Paving the Way for Sustainable UAVs Using Distributed Propulsion and Solar-Powered Systems

Abstract

Hybrid systems offer optimal solutions for unmanned aerial platforms, showcasing their technological development in parallel and series configurations and providing alternatives for future aircraft concepts. However, the limited energetic benefit of these configurations is primarily due to their weight, constituting one of the main constraints. Solar PV technology can provide an interesting enhancement to the autonomy of these systems. However, to create efficient propulsion architectures tailored for specific missions, a flexible framework is required. This work presents a methodology to assess hybrid solar-powered UAVs in distributed propulsion configurations through a two-level modeling scheme. The first stage consists of determining operational and design constraints through parametric models that estimate the baseline energetic requirements of flight. The second phase executes a nonlinear optimization algorithm tuned to find optimal propulsion configurations in terms of the degree of hybridization, number of propellers, different wing loadings, and the setup of electric distributed propulsion (eDP) considering fuel consumption as a key metric. The results of the study indicate that solar-hybrid configurations can theoretically achieve fuel savings of up to 80% compared to conventional configurations. This leads to a significant reduction in emissions during long-endurance flights where current battery technology is not yet capable of providing sustained flight.

1. Introduction

Current environmental policies promote the development of greener designs for many industrial sectors, and aviation is not an exception to this worldwide trend. Accordingly, aircraft design has gradually evolved to implement technologies that reduce fossil fuel consumption and CO₂ emissions. For instance, the European Commission is driving innovative technologies aimed at eliminating CO₂ and promoting sustainable solutions to capture carbon dioxide, thus achieving net-zero emissions. This involves quantifying, monitoring, and verifying CO₂ removal to prevent ecological whitewashing and become the world’s first climate-neutral continent by 2050. The European Union is also advancing the measures package called “Objective 55”, which seeks to reduce greenhouse gas emissions from the transportation, buildings, agriculture, and waste sectors. These measures aim to decrease these gas emissions by at least 55% by 2030 [1,2]. Following the same trend, ICAO and its member states aim to reduce CO₂ emissions by 5% by 2030 through the implementation of Sustainable Aviation Fuels (SAF), Low-Carbon Aviation Fuels (LCAF), and other cleaner energy sources, thereby achieving zero carbon emissions by 2050.

Current policies have spurred the adoption of zero-emission technologies in aviation, with electric propulsion standing out for its low carbon and noise emissions, efficient energy transmission, and overall performance. However, challenges lie in the energy storage system, as the current limitations of commercial lithium-ion batteries, such as their low energy density, pose a significant constraint for high-performance aerial designs [3,4]. On the other hand, UAVs equipped with internal combustion engines stand out for their high energy density and power, making them suitable for missions with extended flight times and the ability to carry significant loads. However, it is important to note that, despite these advantages, internal combustion engine UAVs can generate noise, greenhouse gas emissions, and toxic pollutant emissions, which may restrict their use in sensitive areas [5,6].

In this context, the hybrid-electric propulsion system (HEPS) emerges as a more compelling alternative, as it combines the advantages of traditional propulsion with electric propulsion. This results in a more efficient aerial propulsion system in terms of energy, autonomy, and a reduced environmental footprint [7]. All of this is due to the high flexibility provided by this type of propulsion, allowing for efficient management of available energy sources at each stage of flight [8].

These systems are usually classified according to their architecture, in parallel, series, and a combination of both, with each configuration presenting its own features and components. Therefore, an overall propulsion architecture evaluation based on a modular assessment of components is required to examine which system is best suited for a specific application. For this aim, design space variables, limitations, and optimization objectives need to be identified in order to set up a comprehensive methodology that encompasses these hybrid configurations [9]. Hybrid-electric technology represents an intermediate step between the transition from traditional propulsion to electric propulsion.

The parallel hybrid propulsion system combines an internal combustion engine directly driving the propeller with an interconnected electric motor functioning as a generator. Excess power is captured by the electric motor, converted into electricity, and stored by an energy management system. This system releases energy flow when the internal combustion engine power is low, driving the electric motor to compensate. This design maximizes fuel efficiency and extends flight time, making it an efficient UAV propulsion system [10]. This configuration is designed to operate with the best overall system performance. In this arrangement, in the event of an electrical system failure, the full power of the internal combustion engine is available, providing a high energy efficiency compared to series hybrid propulsion systems [11].

In a series hybrid propulsion system, the combustion engine solely powers a generator, producing electrical energy directed to a storage system. An energy management system regulates the flow of electrical energy among the generator, storage device, and electric motor connected to the propeller. This design ensures a continuous operation of the internal combustion engine during UAV flight, creating an efficient and flexible energy usage system [10].

This type of propulsion offers simplicity of control and lower specific fuel consumption, as these systems operate the internal combustion engine under its optimal thermal conditions [12,13]. Figure 1 shows the conceptual designs of series and parallel hybrid configurations using distributed propulsion, where the solar panels are connected parallel to the electric subsystem to provide additional power when available. Series–parallel propulsion is a combination of the series hybrid propulsion system and the parallel propulsion system. It is characterized by the selective activation of specific propulsion modes based on flight conditions and corresponding energy demands [8].

Hybrid propulsion systems have operational synergies with various advanced technologies developed to meet the environmental expectations mentioned above, such as: (a) airframe–propulsion integration using Boundary Layer Ingestion (BLI), (b) Distributed Propulsion (DP), and (c) solar-powered propulsion, as shown in Figure 2 [14,15].

