Owing to the severe fossil energy shortage and carbon pollution, the extensive electrification of maritime transportation, represented by all-electric ships (AESs), has become an appealing solution to increase the efficiency and environmental friendliness of the industry. To improve energy utilization, not only renewable energy but also thermal energy has been introduced is used in AESs. However, various uncertainties that are associated with renewable energy and ship motions significantly inhibit and complicate the operation and navigation of multi-energy shipboard microgrids. Accordingly, a new coordination of optimal energy management and voyage scheduling is important in reducing both the costs and emissions of AESs. This overview characterizes shipboard microgrids and several emerging technical challenges related to joint power and voyage scheduling, and elucidates prospects for further research, based on a comprehensive survey of the relevant literature.

Due to its high efficiency, reliability, and environmental benefits, the permanent-magnet synchronous motor (PMSM) is increasingly being used in marine propulsion applications. As a promising solution, finite-control-set model predictive current control (FCS-MPCC) has been gaining attention in marine propulsion systems. However, FCS-MPCC for PMSM drives applies only a single switching state within each control cycle. Moreover, its prediction model depends on motor parameters. To address this issue, a dual-vector (DV)-based MPCC (DV-MPCC) incorporating online parameter identification was proposed. Firstly, a DV-MPCC suitable for a two-phase stationary reference frame was introduced. To reduce torque ripple, the DV combination was generated based on the error current vector, and the action time was allocated in accordance with the minimum root mean square error of the current. Furthermore, a model reference adaptive system (MRAS) for multi-parameter identification was developed based on the incremental current state equation. This equation was constructed and used as an adjustable model, enabling accurate estimation of resistance and inductance parameters, even when the flux parameter was completely unknown. Additionally, the proposed method addressed the identification error caused by rank deficiency. Experimental validation confirmed the effectiveness of the proposed method.

In electric vehicle and marine propulsion applications, the stable operation of permanent-magnet synchronous motor (PMSM) drive systems relies on accurate rotor position information. Such information is typically obtained from position sensors, which are prone to high temperature, humidity, vibration, and electromagnetic interference, leading to elevated failure rates; moreover, sensor installation introduces additional interfaces and wiring, thereby reducing system reliability. To address these issues, this paper proposes a sensorless control method based on an adaptive full-order sliding-mode observer (SMO). The proposed method employs the SMO output as the observer feedback correction term rather than the estimated back EMF, thereby avoiding substantial high-frequency noise. Furthermore, an S-shaped nonlinear function is designed to replace the conventional switching function, mitigating high-frequency chattering when the system operates in sliding mode; an adaptive sliding-mode gain function is designed, the sliding-mode gain and the boundary-layer thickness are adaptively tuned as a function of motor speed, which effectively enhances the back EMF estimation accuracy over a wide operating-speed range. The effectiveness of the proposed method is validated on a 2.3-kW PMSM experimental platform.