision-making process involve price, freight loss and damage, average travel time, and delivery time variability. Furthermore, factors such as the number of stops, weather conditions, traffic jams, and the difference in the time required to consolidate freight can vary the travel time [6].

Among the alternatives for integration into a sustainable transportation system, electric trucks stand out for operating in urban freight transport, whose benefits focus on energy efficiency, fleet optimization, and environmental impact reduction [7,8]. However, due to insufficient recharging stations and vehicle autonomy, the current application relies on short logistics operations and light freight [9].

Light truck traffic for last-mile deliveries is heavy in urban centers and between municipalities, which has a major impact on air quality [10]. Furthermore, introducing electric fleets to transportation is essential for promoting the shift toward sustainable energy sources [11].

Electric vehicles are recognized as the most feasible technology option for reducing pollutant emissions to meet business demands [12,13]. Furthermore, EVs contribute to building a sustainable economy through recharging using renewable energy sources. Thus, several countries are taking measures to promote EV development to increase energy security, reduce greenhouse gas emissions, and decrease noise pollution [14,15].

The development of recharging infrastructure is a requirement for implementing electric trucks. According to [16], when using a commercial electric truck fleet, after completing the route, all vehicles return to one location, which makes it of utmost importance to explore the area due to the additional complexity of multiple recharging stations.

Thus, implementing an electric truck fleet impacts the company’s electricity demand. As such, evaluating this impact and verifying the possibility of simultaneous recharges is important [17].

Batteries typically used in electric vehicles present operational challenges such as degradation from fast charging currents and cost fluctuations. These factors must be considered when planning efficient and sustainable distribution routes [9,14]. Furthermore, verifying each consumer unit’s recharge-electricity costs is necessary since energy costs are charged per kilowatt-hour (kWh) with different prices during the day [17].

Therefore, an interesting way to mitigate this problem is by substituting diesel with electricity, using light electric trucks. Light and semi-light electric trucks are suitable for last-mile cargo transportation, especially in urban centers, where their smaller size allows for greater agility in deliveries. However, studies regarding economic factors must be developed to provide a greater incentive for implementing the use of light electric trucks since it significantly impacts the company’s revenues. Furthermore, the recharging of the electric truck fleet impacts the company’s electrical installations, consumption, and demand for electricity.

Considering the growth and massification of EV technology, the future study of the logistical impact of introducing electric trucks in freight transportation becomes important. The study of logistical impact focuses on the effects that decisions or changes may have on an operation’s logistics. In this context, this study proposes developing a methodology for recharging light trucks, considering the impact on the companies’ logistics, routing, and electricity costs. This study aims to show the results of the evaluations developed for replacing light truck fleets and encourage other companies to evaluate the possibility of implementing the technology in their current fleets.

2. Materials and Methods

Electric trucks are increasingly positioned as viable alternatives to combustion models in urban logistics. Therefore, the proposed methodology comprises an analysis of the routes used for transporting beverages, focusing on operational parameters such as distance and cargo volume. This approach was selected because route-based analysis allows for a realistic assessment of electric vehicle performance under specific logistical constraints. Furthermore, it verifies the trucks’ behavior during the workday in the delimited regions to evaluate compatibility with existing operations.

In addition, the methodology evaluates the economic viability of the vehicles’ operational expenses and the environmental impact of reducing carbon dioxide emissions by replacing the company’s current truck fleet with electric alternatives. Thus, this study aims to analyze the logistical impact of light electric trucks on last-mile cargo transportation.

In summary, to evaluate the feasibility of replacing trucks, the methodology is structured around three core dimensions: logistical performance, economic viability, and environmental impact.

First, to evaluate the truck’s performance during a trip, it is necessary to model a route. For this purpose, the Roadshow software was employed to schedule the routes of the trucks currently in operation. Roadshow is software used in business environments to plan vehicle fleet routes and optimize them. The program enables precise analyses regarding the utilization of resources involved in the distribution system, defines territorial boundaries, and includes graphical features, as well as customer and address data [18].

For the implementation, a computer manufactured by Dell Computadores do Brasil LTDA, located in Hortolândia, São Paulo, Brazil, was used, with the following specifications:

• Processor: 13th Gen Intel(R) Core (TM) i7-13650HX 2.60 GHz (Intel, Santa Clara, CA, USA);
• System type: 64-bit operating system;
• Installed RAM: 64.0 GB;
• Graphics card: nVidia RTX4050 (NVIDIA Corporation, Santa Clara, CA, USA).

This route modeling enables an assessment of the logistical impact resulting from the integration of an electric truck into a schedule previously served by a diesel vehicle. By considering the range of the electric trucks, it becomes possible to analyze some effects, such as freight demand, delivery time, and distance traveled, contributing to the dimensions of the logistics optimization.

After that, the methodology is based on developing a feasibility analysis for replacing the diesel truck fleet with an electric fleet. It uses an economic analysis that includes refueling and avoided maintenance costs (maintenance costs with components that do not exist in the electric truck) for the trucks during the specified period. Therefore, expenses associated with fuel consumption are analyzed, and through this, a cost comparison per kilometer driven is developed, directly supporting the economic analysis of operational savings.

