Experimental Evaluation of Electric UAV Powertrain Systems: Comprehensive Review of Propeller Performance, Efficiency, and Flight Endurance

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Unmanned Aerial Vehicles (UAVs) have become indispensable platforms across numerous civilian and military applications, including aerial surveillance, precision agriculture, infrastructure inspection, disaster response, environmental monitoring, and logistics. As UAV missions become increasingly demanding, improving propulsion system efficiency has emerged as one of the most important engineering challenges. The reviewed study investigates how commercially available propeller designs influence the performance of an electric UAV powertrain while maintaining identical batteries, electronic speed controllers (ESCs), and brushless DC motors. Through a comprehensive experimental evaluation involving thirty-two propeller configurations, the research establishes practical performance benchmarks for thrust generation, power consumption, efficiency, endurance, and thrust-to-mechanical power ratio. Rather than relying solely on theoretical modelling, the study provides experimentally validated evidence that supports mission-oriented propeller selection for electric UAV systems and contributes valuable engineering data for future propulsion system optimization.

Bibliographic Information

Item Information
Article Title Experimental Evaluation of Electric Powertrain System for Unmanned Aerial Vehicles
Article Type Research Article (Open Access)
Authors Srikanth Goli; Dilek Funda Kurtuluş; Imil Hamda Imran; Taiba Kouser; Abdulrahman Aliyu; Luai M. Alhems; Azhar M. Memon
Journal Engineering Reports
Volume 8
Issue 1
Publication Year 2026
Article Number e70565
Received 22 January 2025
Revised 22 September 2025
Accepted 7 December 2025
DOI https://doi.org/10.1002/eng2.70565
Official Article View on Wiley Online Library
Publisher John Wiley & Sons Ltd.
License Creative Commons Attribution License (CC BY)
Open Access Yes
ISSN (Online) 2577-8196
Keywords efficiency; endurance; pitch; power; propeller; thrust; UAV

Research Background

Electric propulsion systems have become the dominant choice for modern multirotor and vertical take-off and landing (VTOL) unmanned aerial vehicles because they provide high controllability, low maintenance requirements, and environmentally friendly operation. The performance of these systems depends on the interaction between four primary components: the battery, electronic speed controller (ESC), brushless direct current (BLDC) motor, and propeller. Among these components, the propeller directly converts mechanical power into aerodynamic thrust, making its geometry one of the most influential factors affecting flight endurance, payload capacity, and overall energy efficiency.

Previous studies have investigated various aspects of UAV propeller performance, including aerodynamic efficiency, Reynolds number effects, computational fluid dynamics (CFD), propeller geometry optimization, experimental thrust measurements, and motor–propeller matching. These investigations have significantly expanded the understanding of small-scale propeller aerodynamics. However, many available datasets evaluate only a limited number of propellers or focus primarily on theoretical modelling rather than comprehensive experimental benchmarking under standardized testing conditions.

Selecting an appropriate electric powertrain remains challenging because hundreds of commercially available motors, batteries, ESCs, and propellers can be combined into thousands of possible configurations. Experimentally evaluating every combination is impractical due to the considerable time, financial cost, and laboratory resources required. Consequently, UAV designers often rely on manufacturer specifications or isolated experimental studies that may not accurately represent real operational performance.

Recognizing this challenge, the authors concentrated on one of the most influential variables within the propulsion system—the propeller. By maintaining identical batteries, ESCs, and BLDC motors while systematically varying only the propeller, the study isolates the contribution of propeller geometry to overall powertrain performance. This approach provides engineers with realistic experimental evidence while avoiding the complexity associated with simultaneously changing multiple propulsion components.

The experimental investigation evaluates thirty-two commercially available propellers ranging from 18-inch to 30-inch diameters. The propellers differ in diameter, pitch, material composition, surface finish, winglet configuration, and folding mechanisms. Each configuration is tested using the same Flight Stand 50 dynamometer under controlled laboratory conditions, enabling direct comparison of thrust generation, rotational speed, electrical power consumption, endurance, efficiency, and thrust-to-mechanical power ratio across a broad range of operating conditions.

Beyond generating performance data, the research seeks to bridge the gap between theoretical propulsion optimization and practical engineering design. Instead of proposing a new propeller design, the study develops an experimentally validated performance database using commercially available hardware. Such information is particularly valuable for UAV developers who must select propulsion components for applications requiring different operational priorities, including long-endurance missions, heavy payload transportation, vertical take-off capability, or maximum propulsion efficiency.

Research Objective

The primary objective of this research is to experimentally evaluate how variations in commercially available UAV propellers influence the performance of an electric powertrain system while maintaining identical batteries, electronic speed controllers, and brushless DC motors. The investigation focuses on quantifying the contribution of propeller geometry to propulsion performance using standardized laboratory testing procedures.

