Over the last half-century, the aviation industry has enabled worldwide connectivity at short travel times and at a relatively affordable price point. Over the course of these years, the fuel-efficiency of aircraft has significantly improved, reducing the environmental impact per passenger. However, the current growth of the industry outpaces the fuel-efficiency, nullifying the environmental gains. In the future, radically different aircraft concepts will be required. In this light, electric aviation technologies pose an interesting group of opportunities which can be deployed in different operating conditions. Three specific developments in electric aviation are (i) external electric taxiing, a new paradigm for aircraft to traverse the airport using Electric Towing Vehicles (ETVs), (ii) electric commuter aircraft, the first generation of electric aircraft for commercial purposes, and (iii) electric Vertical Take-Off and Landing (eVTOL) aircraft, used for urban air mobility.

These technologies will impact aviation operations, as well as the way these operations are planned. Battery performance plays a key part in this, as we are faced with the shorter vehicle range, long charging times, underdeveloped charging infrastructure at airports, and new maintenance requirements due to battery degradation. New operations planning models are required to address these challenges and accommodate these constraints. This dissertation aims to contribute to the incorporation of electric aviation technologies by developing these models and optimization algorithms. Special attention is paid to modelling and addressing stochastic elements of operations, and to the interactions between different planning stages, from infrastructure development to rescheduling. The developed algorithms enable solution generation within an appropriate optimization time, and are applied at several case studies at airports and airlines.

The first subject of this dissertation is the creation of a comprehensive model for the implementation of ETVs at large airports, with a focus on ETV scheduling. This ETV schedule comprises an assignment of ETVs to to-be-towed aircraft, together with information when each ETV is to recharge its battery. An efficient ETV schedule, with a tight assignment and well spread charging moments, increases the number of aircraft which can be towed by an ETV, thereby increasing the environmental benefits as well as reducing the required number of ETVs to provide a given service level. We build on existing studies in three ways. These are (i) the development of realistic charging assumptions, (ii) the integration of taxiway traffic coordination, and (iii) the incorporation of disruption management.

The first goal is to benchmark the existing ETV scheduling models with one that has realistic charging assumptions. Specifically, we consider that the charging power decreases when approaching a full charge, and allow for preemptive charging. From a review of the existing models, our first ETV scheduling model is developed. This model is formulated as a mixed-integer linear programming model (MILP), and optimization of this model is performed using a branch-and-bound (B&B) algorithm. The different models are compared in a case study.

Building on this, we develop an optimization model for ETV scheduling that integrates the taxiway traffic coordination with ETV scheduling. This concerns the routing of aircraft and ETVs across the airport taxiways and service roads, while avoiding (near) collisions. An efficient routing reduces the taxiing time of aircraft and driving time of ETVs, while also preventing inefficient stop-and-go situations. A framework is proposed in which a full-day ETV schedule is created by sequentially optimizing surface movements and optimizing the ETV-to-aircraft assignment. For this purpose, two algorithms are developed: two sequential MILPs solved with the branch-and-bound algorithm, and a dynamic model solved by two greedy algorithms. For the surface movement optimization problem, the greedy algorithm is able to achieve a near-optimal routing with significantly reduced computational requirements. Contrasting, the greedy algorithm exhibits a significant gap with respect to the MILP when considering the ETV-to-aircraft assignment and charging schedule creation. This shows the necessity of a non-greedy algorithm for this problem.

This model is completed by the creation of an ETV scheduling algorithm that is able to retain performance under flight schedule disruptions. Disruptions such as early arrivals and late departures are commonplace at large airports, and ETV scheduling algorithms are required to account for this. A dynamic data-driven scheduling model is developed, which both anticipates and reacts to disruptions. It is used to simulate ETV operations at several days at a large airport, with real-time updates of the flight arrival/departure times. Thirty days of historical flight data are used to predict flight delays. The results show that the ability to anticipate disruptions enables more-robust schedules, with a higher environmental benefit per ETV.

The second subject of this dissertation is the implementation of small electric aircraft. The first generation of these aircraft can be deployed in remote areas, such as archipelagoes or fjords. For the charging operations, a battery swapping system is considered. This system has the advantage of significantly reducing the turnaround time, as well as the ability to spread the charging power across the day more evenly. We consider a charging infrastructure sizing and charging operations scheduling model for a network of electric aircraft. An efficient charging schedule reduces the required charging infrastructure, and conversely, an appropriate charging infrastructure reduces operational disruptions.

The scheduling model considers when the battery of each aircraft is recharged, given a specified charging infrastructure. The schedule is made to minimize operational disruptions while spreading electricity demand as best as possible. This model is integrated into the recharge infrastructure sizing model as a subroutine. By considering different levels of traffic around the year, a balanced charging infrastructure is obtained. The model is optimized with a simulated annealing algorithm, where the scheduling model is formulated as a MILP and is addressed with a branch-and-bound algorithm. The method is applied in a case study to a domestic network considering one year of operations. The results show that this approach allows for significant cost reductions.

