Hybrid propulsion is considered a reliable alternative to solely mechanical or electrical propulsion for enhanced ship energy performance. Nevertheless, an increased number of components and interconnections results in more complex ship design problems. The automotive and aviation industries already examine new designs on predefined driving and flying cycles. However, new ships are still assessed on one design point with the regulated Energy Efficiency Design Index (EEDI). Its limited consideration of calm water conditions and installed rated power is characterised as insufficient, if not dangerous. A design methodology that accounts for operational and environmental uncertainty is lacking. This paper proposes a design optimisation framework for the topology selection and sizing of hybrid propulsion systems integrating probability distributions of actual sailing profiles from continuous monitoring. The methodology is demonstrated on the ‘Holland’ class ocean patrol vessels of the Royal Netherlands Navy. Its multi-objective consideration examines a wide design space from an environmental, financial, and technical perspective, solving the mixed-integer nonlinear programming (MINLP) problem with a multi-starting scheme that combines a genetic algorithm and interior point method. The low computational cost is achieved by integrating a state-of-the-art digital twin approach leveraging data-driven and first-principle modelling. The results demonstrate feasible improvements of approximately 4 % for carbon intensity and 11 % for operational expenditure by increasing the size of the electrical motors. The exact configuration and percentage improvement are sensitive to actual operational and environmental conditions, while calm water conditions tend to overestimate savings. Consequently, the use of actual sailing profiles is recommended for more accurate life-cycle predictions.

Maritime industry has set ambitious goals to drastically reduce its greenhouse gas emissions through stipulating and enforcing a number of energy assessment measures. Unfortunately, measures like the EEDI, EEXI, SEEMP and CII do not account for the operational and environmental uncertainty of operations at sea, even though they do provide a first means of evaluating the carbon footprint of ships. The increasing availability of high-frequency operational data offers the opportunity to quantify and account for this uncertainty in energy performance predictions. Current methods to evaluate and predict energy performance at a whole energy system level do not sufficiently account for operational and environmental uncertainty. In this work, we propose a digital twin that accurately predicts the fuel consumption and carbon footprint of the hybrid propulsion system of an Ocean-going Patrol Vessel (OPV) of the Royal Netherlands Navy under the aggregate effect of operational and environmental uncertainty. It combines first-principle steady-state models with machine learning algorithms to reach an accuracy of less than 5% MAPE on both mechanical and electrical propulsion, while bringing a 40% to 50% improvement over a model that does not utilise machine learning algorithms. Results over actual voyage intervals indicate a prediction accuracy of consumed fuel and carbon intensity within 2.5% accounting for a confidence interval of 95%. Finally, the direct comparison between mechanical and electrical propulsion showed no clear energy-saving benefits and a strong dependency of the results on each voyage's specific operational and environmental conditions.

Ship designers hardly ever receive feedback from the actual operation of their designs apart from sea acceptance trials. Similarly, crews operating the vessels do not receive a clear picture of the energy performance and environmental footprint of different options. This paper proposes a methodology based on operational data from continuous monitoring, and applies it to an ocean patrol vessel of the Royal Netherlands Navy in order to identify the impact of diverse operational conditions on energy performance over the whole operating range, but also to examine the decision to equip the vessel with hybrid propulsion. Specifically, it introduces mean energy effectiveness indicator and mean total energy efficiency over discretised vessel speed, as the main tool in quantifying the energy gains and losses to assist in making better-advised design and operational decisions. Moreover, it demonstrates a dataset enrichment procedure, using manufacturers' information, in case not all needed sensors are available. Results suggest that electrical propulsion was 15–25% less efficient than the best mechanical propulsion mode, and on the overall energy performance of the vessel, increasing speed by 1 knot caused a 7% and 14% increase over the minimum (Formula presented.) /mile emissions between 8 and 14, and above 14 knots respectively.