In recent years, the challenge of transitioning to hybrid technology has been addressed, with conceptual design methods developed to optimize size, performance, and flight technique. Using Computational Fluid Dynamics (CFD), Wick et al. demonstrated an 8% increase in aerodynamic efficiency with distributed propulsion compared to conventional configurations [16]. These results, irrespective of propulsion type, suggest extrapolatable benefits to distributed hybrid propulsion. Further research indicates that boundary layer ingestion can enhance propulsion efficiency and reduce losses of turbulent kinetic energy [17,18,19]. The synergy that exists between hybrid propulsion systems and distributed propulsion is a consequence of the flexibility of both systems to allocate the propellers on different parts of the airframe. This has led current research to focus on proposing preliminary design methods for distributed hybrid-electric propulsion. In 2017, Sliwinski developed a retrofit design methodology to size hybrid-electric distributed propulsion and estimated the energy consumption, showing promising emission reductions [20]. Two years later, Hoogreef proposed a preliminary sizing method for hybrid electric aircrafts with distributed propulsion, including the aero-propulsive interaction and a modified class II weight estimation tool, which is used to estimate the energy consumption using a mission analysis method. The results showed that it is possible to achieve an energy consumption reduction of up to 10% relative to conventional turbofan aircrafts [21] using this sizing method. The same year, Vries developed a preliminary sizing methodology for hybrid-electric passenger aircrafts featuring over-the-wing distributed propulsion, which showed a 2.5% weight and energy consumption reduction when compared to conventional aircrafts. Moreover, the results suggested that this method yields low-noise alternatives.

1.1. Scope and Problem Formulation

Despite the extensive research conducted on the subject, to date, no study has addressed how different hybrid propulsion architectures impact the performance of hybrid-solar electric propulsion configurations.

Therefore, this paper introduces a methodology to size a mid-sized fixed-wing UAV and assess various hybrid propulsion systems combined with photovoltaic panels, considering different wing loadings, degrees of hybridization, and varying numbers of propellers.

The aim is to identify the type of propulsion system that best suits specific flight conditions. This study focuses on a potential application scenario for monitoring the Galápagos Islands with 6-hour and 10-hour flight missions, where the proposed propulsion system would provide high autonomy with the lowest possible cost, reduced pollution, and increased efficiency in environmentally protected and sensitive areas such as these islands.

1.2. Benchmarking Assessment for Hybrid Platform Technologies

The progression and development of hybrid architecture technologies for aerial platforms is compliant with the technical limitations of full electric aerial platforms [20,22]. For this reason, it is important to review the state-of-the-art devices that promote suitable and techno-economically feasible designs for environmental and ecosystem monitoring. In the next subsections, the aforementioned components are analyzed to offer an overview of how current and future hybrid systems can perform.

1.2.1. Internal Combustion Engines (ICEs)

Over the past century, Internal Combustion Engines (ICEs) have been the primary power plant used in aviation due to the high energy density of fossil fuels. Furthermore, since the inception of aviation, one of its main challenges has been to design and develop engines with high power-to-weight ratios and lower specific fuel consumption [23]. As a consequence, there is currently a wide range of specific aircraft Internal Combustion Engines (ICEs) available in the market. However, not all ICE are suitable for hybrid propulsion systems. For example, alternative engines, rotary engines, and some gas turbine engines are the most suitable types of ICEs for hybrid propulsion systems [24,25,26,27]. In the case of gas turbines, where turbofans, turbopropellers, and turbojets are the most compatible with parallel propulsion systems, they have demonstrated thermal efficiency ranging from 40% to 50% compared to alternative and rotary engines. The disadvantages of using these gas turbines include their high cost, the need for specialized maintenance, and their overall lower performance compared to other Internal Combustion Engines (ICEs) at low speeds [28,29,30]. On the other hand, rotary engines can be used in both series and parallel configurations. These engines boast a higher power-to-weight ratio and experience fewer losses due to vibrations and friction when compared to four-stroke engines. However, they exhibit higher fuel and oil consumption and a lower thermal efficiency compared to alternative four-stroke engines [31,32,33]. Finally, alternative engines can also be used in hybrid configurations. These engines offer certain advantages, such as their low cost, a wide variety of two-stroke and four-stroke engines, low maintenance, and low complexity compared to gas turbines. On the contrary, these types of engines have a disadvantage in terms of their thermal efficiency, as it ranges between 25% and 30%, and they only demonstrate greater effectiveness at low speeds compared to gas turbines [23,34].

Figure 3 shows comparative info-graphics of ICEs in terms of their power, weight, and performance, which are main metrics in the selection of ICE systems.

1.2.2. Electronic Components and Control

Electronic Components

Electric aircrafts offer a new range of flight capabilities, but at the same time, they create new challenges for aircraft design and construction. These platforms are characterized by their low flight endurance due to the poor energy density of batteries. Additionally, the need for more components in electric propulsion systems increases the failure rate. To overcome these challenges, design efforts in electric aircrafts have been focused on four key areas: the development of advanced motor drives, the development of advanced generator controllers, the design of high specific power electric machines, and high-reliability battery packs. All of these features are integrated into smart grid architectures with redundant power sources, multiple power and communication buses, fault isolation, and complex sensing systems to mitigate hazards.

In hybrid systems, motor drives and generator controllers need to incorporate advanced control algorithms, built-in EMI filtering/bus capacitance, communications interfaces, customized fault-mode behavior, a high switching frequency for smooth operation, and generator controller modes with control algorithms tailored to regulate the DC bus of the hybrid vehicle. The generator set should be designed to maintain engine speed and bus voltage during transient events. In bi-directional power conversion, the alternator can be used as a starter motor for the engine.

High-reliability battery packs need battery management systems (BMS) that enable in-flight recharging of the battery pack in coordination with a hybrid bus and gen-set controller, measurement of cell voltages and temperatures, SOC (state of charge) measurement capability through coulomb counting, disconnection circuitry that will remove the pack from the DC bus in a major fault condition, and a CPU-enabled signal monitoring derived from the battery pack.

