# Developments, Trends, and Challenges in Optimization of Ship Energy Systems
## Appendix A Main Characteristics of Journal Articles on Optimization of Ship Energy Systems
Biobjective: (i) maximize efficiency and (ii) maximize \(W_{\text{net}}/\text{UA}\) for each cycle converted in a single objective with the weighted sum function. Instead of formal optimization, each objective is drawn as a function of various parameters. Certain graphs reveal an optimal point. * [85][PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] _Journal of Energy Storage_**2020**. System: PEM fuel cells and battery in ferry boat. Cold-ironing at harbor. Optimization of sizing of fuel cells and battery and operation optimization (power from fuel cells, from cold-ironing, and from/to battery for a 24-h round trip). Propulsion and electric load for each hour of a 24-h round trip. Objective: total daily cost (capital and operation). Algorithm: Improved Sine Cosine Algorithm (ISCA), while Harmony Search is used to prevent SCA from being trapped in local optima. * [86][PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] _Energies_**2020**. System: three ORC cycles combined. They use heat from the JCW and exhaust gas for evaporation and LNS to be burned in the main engine for condensation of the fluids. Design optimization at full load of the main engine. Only two independent variables: pressures of evaporator and condenser. Various fluids are investigated. Two objectives: (i) maximize energy and exergy efficiencies and (ii) minimize (system investment/payback period). The standard Technique for Order Preference by Similarity to Ideal Situation (TOPSIS) method attempts to choose alternatives that have the shortest distance from the ideal solution. Algorithm: NSGA-II. * [87][PERSON]; [PERSON]; [PERSON] _Energy_**2020**. System: gas turbine-generator, power electronics, cooling circuit, and thermal and electric energy storage. Operation optimization: optimal control. Objective: minimize exergy destruction of the whole system, excluding the gas turbine-generator at each control update. Method: model predictive control. The optimization executes the ship simulation within a call to the nonlinear optimization algorithm fmincon. A multiphysics-based model is used: the electric power system is modeled along with the cooling system and interactions between the two shipboard systems are considered. * [88][PERSON]; [PERSON] _International Journal of Hydrogen Energy_, **2020**. System: fuel cell and battery propulsion system in coastal ferry. Voyage duration: 1 h. Shore connection is available in all ports. Design and operation optimization. Electric power profile for 3500 s. Solution: inner layer solves the optimal power split problem for given sizing of components. The external layer optimizes the average voyage cost and GWP emissions concurrently. The decision variables of the external layer are a vector of the fuel cell and battery module sizing parameters. The trade-off between the two objectives needs to be determined manually based on the Pareto front. Biobjective: (i) minimize average voyage cost and (ii) minimize GWP emissions. Algorithms: Deterministic Dynamic Programming (DDP) for the inner layer and Nondominated Sorting Genetic Algorithm II for the external layer.