1. Introduction
Urban logistics is an essential process for moving goods in the city. It covers all processes and activities related to the delivery of a specific product. Urban logistics aims to control the effective flow of resources in the town, and subsystems following sustainable development, and to meet users’ expectations at an established level [
1]. Urban logistics contributes to added convenience in cities, minimizing costs and overcoming difficulties in the context of adverse effects leading to more air pollution, noise, or traffic jams. Urban logistics focuses on the efficient and effective transport of goods in an urban area, considering the negative impact of urban logistics on congestion, safety, and the environment, which is crucial to residents’ quality of life.
The distribution of goods to customers is a component of last-mile delivery. Because it represents the final stage, this phase is one of the most important for supply chains, as it is where the last customer is contacted. In addition, this phase is frequently used in urban areas, where problems with parking, travel times, and environmental pollution are particularly pressing.
Urban logistics and its operations are linked to a specific purpose. Urban logistics distinguishes the following objectives: technical, economic, and organizational, as presented in
Table 1.
The technical objective is based on the operation of IT processes in order to record goods and their flow efficiently. It supports the company in analyzing the delivered products, and allows it to control the flow of goods. In the case of large companies, it is a goal that is necessary for achieving specific results. The economic objective focuses on the optimization of financial costs, and the use of financial tools. The last organizational goal creates the entire logistics structure, and is responsible for implementing new technologies to improve the city’s goods flow [
2].
In response to the problems of cities and climate change, the European Union has set trends to improve the quality of urban life, including introducing electric vehicles, or improving road surfaces. The electrification target refers to the EU climate package, which is to contribute to the growth of emission-free, sustainable transport. Some of the first significant changes that introduced electric vehicles involved restaurants introducing electric scooters for deliveries. The introduced vehicles were more advantageous than combustion vehicles regarding use, costs, and technical parameters. In response to the trend of zero-emission vehicles, and the popularization of eating out, this work presents an alternative to electric scooters, in the form of UAVs, which can be more sustainable and beneficial to introduce in the city.
Electric vehicles (EVs) are increasingly becoming a sustainable and cost-effective means of transport, taking into account their lower CO
2 emissions, air pollution, and noise, compared to combustion vehicles. The research conducted in [
3] shows that China, the United States, and Great Britain are leaders in the research on electric vehicles and their large-scale applications. In addition, China is a leading country in terms of the research institutions exploring electric vehicles.
Globally, the number of electric vehicles in 2020 has increased fivefold compared to 2016. The number of electric vehicles sold worldwide has reached over 10 million, which means an increase of 43% compared to 2019 [
4]. The share of electric vehicle sales worldwide increased by 70%, reaching a record high of 4.6% in 2020, despite a decrease in registrations of conventional cars, and in total new cars [
5].
UAVs appear increasingly often in many areas of the economy. Among others, authorities use UAVs in geodesy, agriculture, rescue, transport, and control. With their efficiency and usefulness in urban spaces, drones arouse curiosity on many levels. Electricity is one of the main obstacles to unmanned vehicles’ faster implementation in urban air mobility [
6]. Many studies are being carried out concerning climate change and European requirements when planning a new form of transport, urban air mobility. Their use may contribute to replacing internal combustion vehicles in urban logistics or pollution monitoring in the city. Improvements in the technical specification of UAVs aim to popularize this form of transport. To allow the use of drones in urban logistics, quadcopters and octocopters have been developed, which, thanks to their parameters, can replace ground vehicles for deliveries. Therefore, drone model research and improvement work focuses on greater energy efficiency.
Drones, or unmanned aerial vehicles (UAVs) are aircraft whose flight operations are performed remotely, or via the onboard computer. There is neither a pilot nor passengers onboard these vehicles. UAVs can speed up delivery times, because they do not participate in traffic, and use electric motors, which are also good for the environment. However, when designing a drone-based last-mile delivery service, adverse weather conditions, complex urban scenarios, and customer identification issues must all be considered, as well as the energy aspects.
Based on the trend of smart cities, this work aims to propose an alternative means of transport for electric scooters in the city, which may be more favorable regarding energy consumption. The paper describes two models of UAV; one is a quadcopter, and the other is an octocopter; assuming scenarios and making calculations. The calculations were prepared using five energy models considered in different use cases. The models of D’Andrea, Dorling, Figliozzi, Kirchstein, and Tseng were used for calculations. Then, the energy the assumed electric scooter model would consume is compared in three scenarios related to energy consumption by drones.
The paper is organized as follows:
Section 1 provides an introduction to the research question, indicating the potential for UAV application in cities.
