# A Risk Assessment Technique for Energy-Efficient Drones to Support Pilots and Ensure Safe Flying

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## Abstract

**:**

## 1. Introduction

#### 1.1. General Overview of Energy Efficiency and Emission of Different Areas of Transport

_{2}emissions are also significant: g/pkm and g/tkm are the units for this parameter. Airplanes and inland ships emit 285 and 245 g/pkm, while electric trains emit 14 g/tkm [14]. Trucks emit 64–693 g/tkm CO

_{2}(smaller trucks and vans emit more), and freight trains and inland ships emit 50 g/tkm [15]. Of course, when discussing vehicle energy efficiency and pollution, it is equally important to consider transport infrastructure construction, operation, and maintenance costs [16,17,18,19,20,21,22,23]; whole-life costs [24,25,26]; risk analysis of projects [27]; and infrastructure construction energy efficiency and emissions. Another possibility to reduce energy consumption is that the lower the vehicle’s weight, the smaller the requested energy for driving and motion; thus, precise mechanical engineering measurement techniques can be applied. A type of this is digital image processing (DIC) [28,29,30,31,32,33,34], as well as shape optimization with [35] or without DIC [36].

#### 1.2. Overview of Unmanned Aerial Vehicles

#### 1.2.1. History as Well as General and Unique Applications of Unmanned Aerial Vehicles

- aviation;
- aerial and real estate photography, as well as videography;
- mapping and surveying, disaster zone mapping, disaster relief, and hidden area exploration;
- asset inspection and control, aerial surveillance, monitoring poachers, and insurance;
- payload carrying and parcel delivery;
- agriculture, bird control, crop spraying, crop monitoring, and precision farming;
- multispectral/thermal/NIR (near-infrared) cameras;
- live streaming events;
- roof inspections;
- emergency response, search and rescue, and marine rescue;
- forensics;
- construction and mining;
- military and firefighting;
- oil rigs and power line monitoring;
- medical applications;
- meteorology;
- wireless communication;
- and so on.

#### 1.2.2. International Literature Review on Unmanned Aerial Vehicles in Special Applications

^{TM}), with which the authors presented a scientometric and bibliometric analysis. It shows that UAVs can be valuable multi-tasking tools at any stage of an architectural project. Shariq et al. [52] developed new methods for building energy studies using advanced radiometric tools. They explored not only UAV applications. Li et al. [53] investigated a complementary optimization technique for high-atmosphere solar UAVs. It starts optimization from the drive direction and integrates the method into the general optimization technique. Weight and required power reduction are achieved by the new method. Mohan et al. [54] deal with forestation and seed spreading by UAVs; the paper discusses the topic mainly from the viewpoint of plants and soil. Aslan et al. [55] introduce an analysis and evaluation of UAS and UAS solutions in agriculture, based on other publications. González-Jorge et al. [56] provide an overview of the role of UAVs in our lives. It raises and explains agricultural, health, and monitoring opportunities. They present LiDAR (light detection and ranging) capabilities and examples. Ausonio et al. [57] discuss drone swarm methodology for forest fires. They report calculations of the critical volume of water to extinguish fires with given parameters. The paper contains calculations for the length of the suppressible fire front based on environmental factors and UAV swarm characteristics (volume, distance, and speed). Qi et al. [58] published about water–air UAVs and investigated optimal water exit angles. They discussed the flying submarine. Chamola et al. [59] introduced methods for monitoring and disabling UAS; they published no battery information review. Stek [60] issued a publication in 2014. The study focuses on the exploration by UAVs of subsurface archaeological remains in the mountainous Mediterranean landscape of Molise in southern Italy. This paper shows the preliminary data of a case study of a complex landscape site of the Classical-Roman period. Finally, the article presents a detailed study of the archaeological research and cultural heritage management potential and viability of UAVs. Specifically, it deals with the opportunities for incorporating UAV photography into current landscape archaeological research methods, such as field mapping and geophysical surveying.

