Search Results (95)

In order to improve energy utilization efficiency and the flexibility of resource transfer in oceanic-island-group microgrids, a water–electricity–hydrogen flexible scheduling strategy based on a multi-rate hydrogen-powered ship is proposed. First, the characteristics of the seawater desalination unit (SDU), proton exchange membrane electrolyzer (PEMEL), and battery system (BS) in consuming surplus renewable energy on resource islands are analyzed. The variable-efficiency operation characteristics of the SDU and PEMEL are established, and the effect of battery life loss is also taken into account. Second, a spatio-temporal model for the multi-rate hydrogen-powered ship is proposed to incorporate speed adjustment into the system optimization framework for flexible resource transfer among islands. Finally, with the goal of minimizing the total cost of the system, a flexible water–electricity–hydrogen hybrid resource transfer model is constructed, and a certain island group in the South China Sea is used as an example for simulation and analysis. The results show that the proposed scheduling strategy can effectively reduce energy loss, promote renewable energy absorption, and improve the flexibility of resource transfer. Full article

Hybrid-energy vessels offer significant advantages in reducing carbon emissions and air pollutants by integrating traditional internal combustion engines, electric motors, and new energy technologies. However, during operation, the high reliance of hybrid-energy ships on networks and communication systems poses serious data security risks. Meanwhile, the complexity of energy scheduling presents challenges in obtaining feasible solutions. To address these issues, this paper proposes an innovative two-stage security optimization scheduling framework aimed at simultaneously improving the security and economy of the system. Firstly, the framework employs a CNN-LSTM hybrid model (WOA-CNN-LSTM) optimized using the whale optimization algorithm to achieve real-time detection of false data injection attacks (FDIAs) and post-attack data recovery. By deeply mining the spatiotemporal characteristics of the measured data, the framework effectively identifies anomalies and repairs tampered data. Subsequently, based on the improved multi-objective whale optimization algorithm (IMOWOA), rapid optimization scheduling is conducted to ensure that the system can maintain an optimal operational state following an attack. Simulation results demonstrate that the proposed framework achieves a detection accuracy of 0.9864 and a recovery efficiency of 0.969 for anomaly data. Additionally, it reduces the ship’s operating cost, power loss, and carbon emissions by at least 1.96%, 5.67%, and 1.65%, respectively. Full article

This study presents a multi-objective predictive energy management system (EMS) for optimizing hybrid renewable energy systems (HRES) in autonomous marine vessels. The objective is to minimize fuel consumption and emissions while maximizing renewable energy usage and pure-electric sailing durations. The EMS combines nonlinear model predictive control (NMPC) with metaheuristic optimizers—Grey Wolf Optimization (GWO) and Genetic Algorithm (GA)—and is benchmarked against a conventional rule-based (RB) method. The HRES architecture comprises photovoltaic arrays, vertical-axis wind turbines (VAWTs), diesel engines, generators, and a battery storage system. A ship dynamics model was used to represent propulsion power under realistic sea conditions. Simulations were conducted using real-world operational and environmental datasets, with state prediction enhanced by an Extended Kalman Filter (EKF). Performance is evaluated using marine-relevant indicators—fuel consumption; emissions; battery state of charge (SOC); and emission cost—and validated using standard regression metrics. The NMPC-GWO algorithm consistently outperformed both NMPC-GA and RB approaches, achieving high prediction accuracy and greater energy efficiency. These results confirm the reliability and optimization capability of predictive EMS frameworks in reducing emissions and operational costs in autonomous maritime operations. Full article

Since power demand varies due to uncertain environmental conditions, a deterministic power control strategy for hybrid electric propulsion ships contains a limitation in securing robust performance. To overcome this limitation, this study applies a stochastic power control strategy based on the augmented operational dataset. This study generated 150 datasets and derived the optimal control strategy set using a dynamic programming algorithm. By synthesizing a set of optimal control strategies, we divided them into a total of 10 bins according to the battery state of charge (SOC) and implemented a probabilistic map for the power distribution ratio according to the demanded power in each bin. Additionally, the memory and SOC correction factor were utilized to prevent frequent changes in power control and ensure that the SOC remains stable. This strategy resulted in a 3% improvement in efficiency compared to the deterministic method. In addition, it can be implemented in a real-time strategy utilizing stochastic maps. Full article