Section 2 deals with a review of the literature on the application of UAVs for cargo transportation, with a special focus on the problem of energy consumption.
Section 3 characterizes urban air mobility, which is an important concept in terms of urban mobility. A description of the current status of ongoing projects and prospects for development, based on defined goals, is included. The application of electric scooters in the process of food delivery is presented, as well as the technical specifications of the adopted UAVs, and assumptions for the area of meal delivery. Adopted scenarios are presented, taking into account the weight of the UAV and the possible transport distance, as well as a description of the energy consumption models, and their application in the adopted models and scenarios.
Section 4 presents a comparative analysis of the energy consumption of UAVs and electric scooters during food delivery in the city.
Section 4 provides a comparative analysis of the adopted scenarios, considering delivery by UAVs and electric scooters. Finally,
Section 5 summarizes the discussion of this work.
3. Material and Methods
The food delivery process in urban logistics involves different stages of implementation to the transport of people or products. In delivering meals, the time and the quality of the received product count. Meal delivery includes the determination of delivery services, especially meal delivery and grocery delivery. Meal delivery consists of ready meals and food ordered directly online for use. Grocery delivery includes unprepared food, beverages, household items, and personal care products. The delivery process includes food delivery directly from the restaurant, and online delivery services that provide customers with meals from partner restaurants (the platform provider). Grocery delivery includes the process of delivering fresh, unprepared produce from supermarkets or retailers (retail delivery).
Figure 1 presents an upward trend in ordered meals and groceries.
3.1. Electric Scooters in the Process of Delivery
Micromobility in the city’s transport system includes the use of small and light means of transport to cover short distances. Due to their small size and ease of movement around the city, these means of transport have gained tremendous popularity. Electric scooters are gaining particular popularity. The Statista report shows how the growth of electric scooters is taking place, and describes the forecast for the coming years.
Figure 2 describes the number of electric scooters in Poland. Based on data from the Statista platform, there is, and will be, an upward trend of almost twofold over two years.
Analyzing the available data on food delivery platforms led to the selection of a company that uses electric scooters. The data show Poland’s leading apps with free food delivery in February 2023. The most popular are Glovo, Pyszne.pl, Wolt Delivery, and Bolt Food.
Figure 3 shows that Pyszne.pl and Glovo are at the forefront of the most popular applications in Poland. Due to this result, we have used the technical parameters of an electric scooter from Pyszne.pl, which has four models of scooters in its fleet, including two models of electric scooters, and two models of diesel scooters. One model of the Robo SC electric scooter is used for the calculations. Based on the information in
Figure 3, an electric scooter from Pyszne.pl will be used for the comparative analysis of energy consumption by UAVs and selected means of transport.
Table 2 shows the technical parameters of the scooter. The technical specification, visual effects, and adaptation to use in logistics determined the choice of this model.
3.2. Unmanned Aerial Vehicles
Unmanned aerial vehicles (UAVs) comprise an innovative technology commonly used in various professional sectors. From the design point of view, drones can be divided into the following groups: military, industrial (entrepreneurship), and commercial.
In most cases, drone flights are unregulated, and flying is only allowed over selected areas. UAVs are a wide topic in terms of legal regulations. They require adaptation on many levels, for example, of infrastructure, urban planning, and the environment. Regarding the popularization and increasing use of drones, the legal issues involve constant analysis and fine-tuning.
The quadcopter, otherwise known as a classic UAV, is the most popular and versatile drone, due to its specifications and simplicity. Quadcopters have become widespread and used for a variety of purposes. The quadcopter is a heavier-than-air aircraft capable of vertical take-off and landing (VTOL), propelled by four rotors parallel to the ground.
Due to the basic model and smaller dimensions, this type of drone is described as smaller in the calculations. Based on the technical specifications,
Table 3 shows the assumed parameters.
An octocopter is a model of a drone with eight engines. Given the extensive form of octocopters, they enable complete control over the system and maximum performance. In terms of quality and functionality, it is unrivaled. Due to the necessity of comparing the two models, this type of drone shows as the larger one, with better technical specifications.
Table 4 presents the assumed parameters for the octocopter. Due to the larger dimensions and different batteries, the data present different values for the number of rotors, batteries, and surfaces.
3.3. Study Area of Delivery
Deliveries in downtown areas are a very complex process due to the diverse and contradictory nature of the demand, the area’s structure, and the delivery point’s density. The significant number of vehicle-kilometers that freight vehicles travel between logistics centers in suburban areas to consignees in the city center, in addition to often having to drive around to find parking, result in additional fuel consumption and traffic congestion.