#### 1.3. Novelty, Essence, and Structure of the Current Article

## 2. Materials and Methods

#### 2.1. Overview of UAV Design

- motor consumption:
- ○
- 10.8 A per motor at 100% assumed average load;
- ○
- 6.9 A per motor at 75% assumed average load;
- ○
- 4.9 A per motor at 65% assumed average load;
- ○
- 2.5 A per motor at 50% assumed average load;

- consumption of additional units:
- ○
- 2 A assumed consumption due to control and telemetry systems;

- the expected energy demand of the construction (approximate, may vary depending on take-off weight):
- ○
- 3.7 Ah (battery capacity)/44 A (average load)~5 min (expected flight time);
- ○
- 3.7 Ah (battery capacity)/22 A (average load)~10 min (expected flight time);
- ○
- 3.7 Ah (battery capacity)/11 A (average load)~20 min (expected flight time);

#### 2.2. Concept of Risk Analysis and Parameter Determination

_{1}) is influenced by several factors, the exact definition of which is a complex task. One key factor is determining the components’ consumption, which can be based on catalog data as a starting point. In order to determine the consumption, it is necessary to know the Motors (X

_{11}), the Propellers (X

_{12}), the System weight (X

_{13}), and possibly the controller, etc. Of course, additional items can be added (additional X

_{1n}parameter), such as different sensors and cameras, but the average consumption is the critical point in all cases. For simplicity, in the first case, the catalog data were used to calculate the value of X

_{1}, which thus consists of the system voltage and an average load current. It is important to note that the current article does not address the design aspects and their interrelationship; it only uses the consumption context. This form of specification is part of future research. In the calculations, the average consumption was decomposed in W unit (see Equation (1)).

_{2}). On the one hand, optimization can be carried out for Time in the air (X

_{21}), Distance traveled (X

_{22}), or Speed (X

_{23}). In most cases, all three parameters are important factors, but higher speed also means higher consumption and, thus, less time in the air. The algorithm presented in this paper analyzes the time spent in the air (X

_{21}); studying the distance traveled requires more minor changes in the model, while optimization for speed requires more significant changes. The approach is now to optimize for one parameter; the other values will be 0. In the case under consideration, the analysis is performed in time, so X

_{22}and X

_{23}are 0, and the output is determined by X

_{21}(see Equation (2)).

_{3}) include present and expected temperature data. It is important to note that in addition to the current components Wind strength (X

_{31}), Temperature (X

_{32}), and Humidity (X

_{32}), the expected values can be found if they are available. Determining the external temperature effects for the entire flight route is vital, such as temperatures measured at different altitudes, wind speed, and relative humidity. Risk is defined as the probability of a flight failing (see Equation (3)):

_{w}) increase, it becomes more challenging to determine the exact flight time. As a function of this, higher wind strength means higher risk. Therefore, a value above 60 km/h is considered too dangerous as a starting point, regardless of wind direction. (The figures currently specified are empirical values, which may be corrected in the future after several real measurements.)

_{4}) generally contain the battery capacity and voltage values. In this case, the factory data installed on the UAV are used with the battery in its new state (see Equation (4)).

_{4}is Wh.

_{5}) values indicate the current status. The value of SOC (state of charge) (X

_{51}) inbound variable, by default, specifies the assumed charge level of the current. Here, the incoming value also varies between 0 and 1, where the fully charged state means 0. Figure 4 shows the relationship between risk and charge level.

_{51}.

_{52}) is the current battery condition (see Figure 5); it is essential to note that battery wear is affected by several factors: for example, cycle number, storage, or usage. Therefore, based on the information available on the battery status, this parameter is set between 0 and 1.

_{53}) is the present pre-take-off pack temperature. So, in this case, it indicates the current temperature of the battery and the temperature used during storage and assigns it a value of 0–1; the ideal input value is 15 °C to 35 °C. Figure 3 is a guide in terms of risk. In all cases, the first step in determining the output was to determine the risk values of the various parameters. As a function of this, the output of ${X}_{5}$ is (see Equation (5)):

_{6}) includes the required energy to complete the current task. It is important to note that the average consumption is calculated, and peak load and its effect are not included. Furthermore, based on average consumption, the model assumes a sufficient pilot skill level to fly. Some cases are presented later in the analysis, where the effect of higher loads is analyzed in the risk calculation (see Equation (6)).

