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 controller.