The fleet data required for the calculations included average fuel consumption (km/L), total distance traveled, and recorded fuel expenses. This way, the total cost of the kilometers traveled is calculated, according to Equation (1).

$$total cost of km traveled={\displaystyle \frac{total km driven }{consumption average }}\ast diesel price average$$

(1)

This estimation was based on one full year of operational data, from which a standard average fuel consumption and average diesel price were derived. The approach assumes a uniform consumption rate across all routes and conditions, without accounting for variations due to vehicle load, road characteristics, or driver behavior. While these factors can influence fuel efficiency, the use of aggregated annual data was considered appropriate for the scope of this study, which focuses on comparative cost analysis.

In contrast, to estimate the cost per kilometer for recharging electric vehicles, the analysis is based on the average kilowatt-hour (kWh) rate from the electricity bill of the consumer unit under study.

Distance data were also used to estimate CO₂ emissions, enabling the environmental impact analysis through comparison of pollutant outputs between diesel and electric fleets. Moreover, in order to evaluate the reduction in pollutant emissions, the mitigation cost (CoM) is applied in the proposed case. It aims to measure the value of decarbonizing, that is, to leave a cleaner energy matrix. It is estimated in R$ per mass of CO₂. Through Equation (2) [19,20]:

$$CoM = {\displaystyle \frac{\left(battery recharge cost - diesel tank cost\right) [\frac{R\$ }{tank}] }{\left(diesel tank emission - battery charge emission\right) [\frac{kg{CO}_2}{ tank}].}}$$

(2)

Then, an evaluation of the energy demand installed in the company was developed, verifying the limits of the consumer unit to receive the electric fleet. Next, the number of recharging stations required, the recharger model to be installed, and the logistics of recharging schedules are defined.

In order to analyze the expenses with recharges, first, we identify the market environment in which the consuming unit is inserted. In Brazil, electricity trading is related to two market environments: the Regulated Contracting Environment (RCE) and the Free Contracting Environment (FCE), according to Law No 10.848 (15 March 2004) and Decree No 5.163 (30 June 2004) [21,22].

For units included in the Regulated Contracting Environment (RCE), consumers enter into a simplified contract in which the local distributor supplies electricity, with prices and supply conditions regulated by the National Electric Energy Agency (ANEEL) [23]. Tariff groups are defined as follows: Group A comprises high- and medium-voltage consumers supplied at voltages equal to or greater than 2.3 kV or at secondary voltage through underground distribution systems. Group B includes low-voltage consumers, supplied at voltages below 2.3 kV [24]. Additionally, the electricity tariff includes the Energy Tariff (ET), the Tariff for the Use of the Distribution System (TUSD), and other applicable taxes.

In the FCE, energy contracting is carried out through free agreements between parties, including trading companies, authorized generation agents, and special or free consumers that meet regulatory requirements. The type of contract and the price are freely negotiated [25]. Therefore, the impact of vehicle recharging on the electricity cost (R$/kWh) is analyzed, considering the grid usage tariff charged by the local utility and state-specific taxes for peak and off-peak hours. Additionally, the contracted demand is evaluated, and the additional demand resulting from the installation of chargers is estimated to determine whether the current contracted capacity is sufficient to support the new load.

Figure 1 presents the flowchart of the proposed methodology to highlight the work steps that will be developed to achieve the results. However, after using the approach regarding route definition, recharge points, and the impact on the contracted energy tariff, it is evident that the process should be re-evaluated where it needs to be expanded.

3. Results

The case study used real data analyzed from a beverage company headquartered in Lajeado and with distribution centers located in Pelotas, Santo Ângelo, Canoas, and Caxias do Sul, all in the state of Rio Grande do Sul, Brazil.

The company carries out transportation with its fleet of 61 trucks distributed among the units in Rio Grande do Sul. The beverage freight includes a variety of products such as mineral water, tonic water, soft drinks, beer, juices, and energy drinks [26]. The freight produced at the factory is sent to all distribution centers, which are supplied using a demand forecast for the month’s sales volume.

Then, freight products are sent to the delivery points, the destinations where the products are unloaded, which are distributors, restaurants, bars, markets, bakeries, shopping malls, and other enterprises. The delivery schedule is from 7 a.m. to 4 p.m., Monday to Friday, with a 1 h break. The average unloading time per delivery point along the route is 5 min. Moreover, the routes are planned daily after 6 p.m. for execution the following day.

Considering the company’s operational workflow, in order to integrate an electric truck into its delivery routes without impacting the current process, nighttime and simultaneous recharging were adopted exclusively during the period from 6 p.m. to 7 a.m. This timeframe was chosen because the truck remains parked in the garages during these hours, ensuring that charging does not interfere with route planning or execution.

3.1. Route Optimization

The first step comprises developing a route analysis for a single workday, detailing the characteristics of the truck’s scheduled route. Based on the resulting variables, the objective is to assess the logistical impact of replacing diesel trucks with electric trucks. Furthermore, the study aims to verify possible alternatives to meet delivery demand, delivery time, and kilometers traveled in a way that aligns with the operational constraints of electric trucks. It is important to note that, without route monitoring, discrepancies may occur between the planned route and the actual route taken during the workday.

3.1.1. Route—Pelotas Region

A truck with a 5-ton load capacity takes the simulated route for 1 route/day from the distribution center in Pelotas to the delivery destination in the city of Rio Grande.

As shown in Table 1, the truck begins its route with maximum load capacity, making deliveries across 17 stops located in urban areas, as illustrated in Figure 2. Each delivery point is marked with a number indicating the delivery sequence throughout the day and a color representing the delivery status. In other words, whether the product is being delivered on time or is delayed. In this context, the green color indicates that the delivery is on schedule.

