Power Management for Electric Tugboats Through Operating Load Estimation

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 - ontroller.