Optimizing the battery charging and swapping infrastructure for electric short-haul aircraft—The case of electric flight in Norway

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

Optimization of Electric Scooter Rebalancing Tour through Mathematical programming

openThe pursuit of Sustainable Development Goal 11 (SDG11), striving to create inclusive, resilient, and sustainable urban environments, has become a global priority. One of SDG11’s key objectives is to ensure safe, affordable, accessible, and sustainable transport systems for all. In response to this imperative, the concept of electric scooter (e-scooter) sharing has gained remarkable popularity. This innovative model allows users to access e-scooters on demand, providing a personalized last-mile solution that complements public transportation and has the potential to reduce private car ownership and greenhouse gas emissions. As e-scooters take on an increasingly pivotal role in urban mobility, the efficient management of their distri- bution and charging presents a critical challenge. One of the key problems in this sharing system is to ensure that e-scooter is not over-saturated and under-utilized. This thesis embarks on a comprehensive exploration of the com- plexities surrounding this challenge and proposes solutions to enhance the integration of e-scooter sharing into everyday urban life. The core idea revolves around conducting a night tour operation that allow operator to drop full-charge e-scooter, but also swap the battery of the low charged unit to re-balance the e-scooter distribution. Within this thesis, two mathematical programming formulations are presented in order to plan ahead the route and suggested actions at each station during the night tour. The first model, adapted from previous literature work on bike sharing systems rebalancing, provides a benchmark and introduces readers to the night rebalancing tour problem. The second model represents an original improved version, allowing night tour operators to swap only the battery rather than the whole e-scooter unit during operations. Both models confront the challenge of managing exponential growth in constraints and size of the solution space as the number of stations increases. In response, tailor-made branch-and-cut algorithms are developed to efficiently solve this problem. This scalable framework offers the potential to manage extensive e-scooter fleets and station networks within the city, enabling companies to enhance their operations, foster citizen trust, and establish e-scooter sharing systems as a dependable choice for daily last-mile transportation. This thesis aims to make the topic accessible and easily understandable, inviting broader participation and contributions to research in this vital field.The pursuit of Sustainable Development Goal 11 (SDG11), striving to create inclusive, resilient, and sustainable urban environments, has become a global priority. One of SDG11’s key objectives is to ensure safe, affordable, accessible, and sustainable transport systems for all. In response to this imperative, the concept of electric scooter (e-scooter) sharing has gained remarkable popularity. This innovative model allows users to access e-scooters on demand, providing a personalized last-mile solution that complements public transportation and has the potential to reduce private car ownership and greenhouse gas emissions. As e-scooters take on an increasingly pivotal role in urban mobility, the efficient management of their distri- bution and charging presents a critical challenge. One of the key problems in this sharing system is to ensure that e-scooter is not over-saturated and under-utilized. This thesis embarks on a comprehensive exploration of the com- plexities surrounding this challenge and proposes solutions to enhance the integration of e-scooter sharing into everyday urban life. The core idea revolves around conducting a night tour operation that allow operator to drop full-charge e-scooter, but also swap the battery of the low charged unit to re-balance the e-scooter distribution. Within this thesis, two mathematical programming formulations are presented in order to plan ahead the route and suggested actions at each station during the night tour. The first model, adapted from previous literature work on bike sharing systems rebalancing, provides a benchmark and introduces readers to the night rebalancing tour problem. The second model represents an original improved version, allowing night tour operators to swap only the battery rather than the whole e-scooter unit during operations. Both models confront the challenge of managing exponential growth in constraints and size of the solution space as the number of stations increases. In response, tailor-made branch-and-cut algorithms are developed to efficiently solve this problem. This scalable framework offers the potential to manage extensive e-scooter fleets and station networks within the city, enabling companies to enhance their operations, foster citizen trust, and establish e-scooter sharing systems as a dependable choice for daily last-mile transportation. This thesis aims to make the topic accessible and easily understandable, inviting broader participation and contributions to research in this vital field

Scheduling Allocation and Inventory Replenishment Problems Under Uncertainty: Applications in Managing Electric Vehicle and Drone Battery Swap Stations