Figure 2. Route with the delivery points—Pelotas Region.

Figure 2. Route with the delivery points—Pelotas Region.

Moreover, Table 1 presents information about the truck’s route on a service day, including the weight and volume of boxes, the cubage corresponding to the product volume, the total distance traveled in kilometers, and the delivery time required to serve each customer along the route. It also includes information about Boxes Stop, which indicates the number of boxes unloaded at each stop along the route.

Table 1. Route planning data—Pelotas distribution center.

Stop (Un.)	Weight (Kg)	Boxes Stop (Un.)	Km Driven (Km)	Cubage (m3)	Delivery Time (h)
1	265.87	21	76.44	22.40	01:28
2	14.16	3	0.48	0.2	00:06
3	91.49	8	0.56	6.90	00:05
4	120.38	10	1.05	9.14	00:10
	⁝	⁝	⁝	⁝	⁝
16	109.56	14	1.21	8.8	00:07
17	119.48	6	1.13	9.2	00:04
Total	4249.03	365	158.36	223.98	4:31

Table 1. Route planning data—Pelotas distribution center.

Stop (Un.)	Weight (Kg)	Boxes Stop (Un.)	Km Driven (Km)	Cubage (m3)	Delivery Time (h)
1	265.87	21	76.44	22.40	01:28
2	14.16	3	0.48	0.2	00:06
3	91.49	8	0.56	6.90	00:05
4	120.38	10	1.05	9.14	00:10
	⁝	⁝	⁝	⁝	⁝
16	109.56	14	1.21	8.8	00:07
17	119.48	6	1.13	9.2	00:04
Total	4249.03	365	158.36	223.98	4:31

The logistical impact of replacing a diesel truck with an electric truck with a load capacity of 3.5 tons and a range of 200 km was evaluated. The electric truck model used in the analysis was selected because it corresponds to the vehicle recently acquired by the company where the study was conducted. The proposed replacement meets the conditions presented for the delivery time and distance traveled. However, it is impossible to meet the product demand for the load capacity. Therefore, to implement the schedule, adjustments to cargo transportation logistics are proposed. The following alternatives can be chosen:

• Perform the route with two electric trucks for 1 route/day;
• Perform the route with one electric truck with a greater load capacity;
• Perform the route with one electric truck for 2 routes/day. It is observed that the planned kilometers would reach the autonomy limit, running the risk of the electric truck not being able to complete the proposed route.

3.1.2. Route—Lajeado Region

A truck with a load capacity of 7.47 tons travels the simulated route for 1 route/day, departing from the distribution center in Lajeado to the delivery destination in the cities of Lajeado, Santa Clara do Sul, and Mato Leitão. Based on the information in Table 2, the truck begins its route with maximum load capacity, making deliveries at 18 stops, as illustrated in Figure 3. Deliveries prioritize customers with delayed orders, represented by red for more than 3 days and yellow for 1 day, and green indicates that the delivery is within the established deadline.

Figure 3. Route with the delivery points—Lajeado Region.

Figure 3. Route with the delivery points—Lajeado Region.

Table 2 presents detailed information about the truck route.

Table 2. Route planning data from the Lajeado headquarters.

Stop (Un.)	Weight (Kg)	Boxes Stop (Un.)	Km Driven (Km)	Cubage (m3)	Delivery Time (h)
1	3203.59	315	1.29	271.36	01:02
2	1130.4	48	1.77	99.98	00:16
3	98.14	7	14.16	7.99	00:24
4	524.04	34	1.29	39.52	00:13
	⁝	⁝	⁝	⁝	⁝
9	266.4	20	4.43	20	00:10
10	137.86	11	11.59	10.38	00:22
	⁝	⁝	⁝	⁝	⁝
17	154.96	18	1.85	12.27	00:08
18	1058.9	74	18.11	89.82	00:37
Total	7318.26	621	66.39	633.16	04:18

Table 2. Route planning data from the Lajeado headquarters.

Stop (Un.)	Weight (Kg)	Boxes Stop (Un.)	Km Driven (Km)	Cubage (m3)	Delivery Time (h)
1	3203.59	315	1.29	271.36	01:02
2	1130.4	48	1.77	99.98	00:16
3	98.14	7	14.16	7.99	00:24
4	524.04	34	1.29	39.52	00:13
	⁝	⁝	⁝	⁝	⁝
9	266.4	20	4.43	20	00:10
10	137.86	11	11.59	10.38	00:22
	⁝	⁝	⁝	⁝	⁝
17	154.96	18	1.85	12.27	00:08
18	1058.9	74	18.11	89.82	00:37
Total	7318.26	621	66.39	633.16	04:18

When replacing a diesel truck with an electric truck with a load capacity of 3.50 tons and a range of 200 km, it is found that the delivery time and mileage of the proposed vehicle meet the requirements. However, it is impossible to achieve full fulfillment of the cargo demand.

Similar to the previous route, some adjustments in logistics need to be considered to achieve the desired result:

• Perform the route with two electric trucks for 1 route/day;
• Perform the route with one electric truck with a higher load capacity;
• Perform the route using one electric truck for 2 routes/day, including a return to the distribution center to reload products. It noted that only a logistical adjustment can make it possible to provide customer service.