Energy management in hybrid aircrafts is a complex task, since different energy sources and sinks must be handled in accordance with different flight scenarios. Moreover, the power bus control system should deal with large load fluctuations due to variations in power demand, as in the case of the take-off and climb stages. To address these difficulties, the power bus should incorporate a controller that is capable of managing those multiple sources and loads, turning the power bus into a micro smart grid. The micro smart grid (MSG) concept appears as a robust and efficient solution that ensures reliability, efficiency, flexibility, and power delivery in a controlled and smart manner [35]. By using advanced metering infrastructure, real-time data, and a communication bus, the SG can identify problems in the power system and perform correction tasks, transforming the grid into a self-healing system [35]. Moreover, SG architectures are suitable environments to incorporate novel energy sources like solar cells, ultra-capacitors (UC) and fuel cells. In the case of solar cells, the system is conditioned through temperature and sunlight, so a Maximum Power Point Tracking (MPPT) control is needed, increasing the complexity of the system. On the other hand, fuel cells are a promising energy source, and their suitability has been proven in several studies. However, fuel cells have a slow dynamic response affecting the stability of the power bus [36]. As a consequence of its high energy density, supercapacitors (SC) are used in hybrid energy storage systems (HESS) to complement batteries and manage large load fluctuations. However, an adaptive and robust control system is needed to optimize the MSG and its energy storage systems for different flight stages [37].

Electronic Control

The advancement of new energy systems arises from the demand for more efficient and environmentally friendly solutions to meet the needs of the aviation industry. Recent technological advances in electronic devices have renewed interest in hybrid-electric propulsion systems, presenting them as a viable option [38]. These systems not only promote fuel consumption reductions but also offer a greater range compared to purely electric propulsion options.

Some manufacturers and institutions such as Boeing, Airbus, NASA, and Siemens are currently engaged in the development of aircrafts with hybrid-electric propulsion (HEP). These hybrid-electric propulsion systems are gaining attention due to their suitability for aircrafts with fluctuating power requirements. Additionally, they offer operational flexibility thanks to the variety of components and options available. However, it is important to note that this flexibility may entail an increase in the aircraft’s weight [8].

Strategic use and efficient management of energy are key aspects in a hybrid-electric propulsion system due to their multiple energy sources.

Current energy management is divided into rule-based control (based on practical experience and mathematical models) and optimization-based control (analytical or numerical methods), where the selection of the approach depends on specific needs. Fuzzy logic control (FLC) also helps manage electric power distribution, achieving flexible and adaptive control, which can be combined with other energy management strategies (EMS), improving energy distribution and reducing fuel consumption [8].

Optimization-based control EMS is divided into global optimization (dynamic programming and Pontryagin’s minimum principle) and real-time optimization methods (model predictive control, reinforcement learning, and equivalent minimum consumption strategy) [39].

Dynamic programming is a more ideal and optimal energy management algorithm, but with a longer calculation time, while Pontryagin’s minimum principle (PMP) has a shorter calculation time but lower optimal control performance [39].

The algorithm of Model Predictive Control (MPC) has robustness and is suitable for nonlinear and uncertain dynamic systems. Reinforcement Learning (RL) is an intelligent control algorithm used in energy management in hybrid agricultural UAVs. The Equivalent Consumption Minimum Strategy (ECMS) achieves minimum consumption, a good performance, and stable output power, making it an optimal tool for hybrid systems. However, it has the limitation of monitoring and reviewing the battery state of charge (SOC) [8,39].

Despite increasing complexity in operation and design, hybrid-electric propulsion, with a management system operating the engines close to their maximum efficiency conditions and proper energy storage control, can meet environmental requirements and achieve reductions in energy consumption [8].

1.2.3. Electric Motors

Electric motors, when compared to internal combustion engines, do not produce CO₂ emissions, making them an essential technology for meeting the current environmental goals of aircrafts [8]. Electric motors used in aerospace applications need to comply with certain criteria, such as high torque/weight and torque/ampere ratios, high efficiency throughout the operational range, and a set of requirements derived from regulations and standards. For this reason, the selection of adequate electric motors in electric and hybrid-electric aircrafts is a key design step [40]. Electric motors can be classified in many ways; in this case, they will be classified according to the type of operating current, resulting in direct current (DC) motors and alternating current (AC) motors. Among the AC motors, only certain types, such as induction motors and switched reluctance motors, are most useful for hybrid propulsion due to their control simplicity [25]. Hybrid propulsion systems, being a combination of two types of propulsion, must meet essential characteristics for their application. One of the most common requirements is to have a high power-to-weight ratio and outstanding overall efficiency [8]. In the design of electric motors (EMs) for aircraft propulsion, overall efficiency is a fundamental criterion. Research and development of superconducting materials (SMs) are underway with the aim of applying them to the manufacturing of electrical components. This research seeks to reduce the weight of the motors, decrease electrical resistance, and ensure a broad critical temperature range. While this range still presents limitations for certain applications, high-temperature superconducting materials (HTS) emerge as a promising option due to their broader critical temperature range. This makes them suitable for implementation in hybrid electric aircraft propulsion, leading to the creation of both conventional and unconventional superconducting machines [8].

1.2.4. Batteries

Energy storage plays a fundamental role in supporting hybrid electric propulsion (HEP). Batteries are the most well-known and widely used form of energy storage, although tests are also being conducted with fuel cells and supercapacitors [8]. The feasibility of implementing electric propulsion systems for air transport depends on the improvement of the energy storage capacity and the technologies of electrical components. Currently, there is a variety of devices for energy storage, which are selected according to the energy supply system implemented in the UAV. In the specific case of solar-powered unmanned aerial vehicles (SPUAV), lithium batteries are highly suitable due to their high energy density, high voltage, wide temperature range, and continuous operational capacity [41]. Fossil fuels have a specific energy density of approximately 12,000 Wh/kg, while lithium polymer (Li-pol) batteries have a density of approximately 250 Wh/kg. This clearly reflects that batteries provide energy with about 50 times less specific energy than liquid fuels. However, in recent years, there has been an observed improvement in the storage capacity of batteries and other devices [8].

