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Hybrid technology in marine vehicles can significantly reduce fuel consumption and local CO 2 emissions. It has been applied successfully to several ship-types, mostly with conventional, rule-based, strategies. To further improve performance, intelligent control strategies are necessary. This work, inspired by automotive research in Energy Management Strategies, applies the Equivalent Consumption Minimisation Strategy (ECMS) to a ship powered by a hybrid propulsion plant with hybrid power supply that can be recharged with renewable shore power. This hybrid configuration has the additional challenge to determine the optimal power-split between three or more different power sources, in real-time, and to optimally deplete the battery packs over the mission profile. To this end, a Mixed-Integer Non-Linear optimisation Problem is formulated and solved by combining Branch & Bound and Convex optimisation. Dynamic Programming (DP) is used to benchmark the real-time strategies, which are also compared to the current rule-based (RB) controller. Simulation results of a case study tugboat with validated models show that, with unknown load demand, 6% additional fuel savings can be achieved with ECMS. [ABSTRACT FROM AUTHOR]

The combination of electric motors and internal combustion engines in hybrid electric vehicles (HEV) can considerably improve the fuel efficiency compared to conventional vehicles. In order to use its full potential, a predictive intelligent control system using information about impending driving situations has to be developed, to determine the optimal gear shifting strategy and the torque split between the combustion engine and the electric motor. To further increase fuel efficiency, the vehicle velocity can be used as an additional degree of freedom and the development of a predictive algorithm calculating good choices for all degrees of freedom over time is necessary. In this paper, an optimization-based algorithm for combined energy management and economic driving over a limited horizon is proposed. The results are compared with results from an offline calculation, which determine the overall fuel savings potential through the use of a discrete dynamic programming algorithm. [ABSTRACT FROM AUTHOR]