The Europe-Asia-Oceania air route is experiencing rapid growth, traditionally served by direct legacy flights but increasingly dominated by hub-based carriers. These airlines leverage large, single-hub models to capture transfer traffic. However, limited research exists on how to efficiently design and operate multi-hub networks for international connectivity. Existing models either oversimplify hub capacities or focus solely on fleet planning and re-timing, lacking an integrated, schedule-based approach. This research develops an integrated decision support model that combines a capacitated multi-allocation p-hub location problem with airline schedule design under operational constraints and competitive dynamics. A multi-step iterative method connects hub assignment and scheduling using a genetic algorithm to ensure feasibility, connectivity, and profitability. The findings reveal that adding hubs initially boosts network efficiency and profitability, though marginal benefits diminish after a certain number of hubs. The integrated model significantly narrows the gap between theoretical and actualized profitability, showing a 7.6\% increase in daily profit for the scheduling model over iterations. This research offers a crucial decision-support tool for long-term airline network planning, particularly in rapidly expanding aviation markets. Its ability to jointly optimize hub locations and flight schedules under operational constraints provides substantial utility for diverse airline network planning scenarios, leading to more viable and profitable network configurations.

Returnable Transport Items (RTIs) play a critical role in sustainable logistics by enabling asset reuse across supply chains. However, damaged RTIs must be efficiently transported to repair centers and reintegrated into the network to maintain service quality and cost-efficiency. This thesis addresses the challenge of optimizing such repair-integrated transport flows within RTI pooling systems, using Container Centralen as a real-world case study. An Integer Linear Programming (ILP) model is developed to jointly determine the optimal allocation of broken RTIs to repair centers and the subsequent redistribution of repaired items to depots, under constraints such as repair capacity, transport time, and demand fulfillment. The model is implemented in Python using Gurobi and embedded in a user-friendly dashboard for decision support. Through simulation experiments based on historical data and realistic scenarios, the integrated approach demonstrates significant improvements over heuristic strategies, including reduced transport distances, better repair center utilization, and improved service reliability. The findings highlight the practical and theoretical value of integrated logistics modeling for RTI networks and propose several avenues for future research, including the extension to multiple RTI types, incorporation of uncertainty, and scalability enhancements. This work contributes to both operations research literature and practical decision-making in circular supply chain management.

The introduction of the shipping container revolutionised global trade by significantly reducing handling costs, improving efficiency and enabling intermodal transportation. This development paved the way for the expansion of international trade and the development of highly interconnected global supply chains. Congestion, sustainability concerns, and vulnerability to disruption have become major obstacles. Recent examples of such obstacles are the COVID-19 pandemic and the blockage of the Suez Canal. Synchromodality has been identified as a potential solution for mitigating some of these concerns.

As a relatively new concept, synchromodality has mainly been studied at a theoretical level, focusing on its definition and potential. As the concept of synchromodality seems to gain attention from a broader public, more recent research has also focused on the more quantitative side. These quantitative studies primarily model the transport planning side of synchromodality. In these studies, some aspects in the supply chain have been overlooked so far, mainly the impact on critical infrastructure such as container terminals.

This research addresses this overlooked area in the existing academic landscape. Aspects such as the loading, unloading and stacking of containers are explicitly modelled in combination with synchromodal transport planning optimisation. This allows for an assessment of how synchromodality influences container terminal operations and how constraints and the dynamics at container terminals influence the transport planning. A multi-agent system approach is used as a framework for this model, as this presents a good option to model the different stakeholders involved in synchromodal transport.

The performance was analysed based on key performance metrics, including container relocation frequency, dwell times, and cost efficiency. The findings indicate that the integration of synchromodal transport planning with container terminal operations yields significant improvements. An iterative feedback loop between the transport planning agent and terminal agents facilitates more effective decision-making, leading to feasible transport planning, smoother operations, and improved resource utilisation.

Scenario analysis yielded further interesting results in terms of how a synchromodal planner would adapt to disruptions. The two most interesting findings are a decrease in the number of transshipments and a modal shift towards faster, more flexible, but also more expensive and more polluting transport modes.

In conclusion, this research demonstrates that the integration of synchromodal transport planning and container terminal operations improves the efficiency and adaptability of synchromodal logistics networks. Through these advances, this research contributes to ongoing efforts in the planning of synchromodal transport and the optimisation of container terminals, offering valuable information for both academia and industry.