More specifically, the study aims to:

  • Experimentally evaluate thirty-two commercially available UAV propellers under identical testing conditions using a standardized electric powertrain configuration.
  • Investigate the influence of propeller diameter, pitch, material, surface finish, winglet configuration, and folding mechanisms on thrust generation across various motor speeds.
  • Measure and compare important propulsion performance indicators, including thrust, electrical power consumption, rotational speed (RPM), endurance, efficiency, thrust-to-weight ratio, and thrust-to-mechanical power ratio.
  • Develop a comprehensive experimental performance database that assists engineers in selecting mission-specific UAV propulsion systems using commercially available components.
  • Identify propeller configurations that maximize endurance, propulsion efficiency, and thrust production while highlighting engineering trade-offs among competing performance objectives.
  • Provide experimentally validated guidance that supports practical UAV powertrain optimization for applications requiring different payload capacities, endurance levels, and vertical take-off performance.

Why This Research Matters

Although UAV propulsion has been widely investigated, relatively few studies provide comprehensive experimental comparisons involving dozens of commercially available propellers under identical operating conditions. This research addresses that gap by generating a standardized performance benchmark that can be directly applied during practical UAV design and powertrain selection.

  • Provides real experimental evidence. Unlike studies relying primarily on numerical modelling or manufacturer specifications, this work experimentally evaluates thirty-two commercially available propellers using a calibrated dynamometer under controlled laboratory conditions.
  • Supports mission-specific UAV design. Different UAV missions require different propulsion priorities. Some applications emphasize maximum thrust, whereas others prioritize endurance or electrical efficiency. The study demonstrates that no single propeller is universally optimal for every mission profile.
  • Reduces engineering uncertainty. Engineers frequently face uncertainty when selecting among numerous commercially available propellers. The experimentally validated performance database provides objective comparisons that facilitate evidence-based engineering decisions.
  • Improves propulsion efficiency. By quantifying relationships among propeller geometry, thrust generation, electrical power consumption, and endurance, the study contributes practical knowledge for improving overall UAV energy utilization.
  • Highlights engineering trade-offs. The findings demonstrate that maximizing thrust does not necessarily maximize endurance or efficiency. Understanding these trade-offs enables designers to select propulsion systems that align with specific operational requirements rather than relying solely on peak performance values.
  • Bridges theory and engineering practice. The investigation translates laboratory measurements into practical engineering recommendations that can immediately support UAV manufacturers, researchers, and system integrators working with commercially available propulsion hardware.
  • Creates a valuable experimental reference. The comprehensive benchmarking dataset establishes a foundation for future studies involving computational modelling, machine learning, propulsion optimization, and advanced UAV system development.

Research Methodology

The study adopted a controlled laboratory-based experimental methodology to evaluate the performance of electric UAV powertrain systems using commercially available propulsion components. Rather than investigating every possible motor, battery, and electronic speed controller (ESC) combination, the researchers isolated the influence of propeller geometry by maintaining identical electrical components throughout the entire experimental campaign. This approach enabled a direct assessment of how variations in propeller diameter, pitch, material, surface finish, and blade configuration affect thrust generation, electrical power consumption, propulsion efficiency, and flight endurance.

Unlike simulation-based investigations that rely on aerodynamic modelling or computational assumptions, this research employed a fully experimental approach. All propulsion configurations were evaluated under standardized laboratory conditions using a calibrated dynamometer capable of simultaneously measuring aerodynamic and electrical performance. Consequently, the resulting dataset represents experimentally validated performance rather than theoretical predictions, providing practical engineering information that can be directly applied during UAV powertrain selection and optimization.

The overall experimental workflow consisted of selecting commercially available propellers, assembling standardized powertrain configurations, conducting controlled laboratory measurements across multiple operating speeds, processing thousands of recorded measurements, evaluating multiple propulsion performance indicators, and comparing every tested configuration using identical experimental procedures.

Experimental Design

The experimental design was intentionally structured to isolate the contribution of propeller geometry to electric UAV performance. Throughout the investigation, only the propeller was varied, while the battery, brushless DC motor, electronic speed controller, testing equipment, operating conditions, and data acquisition procedures remained unchanged. This experimental strategy minimizes confounding variables and allows performance differences to be attributed primarily to propeller characteristics.

Thirty-two commercially available propellers were evaluated. These propellers covered a wide range of practical UAV applications and represented several commonly available commercial designs. The investigated propellers varied in:

  • Overall diameter (18–30 inches).
  • Blade pitch.
  • Material composition.
  • Surface finishing.
  • Wingtip configuration.
  • Winglet design.
  • Foldable and fixed-blade construction.

To facilitate systematic comparison, the researchers grouped the propellers into five design families according to their manufacturing characteristics. Within each family, propellers shared similar construction concepts but differed in diameter and pitch. This classification enabled comparisons both within individual families and across different propeller designs while maintaining a structured analytical framework.

Powertrain Configuration

Each experimental configuration consisted of four principal propulsion components:

  • A commercially available propeller.
  • A 12-cell lithium-polymer (LiPo) battery with a capacity of 22,000 mAh.
  • A 100 KV brushless direct current (BLDC) motor.
  • A Flame 80 V electronic speed controller (ESC).

