With increasingly stringent emission regulations, various clean fuel engines, electric propulsion systems, and renewable energy sources have been demonstratively applied in marine power systems. The development of control strategies that can effectively and efficiently coordinate the operation of multiple energy sources has become a key research focus. This study uses a modular modeling method to establish a system simulation model for a parallel hybrid ship with a natural gas engine (NGE) as the prime mover, and designs an energy management control strategy that can run in real time. The strategy is based on Pontryagin’s minimum principle (PMP) for power allocation, and is supplemented by a hybrid model predictive control (HMPC) method for speed-tracking control of the power system. Finally, the designed strategy is evaluated. Through simulation and hardware-in-the-loop (HIL) experimental validation, results compared with the Rule-based strategy indicate that under the given conditions, the SOC final value deviation from the initial value is reduced from 11.5% (in the reference strategy) to 0.39%. The system speed error integral is significantly lower at 39.06, compared to 2264.67 in the reference strategy. While gas consumption increased slightly by 2.4%, emissions were reduced by 3.2%.

Amid growing global concerns over environmental sustainability, the shipping industry is under increasing pressure to implement innovative power systems that minimize ecological impact. A promising approach is the marine gas–electric hybrid system, which combines conventional marine propulsion with electric power to offer a cleaner energy solution. Characterized by the integration of continuous and discrete variables, these systems reflect the hybrid nature of gas–electric propulsion. Despite their potential, research on marine hybridization remains limited. To address this gap, a hybrid system model has been developed to optimize energy allocation while accurately capturing the hybrid characteristics of gas–electric systems in ships. Additionally, an energy distribution strategy based on predictive control has been proposed to validate the model’s practical applicability. A weighted evaluation method was employed on a marine gas–electric hybrid test platform to verify the performance of both the model and the control strategy. Results show that different weighting configurations lead to varying torque distribution patterns, confirming the effectiveness of the hybrid system model. Moreover, tuning the weighting parameters within the energy allocation strategy yields diverse control behaviors, further demonstrating the system’s viability for marine applications.

The current lack of harmonized standard test conditions for marine shipping hinders the comparison of performance and compliance assessments for different types of ships. This article puts forward a method for extracting ship loading cycles using machine learning algorithms. Time-series data are extracted from real ships in operation, and a segmented linear approximation method and a data normalization technique are adopted. A hierarchical-clustering type of soft dynamic time-warping similarity analysis method is presented to efficiently analyze the similarity of different time-series data, using soft dynamic time warping (Soft-DTW) combined with hierarchical clustering algorithms from the field of machine learning. The problem of data bias caused by spatial and temporal offset characteristics is effectively solved in marine test condition data. The validity and reliability of the proposed method are validated through the analysis of case data. The results demonstrate that the hierarchically clustered soft dynamic time-warping similarity analysis method can be considered reliable for obtaining test cases with different characteristics. Furthermore, it provides input conditions for effectively identifying the operating conditions of different types of ships with high levels of energy consumption and high emissions, thus allowing for the establishment of energy-saving and emissions-reducing sailing strategies.

The microstructure and mechanical properties of electrically assisted brazing (EA-brazing) joints of aluminum alloy 6061-t6 (AA6061-t6) and S45C steel are experimentally investigated. During the EA-brazing process, an electric current is directly applied to the cylindrical specimen assembly (S45C and AA6061-t6) and fillers of 88% Al and 12% Si (in the middle of the specimen assembly). The temperature of the specimen assembly rises rapidly to the melting point of the filler and remains nearly constant for a period of time using a pulsed electric current. Two types of EA-brazing joints are fabricated, namely Joint-0s (no temperature holding time) and Joint-12s (12 s temperature holding time). The characteristics of the intermetallic compounds (IMCs) formed at the EA-brazing joint interface are analyzed using scanning electron microscopy and energy dispersive spectrometer. Compared to Joint-0s, the Fe-rich IMCs (FeAl) are observed at the interface of Joint-12s due to the 12 s temperature holding time. In addition, the microstructural analysis shows that the thickness of the diffusion layer increases with increasing temperature holding time. The mechanical properties of the EA-brazing joints are evaluated using bending tests. The results of the mechanical test show that the strength of Joint-12s is higher than that of Joint-0s.