Fast diesel engine models for real-time prediction in dynamic conditions are required to predict engine performance parameters, to identify emerging failures early on and to establish trends in performance reduction. In order to address these issues, two main alternatives exist: one is to exploit the physical knowledge of the problem, the other one is to exploit the historical data produced by the modern automation system. Unfortunately, the first approach often results in hard-to-tune and very computationally demanding models that are not suited for real-time prediction, while the second approach is often not trusted because of its questionable physical grounds. In this paper, the authors propose a novel hybrid model, which combines physical and data-driven models, to model diesel engine exhaust gas temperatures in operational conditions. Thanks to the combination of these two techniques, the authors were able to build a fast, accurate and physically grounded model that bridges the gap between the physical and data driven approaches. In order to support the proposal, the authors will show the performance of the different methods on real-world data collected from the Holland Class Oceangoing Patrol Vessel.

Hybrid technology in marine vehicles can significantly reduce fuel consumption and local CO₂ emissions. It has been applied successfully to several ship-types, mostly with conventional, rule-based, strategies. To further improve performance, intelligent control strategies are necessary. This work, inspired by automotive research in Energy Management Strategies, applies the Equivalent Consumption Minimisation Strategy (ECMS) to a ship powered by a hybrid propulsion plant with hybrid power supply that can be recharged with renewable shore power. This hybrid configuration has the additional challenge to determine the optimal power-split between three or more different power sources, in real-time, and to optimally deplete the battery packs over the mission profile. To this end, a Mixed-Integer Non-Linear optimisation Problem is formulated and solved by combining Branch & Bound and Convex optimisation. Dynamic Programming (DP) is used to benchmark the real-time strategies, which are also compared to the current rule-based (RB) controller. Simulation results of a case study tugboat with validated models show that, with unknown load demand, 6% additional fuel savings can be achieved with ECMS.

Shipping urgently needs to reduce its impact on the environment, both due to CO₂, NO_x and particulate matter (PM) emissions and due to underwater noise. On the other hand, multifunction ships such as offshore support vessels, anchor handling and towing vessels, naval vessels and wind farm construction and support vessels require fast and accurate manoeuvring and need highly reliable systems to support reduced or no crew. Diesel mechanical propulsion with controllable pitch propellers provides high efficiency and low CO₂ emissions, but has traditionally been poor in manoeuvrability, can suffer from thermal overloading due to manoeuvring and requires significant measures to meet NO_x and PM emission regulations. The control strategy of diesel mechanical propulsion with fixed combinator curves is one of the causes of the poor manoeuvrability, thermal overloading and cavitation noise during manoeuvring, such as slam start and intermediate acceleration manoeuvres. This paper proposes an adaptive pitch control strategy with slow integrating speed control that reduces fuel consumption, CO₂, NO_x and PM emissions and underwater noise, improves acceleration performance, limits engine loading and prevents engine under- and overspeed. A simulation study with a validated model of a case study Holland class Patrol Vessel demonstrates 5–15% reduction in fuel consumption and CO₂ emissions, compared to the baseline transit control mode in the ship speed range from 6 to 15 kts, during constant speed sailing. Moreover, the adaptive pitch control strategy reduces acceleration time from 0 to 15 kts with the slam start procedure by 32% compared to the baseline manoeuvre control mode and by 84% for an intermediate acceleration from 10 to 15 kts, while preventing thermal overloading of the engine, during straight line manoeuvres. Combining this control strategy with hybrid propulsion, running an electric drive in parallel with the propulsion diesel engine, can potentially further reduce fuel consumption at low speeds while also improving acceleration performance even more. Therefore, hybrid propulsion plants with controllable pitch propellers and adaptive pitch control can provide a significant contribution to the urgent reduction of environmental impact of shipping and to the need for more autonomous and reliable ship systems.