Figure 4 shows the development of electric power sources and fuels over time. The specific energy density of batteries improved very little between the years 2001 and 2018 [42]. Al-Air and Air-Al batteries have a higher specific energy density compared to other battery technologies. However, these batteries are still classified as an emerging technology due to many limitations in their use [43]. Therefore, the use of hybrid UAV systems has emerged as a solution to the fact that battery technology is not yet advanced enough to provide sufficient flight autonomy, efficiency, and low weight.

1.3. Solar-Powered Vehicles

The progressive evolution of solar-powered vehicle technology has enabled significant improvements in panel efficiency. From conventional polycrystalline and monocrystalline silicon-based panels to the use of inorganic nanoparticles in conjunction with organic nanomaterials, the development of solar technologies has facilitated and enabled the inception and progression of a broad spectrum of possible applications. Figure 5 shows the advances of solar PV efficiency for different technologies [44,45,46,47,48].

Figure 5 shows data reported in the literature for laboratory-developed solar panel efficiencies. The figure also shows projections up to 2030. Though optimistic, these projections show rapid advances in newly developed solar technologies. Traditional silicon-based solar panels may be reaching the Shockley–Queisser efficiency limit in the following years, but thin film and third-generation tandem and multi-junction technologies are not subject to this theoretical limitation and have been quickly reaching efficiencies similar or even higher than traditional single-junction cells. At the moment, multi-junction cells present power-to-cost ratios that are too high to be commercially attractive. However, they have been shown to be useful in aerospace applications, where higher power densities are desirable [49]. For this reason, despite the fact that these advances are mostly research-oriented, they may be paving the way for advances in aerospace technologies.

1.4. Solar-Powered Unmanned Aerial Vehicles

Although most UAVs are powered by internal combustion engines due to their greater power and high energy density, this type of propulsion has a significant drawback: the environmental pollution caused by the emitted gases. Additionally, the fuel used comes from petroleum, which is a non-renewable resource [41]. This has driven the use of alternative energies, such as solar energy, which represents a novel option. Thanks to technological advances and growing environmental awareness, this type of energy is experiencing a resurgence [50]. This type of energy has yielded excellent results in UAVs. For example, the Autonomous Systems Lab at the Swiss Federal Institute of Technology Zurich developed a solar-powered UAV that achieved a flight mission of 81 hours, covering a distance of over 2000 km. This project demonstrated the feasibility of solar-powered UAVs, which are capable of perpetual flights in suboptimal meteorological conditions [51].

Since solar energy is not always available, these systems must use a storage method; commonly batteries. This has driven research into better storage systems, such as solid-state and lithium–sulfur batteries, which offer higher energy densities [41]. Another important aspect of these systems is the power system components, such as Maximum Power Point Trackers (MPPT) and the battery management system, which regulate the energy flow and provide detailed information about its status [51].

2. Methodology

Figure 6 shows a general outline of the steps followed to study the potential impact of series, parallel, and solar hybrid systems in the fuel consumption of long-endurance unmanned vehicles. The figure shows the different subsystems used to design a generic hybrid configuration, where different interconnection schemes between the main routine blocks, the design space variables, and outputs of the system for concept generation are indicated. This study considers the basic mission requirements for a long-range monitoring flight, such as flight speed, altitude, and endurance, coupled with the definition of a few operational and aerodynamic characteristics such as payload capacity, aspect ratio, RoC, preliminary efficiencies, and other lifting surface parameters. These parameters were taken for a NACA 2412 airfoil and are shown in

Table 1. Input parameters.

Parameter	Symbol	Value
Maximum lift coefficient	$C_{L_{max}}$	1.2
Maximum lift-to-drag ratio	$L/D_{max}$	15
Zero angle of attack lift coefficient $C_{L_{\alpha }=0}$	0.25
Zero lift drag	$C_{D_0}$	0.024

. This study was performed for a range of flight times between 6 and 10 h, considering fixed-wing aircrafts of variable wing loadings and variable numbers of propellers and electric motors (distributed propulsion). The endurance requirement was defined considering the distance between a set of islands in the Galapagos archipelago. Additionally, the influence of distributed propulsion architectures was factored in by performing efficiency and system weight calculations for different numbers of propulsive devices. This was performed on the basis of previous work, where the correlation between power and weight was estimated for different powertrains and powertrain components [52,53]. The algorithm performs an initial constraint analysis by determining the required power loading for all allowable wing loading conditions under the given conditions Figure 7. Then, the initial masses of the main propulsive components are determined using a set of correlating equations presented in this section. Through an iterative optimization routine tailored for each hybrid architecture, the algorithm determines the maximum electric energy input to the system and the total solar energy generated (for series and parallel solar-hybrid models) to calculate the minimum fuel requirement for a mission.

