Powertrains and future fuels, optimised over real voyages

SSAF extended the VME below deck: simulation, sizing and lifetime optimisation of future-fuel powertrains on wind-assisted vessels, including fuel cells, batteries and the control strategies that run them. Over 9,600 powertrain permutations were simulated across vessels, voyages and control strategies. The training data came from the VME itself, generated from a real vessel’s movements, so every permutation was tested against voyage-realistic conditions.

The machine learning optimisation was developed alongside UCL’s marine autonomy group, with Seabird Technologies and Scotline as project partners.

One result matters commercially. Peak fuel efficiency and peak economic value are different answers. The most valuable powertrain undersizes the fuel cell against peak demand and lets the battery carry the peaks. That conclusion is only reachable when the powertrain, the vessel, the voyage and the economics are solved together.