Determining the delivery area involves many necessary factors. Due to the high importance of the delivery time, the meal delivery area cannot consider a remote area. According to the Pyszne.pl guide, the average distance should be 4 km. As a result of other variables, such as expanding housing estates and emerging office buildings, the range may increase slightly. In addition, the guide from Pyszne.pl justifies its maximum distance of 4km to the customer via the high importance of residents near the restaurant. By focusing on remote areas, the supplier would not be able to return quickly, which might be at the expense of people living near the restaurant [
55].
The delivery area for electric scooters refers to the above-mentioned 4 km. Concerning cities, there are no restrictions, and they are only excluded from traffic-free zones, usually found in the city center. The delivery area for electric scooters has no prohibitions that restrict the delivery process in the city.
The delivery process regarding drones includes the take-off from the base, the flight to the customer, sending the package, and the return to the warehouse. Various cases should be defined to determine the delivery area, including the energy consumption, breakdowns, and repair points. Currently, cities are not adopting the delivery of parcels or food via drones. The urban infrastructure does not have vertiports that will allow free landing. In addition, the lack of legal regulations still does not allow the designation of the supply area.
Additionally, due to their size and technical parameters, UAVs move at much lower heights, which can affect parks, reserves, and farms [
56]. Scenarios form the basis of data, right after the technical parameters of UAVs used in the calculations. Using scenarios allows for better specifications of energy models in various cases. Using two models of UAVs and three scenarios improves the assessment of energy models and final results.
Determining the parameters of a small quadcopter and a large octocopter, they differ in some values that will be useful to determining the scenarios. The total payload of the drone is the basis for determining the data in three scenarios; because the meal is delivered, it is the main element, and its weight is essential.
Due to its small dimensions and worse technical specifications compared to a large octocopter, a small quadcopter is used only in scenario no. 1. The payload of the quadcopter is 0.68 kg. Therefore, the weight of the meal included in scenario no. 1 is 0.5 kg. In addition, the maximum delivery area is described in the Pyszne.pl guide as 4 km. Therefore, scenario no. 1 assumes the shortest distance, of 1.5 km, out of all the scenarios.
Scenario no. 2 contains parameters that differ significantly in weight from scenario no. 1. The assumed weight is 5 kg, the highest payload of all three scales defined in the three scenarios. Specifying a greater weight of food delivered would not reflect accurate deliveries. A weight over 5 kg is probably relatively rare for people delivering food. The distance specified in scenario 2 is 2.7 km, the second-longest distance out of the three scenarios considered. The second scenario only assumes a large octocopter with a maximum payload of 7 kg. Scenario 3 defines a more significant distance to cover than in the two previously described scenarios, 4 km. Scenario 3 uses only the octocopter model for calculations. The assumed weight that the UAV transports for food delivery is 4 kg.
Table 5 shows, in a table, all the scenarios described above. The assumptions consider the type of drone, the distance to be covered, and the weight.
Each model of calculation requires a mass of components. It consists of the mass of the drone body, the battery, and the payload.
Due to the quadcopter’s maximum payload of 0.68 kg, this drone was used only in scenario no. 1. Considering the assumed scenarios, the following component weights were calculated for the quadcopter and octocopter, which are presented in
Table 6. Based on the calculated component weights, the smallest quadcopter has the lowest weight, due to its technical parameters. Then, the weights of the octocopter vary according to specific scenarios. In scenario no. 1, the weight of the goods was the lowest, which meant that the weight of the octocopter component was the lowest of all the scenarios, at 10.5 kg. Then, in scenario no. 2, it was 15 kg, and in scenario 3, it was 12 kg.
3.4. Energy Models
The following subsection shows the energy requirements described in several models. It describes the course of calculations, and presents the results. All models use the assumed scenarios, and consider the energy consumption in three specific cases. Five energy consumption models in different use cases were used for the calculations.
Various energy models for UAVs are available online. Juan Zhang developed some of these models, by assuming his data using selected formulas [
57]. The formulas study energy consumption based on different types of drones, and then compare models. Most of the models used have a similar calculation scheme and use similar values. The D’Andrea model describes a model that combines aerodynamic and design aspects into one parameter that takes into account the ratio of lift to drag, including in the energy model an element of constant power supply to the avionics [
58]. Figliozzi extends the model described by D’Andrea to include empty turns without avionics, to include a parameter for the battery charging efficiency, and to model the lift-to-drag ratio as a function of the speed. Dorling provides the power consumed in a hover as a function of the battery, payload, and weight. The energy consumption model uses elemental forces based on the force of gravity (due to gravity) and the drag force. Kirchstein uses an energy model that is more elaborate than other models [
59], separating the energy for the take-off, climb, steady flight, descent, hover, and landing for delivery. The last approach to modeling drone power consumption is the Tseng model, which uses the battery mass and speed. Tseng only considers drones moving up to 5 m/s.
