Reduction of carbon emissions and greenhouse gases within the transportation sector is crucial due to its significant contribution to environmental degradation. To reduce this impact, it is important to develop and implement energy efficient systems. In addition to exploring renewable fuel alternatives, effective energy management systems are vital. This paper presents an optimization-based control referred to as Hierarchical Model Predictive Control along with a comparative framework, to evaluate its effectiveness as a real-time control strategy. Three energy management strategies are designed for this purpose, with the objective of minimizing the fuel consumption of a hybrid marine powertrain, while optimizing the power distribution among its components to achieve high operational efficiency.

Currently, inland waterway shipping mainly includes barges and bulk transportation with little to no variation in volume, product, and route. Since transport over water emits less CO2 per tonne-km than road transport, a potential way to reduce CO2 emissions is to transport more containers via inland waterways. Large delays cause unreliability, which in their turn cause an opportunity cost and increased operational costs. These increased costs have a negative impact on the competitiveness of inland water transport with regards to road transport.

An alternative to the way planning is handled currently is by solving a Vehicle Rotaton Planning Problem (VRPP). The problem is solved by minimizing a cost function. For example, the total time spent in a port area (called the sojourn time) can be minimized.

Solving the VRPP results in a rotation plan that dictates the order in which vessels visit terminals and how many containers they should transport from one terminal to another. There are multiple ways to solve the VRPP, one of which is through the use of the Distributed Pseudotree Optimization Protocol (DPOP), where the vessel operators reach an optimal solution together by sharing only information about those variables that share the same constraints with other variables from other agents. An attractive property of DPOP is that it is possible to include privacy constraints in the algorithm, such that vessel operators only have to share minimal information about their journey and not lose a competitive advantage to one another. For example, the operators might only want to share variables like the arrival time, and only want to share it with operators that have the approximately same arrival time.

To ensure the problem can be solved and that the message size does not run out of memory bounds, the memory-bounded version of DPOP is implemented. Because Memory-Bounded Distributed Pseudotree Optimization Protocol (MB-DPOP) requires an exponential number of messages when the inference width is above a certain threshold, an alternative way of formulating and solving the problem is considered using the Maximum Gain Messaging (MGM) algorithm and a formulation that includes how much vessel operators are willing to change their current rotation plan for a lower overall cost. With this formulation, when something changes in the problem definition, the entire calculation does not have to be done again because starting from the previous solution is possible.

Two simulated case studies in the port area of Rotterdam are done to performed the performance of the algorithms. The results show while MB-DPOP can guarantee some level of privacy, it does not scale very well and is thus not really useful in real life. While the algorithm yields a global solution and can guarantee a certain level of privacy, its execution time is in the order of hours, even for relatively small problem sizes. Conversely, the MGM algorithm with reformulation scales quite well and can possibly guarantee some level of privacy as well with extension. While the algorithm does not come up with a global solution, it can be a decent trade off between a level of privacy, and scalable algorithms that are centralized, such as a genetic algorithm.

The CCNR will ensure that in 2050 inland ships do not emit any emissions. A PEM fuel cell system running on pure hydrogen could be a solution. However, the new system imposes some difficulties on the design of the inland ship. A calculation model is created to evaluate inland ships with this system and research these difficulties. The calculations are performed for shallow water. An elaboration is given on the design implications to incorporate their effects in the calculations. The inland ships are eventually judged base on their required freight rates. This research concludes the high hydrogen fuel price results in a lower optimal ship speed and a higher required freight rate than those of a conventional inland ship. Furthermore, increasing the installed power or increasing the refuelling frequency can improve the required freight rate.

Nonholonomic mechanical systems encompass a large class of practically interesting robotic structures, such as wheeled mobile robots, space manipulators, and multi-fingered robot hands. However, few results exist on the cooperative control of such systems in a generic, distributed approach. In this work we extend a recently developed distributed \ac{IDA-PBC} method to such systems. More specifically, relying on port-Hamiltonian system modelling for networks of mechanical systems, we propose a full-state stabilization control law for a class of nonholonomic systems within the framework of distributed \ac{IDA-PBC}. This enables the cooperative control of heterogeneous, underactuated and nonholonomic systems with a unified control law. This control law primarily relies on the notion of Passive Configuration Decomposition and a novel, non-smooth control law proposed here. A low-level collision avoidance protocol based on the \ac{APF} method is also implemented in order to achieve dynamic inter-agent collision avoidance, enhancing the practical relevance of this work. Theoretical results are tested in different simulation scenarios in order to highlight the applicability of the derived method.