Airside service roads are essential for the movement of ground support vehicles that enable efficient aircraft turnaround and overall airport operations. Despite their importance, no standardised method exists for evaluating their capacity and performance, limiting the ability of airports to optimise this operational infrastructure. However, the heterogeneity of service road traffic — characterised by vehicles with varying speeds, sizes, and manoeuvrability — complicates capacity estimation and Level of Service (LoS) assessment. This research shows that a combination of microscopic traffic simulation and Multiple Linear Regression (MLR) provides a robust framework for estimating Passenger Car Unit (PCU) values and evaluating service road performance. Using five common airside road configurations, the method estimates PCU values for eleven service vehicle types and establishes LoS boundaries expressed in PCU per hour. The results reveal that PCU values vary significantly across intersection types, with four-way priority intersections exhibiting the highest sensitivity to traffic heterogeneity. Roundabouts are found to facilitate smoother traffic flow. Two three-way intersection types provide similar PCU values, but traditional priority rules facilitate substantially better performance. The results of the component-level models are upscaled to a service road network model, presenting a method for describing network performance. These findings provide a standardised method for assessing airside service roads, enabling airports to improve infrastructure planning and mitigate congestion amid rising aviation demands.

Airport surface movement operations present a promising opportunity for enhancing airport sustainability, as up to 25% of airport emissions originate from taxiing aircraft. This study explores the use of Electric Towing Vehicles (ETVs) to tow aircraft to runways, eliminating the operational use of aircraft engines during taxiing. Given the high cost of ETVs, their effective deployment is essential. This research adopts a Multi-Agent Pickup and Delivery (MAPD) framework to optimize ETV operations, addressing both the task assignment and path planning aspects of this problem. A novel task assignment method based on Ant Colony Optimization (ACO) is proposed, capable of efficiently scheduling towing and charging tasks for ETVs. The proposed method outperforms benchmark Mixed Integer Linear Programming (MILP) methods in terms of runtime and scalability, while delivering a competitive solution quality. The ACO-based task assignment method is integrated with a state-of-the-art motion planning algorithm based on Priority Based Search (PBS) and Safe Interval Motion Planning (SIMP). The resulting MAPD solution method is evaluated through its application in a high-fidelity simulation of Amsterdam Airport Schiphol (AAS). The simulation results indicate that deploying 22 ETVs at AAS can achieve a 57% reduction in the annual addressable CO2 emissions, while ensuring the economic feasibility of the operational concept.

Offshore wind turbines (OWTs) play a critical role in renewable energy, requiring efficient methods to predict fatigue loads on their support structures under harsh environmental conditions. Traditional fatigue assessment methods are effective but costly and impractical at scale. Surrogate models (SMs) offer a cost-effective alternative, but site-specific SMs often fail to generalize across turbines in varying environments and locations. This study explores whether platform-generic SMs trained on a Simulation Database can serve as reliable replacements for site-specific SMs trained on Supervisory Control and Data Acquisition (SCADA) and Structural Health Monitoring (SHM) data for predicting fatigue loads in OWT support structures. The SMs, developed using both deterministic and Bayesian neural networks (NNs), were evaluated in three progressively complex model setups featuring SCADA signals, nacelle accelerations, and tower top strain gauges. Results showed that site-specific SMs generally achieved lower mean average percentage error (MAPE), with deterministic NNs at 47.1m lowest astronomical tide (LAT) in the fore-aft (FA) direction reducing errors from 16.8% to 7.2% and in the side-side (SS) direction from 27.0% to 4.5% as additional signals were introduced. Platform-generic SMs exhibited higher MAPEs due to the broader scope of the model and slight mismatches between simulated and real turbine dynamics, especially in the FA direction. Nonetheless, in the SS direction at 47.1m LAT, platform-generic SMs showed promising performance, achieving errors as low as 7.5% in one configuration. Although Bayesian NNs did not consistently lower errors compared with deterministic approaches, they provided valuable insights into how far test data deviated from the training distribution, helping identify the potential limits of the model. This capability has significant practical implications, as it can potentially serve as a tool for detecting sensor malfunctions or identifying irregular data, ensuring more reliable predictions in real-world applications. Overall, platform-generic SMs still require refinement before they can fully replace site-specific SMs, and Bayesian NNs should not replace deterministic NNs but rather serve as a valuable supplement.

