The company H2 Marine Solutions has designed a zero-emission hydrogen powered boat. This boat is compared to its fossil fuel counterpart more than twice as heavy. The reason for this is that the system components that are used in the hybrid powertrain of the hydrogen powered boat are heavier. The main question of this research is: How can we establish the optimal energy and power of the system components of a hybrid power system with optimal energy management for a zero-emission hydrogen powered boat for different operational profiles? This results in a sizing and control optimization problem. Because these two problems are coupled this is a multi-objective double-layer optimization problem. The most popular strategy to solve this problem is with the control problem nested in the sizing problem \cite{Hybrid-ship}. The most popular algorithms to solve these problems are evolutionary algorithms.

Unfortunately due to the complexity of these algorithms and due to lack of time the sizing and control problems are solved separately in this research. First, the system components of the plant are described and modeled. The components that are modeled are the battery, the fuel cells, and the DC/DC converter. To find the optimal energy management strategy an online optimization strategy is used. This is done because the problem is solved in real-time than and could be used in a real application. The strategy that is chosen to solve the control problem is the Equivalent Consumption Minimization Strategy (ECMS). This strategy translates the electrical energy from the battery into equivalent hydrogen consumption. For every timestep, the equivalent consumption is minimized by the ECMS. Because there are different variants of ECMS three of these variants are discussed and compared in the research. Also, two rule-based energy management strategies are compared. The sizing problem is described by linear equality and inequality constraints. The problem is solved by the Linprog function in Matlab. The objective of the sizing problem is to minimize the weight of the system components. The input in the sizing problem is the energy and power demand of the most energy intensive operational profile. After solving the sizing and control problem the results are combined and the different operational profiles are used as input to show the robustness of the optimization.

The three different energy management strategies all minimize the instantaneous equivalent consumption but show different behaviors when controlling the system components. The optimal energy management strategy is the Smooth Adaptive Penalty (SAP)-ECMS. With this controller, the fuel cells work on a steady operating point and ramp up and down the output power smoothly when necessary. Due to this behavior, the average efficiency of the fuel cell is the highest, and the hydrogen consumption is the lowest compared to the other controllers. The results of the sizing problem show that the weight will decrease when a bigger fuel cell is used in combination with a smaller battery. The consideration between a bigger fuel cell and a smaller battery is a consideration between lower weight and more hydrogen consumption. When a bigger fuel cell is used it is recommended to implement an optimal energy management strategy such as the SAP-ECMS to control the output power of the system components. This is preferable above a rule-based controller which can not find the optimal operating point at all timesteps. Even better energy management strategies may exist or could be made by combining different ECMS's. When the sizing and control problem are solved in a nested strategy more accurate results could be achieved.