Considering several models, it is possible to notice the calculation process leading to the final calculation of the energy. Some calculations use the thrust model and power, and some do not extend their models and give the final formula for energy.
Table 7 shows the relationships between the models in calculating the energy needed to fly the drone.
Some models that provide the final formula for energy do not break down the acquired data in stages.
Table 8 presents the components and factors considered by individual models in the calculations.
3.4.1. D’Andrea Model
The D’Andrea model considers the thrust, the energy needed to cover a certain distance, the energy required from the battery for stable flight, the energy necessary for avionics, the power consumption, the energy required for level flight, and the energy for the headwind.
Three scenarios were used for the calculations, taking into account different weights of goods and distances to be covered. The thrust force is based on the mass (mk), gravity (g = 10 m/s
2), and lift/drag ratio (r = 3). Based on the above data, the formula for the thrust is described as:
Using the formula for the thrust force in the D’Andrea model, we obtain the results presented in
Table 9.
The calculation of the power consumption necessary to maintain a stable flight, including the electronics operation regarding the weight of the drone, takes into account the mass of components (
mk), the force of gravity (
g = 9.8 m/s
2), the power consumed by the avionics (
= 0.1 J/s), the ratio of lift to drag (
r = 3), and the efficiency (
n = 0.5). The resulting formula, based on D’Andrea’s calculations, is:
Based on the D’Andrea formula concerning the power,
Table 10 shows the results obtained. Considering the obtained results, the small quadcopter requires significantly less power, due to its specification.
Based on the results obtained, the energy formula consists of the power (
P) obtained in
Table 11 divided by the speed (
v):
Table 11 shows the results obtained via the basic D’Andrea energy model.
The D’Andrea model also describes the formula for the energy, with the inclusion of headwinds. For this reason, the ratio of the headwind (
= 8.33 m/s) to the speed of the drone (
v) is calculated by:
After calculating the headwind coefficient, the quadcopter eliminates the negative value caused by the headwind speed being more significant than the maximum speed of the drone. The coefficient indicating the headwind for the octocopter is 0.46. On this basis, the calculation of the energy taking into account the headwind can be calculated using the formula:
Table 12 presents the results obtained after taking into account the headwind. Analyzing the obtained results, the energy, including the headwind, is much higher, which, in the case of the large octocopter in scenario 1, is as much as 94% more energy than in the D’Andrea energy model without the headwind.
3.4.2. Figliozzi Model
Figliozzi, in his energy model, describes an equation considering empty returns in a UAV after delivering a meal to a customer. He does not take into account the power consumed by the avionics (
Pavio = 0), models the ratio of lift to drag depending on the speed (with
r(
v)), and provides a unitless parameter for the battery-charging efficiency (
ηr).
The results using the Figliozzi model are given in
Table 13. The obtained results are close to those of the D’Andrea energy model.
3.4.3. Dorling Model
The Dorling model provides a hovering power that depends on the battery. It uses forces based on gravity as well as sag, so the airspeed is zero, and the thrust balances the force.
The force thrust in the final formula consists of the acceleration due to gravity and the weight of the components.
Table 14 describes the results where the thrust of a large octocopter is slightly superior. Based on helicopter theory, Dorling developed a formula for the required power.
Table 15 describes the result of the power required by drones, where n is the number of rotors, and ς is the area of the spinning blade disc of one rotor. In the calculation, the parameter ς is assumed to be the same value for the octocopter and quadcopter.
The energy formula is the same as for the D’Andrea model, consisting of the power [
P] and the UAV speed [
]. After taking into account all parameters, the formula looks like this:
Table 16 shows the final energy required by drones via the Dorling model.
Based on the results obtained, in
Table 16, the energy in the Dorling model resulted significantly lower than in the D’Andrea model. This is due to the significantly different formula for power, which considers the number of rotors and the surface of the propeller.
3.4.4. Kirchstein Model
A Kirchstein model considers an idealized delivery process, with the take-off, climb to cruising speed, level flight, descent, hover, landing, and delivery. The return takes place without a load. It takes into account the energy used for the induced power, parasite power, and profile power. In addition, it includes the power for climbing, avionics, and corrections related to power loss to the electric motor, transmission efficiency, and charging.