The third subject of this dissertation are the eVTOL aircraft. We aim to create a predictive maintenance framework for the eVTOL batteries which is integrated into operations. This maintenance schedule comprises the times which each eVTOL in a fleet is maintained, while ensuring that capacity is not exceeded.
Using battery sensor measurements, health prognostics can be made. The ability to create these and implement them adequately into maintenance operations minimizes the number of breakdowns while maximizing the used battery life. Two models are presented for predictive battery maintenance planning: (i) a two-stage probabilistic remaining useful life (RUL) prognostics and (ii) an end-to-end maintenance cost prognostics framework. When applied to a case study, the results show the merit of the end-to-end planning framework, with fewer breakdowns and lower maintenance costs.

The objective of this dissertation has been the creation of operations optimization algorithms for electrified aviation. Special attention has been paid to the interaction between the planning phases involved: from infrastructure development to asset scheduling to disruption management. Data-driven algorithms have been developed to address the uncertainties which occur within the different phases. The models can provide support for the implementation of these technologies into aviation operations. Future work could address the integration of the different algorithms into an overall planning framework. Additionally, it could address the creation of fairness constraints. Also, when the technology readiness of the ETVs and aircraft is at a higher level, more accurate performance models can be leveraged to improve the quality of the results of the developed algorithms. Overall, this dissertation provides a starting point for airport and airline planners when considering electric aviation technologies.
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Introducing electric vehicles that tow aircraft during taxiing is an emerging technology aimed at supporting climate neutrality for aviation. Planning electric towing operations is, however, impeded by the high uncertainty in aircraft arrival and departure times. We address the question of how to plan the operation of a fleet of Electric Towing Vehicles (ETVs) to maximize environmental benefits, given the uncertainty in aircraft arrival/departure times. For this, we propose a stochastic and dynamic planning framework for ETVs, where stochastic aircraft arrival and departure times are updated during the day. With this, the assignment of the ETVs-to-aircraft to replace conventional taxiing, and ETV battery charging times are planned such that the fuel savings are maximized. At the same time, we ensure that aircraft delays induced by the use of ETVs are minimized. We illustrate our framework for a large European airport. The results show that our framework achieves 79.5% of the highest possible cost reduction (fuel and ETV-induced delay), which is obtained when full knowledge of the arrival/departure times is available in advance. Furthermore, we show that considering the uncertainty in the arrival/departure times, rather than using point estimates of these times, leads to a 17.7% additional cost reduction. Overall, our framework supports the implementation of electric aircraft towing with maximum environmental benefits while considering the dynamic, uncertain arrival and departure times of aircraft. ... Read more

Recent advances in battery technology have opened the possibility for short-haul electric flight. This is particularly attractive for commuter airlines that operate in remote regions such as archipelagos or Nordic fjords where the geography impedes other means of transportation. In this paper we address the question of how to optimize the charging infrastructure (charging power, spare batteries) for an airline when considering a battery swapping system. Our analysis considers the expenditures needed for (i) the significant charging power requirements, (ii) spare aircraft batteries, (iii) the used electricity, and (iv) delay costs, should the infrastructure not be sufficient to accommodate the flight schedule. The main result of this paper is the formulation of this problem as a two-phase recourse model. This is required to account for the variation of the flight schedule throughout a year of operations. With this, both the strategic (infrastructure sizing) and tactical (battery recharge scheduling) planning are addressed The model is applied for Widerøe Airlines, with a network of 7 hub airports and 36 regional airports in Norway. The results show that a total investment of 4412 kW in electricity power supply and 25 spare batteries is needed for the considered network, resulting in a daily investment of €11700. We also quantify the benefits of considering an entire year of operations for our analysis, instead of just one congested day (7% cost reduction) or one average day of operations (31% reduction) at the most congested airport. ... Read more

Following the Paris Accords, the aviation industry aims to become climate neutral by 2050. In this line, electric vehicles that tow aircraft during taxiing are a promising emerging technology to reduce emissions at airports. This paper proposes an end-to-end optimization framework for electric towing vehicles (ETVs) dispatchment at large airports. We integrate the routing of the ETVs in the taxiway system where minimum separation distances are ensured at all times, with the assignment of these ETVs to aircraft towing tasks and scheduling ETV battery recharging. For ETV recharging, we consider a preemptive charging policy where the charging times depend on the residual state-of-charge of the battery. We illustrate our model for one day of operations at a large European airport. The results show that the 913 arriving and departing flights can be towed with 38 ETVs, with battery charging distributed throughout the day. The fleet size is shown to increase approximately linear with the number of flights in the schedule. We also propose a greedy dispatchment of the ETVs, which is shown to achieve an optimality gap of 6% with respect to the number of required vehicles and with 22% with respect to the maximum delay during towing. We also show that both algorithms can be leveraged to account for flight delays using a rolling horizon approach, and that over 95% of the flights can be reallocated if delays occur. Overall, we propose a roadmap for ETV management at large airports, considering realistic ETV specifications (battery capabilities, kinematic properties) and requirements for aircraft collision avoidance during towing. ... Read more