Airports play a significant role in economic development while presenting considerable energy consumption with demands from various stakeholders. Their important environmental impact can be reduced through an optimised energy system design. In this study, a systematic framework for the generation and comparison of airport energy systems is developed. Mixed integer linear programming and multi-objective optimization are employed to generate different system configurations, which are then compared using multi-criteria analysis. The airport energy demands are estimated based on a simple model for the building and the airfield handling area, using a limited set of parameters available for any airport. First results for Amsterdam Schiphol and five other European hub airports indicate that fully renewable energy systems can be competitive, especially when integrating battery storage for daily energy management. Self-sufficiencies of around 80% can be economically viable, relying on energy systems employing photovoltaic cells, a CO2 network, batteries and grid electricity.

Since the 1970s, helicopters have been vital in disaster response but face limitations in cost, infrastructure, and
maneuverability. This thesis presents the design and optimization of an emergency response flyer tailored for the
GoAERO competition. Multirotor configurations with hybrid-electric propulsion are investigated to overcome
the limitations of existing fully electric VTOL technologies.

A Multidisciplinary Design Optimization (MDO) framework is applied to hybrid-electric quadrotor, hexarotor,
and octorotor configurations, optimizing the balance between rotor count, mass, and performance. The objec-
tive is to minimize Maximum Takeoff Mass (MTOM) while enhancing dynamic performance and maximizing
the payload-to-system mass ratio. This study addresses a key research gap by integrating early-stage handling
qualities (HQ) evaluation and exploring trade-offs between optimized rotor count configurations. Aerodynamic
forces and energy consumption are estimated using momentum theory, while system dynamics are analyzed
through Newton-Euler equations and eigenvalue assessments.

Results show that the hexarotor configuration achieves the fastest convergence, balancing design complexity and
design space exploring. Configurations maintain a disk loading between 43–63[kg/m2] with hover efficiency
between 5 and 5.8. A higher rotor count improves hover efficiency and disk loading, making performance com-
parable to eHang and Vahana while achieving nearly twice the cruise speed, rivaling helicopters.

At a hybridization factor (HF) of 0.1, MTOM is reduced by 75%, with only a 3% mass variation between con-
figurations. However, at higher HF, mass increases by 20% due to relatively low energy density of batteries
further impacting structural support demands, underscoring the need for a more detailed support structure model.
Stability analysis confirms neutral hover stability, while a real, negative eigenvalue at cruise identifies the surge
subsidence mode, governed by system mass.

Rotor count significantly impacts design flexibility. Hexacopters and octocopters offer better disk loading homo-
geneity, whereas quadrotors face constrained rotor sizing and elevated blade and disk loading, limiting efficiency.
Quadrotors excel in payload-to-mass ratio and simplicity, making them ideal for productivity missions but less
suited for maneuvering-intensive tasks with lower disk loading and control authority, highlighting the importance
of early HQ considerations.

This thesis makes a valuable contribution to the advancement of eVTOL research by addressing early-stage HQ
assessments where on the basis of other literature and configuration optimization, the hexacopter emerges as the
most viable for the GoAERO competition, striking the best balance between mass, handling qualities, and mission
performance.

Excess inventory negatively impacts financial performance due to increased holding and working capital costs, stock value depreciation and lost sales from other products. For durable finished goods inventory within e-commerce, this issue of excess stock could be amplified given the risk of long-term accumulation and specific nature of goods, as well as the industry's competitive emphasis on broad product assortments and short delivery times. Despite improvements in forecasting, procurement and inventory management, the problem of excess stock is likely to persist, highlighting the importance of effective reactive control. This study proposes a binary programming model for optimizing reactive control strategies for excess inventory through cost minimization, tailored to the use case of a large e-commerce company, which remains specified due to confidentiality. The total costs consist of holding costs, working capital costs, transport costs and opportunity costs from lost revenue opportunities. The model considers three control strategies: 'keep in stock', 'return', 'liquidate', and incorporates warehouse capacity constraints. The model has been deployed for optimizing the excess stock from 27 November 2024, consisting of over 44 thousand excess items. Under limited warehouse capacity, the model yielded a 19% retention rate, 67% return rate, 14% liquidation rate, resulting in total excess stock costs of - €166k. Under unlimited warehouse capacity, eliminating opportunity costs of keep-in-stock, the model yielded a 31% retention rate, 59% return rate, 11% liquidation rate, resulting in total excess stock costs of - €215k. Thus, in both capacity scenarios, profit could be generated through optimal excess stock management. Optimal control decisions showed significant sensitivity to capacity constraints, return eligibility, expected stock days and the financial benefit of clearing compared to regular sale. Excess stock costs are mainly impacted by the opportunity costs of keep-in-stock and clearing, which are mostly sensitive to the rate at which excess stock depreciates in value, and the expected days in stock. Working capital, inventory holding and transport costs only have little effect. Given the clear relationships, optimal control decisions can be closely approximated with simple decision rules based on stock metrics, presenting a time-efficient alternative to exact optimization. Concluding, the optimization of reactive control strategies can significantly improve the performance of excess stock of the company. In the broader context of durable finished goods e-commerce platforms, this study suggest that incorporating opportunity costs alongside direct costs, and differentiating excess stock management strategies based on seasonality, could enhance excess stock management.