_{7}) is used to determine how much energy the battery has at the moment of take-off (see Equation (7)).

_{7}is the currently available energy of the battery, in Joules.

_{8}) can be determined (see Equation (8)).

_{8}is the risk of the specified flight time. If the result is 0 or below, the risk is very low, thus, 0. If it is greater than 0, further scaling is required. The value is between 0 and 1, where 1 represents the higher risk. In the evaluation, the Y-output is determined, as shown in Figure 6.

## 3. Results and Discussion

#### 3.1. Risk Calculation

_{21}, which means analyzing the safety of a 1200 s flight time according to the given conditions.

#### 3.2. Measurement Results

## 4. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

AEC | architecture, engineering, and construction | |

DIC | digital image correlation | |

ECS | electronic speed controller | |

ELRS | express long-range system | |

FC | flight controller | |

FTC | fault tolerant controller | |

IMU | inertial measurement unit | |

IoE | internet of everything | |

IoT | internet of things | |

LiDAR | light detection and ranging | |

LiPo | lithium-polymer | |

MFP | mission feasibility problem | |

MTOM | maximum take-off mass | |

NIR | near infrared | |

PDB | power distribution board | |

PI | proportional integral | |

PID | proportional-integral-derivative (controller) | |

RC | radio control | |

SOC | state of charge | |

SOH | state of health | |

UAS | unmanned air/aerial systems | |

UAV | unmanned air/aerial vehicle | |

UGV | unmanned ground vehicle | |

Symbols | Description (meaning) | Units |

X_{1} | consumption of the construction | W |

X_{11} | UAV motor parameters in terms of consumption | W |

X_{12} | UAV propeller parameters in terms of consumption | W |

X_{13} | UAV system weight | kg |

V_{NS} | system nominal voltage | V |

I_{AVR} | average load current | A |

X_{2} | expected flight time | S |

X_{31} | wind strength risk | – |

V_{w} | wind speed | km/h |

RH | relative humidity | % |

X_{32} | temperature risk | – |

X_{33} | humidity risk | – |

X_{4} | battery factory data | Wh |

X_{41} | factory capacity | Ah |

X_{42} | nominal voltage | V |

X_{5} | battery state | – |

X_{51} | SOC (state of charge) | % |

X_{52} | SOH (state of health) | % |

X_{53} | battery temperature risk | – |

X_{6} | energy demand | J |

X_{7} | available energy | J |

X_{8} | risk of the specified flight time | – |

Y | risk output | – |

T_{LIMIT} | theoretical flight time | s |

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**Figure 7.**Analysis of the effects of different loads as a function of risk (the 1C…5C values mean the discharging current; thus, if a battery is 3.7 Ah, 1C means 3.7 A discharging current, 2C means 7.4 A discharging current, and so on).

**Figure 10.**Relationship between risk and C-rate (the meaning of C-rate can be found in Figure 7).

**Figure 11.**Relationship between the voltage and risk considering the flight time based on real measurements.

Speed (V_{w}) (km/h) | $\mathbf{Risk}\mathbf{Values}\left({\mathit{X}}_{31}\right)$ |
---|---|

V_{w} ≤ 30 | 0.0 |

30 < V_{w} < 60 | 0.5 |

V_{w} ≥ 60 | 1.0 |

Relative Humidity (RH) (%) | $\mathbf{Risk}\mathbf{Values}\left({\mathit{X}}_{33}\right)$ |
---|---|

