PROJECT TITLE :
Power Management for Electric Tugboats Through Operating Load Estimation
This brief presents an optimal power management theme for an electromechanical marine vessel's powertrain. An optimization problem is formulated to optimally split the facility provide from engines and battery in response to a load demand, whereas minimizing the engine fuel consumption and maintaining the battery life, whereby the price perform associates penalties such as the engine fuel consumption, the amendment in battery's state of charge (SOC), and the surplus power that cannot be regenerated. Utilizing the nonlinear optimization approach, an optimal scheduling for the facility output of the engines and optimal charging/discharging rate of the battery is decided whereas 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., beginning time and stopping time for a multiengine configuration optimally, that could be a key distinction from the previously developed optimal power management algorithms for land-based hybrid electrical vehicles. Afterward, a unique load prediction scheme that requires only the information concerning the final operational characteristics of the marine vessel that anticipates the load demand at a given time instant from the historical load demand information throughout that operation is introduced. This prediction theme schedules the engine and battery operation by solving prediction-based mostly optimizations over consecutive horizons. Numerical illustration is presented on an industry-consulted harbor tugboat model, together with a comparison of the performance of the proposed algorithm with a baseline conventional rule-primarily based controller to demonstrate its feasibility and effectiveness. The simulation results demonstrate that the optimal price for electrical tugboat operation is nine.31percent lower than the baseline rule-based mostly controller. Within the case of load uncertainty, the prediction-primarily based algorithm yields a price 8.90p.c below the baseline rule-based mostly - ontroller.
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