The implementation of hybrid propulsion systems in vessels has gained prominence due to their significant advantages in energy efficiency and their reduction in harmful emissions, particularly during low engine load operations. This study evaluates hybrid propulsion system applications in two different tugboats, focusing on fuel consumption and engine load across eight distinct operational scenarios, including Istanbul Strait crossings and towing and pushing manoeuvres. The scenarios incorporate asynchronous electric motors with varying power ratings, lead-acid and lithium iron phosphate batteries with distinct storage capacities, and photovoltaic panels of different sizes. The highest fuel savings of 72.4% were recorded in the second scenario, which involved only towing and pushing operations using lithium iron phosphate batteries. In contrast, the lowest fuel savings of 5.2% were observed in the sixth scenario, focused on a strait crossing operation employing lead-acid batteries. Although integrating larger-scale batteries into hybrid propulsion systems is vital for extended ship operations, their adoption is often limited by space and weight constraints, particularly on tugboats. Nevertheless, ongoing advancements in hybrid system technologies are expected to enable the integration of larger, more efficient systems, thereby enhancing fuel-saving potential. Full article

Fast patrol boats account for a large number among the numerous vessels used in naval fleets. Owing to their operational characteristics, which involve relatively high speeds, they contribute to emissions significantly. This study presents an optimized design concept for a diesel–battery hybrid electric propulsion system integrated into the general ship design process for fast patrol boats. The optimization design uses mixed-integer linear programming to determine the most eco-friendly shares ratio of battery and diesel usage while satisfying high-endurance operational scenarios. A shares ratio of 1.259 tons of diesel to 2.88 tons of batteries was identified as the most eco-friendly configuration capable of meeting a 200-nautical-mile operational scenario at a maximum speed of 35 knots for the selected case study. A quantitative comparison through a global warming potential (GWP) analysis was conducted between conventional diesel propulsion systems and the designed diesel–battery hybrid electric propulsion system, using a life-cycle assessment (LCA) standardized under the ISO framework. The analysis confirmed that the optimized hybrid propulsion system can achieve a GWP reduction of approximately 7–9% compared with conventional propulsion systems. Few studies have applied LCA in this field, and the application of batteries as hybrid secondary energy sources is viable and sustainable for high-endurance scenarios. Full article

The maritime shipping sector is a major contributor to greenhouse gas emissions, particularly in coastal regions. In response, the adoption of electric ferries powered by renewable energy and supported by battery storage technologies has emerged as a viable decarbonization pathway. This study investigates the operational impacts of coordinated electric ferry charging on a medium-voltage distribution network at Gladstone Marina, Queensland, Australia. Using DIgSILENT PowerFactory integrated with MATLAB Simulink and a Python-based control system, four proposed ferry terminals equipped with BESSs (Battery Energy Storage Systems) are simulated. A dynamic model of BESS operation is optimized using a balanced hybrid metaheuristic algorithm combining GA-PSO-BFO (Genetic Algorithm-Particle Swarm Optimization-Bacterial Foraging Optimization). Simulations under 50% and 80% transformer loading conditions assess the effects of charge-only versus charge–discharge strategies. Results indicate that coordinated charge–discharge control improves voltage stability by 1.0–1.5%, reduces transformer loading by 3–4%, and decreases feeder line loading by 2.5–3.5%. Conversely, charge-only coordination offers negligible benefits. Further, quasi-dynamic analyses validate the system’s enhanced stability under coordinated energy management. These findings highlight the potential of docked electric ferries, operating under intelligent control, to act as distributed energy reserves that enhance grid flexibility and operational efficiency. Full article

Electric and hybrid marine vessels are marking a new phase of eco-friendly maritime transport, combining electricity and traditional propulsion to boost efficiency and reduce emissions. The industry’s advancements in charging infrastructure and strict regulations help these vessels lead the way toward a sustainable and economically viable future in shipping. In this review, electric and hybrid marine vessels are discussed, including past applications and trend demonstrations. This paper systematically analyzes maritime vessels’ energy management and battery systems, highlighting advances in lithium-based and alternative battery technologies. Additionally, the review examines the impact of these technologies on sustainability and operational efficiency in the maritime industry. This paper contributes to the field by presenting a holistic view of the challenges and solutions associated with the electrification of maritime vessels, aiming to inform future developments and policymaking in this dynamic sector. Unlike many existing reviews that focus exclusively on battery chemistries or energy management algorithms, this manuscript integrates multiple aspects of maritime electrification—including propulsion types, charging infrastructure, grid systems (MVDC), EMS, BMS, and AI applications—into one cohesive systems-level review. This cross-sectional integration is particularly rare in the literature and enhances the practical value of the review for designers, policymakers, and shipbuilders. Full article