The battery, motor, and ESC remained identical throughout all thirty-two experiments. Only the propeller was replaced between tests. Maintaining identical electrical hardware eliminated variations caused by differences in motor efficiency, battery characteristics, or controller behaviour, thereby ensuring that observed performance differences originated from the propeller itself.

The tested powertrain systems represented realistic UAV propulsion configurations rather than simplified laboratory prototypes. Consequently, the experimental results closely reflect practical engineering conditions encountered during UAV design and component selection.

Experimental Test Facility

All experiments were performed using the Flight Stand 50 developed by Tyto Robotics. This professional dynamometer is specifically designed for propulsion testing and enables simultaneous measurement of thrust, torque, rotational speed, electrical current, voltage, and power. The system provides synchronized measurements from multiple sensors, allowing comprehensive evaluation of propulsion system performance under controlled operating conditions.

The propulsion system was mounted on a rigid platform attached to the dynamometer. Testing was conducted under static conditions without freestream airflow. To minimize environmental interference, the experimental apparatus was positioned inside a spacious safety enclosure measuring approximately 3 m × 3 m × 3 m. The enclosure provided sufficient clearance around the propeller to avoid significant boundary-wall effects while ensuring operator safety during high-speed testing.

The Flight Stand software controlled motor operation through the electronic speed controller while simultaneously recording all measurement channels. This automated acquisition system ensured consistent operating conditions across every propeller configuration.

Controlled Environmental Conditions

All experiments were conducted indoors under carefully controlled environmental conditions to minimize external influences on propulsion performance. Ambient temperature remained between approximately 24 °C and 27 °C throughout the testing period, with an average temperature close to 25 °C. Relative humidity varied between approximately 37% and 48%, averaging about 42%.

Environmental parameters were continuously monitored using a digital thermo-hygrometer to ensure consistency during the entire experimental campaign. Maintaining stable laboratory conditions reduced the likelihood that atmospheric variations would influence thrust generation or electrical measurements, thereby improving the repeatability of the experimental results.

Measurement Procedure

Each propeller was evaluated across multiple operating conditions by varying motor rotational speed. Rather than controlling throttle percentage directly, the experiments were conducted by specifying target rotational speeds (RPM), allowing more accurate comparisons among different propeller configurations.

Measurements were performed between approximately 1,500 RPM and 3,500 RPM. At each operating condition, the propulsion system was allowed to stabilize before data acquisition commenced. This procedure ensured that transient fluctuations did not influence the recorded measurements.

For every powertrain configuration, data were collected continuously for sixty seconds at a sampling frequency of 100 Hz. Consequently, each experiment produced approximately 6,000 individual measurements covering thrust, voltage, current, torque, rotational speed, and other performance parameters. Rather than relying on single observations, the researchers analysed mean values calculated from the complete dataset, thereby reducing random measurement noise and improving statistical reliability.

Noise Reduction and Experimental Repeatability

Before evaluating the propulsion systems, background system noise was measured independently. The recorded noise level was subsequently removed from the experimental measurements to improve data quality. The observed noise level was extremely small, indicating that the measurement system exhibited excellent stability.

To verify repeatability, every experimental configuration was tested multiple times. Successive measurements produced only minor variations, demonstrating that the experimental procedure was highly reproducible. The consistency observed across repeated experiments confirms that the reported performance differences primarily reflect genuine differences among propeller configurations rather than random experimental uncertainty.

Measurement Accuracy and Uncertainty Analysis

The researchers also considered measurement uncertainty as an integral component of the experimental methodology. The Flight Stand 50 had been factory calibrated, and its manufacturer-provided measurement specifications were incorporated into the uncertainty analysis.

According to the reported specifications, the measurement uncertainties were approximately:

  • ±0.5% for thrust measurements.
  • ±0.75% for torque measurements.
  • ±1% for voltage measurements.
  • ±1% for current measurements.
  • Approximately ±1 RPM for rotational speed measurements.

The study further calculated combined measurement uncertainties for the principal experimental variables. This additional analysis strengthens confidence in the reported performance rankings and demonstrates that the observed differences among propeller configurations are substantially larger than the associated measurement uncertainties.

Performance Metrics Evaluated

The experimental investigation evaluated multiple engineering performance indicators to provide a comprehensive assessment of electric UAV propulsion systems. Rather than focusing solely on thrust production, the researchers examined several complementary metrics that collectively describe propulsion efficiency and operational capability.

The principal performance indicators included:

  • Static thrust generation.
  • Motor rotational speed (RPM).
  • Electrical power consumption.
  • Mechanical power output.
  • Propulsion efficiency.
  • Flight endurance.
  • Thrust-to-weight ratio (T/W).
  • Thrust-to-mechanical power ratio (TMPR).

Evaluating multiple performance indicators enabled the researchers to identify engineering trade-offs among competing design objectives. For example, a propeller capable of generating the greatest thrust was not necessarily the most energy efficient or the most suitable for endurance-oriented missions. This multi-criteria evaluation provides a more realistic basis for selecting UAV propulsion systems than relying on a single performance parameter.