Condition Based Maintenance on diesel engines can help to reduce maintenance load and better plan maintenance activities in order to support ships with reduced or no crew. Diesel engine performance models are required to predict engine performance parameters in order to identify emerging failures early on and to establish trends in performance reduction. In this paper, a novel approach is proposed to accurately predict engine temperatures during operational dynamic manoeuvring. In this hybrid modelling approach, the authors combine the mechanistic knowledge from physical diesel engine models with the statistic knowledge from engine measurements on a sound engine. This simulation study, using data collected from a Holland class patrol vessel, demonstrates that existing models cannot accurately predict measured temperatures during dynamic manoeuvring, and that the hybrid modelling approach outperforms a purely data driven approach by reducing the prediction error during a typical day of operation from 10% to 2%.

Ships, in particular service vessels, need to reduce fuel consumption, emissions and cavitation noise while maintaining manoeuvrability and preventing engine overloading. Diesel mechanical propulsion with controllable pitch propellers can provide high fuel efficiency with good manoeuvrability. However, the conventional control strategy with fixed combinator curves limits control freedom in trading-off performance characteristics. In order to evaluate performance of current state-of-the-art and future alternative propulsion systems and their control, a validated propulsion system model is required. To this end, this paper proposes a propulsion model with a Mean Value First Principle (MVFP) diesel engine model that can be parameterised with publicly available manufacturer data and further calibrated with obligatory FAT measurements. The model uses a novel approach to predict turbocharger performance based on Zinner blowdown, the Büchi power and flow balance and the elliptic law for turbines, and does not require detailed information such as compressor and turbine maps. This model predicts system performance within 5% of actual measurements during Factory Acceptance Tests (FAT) of the diesel engines and Sea Acceptance Tests (SAT) of a case study navy ship. Moreover, this paper proposes measures of performance that objectively quantify the fuel consumption, acceleration rate, engine thermal loading and propeller cavitation during trial, design and off-design conditions in specified benchmark manoeuvres, within an hour simulation time. In our experiments, we find that, depending on the control strategy, up to 30% of fuel can be saved, thermal engine loading can be reduced by 90. K, and acceleration time by 50% for a case study Holland class patrol vessel.

Multifunction ships, naval vessels in particular, need to reduce fuel consumption while maintaining manoeuvrability. Hybrid propulsion that runs a main diesel engine and electric drive in parallel can achieve this. However, a parallel control strategy needs to be developed. In this paper, we use a simulation model of a hybrid propulsion system to investigate two parallel control strategies for diesel mechanical and electrical propulsion on multifunction ships. For the case study frigate, parallel control can increase the ship top speed with 3 kts when using two 4 MW electric drives and two 10 MW main diesel engines, compared with the same baseline hybrid propulsion without parallel control. The diesel engine speed control with electric drive torque control strategy increases ship acceleration rate with 17% and reduces average engine thermal loading with 150 K. Moreover, the electric drive speed control with diesel engine torque control strategy can improve acceleration rate by 40%, while eliminating thermal loading fluctuation due to heavy seas, and also reducing engine average thermal loading with 150 K. Future combination of the proposed electric drive speed control strategy with an adaptive pitch control and optimal power split strategy can potentially further increase hybrid propulsion plant performance.

The recent trend to design more efficient and versatile ships has increased the variety in hybrid propulsion and power supply architectures. In order to improve performance with these architectures, intelligent control strategies are required, while mostly conventional control strategies are applied currently. First, this paper classifies ship propulsion topologies into mechanical, electrical and hybrid propulsion, and power supply topologies into combustion, electrochemical, stored and hybrid power supply. Then, we review developments in propulsion and power supply systems and their control strategies, to subsequently discuss opportunities and challenges for these systems and the associated control. We conclude that hybrid architectures with advanced control strategies can reduce fuel consumption and emissions up to 10–35%, while improving noise, maintainability, manoeuvrability and comfort. Subsequently, the paper summarises the benefits and drawbacks, and trends in application of propulsion and power supply technologies, and it reviews the applicability and benefits of promising advanced control strategies. Finally, the paper analyses which control strategies can improve performance of hybrid systems for future smart and autonomous ships and concludes that a combination of torque, angle of attack, and Model Predictive Control with dynamic settings could improve performance of future smart and more autonomous ships.