In the context of the new energy hybrid ship propulsion system (NE-HSPS), the parameters of the rotor speed, torque, and current of the permanent magnet synchronous motor (PMSM) are susceptible to environmental variations and unmodeled disturbances. Conventional nonlinear controllers (e.g., backstepping, PI, and sliding mode) encounter challenges related to response speed, interference immunity, and vibration jitter. These challenges stem from the inherent uncertainties in perturbations and the limitations of the traditional nonlinear controllers. In this paper, a novel Adaptive Finite-Time Command-Filtered Backstepping Controller (AFTCFBC) is proposed, featuring a faster response time and the elimination of overshoot. The proposed controller is a significant advancement in the field, addressing the computational complexity of backstepping control and reducing the maximum steady-state error of the control output. The novel controller incorporates a Nonlinear Finite-Time Command Filter (NFTCF) adapted to the variation in motor speed. Secondly, a novel Nonlinear Sliding Mode Observer (NSMO) is proposed based on the designed nonlinear sliding mode gain function (φ(S_w)) to estimate the load disturbance of the electric propulsion system. The Uncertainty Parameter-Adaptive law (UPAL) is designed based on Lyapunov theory to improve the robust performance of the system. The construction of a simulation model of a hybrid ship PMSM under four distinct working conditions, including constant speed and constant torque, the lifting and lowering of speed, loading and unloading, and white noise interference, is presented. The results of this study demonstrate a significant reduction in speed-tracking overshoot to zero, a substantial decrease in integral squared error by 90.15%, and a notable improvement in response time by 18.6%. Full article

Within the 21st century, in the Maritime Silk Road, wave energy, a clean renewable source, is drawing more interest, especially in areas with power shortages. This paper investigates wave energy in ships, particularly in a hybrid electric bulk carrier, by designing a system that supplements the existing power setup with oscillating buoy wave energy converters. The system includes diesel generators (DGs), a wave energy generation system, heterogeneous energy storage (consisting of battery storage (BS) and thermal storage (TS)), a combined cooling heat and power (CCHP) unit, and a power-to-thermal conversion (PtC) unit. To ensure safe and reliable navigation despite uncertainties in wave energy output, onboard power loads, and outdoor temperature, a robust coordination method is adopted. This method employs a two-stage robust optimization (RO) strategy to coordinate the various onboard units across different time scales, minimizing operational costs while satisfying all operational constraints, even in the worst-case scenarios. By applying constraint linearization, the robust coordination model is formulated as a mixed-integer linear programming (MILP) problem and solved using an efficient solver. Finally, the effectiveness of the proposed method is validated through case studies and comparisons with existing ship operation benchmarks, demonstrating significant reductions in operational costs and robust performance under various uncertain conditions. Notably, the simulation results for the Singapore–Trincomalee route show an 18.4% reduction in carbon emissions compared to conventional systems. Full article

A ship’s diesel–electric hybrid power system is complex, with hardware configuration and energy management parameters being crucial to its economic performance. However, existing optimization methods typically involve designing and optimizing the hardware configuration on the basis of typical operating conditions, followed by the design and optimization of the energy management parameters, which makes it difficult to achieve optimal system performance. Moreover, when co-optimizing hardware configurations and energy management parameters, the parameter relationships and complex constraints often lead conventional optimization algorithms to converge slowly and become trapped in local optima. To address this issue, a hybrid Ivy-SA algorithm is developed for the co-optimization of both the hardware configuration and energy management parameters. First, the main engine and hybrid ship models are established on the basis of the hardware configuration, and the accuracy of the models is validated. An energy management strategy based on the equivalent fuel consumption minimization strategy (ECMS) is then formulated, and energy management parameters are designed. A sensitivity analysis is conducted on the basis of both the hardware configuration and energy management parameters to evaluate their impacts under various conditions, enabling the selection of key optimization parameters, such as diesel engine parameters, battery configuration, and charge/discharge factors. The Ivy-SA algorithm, which integrates the advantages of both the Ivy algorithm (IVYA) and the simulated annealing algorithm (SA), is developed for the co-optimization. The algorithm is tested with the CEC2017 benchmark functions and outperforms 11 other algorithms. Furthermore, when the top five performing algorithms are applied for the co-optimization, the results show that the Ivy-SA algorithm outperforms the other four algorithms with a 14.49% increase in economic efficiency and successfully escapes local optima. Full article