Analytical Framework

Following data acquisition, the researchers compared all thirty-two powertrain configurations using identical analytical procedures. Performance trends were examined with respect to propeller family, diameter, blade pitch, power consumption, propulsion efficiency, endurance, thrust-to-weight ratio, and thrust-to-mechanical power ratio. In addition, representative VTOL UAV calculations were performed to illustrate how the experimentally measured propulsion characteristics translate into practical aircraft performance.

Rather than identifying a universally superior propeller, the analytical framework emphasized mission-specific optimization. This perspective recognizes that UAV propulsion design involves balancing multiple engineering objectives, including lifting capability, endurance, efficiency, payload capacity, and electrical power consumption. Consequently, the resulting performance database serves as a practical engineering reference for selecting propulsion components according to specific operational requirements rather than maximizing a single performance metric.

Key Findings

The experimental evaluation generated one of the most comprehensive comparative datasets currently available for commercially available UAV propellers tested under standardized laboratory conditions. By examining thirty-two propeller configurations while maintaining identical batteries, electronic speed controllers (ESCs), and brushless DC motors, the researchers demonstrated that propeller geometry alone can substantially influence thrust generation, electrical power consumption, propulsion efficiency, endurance, and overall UAV performance. Rather than identifying a universally superior propeller, the results reveal that propulsion optimization is fundamentally mission dependent, with different propeller configurations excelling under different operational objectives.

1. Thrust Increased Consistently with Motor Speed

Across all five propeller families, thrust increased steadily as rotational speed increased from approximately 1,500 RPM to 3,500 RPM. This relationship confirms the expected aerodynamic behaviour of fixed-pitch propellers, where higher rotational speeds accelerate larger volumes of air and consequently produce greater lifting force.

The experiments further showed that propeller diameter had a dominant influence on thrust generation. The 30-inch propellers consistently produced the highest thrust values across every propeller family, whereas the 18-inch propellers generated the lowest thrust under identical operating conditions. This trend demonstrates that increasing propeller diameter substantially enhances lifting capability, making larger propellers more appropriate for heavy-payload and VTOL UAV applications.

2. Propeller Pitch Alone Does Not Determine Performance

One of the most important findings of the study is that blade pitch cannot be considered the sole predictor of propulsion performance. Although higher-pitch propellers frequently generated greater thrust, several configurations with identical pitch values produced noticeably different aerodynamic performance.

The researchers attribute these differences to additional engineering factors, including blade geometry, aerodynamic profile, manufacturing quality, surface finish, structural material, blade twist distribution, wingtip design, and winglet configuration. Consequently, selecting a propeller based solely on diameter and nominal pitch may lead to suboptimal UAV performance.

3. Larger Propellers Required Higher Electrical Power

Electrical power consumption increased continuously as motor speed increased for every tested configuration. Larger-diameter propellers and higher-thrust configurations generally demanded substantially greater electrical power than smaller propellers.

This finding illustrates a fundamental engineering trade-off within electric propulsion systems. While larger propellers provide superior lifting capability, they also increase battery energy consumption. Engineers must therefore balance payload requirements against endurance objectives when selecting propulsion components.

4. P3_30 Produced the Highest Thrust-to-Mechanical Power Ratio

Among all thirty-two evaluated configurations, propeller P3_30 achieved the highest Thrust-to-Mechanical Power Ratio (TMPR), reaching approximately 8.48%. TMPR represents how effectively mechanical power supplied by the motor is converted into useful aerodynamic thrust.

A high TMPR indicates superior propulsion effectiveness because more useful thrust is produced for each unit of mechanical power delivered by the motor. Consequently, P3_30 represents an attractive option for UAV missions that prioritize lifting capability while maintaining efficient mechanical energy utilization.

5. P1_26 Achieved the Longest Flight Endurance

Although larger propellers generated greater thrust, they were not necessarily the most suitable for endurance-oriented missions. The experimental results identified configuration P1_26 as the best performer for flight endurance, achieving an estimated endurance of approximately 16.17 minutes.

This result demonstrates that moderate propeller dimensions can provide a more favourable balance between thrust production and battery energy consumption than larger propellers designed primarily for maximum lifting capability.

6. Highest Overall Propulsion Efficiency

Configuration P1_26 also achieved the highest measured propulsion efficiency of approximately 93.1%. This exceptionally high efficiency indicates that the electrical energy supplied by the battery was converted into useful mechanical propulsion with minimal losses.

The simultaneous achievement of the highest efficiency and longest endurance confirms that propulsion efficiency depends on the combined interaction among propeller geometry, aerodynamic loading, motor operating conditions, and electrical power demand rather than on thrust generation alone.

7. Thrust-to-Weight Analysis Demonstrated Practical UAV Capability

Beyond laboratory measurements, the researchers extended their analysis by evaluating thrust-to-weight ratios for a representative VTOL UAV platform. This practical case study demonstrated how experimentally measured propulsion characteristics translate into actual aircraft performance.

