Comparison of energy demands of drone-based and ground-based parcel delivery services

https://doi.org/10.1016/j.trd.2019.102209Get rights and content

Highlights

  • Introduction of detailed energy demand model for drones.

  • Hovering and wind conditions affects range of parcel delivery drones seriously.

  • Simulation study on energy demand of electric and Diesel trucks as well as drones.

  • Electric trucks require least energy and produce least GHG emissions in most situations.

  • Drones are competitive in rather rural settings (large distances, low customer density).

Abstract

Drones are one of the most intensively studied technologies in logistics in recent years. They combine technological features matching current trends in transport industry and society like autonomy, flexibility, and agility. Among the various concepts for using drones in logistics, parcel delivery is one of the most popular application scenarios. Companies like Amazon test drones particularly for last-mile delivery intending to achieve both reducing total cost and increasing customer satisfaction by fast deliveries. As drones are electric vehicles, they are also often claimed to be an eco-friendly mean of transportation.

In this paper an energy consumption model for drones is proposed to describe the energy demand for drone deliveries depending on environmental conditions and the flight pattern. The model is used to simulate the energy demand of a stationary parcel delivery system which serves a set customers from a depot. The energy consumed by drones is compared to the energy demand of Diesel trucks and electric trucks serving the same customers from the same depot.

The results indicate that switching to a solely drone-based parcel delivery system is not worthwhile from an energetic perspective in most scenarios. A stationary drone-based parcel delivery system requires more energy than a truck-based parcel delivery system particularly in urban areas where customer density is high and truck tours are comparatively short. In rather rural settings with long distances between customers, a drone-based parcel delivery system creates an energy demand comparable to a parcel delivery system with electric trucks provided environmental conditions are moderate.

Introduction

Drones are regarded as one of the technological innovations which may trigger a revolutionary reshaping of transportation industry. This is mainly caused by the prospect of quick, cheap, and flexible deliveries which complies with current trends in the transport industry (Joerss et al., 2016). Therefore, a lot of pilot projects have been launched to exploit the potentials of drones in logistics applications. Examples of large companies experimenting with drones are Google, DHL, and Amazon (Goodchild and Toy, 2018). Next to large-scaled projects of multi-national firms, also smaller-scaled projects of drone delivery systems have been successfully put into practice (Haidari et al., 2016).

Technically, drones are referred to as Unmanned Aerial Vehicles (UAVs) in most official and scientific documents. This term refers to the fact that UAVs are flying vehicles without accompanying pilot. There exist numerous models of UAVs for logistical applications. For logistical applications, payload and radius of operation are the most important technical parameters. At the moment, primarily models with payload capacity up to 5 kg are used (Wang, 2016, Joerss et al., 2016). However, also heavy-load UAVs are available with a payload capacity of up to 40 kg (Multikopter, 2019).

Payload and radius of operation are interdependent as the energy demand depends on the payload. Other important factors influencing the energy demand are weather conditions and traveling speed. E.g., (D’Andrea, 2014) shows that the energy consumption of drones increases drastically in case of head wind conditions. The energy demand and the UAV’s battery capacity determine the radius of operation.

Additional technological issues to be considered in logistical UAV applications are the launching and landing concept as well as the autonomous control capability. Meanwhile, most commercial UAV models also provide fully automatic launching stations Scott et al. (2017). For B2C concepts, however, the UAV must be able to land on ‘rough’ ground or some detachment technology like ropes (Flirty, Google) or parachutes (Zipline) must be available. In a B2C application scenario, it is reasonable to assume that UAVs have to wait hovering until all prerequisites for detaching the cargo are fulfilled (e.g., waiting for a clear detachment area or the customer’s approval).

As electricity-powered vehicles, UAVs are often claimed as efficient and eco-friendly (Goodchild and Toy, 2018, Amazon, 2019). In this paper, some light is shed on this claim considering a particular application scenario. In the following a stationary parcel delivery system is considered where customers are served from a central depot. Parcels can be delivered either by conventional Diesel trucks (DVs), electric trucks (EVs), or UAVs. To assess the three means of transportation, the total energy consumption for serving all customers is calculated. Based on the energy demand, the resulting environmental effects in terms of greenhouse gas (GHG) emissions are derived. The paper is organized as follows: Section 2 briefly reviews the relevant literature on logistical applications of UAVs. In Section 2 the energy consumption models for DVs, EVs, and UAVs are presented. Section 3 outlines the simulation settings and planning procedures for calculating the energy demand of the parcel delivery processes using DVs, EVs, or UAVs. The simulation results are analyzed in Section 4 before the paper closes with a conclusion.

Section snippets

Literature review

In recent years, there is an exploding body of literature on potential application scenarios of drones. Otto et al. (2018) provides an extensive overview about civil applications of UAVs. In the following, we focus on logistics applications where the UAV’s task is to deliver cargo to customers. Typically, it is assumed that only one customer can be served on a UAV trip. Counterexamples can be found in case of urgent and small pieces of cargo like blood samples or drugs (see Scott et al., 2017,

Energy consumption models of DVs and EVs

Calculating the energy demand of ground-based delivery vehicles and the associated GHG emissions has attracted a lot of attention in the scientific community. Most energy consumption models rely on the physical formulation of the power demand for moving a point of massPM=Proll+Pair+Pgrade+Pinert=g·croll·ν·m+ρ·cair2·103·A·ν3+g·i·ν·m+nacc·0.5042·103·3.6·v3·mwhere m denotes the mass, i is the road grade, v and ν the speed (in km/h and m/s, respectively), A the frontal surface area, cair the

General assumptions

In the following, a comparative simulation study for stationary parcel delivery services with UAVs, DVs, and EVs is outlined. Thereby, a number of assumptions is made which are summarized in Table 1.

In the subsequent computational experiments, the region of the city Berlin is considered. If parcels are supplied by UAVs in a stationary delivery system, a set of depots needs to be determined. As outlined above, a radius of operation of 9 km is assumed for UAVs. Thus, when setting up an

Energy consumption

Fig. 8 displays the total WTW energy demand of DVs, EVs, and UAVs depending on the traffic conditions, the radius of the customer area, and the number of customers per stop.

Fig. 8 shows that the total WTW energy demands of DVs and EVs depend on customer radius and traffic conditions, whereas the numbers of customers per stop plays no role. As expected, EVs always consume less energy than DVs in total. For low and medium traffic congestion, DVs require about 40–50% more energy than EVs. When

Summary

The analyses shown in this paper highlight that drones show some disadvantages regarding energy efficiency when used as parcel carriers. As parcel delivery drones are multicopters designed to cover large distances, they show a limited energy efficiency when hovering such that spending even short times hovering generates noticeable energy demands affecting a drone’s radius of operation seriously. Wind conditions have a similar impact on a drone’s radius of operation. When compared with

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