3.2. Operational Feasibility of Fleet Replacement

The study aims to demonstrate that replacing the diesel truck fleet with electric trucks can be a feasible alternative from an operational perspective for the last mile, particularly in urban freight transportation. To evaluate this, the JAC Motors iEV1200T electric truck (Grupo SHC, located in São Paulo, Brazil) was selected due to its 200 km range [27]. The truck is a 3/4 model with a maximum speed of 90 km/h, suitable for freight transportation in urban regions.

The recharger model defined for this study is an EDP SMART Wall Box with a type T2 plug, 7.4 kW power, 220 V voltage, and an alternating current of 32 A [28].

Truck in Operation

The JAC Motors iEV1200T electric truck was used in an evaluation period to conduct tests to analyze the behavior during freight transportation. The vehicle was stationed at the Lajeado distribution center, a place assigned to recharge the battery at night and load the products scheduled for deliveries.

Table 3. Monitoring of the electric truck.

Test (Un)	1	2	3	4	5
Routes	Lajeado	Lajeado	Estrela	Lajeado	Lajeado/Estrela/ Cruzeiro
Km Road	29.93	18.11	28.16	31.06	45.46
Weight (Kg)	3372.14	3168.17	3281.18	3345.55	3385.30
Deliveries	15	15	16	23	20
km Traveled	55	40	51	45	88
Used Battery	39%	31%	30%	33%	50%

shows the results obtained for five days of operation. The kilometers traveled resulted in 279 km, with a total of 16,552.34 kg of products distributed. Additionally, it was observed that the projected mileage (Km Road) differed from the actual mileage achieved, revealing a discrepancy between the planned and the executed route, mainly due to the lack of monitoring during the journey. Consequently, based on the route traveled, it is possible to determine the percentage of battery consumption and correlate battery usage with the distance traveled.

The electric truck showed good performance for operating in urban regions. The tests were conducted in regions close to the company’s units, with fewer kilometers, precisely to meet the battery’s autonomy.

3.3. Expense Analysis

In order to develop an evaluation of fuel expenses in the truck fleet responsible for carrying out the products’ distribution, a survey of the expenses registered in the company was conducted. After that, an analysis was conducted to assess the fuel savings per vehicle and to compare the cost per kilometer traveled resulting from the fleet replacement. The data for the combustion trucks used stems from the data collected at the company over the course of a year. It is important to note that the analysis was conducted on a fleet of 61 trucks, distributed between the headquarters and the distribution centers. However, for this study, we decided to show the detailed comparison only for the headquarters.

At the Lajeado, the fleet comprises 15 trucks, totaling 476,750 km traveled.

Table 4. Cost comparison between EVs and diesel.

Truck Data			Combustion Model		Electric Model
Un	Region	Total km Driven	Supply	Consumption Average	Recharges	Consumption Average
			km Driven		km Driven
		Annual	DIESEL—R$	km/L	Electricity—R$	km/kWh
1	Headquarters	7908	R$ 14,179.07	3.43	R$ 1650.70	2.06
2	Headquarters	27,122	R$ 40,683.00	4.1	R$ 5661.39	2.06
3	Headquarters	44,719	R$ 74,330.23	3.7	R$ 9334.55	2.06
4	Headquarters	45,279	R$ 80,949.38	3.44	R$ 9451.44	2.06
5	Headquarters	33,463	R$ 64,311.70	3.2	R$ 6985.00	2.06
6	Headquarters	39,287	R$ 69,830.94	3.46	R$ 8200.68	2.06
7	Headquarters	17,925	R$ 24,389.10	4.52	R$ 3741.63	2.06
8	Headquarters	29,409	R$ 37,446.24	4.83	R$ 6138.77	2.06
9	Headquarters	34,294	R$ 59,578.56	3.54	R$ 7158.46	2.06
10	Headquarters	33,569	R$ 58,154.75	3.55	R$ 7007.12	2.06
11	Headquarters	33,013	R$ 53,149.20	3.82	R$ 6891.06	2.06
12	Headquarters	38,527	R$ 44,123.10	5.37	R$ 8042.04	2.06
13	Headquarters	24,743	R$ 44,755.72	3.4	R$ 5164.80	2.06
14	Headquarters	21,092	R$ 42,529.77	3.05	R$ 4402.70	2.06
15	Headquarters	46,400	R$ 55,517.51	5.14	R$ 9685.44	2.06
	Total	476,750	R$ 751,798.08	3.9	R$ 99,515.78	2.06

shows the cost comparison between EVs and diesel. In this comparison, we considered the cost of R$0.43 per kWh (approximately USD 0.080 per kWh) for energy supply, based on the electricity bill of the evaluated consumer unit under the free customer classification. For the cost of fueling combustion trucks, an average diesel price of around R$6.15 (approximately USD 1.14) was considered.

According to the FIPE Table [29], the iEV1200T electric truck has an average value of R$90,811.43 (approximately USD 91.19). Considering the replacement of vehicle 3, which has a total mileage of 44,719 km, with an average consumption of 3.7 km/l and an annual diesel cost of R$74,330.23 (approximately USD 13,782.42), a saving of R$64,995.68 (approximately USD 12,074.42) is estimated in refueling alone. Therefore, the investment made in the acquisition of the electric truck could be recovered in approximately 7 years and 6 months, exclusively through the reduction in fuel costs.