The analysis showed that only specific powertrain configurations generated sufficient thrust to achieve thrust-to-weight ratios exceeding unity, a fundamental requirement for vertical take-off and hovering flight. Consequently, the experimental data provide practical engineering guidance for selecting propulsion systems capable of supporting realistic UAV payload requirements.

8. Experimental Benchmarking Enables Mission-Oriented Propeller Selection

Rather than recommending a single "best" propeller, the study demonstrates that optimal propeller selection depends entirely on mission objectives. UAVs designed for aerial mapping, surveillance, cargo transportation, agricultural spraying, infrastructure inspection, or long-endurance observation require different propulsion characteristics.

Accordingly, the researchers developed comprehensive performance rankings based on thrust generation, endurance, efficiency, power consumption, thrust-to-weight ratio, and thrust-to-mechanical power ratio. These rankings provide engineers with practical decision-support information for selecting commercially available propellers according to specific operational requirements.

Scientific Contribution

Beyond reporting experimental measurements, this study makes several meaningful contributions to the field of electric UAV propulsion engineering. The research advances current knowledge by providing experimentally validated performance data under standardized conditions while simultaneously establishing a practical framework for mission-oriented powertrain selection.

  • Provides one of the largest standardized experimental benchmark datasets. Thirty-two commercially available propellers were evaluated using identical propulsion hardware, producing a comprehensive database that supports future experimental and computational research.
  • Demonstrates the dominant influence of propeller geometry. The study experimentally confirms that propeller characteristics significantly influence propulsion performance even when batteries, ESCs, and motors remain unchanged.
  • Bridges theoretical propulsion analysis and engineering practice. Rather than relying solely on aerodynamic theory or numerical simulations, the investigation provides experimentally verified engineering data that can immediately support UAV system development.
  • Introduces a practical multi-criteria evaluation framework. Instead of evaluating propulsion performance using thrust alone, the research simultaneously considers efficiency, endurance, electrical power consumption, thrust-to-weight ratio, and thrust-to-mechanical power ratio.
  • Highlights the importance of engineering trade-offs. The findings demonstrate that maximizing one performance indicator often reduces another, emphasizing the need for balanced multi-objective optimization during UAV powertrain design.
  • Provides high-quality experimental data for future model validation. The benchmark dataset can be used to validate Computational Fluid Dynamics (CFD), analytical propulsion models, digital twins, optimization algorithms, and machine learning approaches developed for UAV propulsion systems.

Industrial Implications

The practical significance of this research extends well beyond academic investigation. Because the experiments employed commercially available propulsion components under realistic operating conditions, the findings can be directly incorporated into industrial UAV development, component selection, and mission planning.

  • Supports evidence-based powertrain design. UAV manufacturers can select commercially available propellers using experimentally validated performance data rather than relying exclusively on manufacturer specifications.
  • Improves mission planning. Different propulsion configurations can be selected according to operational priorities such as endurance, payload capacity, hovering capability, or energy efficiency.
  • Reduces engineering development time. The benchmark database minimizes trial-and-error testing during propulsion system integration by providing comparative performance information across numerous commercially available propellers.
  • Enhances battery utilization. Selecting more efficient propeller configurations enables longer flight duration without increasing battery capacity, thereby improving overall UAV productivity.
  • Supports heavy-lift UAV development. High-thrust propeller configurations identified in the study provide practical references for cargo drones, emergency response platforms, precision agriculture, and industrial inspection systems.
  • Facilitates optimization of commercial UAV platforms. Manufacturers, system integrators, and UAV operators can utilize the reported performance rankings to optimize propulsion systems according to application-specific engineering requirements.
  • Provides a foundation for next-generation intelligent propulsion design. The experimental dataset offers valuable reference data for integrating artificial intelligence, digital twin technology, multidisciplinary optimization, and predictive performance modelling into future UAV propulsion development.

Overall, the industrial value of this research lies in transforming detailed laboratory measurements into actionable engineering knowledge. By experimentally demonstrating how commercially available propellers influence propulsion efficiency, endurance, and thrust generation, the study provides UAV designers with reliable guidance for developing safer, more energy-efficient, and mission-optimized electric aerial vehicles.

Research Limitations

Like any well-designed engineering investigation, this study was conducted within clearly defined experimental boundaries. Rather than attempting to evaluate every possible aspect of UAV propulsion, the researchers deliberately focused on the influence of commercially available propellers while maintaining identical batteries, electronic speed controllers (ESCs), and brushless DC (BLDC) motors. This controlled experimental strategy successfully isolated the effect of propeller geometry on powertrain performance; however, it also defines the scope within which the reported findings should be interpreted.

Understanding these limitations is important because they provide valuable context for interpreting the results and identifying opportunities for future investigations. Importantly, none of these limitations diminish the scientific value of the study. Instead, they reflect practical engineering decisions that allowed the researchers to obtain reliable and repeatable experimental data under standardized laboratory conditions.