The general structure of the sizing module is shown in Figure 7. The steps the algorithm takes to compute the weight and fuel consumption data for each wing loading/DoH input are as follows:

• The powertrain is sized to meet the maximum power requirement of the wing loading configuration. Figure 7 is a reference to the sizing of a solar parallel-hybrid powertrain. In this step, a preliminary fuel mass is estimated based on the mission requirements. This preliminary fuel mass is taken as the theoretical maximum fuel consumption of a conventional (non-hybrid) powertrain. It is also worth noting that there is a direct influence of the distributed propulsion on the estimation of the powertrain size in this step based on the required power from the electric motors.
• An initial estimation of the mass of the battery pack is performed based on the available weight.
• In solar configurations, the available solar power curve throughout the mission is calculated based on the available wing surface, which is a function of wing loading and MTOM. A total of 90% of the wing surface is assumed to be available for solar panel installation. Solar power has an impact on charge and discharge times, as it is assumed to provide energy continuously to the batteries when available.
• Charge and discharge times are estimated as a function of the available battery energy within the limits of the state of charge (SoC) and the power consumption during the mission. These timeframes are used in a later step to determine the total time during which the ICE is operating at its maximum load and under its limit load.
• The charge and discharge cycles are then optimized to maximize charge and minimize the number of complete discharge cycles. This is accomplished by analyzing the mission energy profile and estimating the ideal minimum and maximum SoC the battery should reach before each recharge and discharge cycle begins, respectively. This is done to maximize the total energetic output of the batteries, which in turn reduces the amount of excess fuel needed for the mission.
• The total fuel mass is estimated based on the power requirements of the mission.
• The updated available weight (reduced fuel mass) enables the estimation of a new allowable battery mass.
• The iteration error is calculated between the updated battery mass and the initial battery mass estimate. When this error reaches an acceptable value (0.01), the configuration is considered acceptable. This is based on the observation that a low enough error means that a balance between the electric and combustive power sources has been reached.
• If the error is below an acceptable value, the updated battery mass is taken as the initial estimate, and the process is repeated.

2.1. Design Constraint Definition

The design space limitations were determined for take-off, climb, and cruise conditions using the following equations from references [54,55]:

$${\left({\displaystyle \frac{P}{W}}\right)}_{TO}={\displaystyle \frac{\mu -(\mu +\frac{C_{D_G}}{C_{L_R}})\left[exp\left(0.6\rho g{C}_{D_G}{S}_{TO}\frac{1}{W/S}\right)\right]}{1-exp\left(0.6\rho g{C}_{D_G}{S}_{TO}\frac{1}{W/S}\right)}}$$

(1)

$${\left({\displaystyle \frac{P}{W}}\right)}_{RoC}=V_{RoC}\left({\displaystyle \frac{V_V}{V_{RoC}}}+{\displaystyle \frac{q}{W/S}}{C}_{D_{m}in}+{\displaystyle \frac{k}{q}}{\displaystyle \frac{W}{S}}\right)$$

(2)

$${\left({\displaystyle \frac{P}{W}}\right)}_{cruise}=V_{cruise}\left(q{C}_{D_{m}in}\left({\displaystyle \frac{1}{W/S}}\right)+k{\displaystyle \frac{1}{q}}{\displaystyle \frac{W}{S}}\right)$$

(3)

These equations were used to obtain the design space (power loading vs wing loading) for the selected analysis characteristics, which in turn was used to obtain the power requirement of each wing loading configuration and size the powertrain accordingly.

Figure 8 presents a graphical representation of the missions. In addition, the top-level requirements and mission description are shown in

Table 2. Mission requirements.

Requirements		Mission	Hybrid	Hybrid-Solar
Take-off ground roll	110 m	Take-off	At MSL	At MSL
Rate of climb	4.5 m/s	Climb	To 2000 m	To 5000 m
Stall speed	15 m/s	Cruise	6–10 h	6–10 h
Payload	40 kg	Cruise velocity	25 m/s	32 m/s
		Descent and landing	To MSL	To MSL

. All the performance requirements are stated for standard day conditions.

2.2. Propulsion Modeling

The propulsive device is one of the main components of the propulsion system. For this reason, an initial sizing of the propulsive device was performed to obtain an estimate of the propulsive efficiency and thrust generation. This was used to determine the specific fuel consumption of a given design point. The size of the propellers can be approximated based on the approach given by Raymer [56]:

$$D_{\mathrm{propeller}}=K_p\sqrt[4]{P_{\mathrm{prop}}}$$

(4)

where $K_p$ is the coefficient for the number of blades and $P_{\mathrm{prop}}$ is the power needed by the propeller. The preliminary efficiency of the propellers was obtained following the iterative procedure outlined in ref. [55], where the efficiency is a function of the number of propellers, prop diameter, flight speed, and max power.

Propulsion System Integration

Figure 1 presents the efficiency diagrams for parallel and series propulsion systems. This study uses fixed efficiencies to determine the energy delivered to each component. In the case of fuel, Specific Fuel Consumption (SFC) is used. Higher-order methods can be used in this stage, but this process would increase computational cost significantly.

For the modeling of the parallel systems, the combustive propulsion system was sized for maximum power requirements. This was done to ensure continuous power provision during flight without the necessity of excessively massive battery packs. This allows us to obtain feasible designs for a wider range of wing load configurations. The battery packs were then sized for increasing fractions of the maximum power to evaluate the effects of the degree of hybridization on fuel consumption, where the degree of hybridization is the ratio of combustive power to total installed power. The DoH then ranges from 0.5 to 1 for the parallel configurations. One of the biggest challenges of using parallel-hybrid system relies on the fact that coupling the ICE output to the propellers and the electric motor can prove to be difficult. The added weight of the coupling devices and the complexity of the power control systems make parallel hybrid systems less attractive. Combining parallel-hybrid configurations and distributed propulsion architectures add a new level of complexity to these systems, since special gearboxes are required to couple a single output shaft to multiple propulsion devices. This can be achieved through multiple-output linear drives that connect multiple output shafts using a special arrangement of bevel gears or certain complex arrangements of planetary gears. This can be heavy and expensive.