Sensitivity analysis is a technique used to assess how changes in input variables impact the results of a model. In the context of the price of diesel, for example, a 10% increase, raising the price to R$6.76 (approximately USD 1.26), would result in an annual cost of refueling of R$826,366.67 (approximately USD 153,846.57), which would still represent a saving of R$726,850.89 (approximately USD 135,305.08) when using an electric model. On the other hand, a 10% reduction in the price of diesel, to R$5.53 (approximately USD 1.03), would lead to an annual cost of R$676,007.05 (approximately USD 125,843.06), with a saving of R$576,491.27 (approximately USD 107,353.34) compared to the same scenario.

Now analyzing the sensitivity of the electric recharging rate, an increase to R$0.93/kWh would increase the annual cost to R$215,231.80 (approximately USD 40,086.15), reducing the savings to R$536,566.28 (approximately USD 99,909.91). A reduction in the tariff to R$0.50/kWh would reduce the cost to R$115,716.02, increasing the savings to R$636,082.06 (approximately USD 118,352.45) when using an electric model.

After developing a detailed analysis comparing the fueling costs between diesel and electric energy, we can see financial gains in replacing the fleet. Thus, considering the result of the individual savings obtained by the 61 trucks, it was possible to determine the total savings generated during the year with the fleet fueling, as shown in Figure 4.

After evaluating the fleet, it was possible to identify annual savings of R$2,254,583.83 (approximately USD 419,778.44), resulting in savings equivalent to 83.90% of the amount previously spent on fuel.

Besides generating savings with fueling, the JAC Motors iEV1200T truck model has a lower maintenance cost than conventional diesel trucks due to the absence of some components in the electric models [27].

In order to develop the evaluation of avoided expenses with maintenance in the active truck fleet, we surveyed the costs recorded by the company over the course of a year. We considered the costs with Arla 32, gearbox, radiator, air filter, oil filter, fuel filter, belts, nozzle, and injection pump, which do not exist in the electric truck. Thus, we obtained a total maintenance expense of approximately R$467,738.53 (approximately USD 87,080.21), i.e., an avoided value in an electric fleet.

Although the electric vehicle does not require the maintenance items mentioned above, it still incurs costs related to tires, brakes, suspension, and battery care. The scheduled maintenance for the iEV1200T truck costs approximately R$606.00 at 10,000 km, R$1902.00 at 20,000 km, and R$606.00 at 30,000 km [27].

3.3.1. Mitigation Cost

As proposed in the methodology, the CoM is calculated through Equation (1) and measured in R$ per mass of CO₂. The index refers to carbon emissions when replacing diesel trucks with electric ones. The emission mitigation calculation refers to the vehicle’s tank and wheel.

Initially, the variables used in the calculation were measured, considering a carbon emission intensity for electricity of 0.2 kg CO₂/kWh [16]. For every 1 L of diesel, an average of 2.603 kg of CO₂ is emitted [30]. Thus, a diesel truck with a 200 km range and an average efficiency of 3.8 km/L requires approximately 53 L of diesel, resulting in an average emission of 137.96 kg of CO₂ per truck. However, for the company with a 61-truck fleet, this results in 8415.56 kg of CO₂.

Light diesel trucks emit an average of 0.771 g/km of nitrogen oxides and 0.007 g/km of particulate matter [30]. Thus, for a light diesel truck traveling 200 km, the total emissions would be 154.2 g of nitrogen oxides and 1.4 g of particulate matter. For electric trucks, there is no direct emission of these gases, which makes them significantly less polluting in urban operation.

The calculation is performed at each company unit, as they have different energy tariffs, resulting in a variation in the CoM results, as shown in

Table 5. Mitigation costs.

Region	Costs (R$)	CoM (R$. kgCO2)
Santo Ângelo	R$ 0.45	−2.0
Caxias do Sul	R$ 0.44	−2.0
Pelotas	R$ 0.26	−2.3
Canoas	R$ 0.24	−2.3
Lajeado	R$ 0.21	−2.4

. The results obtained in CoM are negative. In other words, instead of cost, it results in savings. When recharging an electric truck, on average, R$2 is saved for each kg of CO₂ avoided in the atmosphere.

3.3.2. Fleet Replacement Proposal

In order to evaluate the impact on the company when receiving a fleet of electric trucks, we aimed to present a fleet replacement proposal. The proposal is based on a replacement in the available quantity of trucks in operation by the JAC Motors iEV1200T model. This electric truck has a 3.50-ton load capacity and is affordable for the Brazilian market.

The insertion of an electric truck fleet impacts the company’s energy demand. Therefore, we sought to analyze this impact and verify the possibility of simultaneous recharges. The expenses for recharging in each location were also analyzed since they have different energy costs and pricing, as shown in

Table 6. Energy costs.

Regulated Contracting Environment (RCE)
Group B
Santo Ângelo	Costs kWh	R$ 0.93
Caxias do Sul	Costs kWh	R$ 0.91
Group A
Pelotas	Costs kWh peak hours	R$ 2.44
Pelotas	Costs kWh off-peak hours	R$ 0.53
Free Contracting Environment (FCE)
Lajeado	Costs kWh peak hours	R$ 0.43
Lajeado	Costs kWh off-peak hours	R$ 0.43
Canoas	Costs kWh peak hours	R$ 1.15
Canoas	Costs kWh off-peak hours	R$ 0.50

.

Thus, in group A’s RCE, the average kWh costs for peak and off-peak hours are analyzed. For Group B, the average kWh cost is analyzed. For the FCE, the energy cost in kWh is analyzed, including the taxes assigned in the free market and the energy provider.

The peak-hour tariff position corresponds to 3 consecutive hours during the day, except on weekends and holidays. Additionally, off-peak hours correspond to the other complementary hours for every day of the week.