1. Evaluation Was Limited to a Single Motor, Battery, and ESC Configuration

The investigation intentionally maintained identical electrical components throughout all experiments. Every propeller was tested using the same 100 KV brushless DC motor, the same 12-cell 22,000 mAh lithium-polymer battery, and the same Flame 80 V electronic speed controller.

Although this approach enabled a fair comparison among propeller configurations, the reported performance rankings may differ when alternative motors, batteries, ESCs, or voltage systems are used. Consequently, the findings should be interpreted as representative of the specific propulsion configuration investigated rather than universally applicable to every electric UAV powertrain.

2. Static Ground Testing Does Not Fully Represent Actual Flight Conditions

All experiments were performed under static laboratory conditions using a calibrated dynamometer. During testing, the propulsion system remained stationary while thrust, power consumption, and rotational speed were measured without forward flight.

In practical UAV operations, propellers experience continuously changing aerodynamic environments influenced by forward velocity, aircraft attitude, wind disturbances, turbulence, climb and descent manoeuvres, payload variations, and control inputs. These dynamic aerodynamic effects cannot be completely reproduced during static bench testing. Therefore, actual in-flight performance may differ from the laboratory measurements reported in this study.

3. Only Commercially Available Propellers Were Investigated

The study exclusively evaluated commercially manufactured propellers that are readily available for UAV applications. While this decision enhances the practical relevance of the findings, it excludes experimental propeller geometries, custom-manufactured blades, additive manufacturing designs, and advanced biomimetic propellers that may exhibit different aerodynamic characteristics.

Consequently, the results should not be interpreted as representing the complete range of possible UAV propeller designs.

4. Aerodynamic Mechanisms Were Not Directly Investigated

The primary objective of the research was experimental performance evaluation rather than detailed aerodynamic analysis. Although the experiments clearly demonstrated differences in thrust, efficiency, endurance, and power consumption, the study did not directly investigate airflow structures surrounding individual propellers.

Advanced flow visualization techniques such as Particle Image Velocimetry (PIV), smoke visualization, laser diagnostics, or Computational Fluid Dynamics (CFD) simulations were outside the scope of the investigation. Consequently, the precise aerodynamic mechanisms responsible for some observed performance differences remain open for future study.

5. Structural and Mechanical Characteristics Were Not Evaluated

The investigation primarily examined propulsion performance and energy utilization. Other engineering considerations—including structural strength, blade fatigue, vibration behaviour, acoustic noise, long-term durability, impact resistance, and material degradation—were not experimentally evaluated.

These characteristics become particularly important for UAVs operating in demanding industrial environments where repeated flight cycles, harsh weather conditions, or high mechanical loading may influence long-term propeller reliability.

6. Environmental Conditions Were Carefully Controlled

All measurements were conducted indoors under relatively stable temperature and humidity conditions. While this approach improved experimental repeatability, it did not examine the influence of changing environmental conditions such as high-altitude operation, extreme temperatures, strong crosswinds, rain, dust, or varying atmospheric density.

Such environmental factors may significantly influence propulsion efficiency during real-world UAV missions and therefore deserve further investigation.

7. Mission Performance Was Evaluated Using Representative Calculations

The thrust-to-weight analysis employed a representative VTOL UAV as an engineering case study to demonstrate practical application of the experimental results. Although this example illustrates how the measured propulsion data can support UAV design, actual aircraft performance depends on numerous additional factors, including airframe aerodynamics, flight control algorithms, payload characteristics, mission profiles, and operational environments.

Therefore, the presented VTOL analysis should be regarded as an engineering illustration rather than a universal prediction applicable to every UAV platform.

Future Research Opportunities

The comprehensive experimental dataset generated by this study provides an excellent foundation for future investigations into electric UAV propulsion systems. While the present research establishes valuable experimental benchmarks, numerous scientific and engineering questions remain open for further exploration. Future studies can build upon these findings by integrating advanced aerodynamic analysis, intelligent optimization techniques, multidisciplinary engineering approaches, and real-world flight validation.

1. Experimental Flight Validation

An important next step would be to validate the laboratory findings through controlled flight experiments. Comparing static bench-test results with actual flight performance would improve understanding of how forward velocity, aircraft manoeuvres, payload changes, and atmospheric disturbances influence propulsion efficiency under operational conditions.

2. Integration with Computational Fluid Dynamics (CFD)

Although the present study experimentally quantified propulsion performance, combining these measurements with high-fidelity Computational Fluid Dynamics simulations would provide deeper insight into the aerodynamic mechanisms responsible for observed differences among propeller configurations.

CFD could be employed to investigate blade loading, pressure distribution, tip vortices, induced velocity fields, wake development, and aerodynamic losses that cannot be directly observed during laboratory measurements.

3. Investigation of Advanced Propeller Geometries

Future studies could expand beyond commercially available propellers by evaluating innovative blade concepts, including variable-pitch propellers, morphing blades, bio-inspired geometries, winglet optimization, additive-manufactured propellers, and composite structures specifically designed for UAV applications.