Owing to the global concern regarding fossil energy consumption and carbon emissions, the power supply for traditional diesel-driven ships is being replaced by low-carbon power sources, which include hydrogen energy generation and photovoltaic (PV) power generation. However, the uncertainty of shipboard PV power generation due to weather changes and ship motion variations has become an essential factor restricting the energy management of all-electric ships. In this paper, a deep reinforcement learning-based optimization algorithm is proposed for a green ship energy management system (EMS) coupled with hydrogen fuel cells (HFCs), lithium batteries, PV generation, an electric power propulsion system, and service loads. The focus of this study is reducing the total operation cost and improving energy efficiency by jointly optimizing power generation and voyage scheduling, considering shipboard PV uncertainty. To verify the effectiveness of the proposed method, real-world data for a hybrid hydrogen- and PV-driven ship are selected for conducting case studies under various sailing conditions. The numerical results demonstrate that, compared to those obtained with the Double DQN algorithm, the PPO algorithm, and the DDPG algorithm without considering the PV system, the proposed DDPG algorithm reduces the total economic cost by 1.36%, 0.96%, and 4.42%, while effectively allocating power between the hydrogen fuel cell and the lithium battery and considering the uncertainty of on-board PV generation. The proposed approach can reduce energy waste and enhance economic benefits, sustainability, and green energy utilization while satisfying the energy demand for all-electric ships. Full article

The aim of this article is to examine existing technologies for the use of electrical energy and to develop proposals for their improvement on maritime vessels. As a criterion for evaluating the effectiveness of alternative energy sources on ships, factors such as greenhouse gas emissions levels, production and transportation characteristics, onboard storage conditions, and technoeconomic indicators have been proposed. The analysis of fuel types reveals that hydrogen has zero greenhouse gas emissions. However, transportation and storage issues, along with the high investment required for implementation, pose barriers to the widespread use of hydrogen as fuel for maritime vessels. This article demonstrates that solar energy can serve as an alternative to gases and liquid fuels in maritime transport. The technologies and challenges in utilizing solar energy for shipping are analyzed, trends in solar energy for maritime transport are discussed, and future research directions for the use of solar energy in the maritime sector are proposed. The most significant findings include the identification of future research directions in the application of solar energy in the maritime sector, including the adaptation of concentrated solar power (CSP) systems for maritime applications; the development of materials and designs for solar panels specifically tailored to marine conditions; the development of methods for assessing the long-term economic benefits of using solar energy on vessels; and the creation of regulatory frameworks and international standards for the use of solar energy on ships. Furthermore, for hybrid photovoltaic and diesel power systems, promising research directions could include efforts to implement direct torque control systems instead of field-orientated control systems, as well as working on compensating higher harmonics in the phase current spectra of asynchronous motors. Full article

This research aims to assess the integration of different fuel cell (FC) options with battery and waste heat recovery systems through a mathematical modelling process to determine the most feasible retrofit solutions for a marine electricity generation plant. This paper distinguishes itself from existing literature by incorporating future cost projection scenarios involving variables such as carbon tax, fuel, and equipment prices. It assesses the environmental impact by including upstream emissions integrated with the Energy Efficiency Existing Ship Index (EEXI) and the Carbon Intensity Indicator (CII) calculations. Real-time data have been collected from a Kamsarmax vessel to build a hybrid marine power distribution plant model for simulating six system designs. A Multi-Criteria Decision Making (MCDM) methodology ranks the scenarios depending on environmental benefits, economic performance, and system space requirements. The findings demonstrate that the hybrid configurations, including solid oxide (SOFC) and proton exchange (PEMFC) FCs, achieve a deduction in equivalent CO₂ of the plant up to 91.79% and decrease the EEXI and the average CII by 10.24% and 6.53%, respectively. Although SOFC-included configurations show slightly better economic performance and require less fuel capacity, the overall performance of PEMFC designs are ranked higher in MCDM analysis due to the higher power density. Full article

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%. Full article