Santo Ângelo Distribution Center

The Santo Ângelo distribution center belongs to Group B, with the conventional tariff modality consisting of a single energy consumption tariff. Thus, for the analysis of recharge expenses, only the energy cost in kWh with the taxes assigned is considered for nighttime and simultaneous recharges from 6 p.m. to 7 a.m.

Therefore, a truck with 200 km autonomy and an average energy cost in kWh of R$0.93 (approximately USD 0.17) will spend about R$90.21 per recharge (approximately USD 16.79). The cost of the two daily recharges amounts to about R$180.42 (approximately USD 33.59). Considering 21 recharging days per month, the expenses would result in R$3788.82 (approximately USD 705.30).

Considering the power input at low voltage, it is possible to recharge the two trucks with 7.4 kW rechargers simultaneously. According to GED 13 [31], an installed charge of up to 75 kW must be applied for this connection. However, it is important to analyze the installed charge added to the power of the chargers before connecting because it may be necessary to adjust the power input measurement to meet a higher demand than that supported by the current connection.

Caxias Do Sul Distribution Center

The Caxias do Sul distribution center belongs to Group B, with the conventional tariff modality consisting of a single energy consumption tariff. Thus, for the analysis of recharge expenses, only the energy cost in kWh with the taxes assigned is considered for nighttime and simultaneous recharges from 6 p.m. to 7 a.m.

Therefore, a truck with a 97-kWh battery and an average energy cost in kWh of R$0.91 (approximately USD 0.17) will spend approximately R$88.27 per recharge (approximately USD 16.44). The cost of the ten daily recharges amounts to about R$882.70 (approximately USD 164.32). Considering 21 recharging days per month, the expenses would result in R$18,536.70 (approximately USD 3451.83).

However, the simultaneous loading of the unit’s ten trucks is not possible at the low-voltage power input. According to GED 13 [31], an installed charge of up to 75 kW must be applied for this connection. Furthermore, only with the connection of the ten chargers does the total power reach 74 kW, and when added to the installed charge at the site, it exceeds the established limit.

Thus, it is important to analyze the site’s installed charge and verify the maximum number of chargers that can be used for the installation. Moreover, if necessary, the power input measurement must be adjusted to meet a demand greater than the current connection can handle. Since the electrical installation capacity does not meet the fleet’s needs, part of these trucks must be transferred to the other units.

Pelotas Distribution Center

The Pelotas distribution center belongs to Group A, with a contracted demand of 55 kW and the green hourly tariff mode, which consists of a single demand tariff and differentiated energy consumption tariffs, according to hours of use, according to Law N° 10,848 (15 March 2004) [21]. In order to analyze the expenses with recharges, the energy cost in kWh with the taxes assigned for peak and off-peak is considered, along with the contracted demand for nighttime and simultaneous recharges from 6 p.m. to 7 a.m.

The replacement comprises six electric trucks, with an average energy cost in kWh on-peak of R$2.44 (approximately USD 0.45) and off-peak of R$0.53 (approximately USD 0.10). It costs about R$94.15 per recharge (approximately USD 17.53), and for the proposed trucks, the cost of the six daily recharges is about R$564.94 (approximately USD 105.22). For 21 days of recharges during the month, the expenses would result in R$11,863.74 (approximately USD 2208.93). Furthermore, considering the average value for the demand (kW) charged by the provider of R$28.91 (approximately USD 5.38), it results in an expense of R$1,283.60 (approximately USD 238.03), referring to the demand for active power made available by the distributor for the load of 44.4 kW.

Each truck requires a recharger with a 7.4 kW charge, resulting in a total power of 44.4 kW. However, according to the consumption history, the company registers an average maximum demand of 40 kW. Therefore, to meet the simultaneous recharges, it is necessary to adjust the demand contract so that the company can meet this new charge since it has a three-phase ZAGO transformer with 150 kVA power, which operates comfortably.

Canoas Distribution Center

The Canoas Distribution Center is part of the free consumer group, operating under a single contracted demand of 140 kW and utilizing the green tariff modality. This tariff structure includes a single demand charge and variable energy consumption rates based on time-of-use, in accordance with Law No. 10,848 of 15 March 2004 [21].

For the analysis of charging costs, the energy price per kWh is considered based on free market rates, along with applicable taxes from the utility provider for both peak and off-peak periods. The contracted demand is assumed to cover nighttime and simultaneous charging sessions occurring between 6:00 p.m. and 7:00 a.m.

Ordinance n° 465 [32] establishes that, after 1 January 2023, consumers with charges equal to or greater than 500 kW, served at any supply voltage, may purchase electricity through incentive or conventional energy purchase agreements. Consumers who choose to use energy with incentives from renewable sources receive discounts between 50% and 100% on the tariff for the electricity transmission or distribution system [33]. For the Canoas distribution center, the FCE is addressed with energy incentive purchase contracts, with a 50% discount on the tariff for the use of the distribution system (TUDS).

The electric truck fleet should comprise 29 trucks with 97 kWh batteries. Thus, recharging requires an idle recharger with 7.4 kW power for each truck, totaling a 214.6 kW power output.

At a single contracted demand of 140 kW, during the month of March, the highest record amounted to 92 kW for peak hours and 84 kW for off-peak hours in the evening. The contract does not meet the rechargers’ new charge demand of 214.6 kW. However, to perform the simultaneous recharges of the proposed trucks, it is only necessary to change the demands contract, with no change in the substation, because the unit has the installation of a three-phase transformer with a 500 kVA power, with the capacity to meet the new charge forecast.