In the present study, a Model Predictive Control (MPC) - based, Energy Management Strategy (EMS) is proposed, aiming at the minimisation of the fuel consumption of vessels with hybrid propulsion and power supply. This approach is capable of utilising online information regarding realistic future predictions of the propulsive power demand, addressing the current gap in the literature regarding the implementation of MPC EMSs onboard ships, and pragmatic future mission information utilisation. To the author's best knowledge, MPC for fuel consumption minimisation has not yet been implemented at this timescale in maritime propulsion plants with hybrid power supply. Furthermore, online mission information from the Integrated Bridge Systems (IBSs) and the governor's human input cannot be utilised by EMSs onboard ships, due to the lack of a mathematical pathway to enable such an information feedback flow. Increasingly powerful processing units in ship controllers and a wider application of batteries in marine powertrains motivated such a computationally heavy and, simultaneously, highly fuel-efficient control approach. The proposed controller framework has been tested and verified in a case study using a Mean Value First Principle (MVFP) propulsion plant model of a hybrid naval vessel with batteries in Simulink®, provided by DAMEN® SNS. By using provided mission data, realistic mission profiles were generated, in which the proposed controller is validated against perfectly and piecewise, non-perfectly informed Dynamic Programming (DP) solutions in an information barrier scheme, yielding close to optimal performance, and a fuel consumption reduction of up to 3.5% when compared to any non-long-term feedback enabled controller.

Damen Shipyards Gorinchem has found deficiencies in the process of developing concept designs of Offshore Patrol Vessels (OPVs). There is a need to increase the efficiency of this process, meaning that (1) the speed has to be increased, (2) the accuracy of designs has to be increased and (3) the risk of deviations of the project plan has to be reduced. Analysis of the current concept design phase shows that the designer should get more insight in the design challenge, and that a more systematic and uniform design approach is required amongst different involved departments. Additionally, the lead time of developing concept designs is mostly delayed by two major bottlenecks: development of the hull shape and obtaining a resistance prediction. In this thesis a conceptual design tool is developed to resolve these deficiencies, by providing a platform for fast hull concept exploration. Key of this approach is to pre-compute time-intensive design aspects such that the most critical activities are done before starting the design process. It uses a set of parent hulls for which the resistance is obtained by RANS CFD simulations. Based on the parents, a semi-parametric modelling technique is used to create new hull geometry. A kriging surrogate model is used to provide a RANS CFD resistance prediction to that new hull geometry. A brute-force approach is then adopted to fill a database with feasible hull shapes, which can be used by the conceptual design tool for hull shape concept exploration. To test this concept a proof-of-concept database of 245.005 hulls of double-ended ferries is generated in a few days on a laptop with good memory capabilities. The geometry results and resistance predictions in the database have been assessed with a sensitivity study, cross-validation and comparison with the parent hulls. It is shown that resulting geometry is smooth and well-faired. The resistance results are sufficiently accurate for use in the concept design phase, but applicability is limited for low prismatic coefficients. For application to OPVs further research into application of this approach to hull geometries with varying local details (bulb shape, tunnels etc.) is necessary. The approach seems promising to improve the concept design phase, as it (1) reduces time pressure in the design process significantly, (2) provides insight in how the resistance is affected by main dimensions of the vessel, and (3) reduces the number of departments which is actively involved in the design process. Putting the design tool in practice should reveal if this is indeed the case.

During the start of a project organisations make estimation on the expected amount of work and the corresponding project duration, the lead time. To make these estimations with a low uncertainty is important because a false estimation could result in cost overruns and project delay. This paper proposes an estimation and planning maturity framework where organisation can asses which methods can be used to ensure the estimation and planning are performed to the lowest possible uncertainty. The framework was implemented at a case study organisation, it was found that selecting the correct estimation and planning methods can result in a lead time estimation with a low uncertainty.