RH ≤ 60 | 0.0 |

60 < RH < 80 | 0.5 |

RH ≥ 80 | 1.0 |

**Table 3.**Theoretical time limits (in sec unit; the meaning of 1C…5C can be seen in Figure 7).

Loading Cases | 1C | 2C | 3C | 4C | 5C |
---|---|---|---|---|---|

Ideal | 3600 | 1800 | 1200 | 900 | 720 |

Temp 9 °C | 2880 | 1440 | 960 | 720 | 576 |

Temp 0 °C | 1800 | 900 | 600 | 450 | 360 |

Temp 0 °C and wind 30 km/h | 1440 | 720 | 480 | 360 | 288 |

SOC 90% | 3240 | 1620 | 1080 | 810 | 648 |

SOC 80% and temp 9 °C | 2304 | 1152 | 768 | 576 | 461 |

SOC 80%, temp 0 °C, and wind 30 km/h | 720 | 480 | 192 | 185 | 144 |

**Table 4.**Determination of the Y parameter regarding the C-rates (the meaning of 1C…5C can be seen in Figure 7).

Cases | 1C | 2C | 3C | 4C | 5C |
---|---|---|---|---|---|

Ideal | 0.00 | 0.00 | 0.00 | 0.50 | 0.88 |

Temp 9 °C | 0.00 | 0.00 | 0.50 | 0.88 | 1.00 |

Temp 0 °C | 0.00 | 0.50 | 1.00 | 1.00 | 1.00 |

Temp 0 °C and wind 30 km/h | 0.00 | 0.88 | 1.00 | 1.00 | 1.00 |

SOC 90% | 0.00 | 0.00 | 0.25 | 0.75 | 0.95 |

SOC 80% and temp 9 °C | 0.00 | 0.00 | 0.81 | 1.00 | 1.00 |

SOC 80%, temp 0 °C, and wind 30 km/h | 0.88 | 1.00 | 1.00 | 1.00 | 1.00 |

Parameters | Ideal | SOC 80% and Temp 9 °C | SOC 80%, Temp 0 °C, and Wind 30 km/h |
---|---|---|---|

X_{1} | 164.28 | 164.28 | 164.28 |

X_{21} | 1200.00 | 1200.00 | 1200.00 |

X_{2} | 1200.00 | 1200.00 | 1200.00 |

X_{31} | 0.00 | 0.00 | 0.50 |

X_{32} | 0.00 | 0.20 | 0.50 |

X_{33} | 0.00 | 0.00 | 0.00 |

X_{3} | 0.00 | 0.20 | 0.75 |

X_{4} | 54.76 | 54.76 | 54.76 |

X_{51} | 0.00 | 0.00 | 0.00 |

X_{52} | 0.00 | 0.20 | 0.20 |

X_{53} | 0.00 | 0.00 | 0.00 |

X_{5} | 0.00 | 0.20 | 0.20 |

X_{6} | 197,136.00 | 197,136.00 | 197,136.00 |

X_{7} | 197,136.00 | 157,708.80 | 157,708.80 |

X_{8} | 0.00 | 0.36 | 0.80 |

Y | 0.00 | 0.81 | 1.00 |

Parameters | Values | |
---|---|---|

Consumption of the construction | X_{1} [W] | 121.36 |

Mission parameters | X_{2} [s] | 1300.00 |

External temperature effects | X_{3} [0–1] | 0.00 |

Battery parameters | X_{4} [Wh] | 54.76 |

Battery state | X_{5} [0–1] | 0.20 |

Energy demand | X_{6} [J] | 157,768.00 |

Available energy | X_{7} [J] | 157,708.80 |

Risk | X_{8} [0–1] | 0.00 |

Estimate flight time | T_{limit} [s] | 1300.00 |

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## Share and Cite

**MDPI and ACS Style**

Kocsis Szürke, S.; Perness, N.; Földesi, P.; Kurhan, D.; Sysyn, M.; Fischer, S. A Risk Assessment Technique for Energy-Efficient Drones to Support Pilots and Ensure Safe Flying. *Infrastructures* **2023**, *8*, 67.
https://doi.org/10.3390/infrastructures8040067

**AMA Style**

Kocsis Szürke S, Perness N, Földesi P, Kurhan D, Sysyn M, Fischer S. A Risk Assessment Technique for Energy-Efficient Drones to Support Pilots and Ensure Safe Flying. *Infrastructures*. 2023; 8(4):67.
https://doi.org/10.3390/infrastructures8040067

**Chicago/Turabian Style**

Kocsis Szürke, Szabolcs, Norbert Perness, Péter Földesi, Dmytro Kurhan, Mykola Sysyn, and Szabolcs Fischer. 2023. "A Risk Assessment Technique for Energy-Efficient Drones to Support Pilots and Ensure Safe Flying" *Infrastructures* 8, no. 4: 67.
https://doi.org/10.3390/infrastructures8040067