The calculation of the monthly expenses with recharges considered the increased demand scenario of 214.6 kW. It considered the kWh cost and energy demand cost for the simultaneous recharging of 29 trucks for 21 days per month.

Table 7. Recharging expenses.

Expenses with Recharges
kW USD Single (with/discount)	R$	10.41105800
Cost—Demand	R$	2234.21
Energy consumed Peak (kWh) month		13,519.80
Energy consumed Off-Peak (kWh) month		45,066.00
TUSD kWh Peak + TE (ACL)	R$	1.15
TUSD kWh Out Peak + TE (ACL)	R$	0.50
Peak Cost—Energy	R$	15,547.77
Off-Peak Cost—Energy	R$	22,533.00
Total for 29 recharges	R$	40,314.98

shows the total expenses obtained.

Lajeado Headquarters

The Lajeado distribution center operates as a free consumer, with a contracted demand of 2000 kW during peak hours and 2600 kW during off-peak hours, under the blue tariff modality. This tariff structure features differentiated rates for both energy consumption and demand, depending on the time of use.

For the analysis of charging costs, the energy price per kWh from the free market is considered, along with utility provider charges and the contracted demand for both peak and off-peak periods. The analysis assumes nighttime and simultaneous charging sessions between 6:00 p.m. and 7:00 a.m.

Additionally, the FCE with energy incentive purchase contracts is addressed with a 50% discount on the tariff for the use of the distribution system (TUDS).

The study proposes replacing 14 electric trucks with 97 kWh batteries. This requires a slow-charge charger with a power of 7.4 kW for each truck, totaling 103.6 kW.

To enable simultaneous charging of the truck fleet without exceeding contracted limits, it is recommended to revise the current demand contracts. Despite this, the Lajeado distribution center has sufficient electrical infrastructure to support the expected additional load, as it is equipped with a 4 MVA transformer.

To calculate the monthly charging expenses, a scenario was considered in which the contracted demand increases by 103.6 kW during both peak and off-peak periods. The calculation considers the cost per kWh and the demand charges with the simultaneous charging of 14 trucks over 21 days in the month. The estimated cost is presented in

Table 8. Recharging monthly expenses.

Expenses with Recharges
kW USD Peak (with discount)	R$	15.55528500
kW USD Off-Peak (with discount)	R$	10.41105800
Peak Cost—Demand	R$	1611.53
Off-Peak Cost—Demand	R$	1078.59
Energy consumed Peak (kWh) month		6526.8
Energy consumed Off-Peak (kWh) month		21,756
TUSD kWh Peak/Off-Peak + TE (ACL)	R$	0.43
Peak Cost—Energy	R$	2806.52
Off-Peak Cost—Energy	R$	9355.08
Total for 14 recharges	R$	14,851.72

.

4. Discussion

As one of the alternatives to be integrated into a sustainable transportation system, electric trucks bring benefits such as energy efficiency, reduced emissions of pollutant gases, and lower operating costs. They also contribute to a sustainable future when recharged with energy from renewable sources [34].

Thus, some factors that contribute to the growing adoption of electric modes occur through advances in energy storage technologies, policies to support the mitigation of pollutant emissions, and new regulations to reduce oil consumption [35]. This includes the application of the ESG (environmental, social, and governance) approach in logistics, as well as the use of green logistics based on the United Nations Sustainable Development Goals (SDGs). Companies that adopt ESG practices are seen as more sustainable, ethical, and prepared for the future [35].

Electric vehicles (EVs) provide opportunities for urban traffic intervention strategies to improve air quality, such as the introduction of low-emission zones (LEZs). Therefore, LEZs are created to limit the access of vehicles with high pollutant emissions in specific regions. In other words, there is a strong incentive for the zones to promote the renewal of the fleet of vehicles in operation with low-emission models [36,37].

In order to seek initiatives to improve air quality, urban centers encourage road freight transport to adopt less polluting modes, such as light electric trucks. Tax incentives, as a form of indirect support, have been a fundamental tool used by governments to foster the development of new technologies and drive innovation [11,38].

Public policies and regulatory measures are essential guiding factors to encourage electrification in transportation. As an example, three important public policies in 2024 in Brazil stand out: the incidence of the Selective Tax, the development of the Mover Program, and the new Brazilian industrial policy, called Nova Indústria Brazil (NIB) [38].

The measures may include targets for the introduction of new technologies, state and municipal tax benefits, and subsidies for the manufacture, purchase, marketing, import, and ownership of EVs. In addition, they include subsidies and incentives for the adoption of charging infrastructure and for the research and development of technological solutions [38].

Typically, companies that own EVs have some charging points at their distribution centers, which are used to recharge vehicles during idle periods. However, some routes are too long for the vehicles to have enough range to complete the journeys in transit without the need for an additional recharge. Therefore, electric charging stations installed in public spaces provide a suitable option for EVs that need to recharge during the journey [39].

The charging behavior of the electric truck fleet at distribution centers is seen as predictable, since the connection points to the electrical grid are fixed and operate at predefined times, which simplifies the planning of the electrical system. With this, the demand to be met can be adapted to the energy generation, transmission, and distribution system and minimize the impact on the electrical grid [40].

