Reducing energy consumption and carbon emissions from ships is a major concern. The development of hybrid technologies offers a new direction for the rational distribution of energy. Therefore, this paper establishes a torque model for internal combustion engines and motors based on first principles and fitting the data collected from the test platform; in turn, it develops a model for fuel consumption and carbon emissions. Furthermore, the effect of irregular waves using an extended Kalman filter is estimated as well as feedback to the controller as a disturbance variable. Then, a parallel hybrid ship energy management strategy based on a new real-time nonlinear model of predictive control is designed to achieve energy conservation and emission decrease. A hybrid algorithm of chaotic optimization combined with grey wolf optimization is utilized to solve the nonlinear optimization problem in the nonlinear model predictive control strategy and a local refined search is performed using sequential quadratic programming. Through the comparison of fuel consumption, carbon emissions, real-time performance, and the engine load path, the superiority of the nonlinear model predictive control energy management strategy based on the chaotic grey wolf optimization algorithm is verified.

This paper proposes a novel dual-loop voltage-current control scheme for a three-phase three-level converter system. In the voltage-loop, a variable-exponential-gain mechanism is introduced, enabling the proposed controller to combine the benefits of proportional-integral (PI) control and super-twisting algorithm (STA). Moreover, time-varying control gains are coupled with the variable-exponential-gain to match the controller characteristics across operating conditions. A Lipschitz-continuous function is further introduced to mitigate chattering. The voltage-loop performance under steady-state and dynamic constraints is validated via Monte Carlo sensitivity analysis. For the current-loop, a feedback-parallel optimization finite-control-set model predictive control strategy is proposed. By partitioning the space into small triangular sectors and applying boundary mapping, the number of candidate voltage vectors is reduced. A vector selection mechanism is then formulated to minimize the tracking errors along the $\alpha$ and $\beta$ axes in parallel. In addition, the previously selected optimal voltage vector enforces level matching between the $\alpha$/$\beta$ components, which identifies the axis to prioritize for post-minimization fine-tuning. The weighting coefficient is eliminated using the sign-matching principle, thereby reducing computational complexity. For the voltage-loop, uniform ultimate boundedness and robustness bound analysis are provided. For the current-loop, the neutral-point potential suppression mechanism without a weighting coefficient is analyzed, and a single-step error boundedness proof is established. Simulation and experimental results validate the superiority of the proposed dual-loop control scheme.