This work explores a paradigm in which isolated aircraft components are analyzed separately and combined together in a multi-body system representative of the whole geometry. The FLAPERON (FLight mechanics Analysis and PERformance OptimizatioN) toolbox is developed and employed to assess the impact of vortex-filament-based aerodynamic interactions on the simulated aircraft's response when considering aerodynamic data only at the component level. Physical networks created in Simscape are used to simulate symmetrical flight and assess key stability characteristics of a configuration including a main wing, a horizontal stabilizer, and a vertical stabilizer. Vortex filaments are modeled for the main wing and the horizontal stabilizer. The introduced aerodynamic interactions change the simulated response of the aircraft in accordance with predictions. This work suggests that multi-body analysis of flying qualities is possible based on the data prescribed at the component level.

Strategic airlift is an essential capability for the rapid global movement of cargo, particularly in crisis operations where timelines are short and cargo demands extreme. Despite the European Union's collective purchase of A400M aircraft, capability gaps remain due to the aircraft's limited ability to airlift heavy and outsized equipment. This study applies Knowledge-Based Engineering aircraft design tools in conjunction with Agent-Based Simulation techniques to evaluate a fleet's ability to move cargo rapidly across strategic distances in high-stakes operational scenarios. One focus of the study is on the often-overlooked constraint of cargo hold volume and its effects on a fleet's ability to transport cargo. The study found that this volume constraint can reduce airlift capacity by roughly 20% in scenarios with medium-density cargo. Further, through design space exploration, this study identifies key top-level aircraft requirements for a new airlifter to work effectively in the European fleet of airlifters. Analysis reveals that a next-generation aircraft requires a wide fuselage capable of double-file loading, an extended fuselage length of 39m, an increased payload capacity of 120 tons, and a cruise speed of Mach 0.8. Such a design could enhance the fleet performance and reduce fleet requirements by roughly 30%.

One of the biggest trade-offs in aviation is between a vehicle’s ability to take-off and land vertically and its efficiency in cruising flight. The recent drive for electric vehicles creates space to consider new solutions to this trade-off. Tilt wing aircraft offer significant advantages in vertical take-off and landing (VTOL) operations due to their versatile design. One of the difficulties in designing configurations with little historical data is that they are difficult to predict. The challenge of formulating a flight dynamics model valid through the transition phase from vertical to horizontal flight is undertaken in this study. This research aims to investigate the tilt wing aircraft behaviour through the wing tilt transition, as well as the stability characteristics. A six degree of freedom model implemented on the Canadair CL-84 Dynavert compares favourably to flight test data, albeit with low-fidelity especially around hovering flight. The likely cause is the simple rotor to wing slipstream interference modelled. The stability characteristics reveal large spikes in positive stability of the longitudinal derivative Cmu and Cmw due to the behaviour of the wings and the slipstream effect on the horizontal tail, with normalized values up to 5.5 and 150 respectively. Some unstable flight configurations are found within the flight envelope at 41 degrees and 28 degrees due to wing stall.

Inherent subjectivity, inefficiencies, and the substantial cost related to human-based visual inspection of high-pressure turbine (HPT) blades has driven research in alternative automated techniques. The
combination of computer vision (CV) and deep learning (DL) provides a compelling alternative. However, as DL models increase in capability, their large parameter spaces require substantial data to
achieve robust training. Therefore, this study explores the use of two Auxiliary Classifier Generative Adversarial Networks (AC-GANs) to augment proprietary datasets for two failure modes: (a) a 3-
channel red-green-blue (RGB) model for obstructed holes, and (b) a 4-channel RGB plus depth (RGBD) model for foreign object damage, with the depth reconstructed using monocular depth estimation. A Differential Evolution Optimizer (DEO) was used for hyperparameter optimization of a ResNet-18 classification target model. In the obstructed hole dataset, GAN-based augmentation showed significantly improved accuracy, recall, F1, and AUC-ROC (p < 0.05), competing with traditional methods using less augmentation. In the foreign object damage dataset, the inclusion of depth information significantly enhanced accuracy, precision, F1, and AUC-ROC performance (p < 0.05). However, the RGBD augmentation mainly resulted in a trade-off between precision and recall, without statistically significant differences (p > 0.05).