In this dissertation, motivated by electric vehicle (EV) and drone application growth, we propose novel optimization problems and solution techniques for managing the operations at EV and drone battery swap stations. In Chapter 2, we introduce a novel class of stochastic scheduling allocation and inventory replenishment problems (SAIRP), which determines the recharging, discharging, and replacement decisions at a swap station over time to maximize the expected total profit. We use Markov Decision Process (MDP) to model SAIRPs facing uncertain demands, varying costs, and battery degradation. Considering battery degradation is crucial as it relaxes the assumption that charging/discharging batteries do not deteriorate their quality (capacity). Besides, it ensures customers receive high-quality batteries as we prevent recharging/discharging and swapping when the average capacity of batteries is lower than a predefined threshold. Our MDP has high complexity and dimensions regarding the state space, action space, and transition probabilities; therefore, we can not provide the optimal decision rules (exact solutions) for SAIRPs of increasing size. Thus, we propose high-quality approximate solutions, heuristic and reinforcement learning (RL) methods, for stochastic SAIRPs that provide near-optimal policies for the stations. In Chapter 3, we explore the structure and theoretical findings related to the optimal solution of SAIRP. Notably, we prove the monotonicity properties to develop fast and intelligent algorithms to provide approximate solutions and overcome the curses of dimensionality. We show the existence of monotone optimal decision rules when there is an upper bound on the number of batteries replaced in each period. We demonstrate the monotone structure for the MDP value function when considering the first, second, and both dimensions of the state. We utilize data analytics and regression techniques to provide an intelligent initialization for our monotone approximate dynamic programming (ADP) algorithm. Finally, we provide insights from solving realistic-sized SAIRPs. In Chapter 4, we consider the problem of optimizing the distribution operations of a hub using drones to deliver medical supplies to different geographic regions. Drones are an innovative method with many benefits including low-contact delivery thereby reducing the spread of pandemic and vaccine-preventable diseases. While we focus on medical supply delivery for this work, it is applicable to drone delivery for many other applications, including food, postal items, and e-commerce delivery. In this chapter, our goal is to address drone delivery challenges by optimizing the distribution operations at a drone hub that dispatch drones to different geographic locations generating stochastic demands for medical supplies. By considering different geographic locations, we consider different classes of demand that require different flight ranges, which is directly related to the amount of charge held in a drone battery. We classify the stochastic demands based on their distance from the drone hub, use a Markov decision process to model the problem, and perform computational tests using realistic data representing a prominent drone delivery company. We solve the problem using a reinforcement learning method and show its high performance compared with the exact solution found using dynamic programming. Finally, we analyze the results and provide insights for managing the drone hub operations

Optimizing Sustainable Transit Bus Networks in Smart Cities

Urban mobility has been facing several challenges in the recent years due to the increasing populations and private vehicles ownership, which led to several negative environmental and social impacts in big cities. In this dissertation, we investigate how decision support systems can enhance the role of transit bus networks in the transition to more sustainable and convenient urban mobility

The Digital Transformation of Automotive Businesses: THREE ARTEFACTS TO SUPPORT DIGITAL SERVICE PROVISION AND INNOVATION

