Predictive power-split system of hybrid ship propulsion for energy management and emissions reduction

In this work, an energy management system to address the optimal power-split problem in hybrid ship propulsion is developed. The torque of the diesel engine and the electric machine is regulated based on a predictive strategy with a weighting factor which determines the trade-off between fuel consumption and NOx emissions minimization. The modeling for the controller design is based on first principles and data gathered from the hybrid plant. In addition a disturbance observer is designed to estimate the propeller load characteristics. A neural network model that predicts rotational speed reference within the prediction horizon complements the control system design. It is used along with the observer to calculate the future load demand. A parametric simulation study is performed for the trade-off evaluation between fuel consumption and NOx emissions reduction of the control scheme. The control scheme is experimentally implemented and tested in real-time operation, where it has to cope with environmental disturbance rejection and follow the desired rotational speed reference, while performing the power-split in respect to the fuel to NOx weighting parameter and operate the plant within the desirable constraints.

Automated summary

In this work, an energy management system for optimal power-split in hybrid ship propulsion is developed. The torque of the diesel engine and electric machine are regulated based on a predictive strategy with a weighting factor for fuel consumption and NOx emissions. A disturbance observer is designed to estimate propeller load characteristics, and a neural network model predicts rotational speed reference. Parametric simulation evaluates the trade-off between fuel consumption and NOx emissions reduction, and the control scheme is experimentally implemented to handle environmental disturbance rejection while following desired speed reference.