We present an adaptation of the tri-level Operator-Attacker-Defender (OAD) model for designing resilient supply chains, addressing disruptions such as supplier failures, production shortfalls, and disabled transportation links. These disruptions impact the supply chain's performance regarding its ability to meet customer demand. Originally proposed by Alderson et al. in 2011, the OAD model integrates three levels of decision-making: the operator optimizing system performance, the attacker seeking to disrupt it, and strategic system design decisions to mitigate disruptions. Our conceptual model is based on a multi-commodity flow network with a defined performance metric, formulating the tri-level optimization problem accordingly. Due to its complexity, we employ a decomposition-based solution strategy. Extensive computational experiments demonstrate the model's tractability across various supply chain scenarios. A real-world case study on a global pharmaceutical supply chain illustrates the OAD model's effectiveness against climate-related disruption. With its ability to help explore trade-offs between operational efficiency, costs, and resiliency, this model contributes to Resilient Supply Chain Design (RSCD) by providing a deterministic framework for designing supply chains capable of withstanding disruptions.

This study addresses the operational challenges of sustainable aircraft towing at hub airports, partic- ularly focusing on taxi delays. Using a queue-based discrete event simulation, airport taxi operations are modeled to evaluate the impact of tow-trucks on taxi delays and runway throughput. Key parameters in- clude service times for coupling and decoupling, tow-truck availability, and parallel service capacity. The findings reveal that taxi delays and runway throughput are significantly affected by the mean and variability of service times. Allowing multiple parallel service stations in front of the runway can reduce delays but may require infrastructure modifications. The study concludes that while operational towing can reduce emissions, its successful implementation depends on careful management and optimized traffic strategies to avoid compromising airport efficiency.

To accommodate the rising demand for airport resources and airspace capacity, a more efficient and robust orchestration of the components that facilitate air travel is required than is the case today. Over recent years, camera systems for automatic identification of ground-handling events were developed, providing the opportunity to automatically detect ground-handling delays as they manifest. Detecting ground-handling delays early allows the Air Navigation Service Providers (ANSPs), ground handlers and aircraft operators to reallocate their resources, facilitating more stable planning and robustness of the air traffic network. This study proposes a novel interpretable machine learning approach to predict initial ground-handling end time adherence. This approach is based on a discrete Bayesian network composed of a central network structure and modular sub-networks, representing distinct turnaround processes often found on the critical path. The Bayesian network structure and discretization were found to be adequate to model most ground-handling operations for narrow-body aircraft. The model proved to be more accurate than the ground handler in predicting initial Target Off-Block Time (TOBT) adherence at all prediction moments. At the initial TOBT-0 [minutes], a maximum in model accuracy was obtained amounting to 65.76%. In addition, the model demonstrated the capability to detect ground-handling delays earlier. At the initial TOBT-20 [minutes], an average recall of 50.4% was obtained for delays within 5 and 15 minutes. In contrast, the ground handler identified only 4.3% of small ground-handling delays at this prediction moment. The discrete Bayesian network approach is also inherently interpretable, providing transparency into the decision-making process of the model.

This research presents a mathematical model for routing that incorporates multiple depots, occasional drivers, and multiple depot visits, a problem faced by many companies in reality. An Adaptive Large Neighborhood Search (ALNS) algorithm was employed, using Random and Worst removal operators, Basic Greedy and Regret-2 insertion strategies, a roulette wheel for operator selection, and simulated annealing for acceptance criteria. Computational experiments validated the effectiveness of the ALNS algorithm and model.Sensitivity analysis revealed that adding depots (ODs) can reduce routing costs by up to 15.21%, with various OD capacities offering additional savings (7.86% for 60% capacity and 9.97% for 70% capacity). A case study with the Dutch e-commerce company Ochama, scaled to 150 requests, confirmed practical applicabil- ity, achieving cost reductions of 11.37% for small vehicles and 6.85% for medium-sized vehicles. The results highlight that integrating ODs into routing strategies can significantly lower costs, with optimal outcomes dependent on market research to balance savings, service quality, and OD capacity.

The advent and rise of low-cost, high-frequency short-haul flights in Europe increasingly necessitates the need of more sustainable travel methods for trips with a distance under 1000 km. Many different proposals and policies have been put in place, including full flight bans for trips under 500 km, but the research space still has not embraced a possible co-existence of major air and ground transport options in this segment.

