Metaheuristics-based Optimal Powertrain Sizing for a Hybrid Electric Passenger Ferry

A global shift toward sustainable transportation has prompted the maritime industry to lead in the electrification of water vessels. Battery electric powertrains show great promise in reducing fuel consumption and greenhouse gas (GHG) emissions; however, high capital expenditures are required to deploy this technology. In other studies, the design of hybrid electric vehicles using metaheuristic algorithms has successfully minimized total costs and equivalent carbon dioxide emissions. This study developed a design methodology for a hybrid electric powertrain for inland waterway ferries, utilizing genetic algorithm (GA) and particle swarm optimization (PSO), techniques that have been used in land vehicles. A hybrid electric powertrain model was used to simulate its real-life operation. An evaluation of the total cost of ownership (TCO) and lifetime carbon (CO2) emissions measured its economic feasibility and environmental impact. The study successfully exploited GA and PSO to design a hybrid electric passenger ferry, reducing up to 39.05% of TCO and 28.51% of CO2 emissions.