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This brief presents an optimal power management scheme for an electromechanical marine vessel's powertrain. An optimization problem is formulated to optimally split the power supply from engines and battery in response to a load demand, while minimizing the engine fuel consumption and maintaining the battery life, wherein the cost function associates penalties corresponding to the engine fuel consumption, the change in battery's state of charge (SOC), and the excess power that cannot be regenerated. Utilizing the nonlinear optimization approach, an optimal scheduling for the power output of the engines and optimal charging/discharging rate of the battery is determined while accounting for the constraints due to the rated power limits of engine/battery and battery's SOC limits. The proposed optimization algorithm can schedule the operation, i.e., starting time and stopping time for a multiengine configuration optimally, which is a key difference from the previously developed optimal power management algorithms for land-based hybrid electric vehicles. Afterward, a novel load prediction scheme that requires only the information regarding the general operational characteristics of the marine vessel that anticipates the load demand at a given time instant from the historical load demand data during that operation is introduced. This prediction scheme schedules the engine and battery operation by solving prediction-based optimizations over consecutive horizons. Numerical illustration is presented on an industry-consulted harbor tugboat model, along with a comparison of the performance of the proposed algorithm with a baseline conventional rule-based controller to demonstrate its feasibility and effectiveness. The simulation results demonstrate that the optimal cost for electric tugboat operation is 9.31% lower than the baseline rule-based controller. In the case of load uncertainty, the prediction-based algorithm yields a cost 8.90% lower than the baseline rule-based controller.Index Terms-Hybrid electric vehicle (HEV), load estimation, marine powertrain, marine vessel, optimization, power management.

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This report presents the work done to develop generator and gearbox models in the Matrix Laboratory (MATLAB) environment and couple them to the National Renewable Energy Laboratory's Fatigue, Aerodynamics, Structures, and Turbulence (FAST) program. The goal of this project was to interface the superior aerodynamic and mechanical models of FAST to the excellent electrical generator models found in various Simulink libraries and applications. The scope was limited to Type 1, Type 2, and Type 3 generators and fairly basic gear-train models. The final product of this work was a set of coupled FAST and MATLAB drivetrain models. Future work will include models of Type 4 generators and more-advanced gear-train models with increased degrees of freedom. As described in this study, the developed drivetrain model can be used in many ways. First, the model can be simulated under different wind and grid conditions to yield further insight into the drivetrain dynamics in terms of predicting possible resonant excitations. Second, the tool can be used to simulate and understand transient loads and their couplings across the drivetrain components. Third, the model can be used to design the various flexible components of the drivetrain such that transmitted loads on the gearbox can be reduced. Several case studies are presented as examples of the many types of studies that can be performed using this tool.

Automated fiber placement (AFP) is an advanced manufacturing method for composites, which is especially suitable for large-scale composite components. However, some manufacturing defects inevitably appear in the AFP process, which can affect the mechanical properties of composites. This work aims to investigate the recent works on manufacturing defects and their online detection techniques during the AFP process. The main content focuses on the position defect in conventional and variable stiffness laminates, the relationship between the defects and the mechanical properties, defect control methods, the modeling method for a void defect, and online detection techniques. Following that, the contributions and limitations of the current studies are discussed. Finally, the prospects of future research concerning theoretical and practical engineering applications are pointed out.