Reducing aircraft taxiing emissions will deliver a significant contribution to the worldwide goal of net-zero greenhouse gas emissions in the aviation industry. Replacing jet-engine taxiing by towing aircraft with electric towing vehicles is expected to reduce taxiing emissions by roughly 80%. Introducing a fleet of towing vehicles introduces operational challenges to an airport. Although there has been research focused on optimizing the assignment of vehicles to aircraft, such an assignment will require changes during a day of operations, when disruptions such as flight delays occur. This paper proposes two models, a strategic and a disrupted model, with which an adaptive vehicle-to-aircraft assignment is created. The models are formulated as Mixed Integer Linear Problems, and both maximize the number of towed aircraft and minimize the schedule changes for vehicle operators. The approach illustrated includes vehicle and aircraft routing, conflict avoidance, and a model for energy usage. We apply the models to Amsterdam Airport Schiphol, where the disrupted model is able to create assignments that remain the same in subsequent time steps for an average of 55% of the vehicles, on a busy day, when towing all aircraft. Furthermore, the results show that minimizing schedule changes does not come at the expense of fewer towed aircraft, i.e. of smaller emission savings. Lastly, we investigate the impact of fleet size and general on-time performance on the assignments created by the model. ... Read more

Point clouds contain high detail and high accuracy geometry representation of the scanned Earth surface parts. To manage the huge amount of data, the point clouds are traditionally organized on location and map-scale; e.g. in an octree structure, where top-levels of the tree contain few points suitable for small scale overviews and lower levels of the tree contain more points suitable for large scale detailed views. The drawback of this solution is that it is based on discrete levels, causing visual artifacts in the form of data density shocks when creating the commonly used perspective views. This paper presents a method based on an optimized distribution of points over continuous levels, avoiding the visualization shocks. The traditional distribution ratio's of data amounts over discrete levels of raster or vector data is considered the reference. How to convert this to point clouds with continuous levels (still benefiting from the proven advantages of the data distribution in discrete levels for efficient access at a wide range of scales)? In our solution, for each point a cLoD (continuous Level of Detail) value is computed and added as dimension to the point. A SFC (Space Filling Curve)-based nD data clustering technique can be used to organize the points, so that they can be efficiently queried. It should be noted that also other multi-dimensional indexing and clustering techniques could be applied to realize continuous levels based on the cLoD value. Besides the mathematical foundation of the approach also several implementations are described, varying from a 3D web-browser based solution to an augmented reality point cloud app in a mobile phone. The cLoD enables interactive real-time visualization using perspective views without data density shocks, while supporting continuous zoom-in/out and progressive data streaming between server and client. The described cLoD based approach is generic and supports different types of point clouds: from airborne, terrestrial, mobile and indoor laser scanning, but also from dense matching optical imagery or multi-beam echo soundings. ... Read more

The aviation industry aims for net-zero emissions by 2050. In this line, achieving climate-neutral ground operations is one of the first objectives. Electric vehicles that tow aircraft during taxiing are a promising technology to achieve climate-neutral ground operations. In this paper, we consider the dispatching of electric towing vehicles at an airport. We study the impact of the maximum battery capacity of these vehicles and the battery recharging protocols, on the total number of electric towing vehicles required at an airport. We propose a mixed-integer linear program to determine the size of the fleet of electric towing vehicles under various battery capacities and various battery recharging protocols. We illustrate our model for one day of operations at Amsterdam Airport Schiphol. The results show that 41, 29, and 24 ETVs are required to tow all aircraft when batteries capacities of 100 kWh, 320 kWh, and 500 kWh are considered, respectively. Compared with the best performing approach existing in literature, our model reduces the required size of the fleet of electric towing vehicles by 27% when considering a nominal battery size of 320 kWh. ... Read more

This paper reports the results of an international benchmark exercise on the measurement of fibre bed compaction behaviour. The aim was to identify aspects of the test method critical to obtain reliable results and to arrive at a recommended test procedure for fibre bed compaction measurements. A glass fibre 2/2 twill weave and a biaxial (±45°) glass fibre non-crimp fabric (NCF) were tested in dry and wet conditions. All participants used the same testing procedure but were allowed to use the testing frame, the fixture and sample geometry of their choice. The results showed a large scatter in the maximum compaction stress between participants at the given target thickness, with coefficients of variation ranging from 38% to 58%. Statistical analysis of data indicated that wetting of the specimen significantly affected the scatter in results for the woven fabric, but not for the NCF. This is related to the fibre mobility in the architectures in both fabrics. As isolating the effect of other test parameters on the results was not possible, no statistically significant effect of other test parameters could be proven. The high sensitivity of the recorded compaction pressure near the minimum specimen thickness to changes in specimen thickness suggests that small uncertainties in thickness can result in large variations in the maximum value of the compaction stress. Hence, it is suspected that the thickness measurement technique used may have an effect on the scatter. ... Read more