However, when running this modeling scheme for both parallel and series configurations it was found that using the same power model used in the parallel system during the sizing of the series systems resulted in fuel consumption figures that were narrowly comparable to those obtained in the parallel configurations, since the only considerable difference between the systems was the added weight provided by the generator and higher efficiency losses in the electrical components of the series hybrid. For this reason, and considering that one of the advantages of series configurations is the ability to operate the combustion engine at high-efficiency points, the series system uses a different approach to component sizing.

For the modeling of series systems, the electrical subsystem was taken as the main propulsive power plant of the UAV, where the battery pack was sized to be able to provide the maximum power requirement, and the total number of cells in series and parallel was determined according to the total energy consumption of the mission and the energy provided by the ICE and the solar cells. In this arrangement, the DoH ranges from 0 to 0.5. However, for series-hybrid systems that do not integrate solar panels, this modeling scheme results in configurations that require oversized propulsive components that exceed MTOM requirements. For this reason, only series-solar schemes were analyzed. It is also worth noting that MTOM was taken as a fixed value due to the logistics and transportation limitations existing for equipment shipping to the Galapagos. The maximum MTOM was defined as a function of the payload requirement based on an analysis of the typical empty weight ratio values of light aircrafts.

To determine the fuel savings for each flight condition, the following calculations were performed:

• An initial estimation of the total fuel consumption of a conventional propulsion system (ICE only) was determined as the product of the power required at each flight condition, the total flight time of said flight condition, and an average value for the specific fuel consumption of a two-stroke engine taken from ref. [52].
• In the parallel-hybrid configuration, the total fuel consumption during each flight condition was analyzed, considering the charge and discharge cycles of the electric subsystem. In other words, the total time of flight during which the ICE operates either at a maximum load during charge (maximum required power for sustained flight at a given flight condition and excess power used to charge the batteries) or shares the power requirement with the electric subsystem during discharge is calculated for each flight condition.
• In the series-hybrid configuration, the electric subsystem is sized to provide power throughout the mission without needing to charge the battery pack mid-flight. Charging is performed only during flight conditions where the ICE has an excess of power it can provide to the battery pack, and the ICE provides a share of the total power required for sustained flight defined by the DoH.
• In the solar configurations, the influence of the solar panels in the power output of the batteries was considered in the computation of charge and discharge times. The energy management of the electric subsystem of the parallel-solar configuration was assumed to perform in such a way that an optimum charge/discharge cycle is determined during cruise flight at a given operational point to maximize the period of time during the mission in which the batteries are operational. This minimizes the time the ICE provides the total power required for sustained flight. Additionally, the discharge time of the batteries (time it takes the battery to reach its minimum SoC) is maximized due to the fact that additional power is being fed to the electrical subsystem.
• The total fuel consumption of the mission was calculated in the different hybrid configurations considering the previous points, and the fuel savings were calculated as the ratio between the fuel consumption of the hybrid systems and the fuel consumption of the conventional system.

2.3. Battery System Model

As mentioned previously, hybrid propulsion systems combine the advantages of conventional and electric propulsion systems. In this context, hybrid systems seek to reduce the size of the battery pack while increasing endurance [24]. Nevertheless, batteries are still a key component in hybrid systems, since they supply a large part of the energy required by the aircraft during the take-off and climb phases, as well as all the energy required by other electric components for control, navigation, and data acquisition [57]. This study uses battery packs consisting of several series and parallel arrangements of cells according to flight requirements. The mass of the battery pack (5) and (6) is determined by estimating the energy demand in each flight phase to determine the number of cells required based on the correlations presented by our research team in reference [53]:

$$M_{\mathrm{Cell}}=A·C_{\mathrm{cell}}^B$$

(5)

$$M_{\mathrm{Batt}}=M_{\mathrm{Cell}}·{\displaystyle \frac{E_{\mathrm{Batt}}}{E_{\mathrm{Cell}}}}$$

(6)

where A and B are coefficients for unitary cell weight with values of 0.0446 and 0.9273, respectively [53].

2.4. ICE Modeling

This work considers the use of a four-stroke reciprocating engine, since this type of engine is easy to purchase, maintain, and integrate into different hybrid configurations. However, these engines also present certain limitations such as a loss of power with increasing operation altitudes. For this reason, this study uses the Gagg and Ferrar equation to determine the engine’s power fall-off as a function of air density. Then, the ICE is sized using the reference (7) presented in ref. [53]:

$$M_{ICE}=T·{P_{ICE}}^U$$

(7)

where T and U are coefficients for ICE weight (See

Table 3. Weight of ICE engines. $M_{ICE}=T·P_{ICE}^U$ [53].

	T	U
Two-stroke	0.0003	1.0530
Four-stroke	0.0013	0.8952

).

Solar Irradiation Model

In order to estimate the generated solar power, the Bailey and Bower model was used, which considers flight level, latitude, day of the year, and time of day:

$$P_{produced}=I_b·{\eta }_{solar}·S_{solar}·Sin\left(\alpha \right)$$

(8)

where $I_b$ is the power of solar irradiation per unit area, ${\eta }_{Solar}$ is the efficiency of the photo-voltaic generation system, $S_{solar}$ is the surface covered by solar panels, and $\alpha$ is the solar altitude angle, as shown in Figure 9 and Equation (9).

$$Sin\left(\alpha \right)=Cos\left(L_{lat}\right)·Cos\left(H\right)·Cos\left(D_{angle}\right)+Sin\left(H\right)·Sin\left(D_{angle}\right)$$

(9)

In this equation, L is the latitude, D is the declination angle, and H is the solar hour angle, defined as follows:

$$H=\left({\displaystyle \frac{t}{24}}\right)·360$$

(10)

$$D_{\mathit{angle}}=23.5·sin\left(360·{\displaystyle \frac{d}{365}}\right)$$

(11)

In these equations, t is the time difference in hours between the required time and 12 p.m., and d is the difference in days from 20 March (vernal Equinox).

