A life cycle carbon emission assessment method is proposed to comprehensively evaluate the carbon emission reduction capacity of hydrogen-electric hybrid ships.

First, an energy flow model of the power system is established using the Matlab/Simulink software platform. Hydrogen consumption and electricity consumption under actual operating conditions are then calculated, and the life cycle carbon emissions are obtained. Finally, sensitivity analysis is performed on carbon emissions from different power sources and hydrogen sources.

The results of an inland river ferry show that compared with the traditional diesel engine power system, the hydrogen-electric hybrid system can reduce carbon emissions by 30.24% in one operation cycle. The sensitivity analysis results show that the carbon emissions of the hydrogen-electric hybrid system are not necessarily better than those of the traditional diesel power system, so it is recommended to use renewable energy for hydrogen production and power generation to significantly reduce carbon emissions (up to 94.2%).

The results of this study can provide references for the power system design of green ships.

The marine diesel-electric hybrid system reasonably distributes the output power of the diesel engine and motor, which can significantly reduce fuel consumption and emissions. Aiming at the contradiction between the optimal performance and real-time operation of traditional energy management strategies applied to hybrid power systems, this study proposes to implement model predictive control (MPC) to achieve the instantaneous optimization of energy management.

First, an energy flow model of a passenger-ferry hybrid system consisting of dual diesel generator sets, energy storage systems and shore power is established by employing the reverse modeling method. An MPC energy management algorithm that can be solved online by rolling optimization under system constraints is then proposed, taking the total greenhouse gas (GHG) emissions of fuel consumption and electric energy consumption as the objective function. Finally, the sensitivity analysis of the variable prediction horizon lengths is carried out.

The simulation results show that the MPC method can reduce fuel consumption by 4.85% and total carbon dioxide emissions by 3.54% respectively compared with the traditional rule-based control method.

The MPC method achieves lower fuel consumption, lower carbon emissions and lower computing load than the traditional rule-based control method, giving it promising potential for real ship application.