RH Marine optimises vessel’s power plant operational costs

Dutch leading system integrator RH Marine succeeded in incorporating battery life prediction for vessels into its fuel-saving Energy Management System.

This makes expensive batteries last longer and reduces Total Cost of Ownership of ships while keeping the reduced fuel consumption intact.

For her research paper on ‘New developments in Energy Management’, product manager hybrid portfolio Despoina Mitropoulou of RH Marine together with Louis Elling from the Netherlands Defence Academy won the third prize at the Sir Donald Gosling Award. This annual prize for young engineers was awarded during the 14th International Naval Engineering Conference and Exhibition in Glasgow. Over a hundred technical papers were presented there.

Because of an increasing demand for greenhouse gas reduction, more and more vessels are looking into using various energy sources. Besides diesel propulsion, extra energy sources like batteries, microturbines, fuel cells and variable speed generators are considered. In such systems, energy management becomes crucial.

RH Marine developed its own award-winning Rhodium Energy Management System, using a self-learning artificial intelligence algorithm that automatically distributes the power demand over the available diesel generators and batteries in the most optimal way. Analysed measured data shows that on seagoing ferries, the EMS saves fuel up to 12%, and 11% on superyachts. Putting battery shore charging in the mix even reduces fuel consumption on ferries with 38%.

To optimise the total performance of a ship, RH Marine wants to further develop its EMS. For instance, by incorporating battery life prediction, power consumption forecasting, generator wear and tear and maintenance requirements of the equipment. The first step was to incorporate battery life prediction and power consumption forecast.

Using a simulation for the unpredictable load profile of a hybrid superyacht, Mitropoulou and Elling demonstrated that battery life can be extended by carefully observing lifetime determining quantities, like the state of charge and the magnitude of charging and discharging currents, thus optimising the TCO.

Additionally, their simulation model for a ferry with a forecasted load demonstrated that power consumption forecast can improve the desired operation.

In their simulation, they adapted the optimisation algorithm, making it also consider the battery life as an important goal, besides focussing on saving fuel. This method resulted in the battery achieving its requested lifetime while preserving fuel savings.

However, it did also show that optimising battery life can lead to more start-stop cycles and subsequent wear and tear of the generator. “That is undesired, so we are adjusting our control algorithm to achieve a balance between these two goals,” says Mitropoulou. “The most important of our findings is that this improved EMS can be used for all kinds of power sources in the future and will minimise Costs of Ownership.”