To estimate the available irradiation, it is necessary to calculate the transmittance using Equation (12), which is the ratio between the extraterrestrial radiation and solar radiation that passes through the atmosphere.

$$T=0.5·\left(e^{-0.65m(z,A)}+e^{-0.095m(z,A)}\right)$$

(12)

Here, $m(z,A)$ is the air mass at altitude z above sea level, which is estimated using the following equation:

$$m(z,A)=m(0,A)·\left[{\displaystyle \frac{p\left(z\right)}{p\left(0\right)}}\right]$$

(13)

In this equation, $p\left(z\right)$ is the atmospheric pressure at altitude z, and the air mass at sea level, $m(0,A)$, can be calculated using the following equation:

$$m(0,A)={\left[1.229+{\left(614·SinA\right)}^2\right]}^{0.5}-614·SinA$$

(14)

Therefore, the surface irradiation at altitude z is as follows:

$$I_b=I_0·T$$

(15)

3. Results and Discussion

In this section, the proposed methodology is evaluated using hybrid distributed propulsion configurations as a baseline to design preliminary hybrid-solar propulsion systems, which are incorporated in order to analyze their applicability in hybrid configurations as a means of enhancing autonomy and efficiency in environmental monitoring missions.

3.1. Model Validation

The first part of the methodology was used and validated in a previous work [52], which presented a comparison between parallel and series hybrid systems. Similarly, the data presented by Panagiotou [58] are used to verify the performance of the solar model. To validate the functionality of the solar system and its influence on the entire sizing model, fuel consumption and solar energy collection were compared to the results obtained in ref. [58].

The characteristics of the airframe and the mission proposed in this study are presented in

Table 4. Airframe and mission requirements in [58].

Parameter	Unit	Value
MTOM	Kg	370
Payload	Kg	50
Flight altitude	km	7
Endurance	h	48
Flight speed	km/h	70
Stall speed	km/h	27
W/S	N/$m^2$	1.51
AR	–	19
Airfoil	–	FX 63-137

. These arguments were input into the algorithm, and the results are presented and contrasted in

Table 5. Model validation.

		Proposed Sizing Model	Panagiotou [58]	Error (%)
Fuel weight	Kg	101.52	105	3.31
Solar energy collected	kWh	107.75	113.5	5.06

.

The agreement between the two methods is good, and the difference between the results is expected for a parametric model.

3.2. Design Space Analysis

The first step to determine the possible aircraft configurations to be considered in the sizing routine was to perform a constraint analysis for the take-off, climb, and cruise flight stages. Figure 10 shows the resulting curves. The basic aerodynamic characteristics of the aircraft and wing airfoils, as well as the operational conditions of the mission, were the inputs used to obtain the graphs. In this figure, lines A, B, and C represent the wing loadings at which the maximum power requirements transition between the cruise, climb, and takeoff flight phases, respectively.

The first portion of the curve was disregarded during analysis due to the high power requirements of the cruise phase in this initial range of values for low wing loading configurations. These configurations result in a high fuel consumption and battery pack size, which increases the weight in an excessive margin. The upper limit for the values of the horizontal axis was set according to stall limitations. The highest value of power loading for each wing loading input was taken to determine the maximum power requirements and size the powertrain (ICE, batteries, electric motors) to meet said requirements.

3.3. Hybrid-Electric

This section presents the comparison between parallel and series hybrid propulsion systems using three, five, and seven propellers, which are the baseline for sizing the solar systems. As mentioned regarding propulsion system integration, the series hybrid model did not provide enough data for a meaningful analysis and comparison. This was because the modeling approach for a series-hybrid setup alone could not generate sufficient power to support extended flights in long-duration missions while staying within the maximum takeoff weight (MTOM) limits for most of the analyzed data points.

Parallel-Hybrid

Figure 11a,b shows the percentage of fuel saved by the parallel hybrid configuration for different numbers of propellers (np) and wing loading (W/S) values when decreasing the degree of hybridization (DoH) for 6 and 10 h missions. The degree of hybridization (DoH) is defined as the ratio of the total power provided by the battery compared to the total power required. The results show that fuel consumption is considerably higher for configurations with higher wing loadings, particularly at lower DoH (higher battery power), regardless of the increased mass of the electrical system.

When comparing the difference between using three, five, and seven propellers, it was found that the fuel required for each configuration was almost the same across the different missions, though the consumption of the five-propeller systems in general tended to be slightly higher than that of the other two configurations. However, the difference was almost negligible. This may be due to the fact that the simplified equations used to calculate the preliminary efficiencies and diameters of the propellers in this design stage were unable to adequately capture the impact of distributed electric propulsion in fuel consumption. Figure 12 shows an evaluation of the SFC of each configuration as a fraction of the SFC of a conventional system. The transition between lines A, B, and C has an effect over the power management strategies and the sizing routines used in the model, and this is reflected in the fuel consumption and consequently in the trend of the relative SFC curves. The same trend observed in the previous figure can be seen in more detail in this plot, with higher wing loadings yielding lower fuel consumption.

3.4. Solar-Hybrid

This section presents the sizing of the solar hybrid electric system using the hybrid propulsion systems of the previous section as a baseline.