Digitalisation and increasing competitive pressure drive original equipment manufacturers (OEMs) to switch their focus towards the provision of digital services and open-up towards increased collaboration and customer integration. This shift implies a significant transformational change from product to product-service providers, where OEMs realign themselves within strategic, business and procedural dimensions. Thus, OEMs must manage digital transformation (DT) processes in order to stay competitive and remain adaptable to changing customer demands. However, OEMs aspiring to become participants or leaders in their domain, struggle to initiate activities as there is a lack of applicable instruments that can guide and support them during this process. Compared to the practical importance of DT, empirical studies are not comprehensive. This study proposes three artefacts, validated within case companies that intend to support automotive OEMs in digital service provisioning. Artefact one, a layered conceptual model for a digital automotive ecosystem, was developed by means of 26 expert interviews. It can serve as a useful instrument for decision makers to strategically plan and outline digital ecosystems. Artefact two is a conceptual reference framework for automotive service systems. The artefact was developed based on an extensive literature review, and the mapping of the business model canvas to the service system domain. The artefact intends to assist OEMs in the efficient conception of digital services under consideration of relevant stakeholders and the necessary infrastructures. Finally, artefact three proposes a methodology by which to transform software readiness assessment processes to fit into the agile software development approach with consideration of the existing operational infrastructure. Overall, the findings contribute to the empirical body of knowledge about the digital transformation of manufacturing industries. The results suggest value creation for digital automotive services occurs in networks among interdependent stakeholders in which customers play an integral role during the services’ life-cycle. The findings further indicate the artefacts as being useful instruments, however, success is dependent on the integration and collaboration of all contributing departments.:Table of Contents Bibliographic Description II Acknowledgment III Table of Contents IV List of Figures VI List of Tables VII List of Abbreviations VIII 1 Introduction 1 1.1 Motivation and Problem Statement 1 1.2 Objective and Research Questions 6 1.3 Research Methodology 7 1.4 Contributions 10 1.5 Outline 12 2 Background 13 2.1 From Interdependent Value Creation to Digital Ecosystems 13 2.1.1 Digitalisation Drives Collaboration 13 2.1.2 Pursuing an Ecosystem Strategy 13 2.1.3 Research Gaps and Strategy Formulation Obstacles 20 2.2 From Products to Product-Service Solutions 22 2.2.1 Digital Service Fulfilment Requires Co-Creational Networks 22 2.2.2 Enhancing Business Models with Digital Services 28 2.2.3 Research Gaps and Service Conception Obstacles 30 2.3 From Linear Development to Continuous Innovation 32 2.3.1 Digital Innovation Demands Digital Transformation 32 2.3.2 Assessing Digital Products 36 2.3.3 Research Gaps and Implementation Obstacles 38 3 Artefact 1: Digital Automotive Ecosystems 41 3.1 Meta Data 41 3.2 Summary 42 3.3 Designing a Layered Conceptual Model of a Digital Ecosystem 45 4 Artefact 2: Conceptual Reference Framework 79 4.1 Meta Data 79 4.2 Summary 80 4.3 On the Move Towards Customer-Centric Automotive Business Models 83 5 Artefact 3: Agile Software Readiness Assessment Procedures 121 5.1 Meta Data 121 5.2 Meta Data 122 5.3 Summary 123 5.4 Adding Agility to Software Readiness Assessment Procedures 126 5.5 Continuous Software Readiness Assessments for Agile Development 147 6 Conclusion and Future Work 158 6.1 Contributions 158 6.1.1 Strategic Dimension: Artefact 1 158 6.1.2 Business Dimension: Artefact 2 159 6.1.3 Process Dimension: Artefact 3 161 6.1.4 Synthesis of Contributions 163 6.2 Implications 167 6.2.1 Scientific Implications 167 6.2.2 Managerial Implications 168 6.2.3 Intelligent Parking Service Example (ParkSpotHelp) 171 6.3 Concluding Remarks 174 6.3.1 Threats to Validity 174 6.3.2 Outlook and Future Research Recommendations 174 Appendix VII Bibliography XX Wissenschaftlicher Werdegang XXXVII Selbständigkeitserklärung XXXVII

Grid flexibility by electrifying energy systems for sustainable aviation

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDecarbonisation of aviation goals set by Flightpath 2050 Europe’s Vision for Aviation requires that the airports become emission-free by 2050. This thesis original contribution to knowledge is to explore the incorporation of aviation electrification technologies, including electric aircraft (EA), electrified ground support equipment (GSE), and airport parking electric vehicles (EVs), into power systems, evaluating their influence on grid infrastructure and operations, as well as their potential to support the grid operation. A comprehensive review of aviation electrification technologies revealed a research gap in the integration of these technologies into the power systems. The thesis contributes to electricity network infrastructure planning for electrification of aviation and airport-based distributed energy resources (DER) that provide ancillary services to the power grid. A multi-objective airport microgrid planning framework is developed, comparing EA charging strategies and revealing that battery swap performs better. Vehicle-to-grid (V2G) strategy with parking EVs improves the microgrid's performance. A techno-economic assessment of wireless charging systems for electric airport shuttle buses shows better economic performance than conventional buses and other charging options. A novel Aviation-to-Grid (A2G) flexibility concept provides frequency response services to the GB power system using EA battery charging systems, with typical A2G service capacity showing significant variation across eight UK airports. A deep reinforcement learning (DRL)-based A2G dispatch approach evaluates the impact of EA charger capacity on energy dispatch results, with higher capacities leading to higher revenue and lower operation costs. To summarise, this thesis addresses the research gaps in integrating aviation electrification technologies into power systems, offering valuable insights for airport operators aiming to decarbonise air transport activities through the adoption of these technologies. The study also provides an understanding of the impacts on grid operators in terms of infrastructure planning and operations. This comprehensive approach ensures a cohesive understanding of the challenges and opportunities presented by aviation electrification and its integration into power systems