Furthermore, the application of methods involving smart charging in freight transport logistics seeks to promote on-demand mobility. Smart charging aims to reduce the impact on the performance of the electricity distribution network during recharging, reduce simultaneity in charging, and optimize supply and demand, especially during off-peak hours, in addition to minimizing freight transportation costs and reducing traffic congestion [41]

Therefore, the application of electric trucks in last-mile delivery requires the assessment of vehicle autonomy, charging time, and the location of charging stations. Thus, accurate prediction of autonomy, routing focused on energy consumption, and optimization of charging stops become crucial issues for the efficient operation of EVs. Therefore, the integration of public policies, infrastructure, and logistics planning is essential to enable electrification in the last mile [3,17].

However, the expansion of electric vehicle (EV) use presents several challenges. Studies such as Olsson [1] and Juan [11] have examined strategic, operational, environmental, and routing issues faced by EV distribution fleets. The authors of [10] focused on the application of EVs in last-mile delivery logistics. Another study [39] addressed the minimum-cost EV routing problem in Beijing, while [42] analyzed energy-efficient routing strategies that also consider recharging costs. The authors of [15] evaluated the impact of EV adoption on CO₂ emissions in municipal transport in Laos and Thailand. In [43], the importance of optimization for reducing costs and maximizing profits in product distribution was emphasized. Collectively, these studies support the relevance of evaluating optimized routing strategies from both economic and environmental perspectives, reinforcing the importance of this paper for the transportation sector.

Electric trucks powered by renewable energy sources can optimize energy consumption throughout the well-to-wheel cycle, enhancing the sustainability of fleet electrification. Consequently, recharging with renewable electricity is directly associated with the reduction in pollutant emissions.

In addition to the operational, economic, and environmental benefits presented in this study, it is important to highlight that a comprehensive analysis of environmental impacts requires the performance of a life cycle analysis (LCA). An LCA considers the environmental impacts associated with the manufacture of vehicles during the operational process and the disposal and recycling processes at the end of the useful life of the components. Therefore, future studies should incorporate the LCA in order to provide a more complete understanding of the environmental footprint associated with fleet electrification. This approach ensures that the transition to electric mobility is aligned not only with operational and economic objectives but also with broader sustainability goals.

Smart charging aims to redesign the future of urban mobility by integrating transportation technologies and allowing data integration to generate valuable insights. Future research should evaluate a real-time energy tariff management system and include features for scheduling recharges.

Moreover, future studies could explore the integration of renewable energy sources, such as a solar park combined with energy storage, could be explored to support the recharging needs of electric trucks. Such an approach has the potential to reduce energy costs and achieve a shorter investment payback period.

Another important point for future research is the validation of the cost estimation model and the inclusion of sensitivity analysis. While this study applied a formula based on annual averages, future work could explore how variations in load, route conditions, and driver behavior affect fuel consumption and cost. A sensitivity analysis would help quantify the impact of these factors.

Finally, the study can help companies make decisions when evaluating the feasibility of replacing their current fleets with electric vehicles. The implementation of the project directly impacts the quality of life of the population in urban areas, mainly by reducing costs in the company’s logistics sector. These improvements have helped the company increase its market share, increasing the number of customers served, in addition to contributing to a future with sustainable transportation.

5. Conclusions

This study presented a methodology to evaluate the insertion of light electric trucks in the freight transportation of beverages. It aimed to analyze aspects such as the decrease in diesel consumption, environmental impact, tariff contracting, and the leasing of recharging stations that will impact the investments in the energy grid adequacy. We sought to collect elements for a comprehensive analysis to demonstrate the benefits of replacing diesel trucks with electric trucks for product delivery.

In the case under study, it was observed that replacing a conventional vehicle with an electric truck with a 200 km range did not meet all the criteria required to fulfill the delivery schedule, particularly in terms of distance traveled. Therefore, adjustments in freight logistics were proposed to adapt to each specific situation.

The evaluation of the route profiles showed good performance for cargo distribution in urban areas and easy handling for parking due to the size of the proposed model. However, routes planned for execution were limited to the regions near the units due to the autonomy and characteristics of the available recharging stations.

Nevertheless, according to the situations reported, we can conclude that to make electric truck technology feasible for longer-distance operations in freight transportation, fast recharging stations should be made available on highways to increase the distribution coverage region. The fast-recharging stations make the technology of larger electric trucks feasible, which can transport more products to regions farther away from the units.

Moreover, the feasibility analysis of replacing the company’s fleet proved to be feasible, as it presented significant savings in fuel and maintenance costs during the period defined for the evaluation. Comparing the fueling costs between diesel oil and electric energy, we estimated a savings of R$2,254,583.83 (approximately USD 419,778.44). It achieved savings of 83.90% of the amount spent on fuel. For maintenance expenses, referring to some items established for the study, an economy of R$467,738.53 (approximately USD 87,080.21) was obtained. Therefore, we achieved a positive result for its application, generating a total economy of R$2,722,322.36 (approximately USD 506,957.52) with the replacement of 61 diesel trucks with 61 electric trucks.

For the CoM equation regarding carbon emissions, pollutant emission reduction can be evaluated. Therefore, it is possible to reduce the expenses to decarbonize the energy matrix and simultaneously reduce pollutant emissions by adopting the electric model.

The proposed methodology for each criterion can help companies in their decision-making when assessing the feasibility of replacing their current fleets with EVs. It is worth mentioning that the methodology can be applied in other companies by simply making adaptations to the local information.