After transformation, where
va = 0, we are left with the following formula:
Table 17 shows the results of the force thrust via the Kirchstein model. Considering other models, the obtained results are the same as those obtained via the Dorling model. Both models use the same dependence.
The Kirchstein model presenting the required energy consists of several parts. The first term takes into account the induced power, including the factor “K” (the lifting power) and “w” (the downwash coefficient). The “w” factor can be determined using the formula for thrust contained in the D’Andrea model.
The next part contains the air density, the drag coefficient of the drone component k [unitless], the projected area of the drone component k [
and the speed of the drone. Based on Kirchstein’s article, the CDk and Ak coefficients were calculated based on assumptions for small and large drones. The third and fourth parts deal with the power of the profile, where the constants κ2 and κ3 reflect the details of the rotors and the environment. The last term in the equation reference refers to avionics.
Table 18 presents the results of the Kirchstein model. Due to the similar assumptions of the coefficients, the results for the large drone do not differ much.
3.4.5. Tseng Model
Tseng presents his model as a nine-period non-linear regression model. It considers the horizontal and vertical speeds, acceleration, load weight, and wind speed. After reduction, the formula is as follows:
The Tseng model takes into account the energy consumption equation at speeds of up to 5 m/s, which is why we only classify the small quadcopter. The result for the small quadcopter is presented
Table 19.
5. Discussion and Conclusions
Cities are implementing changes towards sustainability, and implementing the concept of sustainable mobility. Urban air mobility is a concept that uses eVTOL and UAVs. The article indicates a research problem concerning the type of electric transport (scooters/UAVs), and demonstrates which has a lower electricity demand when delivering food from restaurants to individual customers. For this purpose, an energy efficiency analysis using unmanned aerial vehicles and electric scooters to transport takeaway food was carried out, which is a solution that fits into the zero-emission transport policy. Calculations were used to carry out a comparative analysis of energy consumption for three adopted scenarios related to the energy consumption by drones. The analysis method used the energy models of D’Andrea, Dorling, Figliozzi, Kirchstein, and Tseng. This paper compares the energy consumption of UAVs and electric scooters in urban logistics. The research problem justifies the theory that drones can be more beneficial than electric scooters in delivering food. We assumed the technical parameters based on the technical specification data, and adopted some data based on the size of the drones. Calculations of energy consumption were made, considering energy models. We created scenarios that allowed us to determine whether the variability of weight or distance significantly affected the final result. The final analysis of drones compared with electric scooters shows the dependencies in the energy models. The paper shows how energy models differ from each other. The equation for the D’Andrea and Dorling energy models is the same, but the upstream calculation is significantly different, due to previous calculations, such as the power and force thrust. Determining the lift-to-drag ratio r, and the power transfer efficiency η, without making measurements, may be crucial to determining unmanned aerial vehicles’ energy consumption. The developed results show how the lack of taking into account of one factor, e.g., a headwind, can affect the final result. The results obtained via the models determined how many parameters are still needed to unify a specific energy model. Nevertheless, preliminary formulas and attempts to determine energy use show results favoring urban air mobility. Investment in specialized control systems and navigation and control systems in the future may help control energy consumption. In addition, adapted infrastructure in the form of vertiports and better adaptation batteries are also necessary elements in air traffic.
The conducted analyses and research in the implementation of UAVs are important from the point of view of the accessibility of this type of transport for the population. Public transportation is heavily crowded during peak hours, and UAV implementation for food delivery can cover a wide range of delivery options. UAV deployment is the future in the suburbs because, as described in [
55], people do not always want to use services, due to limited transportation availability. The solution of food delivery with a UAV to the suburbs of the city could be extremely important for the population. It is worth mentioning that the methodology adopted in this study can only be applied if relevant data are available for UAVs and electric scooters. The results of this study may be helpful to policymakers and stakeholders in evaluating the implementation of food transport UAVs for individual customers.
Future work should extend the models described in the paper with additional information that will be investigated or determined. More significant development of UAVs and infrastructure for urban air mobility can significantly change the existing ambiguities, and introduce changes in the perception of some aspects. Important analyses should be carried out in the development of smart, sustainable, and safe vehicles, in terms of creating an aircraft with low operating costs (which will be beneficial, justifiable for customers, and profitable for companies), and producing low noise levels. Proper technical support and serviceability are required for the smooth operation of unmanned aerial vehicles. An important aspect is air traffic management, which must take into account the infrastructure, legal regulations, and planning of airspace available for UAVs.