Towards electric bus system: planning, operating and evaluating

The green transformation of public transportation is an indispensable way to achieve carbon neutrality. Governments and authorities are vigorously implementing electric bus procurement and charging infrastructure deployment programs. At this primary but urgent stage, how to reasonably plan the procurement of electric buses, how to arrange the operation of the heterogeneous fleet, and how to locate and scale the infrastructure are urgent issues to be solved. For a smooth transition to full electrification, this thesis aims to propose systematic guidance for the fleet and charging facilities, to ensure life-cycle efficiency and energy conservation from the planning to the operational phase.One of the most important issues in the operational phase is the charge scheduling for electric buses, a new issue that is not present in the conventional transit system. How to take into account the charging location and time duration in bus scheduling and not cause additional load peaks to the grid is the first issue being addressed. A charging schedule optimization model is constructed for opportunity charging with battery wear and charging costs as optimization objectives. Besides, the uncertainty in energy consumption poses new challenges to daily operations. This thesis further specifies the daily charging schedules with the consideration of energy consumption uncertainty while safeguarding the punctuality of bus services.In the context of e-mobility systems, battery sizing, charging station deployment, and bus scheduling emerge as crucial factors. Traditionally these elements have been approached and organized separately with battery sizing and charging facility deployment termed planning phase problems and bus scheduling belonging to operational phase issues. However, the integrated optimization of the three problems has advantages in terms of life-cycle costs and emissions. Therefore, a consolidated optimization model is proposed to collaboratively optimize the three problems and a life-cycle costs analysis framework is developed to examine the performance of the system from both economic and environmental aspects. To improve the attractiveness and utilization of electric public transportation resources, two new solutions have been proposed in terms of charging strategy (vehicle-to-vehicle charging) and operational efficiency (mixed-flow transport). Vehicle-to-vehicle charging allows energy to be continuously transmitted along the road, reducing reliance on the accessibility and deployment of charging facilities. Mixed flow transport mode balances the directional travel demands and facilities the parcel delivery while ensuring the punctuality and safety of passenger transport

Efficient management of industrial electric vehicles by means of static and dynamic wireless power transfer systems

Industrial companies are moving toward the electrification of equipment and processes, in line with the broader energy transition taking place across the economy. Particularly, the energy efficiency and, consequently, the reduction of environmental pollution of intralogistics activities have become a competitive element and are now an actual research and development objective. A wireless power transfer is a contactless electrical energy transmission technology based on the magnetic coupling between coils installable under the ground level and a coil mounted under the vehicle floor, and it represents an excellent solution to decrease the demand for batteries by reducing vehicle downtimes during the recharge. This work aims to define a methodology to determine the optimal positioning of wireless charging units across the warehouse, both for static and dynamic recharging. To this aim, firstly, a mathematical model of the warehouse is proposed to describe transfers and storage/retrieval operations executed by the forklifts. Then, an integer linear programming problem is applied to find the best possible layout of the charging infrastructures. The optimal solution respects the energetic requirements given by the customer and minimizes the overall system cost. The proposed approach was applied to optimize the installation in a real-size warehouse of a tire manufacturing company. Several scenarios were computer generated through discrete event simulation in order to test the optimizer in different warehouse conditions. The obtained results show that integrated dynamic and static WPT systems ensure a constant state of charge of the electric vehicles during their operations