This research addresses this by developing a reproducible method using openly accessible data to assess air and rail network capacities of three of the busiest air transport routes in Europe. A capacity analysis is conducted and the modelling of travel time, travel cost, change in carbon dioxide emissions and passenger experience is performed, in order to investigate the feasibility and logistics of shifting passengers between air and rail.

The study finds that 30-50% of passengers could shift completely to trains, given sufficient rail network capacity, significantly reducing total carbon dioxide emissions and improving passenger experience throughout.

A novel security checkpoint concept has been proposed to address the continuing growth in passenger traffic that airports are experiencing globally. The new security checkpoint intends to employ Automated Guided Vehicles (AGVs) instead of conveyor belts for baggage tray transportation. However, the new concept poses new challenges to the classic Multi-Agent Path Finding (MAPF) problem due to its small environment and high agent density. The AGV fleet will thus have to effectively coordinate their paths to avoid gridlocks within the crowded security checkpoint environment. Firstly, this paper presents a detailed Agent-Based Model (ABM) to accurately represent the new automated security checkpoint and estimate its passenger throughput. Secondly, the paper presents a new MAPF algorithm for the AGV fleet: the Windowed Hierarchical Idle-Push (WHIP) algorithm. WHIP is a prioritised MAPF solver that uses Push-operations to efficiently resolve the bottlenecks of the automated security checkpoint, avoiding gridlock even at very high agent densities. Experimentally, WHIP achieves path efficiencies 5 percentage points higher than leading prioritised MAPF algorithms, while requiring only half the computational runtime. Using the WHIP algorithm, the automated security checkpoint is capable of achieving an 18% increase in passenger throughput with respect to existing security checkpoints using conveyor belts.

To reduce greenhouse gas emissions in aviation, innovative propulsion systems such as battery-powered electric aviation are essential. These systems are carbon neutral when powered by 100% green electricity. However, widespread adoption requires overcoming challenges including improving energy density, lowering costs, and maintaining safe thermal operation (incl. thermal stability). This thesis addresses the challenge of maintaining battery thermal stability during flight by developing a combined electronic circuit and thermal network model for the NLR-owned Pipistrel Velis Electro aircraft. Using flight test data from this aircraft as a validation source, the model evaluates three battery thermal management strategies: two liquid cooling methods (ribbon and cold plate) and one gas cooling method (air cooling).
The electronic equivalent circuit model, used to simulate voltage characteristics and heat production in a single lithium ion battery cell, requires pulse current characterization tests for accurate parameter estimation.
Extensive testing has been conducted to gather these data. The model achieves accurate voltage modeling accuracy with a low root mean square error Adding more than one RC branch to the circuit did not significantly improve the accuracy of the model.
The electronic equivalent circuit and lumped parameter thermal network models were validated with two flight data sets. Both ribbon and cold plate cooling solutions effectively matched the validation temperatures, performing similarly. In contrast, the air cooling solution was less effective. In case of ribbon cooling, the maximum cell temperature was highly sensitive to its geometric parameters, specifically the angle and height of the ribbon. The sensitivity of the cold plate solution in terms of maximum battery temperature was influenced by the diameter of the cooling channel and the thickness of the plate. The air cooling showed sensitivity in terms of maximum battery temperature relative to the inter-cell gap width. For the ribbon model, varying the number of thermal nodes led to a convergence in the maximum battery temperature as the node count increased.
Using the validated ribbon cooling model, two operational scenarios were analyzed. The first scenario involved charging operations, where simulations closely matched temperature validation data, showing only a minor temperature rise in the battery pack. The second scenario tested cold weather operations with ambient temperatures reduced to approximately 0 ◦C. Here, two simulations were conducted: one with the battery preheated to 20 ◦C and another without preheating. Without preheating, the battery pack’s temperature neared the operational lower limit of 0 ◦C. Preheating prevented reaching this lower limit. It is recommended to preheat the battery pack using an external charger, as using the battery’s own energy for preheating is inefficient.
The thesis was concluded by using the developed modeling approach to size and model a battery pack for the Eviation Alice, a larger aircraft. The approach was successfully scaled to this large use case with a known power profile. Furthermore, due to significant ambient temperature effects and higher operational altitudes compared to the Pipistrel Velis Electro, thermal insulation will be necessary for the battery pack to maintain temperatures above the lower operational limit of 0 ◦C during typical missions.