4. Multi-Objective Propulsion Optimization

The findings demonstrate that maximizing thrust, efficiency, endurance, and payload capability simultaneously is challenging because these objectives frequently conflict with one another. Future research could employ multi-objective optimization algorithms to identify propulsion configurations that provide balanced performance across multiple engineering criteria.

Techniques such as genetic algorithms, particle swarm optimization, Bayesian optimization, surrogate modelling, and evolutionary computation offer promising opportunities for future propulsion system design.

5. Artificial Intelligence and Machine Learning Applications

The extensive experimental dataset generated by this study represents an excellent resource for developing data-driven prediction models. Machine learning algorithms could be trained to estimate propulsion performance directly from propeller geometry, enabling rapid selection of optimal powertrain configurations without extensive experimental testing.

Artificial intelligence could also support predictive maintenance, intelligent mission planning, adaptive propulsion control, and autonomous optimization of future UAV systems.

6. Dynamic and Transient Operating Conditions

Future investigations should examine propulsion performance during realistic flight manoeuvres rather than only steady-state operating conditions. Dynamic throttle changes, rapid acceleration, climbing, descending, hovering transitions, and aggressive manoeuvres may influence propulsion characteristics differently from the static operating conditions evaluated in this study.

7. Structural Reliability and Service Life

Long-term structural performance remains an important research topic. Future investigations could evaluate blade fatigue, vibration characteristics, impact resistance, crack propagation, material ageing, and durability under repeated operational cycles to complement the aerodynamic performance measurements presented in this study.

8. Environmental and Sustainability Assessment

As UAV applications continue to expand, environmental sustainability is becoming increasingly important. Future research may evaluate lifecycle energy consumption, recyclable materials, environmentally friendly composite manufacturing, noise reduction strategies, and sustainable propulsion technologies that minimize environmental impact while maintaining high aerodynamic performance.

9. Development of Intelligent Digital Twins

Another promising research direction involves integrating experimental propulsion data with digital twin technology. High-fidelity digital twins capable of continuously updating propulsion performance using real operational data could support predictive maintenance, mission optimization, fault diagnosis, and autonomous decision-making for next-generation UAV fleets.

Potential for Public Policy Citation

Although this research primarily addresses UAV propulsion engineering, its findings also have broader implications for public policy, industrial standards, and technology development. As governments increasingly incorporate unmanned aerial vehicles into national infrastructure, environmental monitoring, emergency response, agriculture, transportation, and public safety operations, evidence-based guidance on propulsion efficiency becomes increasingly valuable for policymaking.

The experimentally validated performance data reported in this study can support regulatory agencies, standards organizations, and government institutions seeking objective technical references for evaluating UAV propulsion technologies. By providing standardized performance benchmarks for commercially available propulsion systems, the research contributes to more transparent engineering assessment procedures and technology evaluation frameworks.

The study may also inform policy discussions related to sustainable aviation technologies. Improving propulsion efficiency directly contributes to longer flight endurance, reduced battery consumption, lower operational costs, and more efficient use of energy resources. These outcomes align closely with international initiatives promoting energy-efficient transportation, environmentally responsible engineering, and sustainable technological innovation.

In addition, the findings have potential relevance for government agencies responsible for developing technical guidelines governing UAV deployment in emergency response, disaster management, infrastructure inspection, precision agriculture, environmental monitoring, border surveillance, and humanitarian operations. Reliable propulsion performance data can support procurement decisions by helping organizations select powertrain configurations appropriate for specific operational requirements.

While the study does not propose regulatory recommendations directly, its experimentally validated benchmark database provides objective engineering evidence that policymakers, certification bodies, standards organizations, and public-sector technology developers may cite when formulating future technical standards and best-practice guidelines for electric UAV propulsion systems.

Who Should Read This Paper?

This paper will be particularly valuable for readers involved in the design, optimization, evaluation, and application of electric UAV propulsion systems. Because the study combines rigorous experimental testing with practical engineering interpretation, its findings are relevant to both academic researchers and industrial practitioners.

  • UAV propulsion engineers seeking experimentally validated guidance for selecting commercially available propellers based on thrust, efficiency, endurance, and payload requirements.
  • Aerospace researchers investigating electric propulsion systems, propeller aerodynamics, vertical take-off technologies, and UAV performance optimization.
  • Mechanical engineers interested in experimental performance evaluation, energy conversion, rotating machinery, and propulsion system design.
  • Electrical engineers working on battery systems, electronic speed controllers, brushless motors, power electronics, and electric mobility technologies.
  • Researchers developing Computational Fluid Dynamics (CFD) models who require high-quality experimental benchmark data for validating numerical simulations of UAV propulsion systems.
  • Artificial intelligence and optimization researchers interested in developing predictive models, digital twins, or machine learning algorithms for intelligent propulsion system design.
  • UAV manufacturers and system integrators responsible for selecting propulsion components that balance payload capacity, endurance, efficiency, and operational cost.
  • Government agencies and standards organizations seeking reliable engineering evidence to support procurement decisions, technical guidelines, certification activities, and future UAV performance standards.
  • Graduate students and early-career researchers looking for an excellent example of experimental engineering research involving systematic laboratory testing, uncertainty analysis, comparative performance evaluation, and practical interpretation of propulsion system behaviour.