3.4.1. Solar Generation

Figure 13 shows the available and collected solar power per unit area at different latitudes and times of day. The solid lines represent the solar power collected by the system. Figure 13a shows the solar power during the winter solstice (21 December) and summer solstice (21 June), as well as during the spring equinox (20 March) and autumn equinox (around 22 September). These days correspond to the moments with the lowest and highest solar intensities, respectively. A slight difference of almost 15% was observed in the received solar power, while the generated energy in both cases showed a minimal variation of approximately 10% during the solstice and equinox. Furthermore, it is noteworthy that the received solar power was nearly seven times greater than the generated power. This phenomenon is attributed to various factors, such as the efficiency of the solar panel, energy losses, the type of solar panel, and climatic conditions, among others. Figure 13b illustrates solar power at different latitudes on 21 June, during the summer solstice in the northern hemisphere and the winter solstice in the southern hemisphere. This study relies on the minimum solar power at a latitude of −10 on that date to ensure continuous solar energy generation throughout the year.

Figure 14a depicts a 24 h flight mission requiring a cruise power of 1 kW/m², where less than 30% of the received solar power is converted into generated power from 6 a.m. to 6 p.m. This configuration helps reduce the fuel consumption required by the internal combustion engine by approximately 10% to 20% throughout the flight. Figure 14b illustrates a 24 h flight mission requiring a cruise power of 0.5 kW/m², where less than 30% of the received solar power is converted into generated power during available sunlight hours. Using this configuration, fuel consumption savings of approximately 20% can be achieved throughout the flight.

The aim of implementing solar panels is to improve the endurance of the hybrid systems or reduce the amount of power required from the combustion engine, therefore lowering fuel consumption and emissions.

3.4.2. Series-Solar Hybrid

The analysis of the series configuration revealed trends similar to those of the parallel hybrid. The curves are depicted only within ranges where feasible designs can operate without exceeding weight limits while maintaining flight for the required mission duration. The results in Figure 15 show that for lower DoH values, where the fuel power plant is smaller and battery packs are larger, fuel savings are higher, as expected. On the other hand, fuel consumption increases significantly when using larger power plants. However, achievable configurations seem to be limited for lower DoH requirements, where only a small region produces feasible designs.

This becomes more obvious in Figure 16, where the datapoints for DoH values lower than 0.3 were insufficient to produce mensurable results. It is worth noting that for series-solar configurations, the influence of wing loading is not as direct on the SFC fraction as in the parallel configuration. This is because lower wing loading values offer the advantage of providing more space for solar panel installation.

3.4.3. Parallel-Solar Hybrid

Once analyzed, the results from the parallel-solar configuration show that this configuration is able to yield the best fuel savings when compared to the other models. Figure 17a,b show that for missions of lower durations and high wing loading values, it is possible to operate almost without using the combustion engine. However, it should be noted that in reality, even though stall constraints may be satisfied at high wing loading values, the small wing area necessary to satisfy the wing loading values of the right-most region of the graph may not be realistic for component allocation (motors, propellers, cells, etc.).

As in the series-solar analysis, Figure 18 shows a trend unlike that of the parallel model, where a high fuel efficiency point is clearly discernible in most of the curves at a point different than the highest possible wing load.

Taking these attributes into consideration, and considering that no significant improvement is clear when increasing the number of propellers, a wing loading of 175 was taken as a point of analysis to determine how the endurance requirement of the mission affects a three-propeller configuration. This configuration was taken due to the increased complexity and cost of installing five or seven propellers. The results shown in Figure 19 show that increasing this requirement has a very small effect on the SFC of the system when compared to a conventional configuration.

4. Conclusions

The present work contributes to the field of mid-sized UAVs by introducing a comprehensive methodology for their preliminary sizing equipped with a hybrid distributed propulsion system, with the potential for integration into solar-electric systems. This methodology addresses the influence of the degree of hybridization, which plays a pivotal role in determining the optimal size of each propulsion system component.

One of the most noteworthy findings of our study is the substantial impact of the solar module on UAV performance. When considering series-hybrid designs, we observed that the inclusion of solar panels could yield working designs tailored for high-endurance missions. These designs also demonstrated remarkable fuel savings, reaching reductions of up to 60% compared to conventional combustion-only configurations. This enhancement in fuel efficiency shows the potential of solar hybrid electric propulsion systems for UAVs, especially when extended mission durations are required.

In the case of parallel-hybrid designs, the integration of solar panels also yielded notable benefits. UAVs equipped with parallel-hybrid systems demonstrated an increase in fuel savings ranging from 10% to 20% compared to their non-hybrid counterparts. Moreover, the most striking finding in this context was the potential to achieve fuel reductions of up to 80% when compared to conventional systems. This remarkable level of efficiency improvement is especially appealing for missions that demand extended operational periods while minimizing fuel consumption and environmental impacts.

While both series and parallel hybrid systems demonstrated superior performance compared to conventional propulsion systems, it is worth noting that parallel hybrid systems consistently outperformed series hybrid systems in our analyses. This outcome is in line with expectations, as the series hybrid system was modeled to operate through larger electric motors and an additional generator, which contributes to increasing the weight and reducing system efficiency. The superior performance of parallel hybrid systems further emphasizes the importance of selecting the right hybridization approach to maximize UAV efficiency for a given set of mission requirements. However, it must be pointed out that parallel hybrid systems tend to require more complex control systems and mechanical arrangements, which often translates to higher costs and increased operational complexity.

To further enhance the comprehensive design of hybrid propulsion systems for UAVs, we recognize the need for a subsequent study that incorporates an aerodynamically specialized module. Such an extension would provide a more holistic perspective to the propulsion system design process, taking into account the critical role of aerodynamics in UAV performance optimization.

The methodology presented in this work represents a significant step forward in the field of UAV hybrid propulsion system design. The integration of a solar module, along with the consideration of hybridization degree, provides a powerful tool for tailoring UAV propulsion to meet specific mission requirements efficiently. Our findings suggest that hybrid propulsion systems, particularly parallel configurations, hold great promise for achieving exceptional fuel savings, performance improvements, and reduced environmental impacts.