Overall, this paper represents an important resource for anyone seeking a deeper understanding of how commercially available propeller characteristics influence electric UAV performance and how experimentally validated propulsion data can support evidence-based engineering design.

Final Thoughts

The rapid expansion of unmanned aerial vehicle (UAV) applications has intensified the demand for propulsion systems capable of delivering higher efficiency, longer endurance, greater payload capacity, and improved operational reliability. While considerable attention has traditionally focused on battery technology and electric motors, this study clearly demonstrates that propeller selection remains one of the most influential yet frequently underestimated factors affecting overall UAV performance.

Through a carefully controlled experimental investigation involving thirty-two commercially available propellers, the authors provide convincing evidence that relatively small differences in propeller geometry can produce substantial variations in thrust generation, electrical power consumption, propulsion efficiency, flight endurance, and thrust-to-mechanical power conversion. By maintaining identical batteries, electronic speed controllers, and brushless DC motors throughout the experiments, the study successfully isolates the contribution of propeller design and offers one of the most comprehensive experimental benchmarks currently available for electric UAV propulsion systems.

Perhaps the most valuable contribution of this research is its emphasis on engineering decision-making rather than simply identifying a single "best" propeller. The findings consistently demonstrate that propulsion optimization is inherently mission dependent. A propeller optimized for maximum thrust may not provide the highest endurance, while the most energy-efficient configuration may not deliver the greatest payload capacity. Consequently, UAV designers must carefully balance multiple performance criteria according to specific operational objectives instead of relying on a single engineering metric.

The study also illustrates the enduring importance of experimental validation in an era increasingly dominated by computational modelling and artificial intelligence. Although simulation techniques continue to advance rapidly, high-quality laboratory measurements remain indispensable for validating numerical models, improving predictive algorithms, and supporting evidence-based engineering practice. The comprehensive dataset generated by this research therefore represents not only a valuable engineering resource for current UAV development but also a strong foundation for future investigations involving Computational Fluid Dynamics (CFD), digital twins, multidisciplinary optimization, and machine learning-driven propulsion design.

Overall, this paper successfully bridges the gap between theoretical propulsion analysis and practical engineering application. Its rigorous experimental methodology, comprehensive comparative evaluation, and mission-oriented interpretation make it a valuable reference for researchers, engineers, manufacturers, and policymakers working to advance the next generation of efficient, reliable, and intelligent electric UAV systems.

Suggested Citations

UNP–Teknomekanik Style

Goli S, Kurtuluş DF, Imran IH, Kouser T, Aliyu A, Alhems LM, Memon AM. Experimental Evaluation of Electric Powertrain System for Unmanned Aerial Vehicles. Engineering Reports. 2026;8(1):e70565. https://doi.org/10.1002/eng2.70565

APA 7th Edition

Goli, S., Kurtuluş, D. F., Imran, I. H., Kouser, T., Aliyu, A., Alhems, L. M., & Memon, A. M. (2026). Experimental evaluation of electric powertrain system for unmanned aerial vehicles. Engineering Reports, 8(1), e70565. https://doi.org/10.1002/eng2.70565

IEEE

S. Goli, D. F. Kurtuluş, I. H. Imran, T. Kouser, A. Aliyu, L. M. Alhems, and A. M. Memon, "Experimental Evaluation of Electric Powertrain System for Unmanned Aerial Vehicles," Engineering Reports, vol. 8, no. 1, Art. no. e70565, 2026, doi:10.1002/eng2.70565.

Harvard

Goli, S., Kurtuluş, D.F., Imran, I.H., Kouser, T., Aliyu, A., Alhems, L.M. & Memon, A.M., 2026. Experimental Evaluation of Electric Powertrain System for Unmanned Aerial Vehicles. Engineering Reports, 8(1), e70565. Available at: https://doi.org/10.1002/eng2.70565.

Vancouver

Goli S, Kurtuluş DF, Imran IH, Kouser T, Aliyu A, Alhems LM, Memon AM. Experimental evaluation of electric powertrain system for unmanned aerial vehicles. Engineering Reports. 2026;8(1):e70565. doi:10.1002/eng2.70565.

Chicago Author–Date

Goli, Srikanth, Dilek Funda Kurtuluş, Imil Hamda Imran, Taiba Kouser, Abdulrahman Aliyu, Luai M. Alhems, and Azhar M. Memon. 2026. "Experimental Evaluation of Electric Powertrain System for Unmanned Aerial Vehicles." Engineering Reports 8 (1): e70565. https://doi.org/10.1002/eng2.70565.

MLA 9th Edition

Goli, Srikanth, et al. "Experimental Evaluation of Electric Powertrain System for Unmanned Aerial Vehicles." Engineering Reports, vol. 8, no. 1, 2026, article e70565, Wiley, https://doi.org/10.1002/eng2.70565.

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