Hybrid Ship Research Articles

An integrated energy efficiency optimization method of the wind-assisted hybrid ship for the shipping decarbonization.

The utilization of wind energy can provide auxiliary thrust and hence reduce the fuel consumption as well as carbon dioxide (CO2) emissions of wind-assisted ship. However, the use of sails would deviate main engine (ME) from its optimal operating point, which would reduce the engine fuel efficiency. The adoption of the shaft generator (SG) can maintain the ME running at the optimal fuel efficiency point in this condition. However, it is crucial to achieve the integrated optimization of the sailing speed, route, sails' angle of attack, and SG output power (SG-OP) to enhance the wind-assisted ship energy efficiency. Therefore, an integrated energy efficiency optimization method is proposed for reducing the ship fuel consumption. The results reveal that this method can effectively increase the energy efficiency of the wind-assisted hybrid ship, decreasing fuel consumption and energy efficiency operational index (EEOI) by about 5.25% and 7.36%, respectively.

A Review on Ferry Electrification: A Path towards Sustainable Maritime Transport

This paper reviews research and development efforts for ferries' electrification, focusing on energy supply grids, communication networks, charging systems, and energy storage. It analyzes energy storage technologies, battery charging requirements, shore side infrastructure design, power system architecture, and alternative battery charging stations[1]. The study evaluates the effectiveness of using an electric/hybrid ferry boat for tourist transportation in a real case. The optimal system configuration depends on engine power, energy storage, photovoltaic system sizes, and percentage of hybrid or pure electric usage. The analysis can be replicated to other cases, showing the potential of new technologies for sustainable boating and improving passenger service, especially in terms of comfort[15]. One of the most important steps in lowering greenhouse gas emissions and the marine industry's reliance on fossil fuels is the electrification of ships. This analysis examines the technical developments, difficulties, and environmental effects of switching from conventional fossil fuel-powered ships to electric and hybrid ships. The future of fully electric ships, legislative efforts, and how these advancements relate to international sustainability goals are also covered in the study.

Optimization-Based Energy Management Algorithm for 2-Stroke Hybrid Ship with Controllable Pitch Propeller

This paper examines the fuel consumption savings of a hybrid ship powertrain with 2-stroke main engine by implementing a novel adaptive equivalent consumption minimization strategy that utilizes a controllable pitch propeller. A non-hybrid powertrain model was developed as a demonstrator and real-world data were used for fuel consumption and efficiency maps. The baseline powertrain model was extended to a hybrid by introducing a shaft generator, a battery, a controllable pitch propeller, and the supervisory control algorithm. The potential benefits of the proposed powertrain are examined over different operation phases including port stay, open sea sailing, and port approach. The result showed that the energy efficiency gains can reach up to 6% under the open sea sailing phase. Furthermore, the controllable pitch propeller offers additional energy efficiency benefits of 2% under the port approach phase, utilizing the proposed algorithm. If the proposed powertrain is produced and the implemented algorithm is adopted, this could lead to substantial carbon dioxide emissions and fuel consumption savings at sea.

Energy Management Strategies for Hybrid Propulsion Ferry with Different Battery System Capacities

The International Maritime Organization (IMO) has been continuously strengthening environmental regulations to reduce greenhouse gas emissions from ships, which has led to increased attention on hybrid ship propulsion systems combining hydrogen fuel cells and batteries. This study analyzes the energy management strategy of a hybrid ship propulsion system in relation to changes in the battery system’s energy capacity. The target vessel was set as a 500 kW-class ferry operating for 24 h, and the maximum current rate (C-rate) and effects of the equivalence factor, which are key elements of the energy management problem, in relation to changes in energy capacity were investigated. The results show that while changes in the battery system’s energy capacity do not significantly affect the optimal operating point of the hybrid ship propulsion system, they are highly influenced by the response speed of the hydrogen fuel gas supply system and fuel cells, as well as the maximum C-rate required by the battery system. Furthermore, the equivalence factor, one of the key parameters in the optimization problem, tends to vary depending on the degree of charging and discharging, as it affects the equivalent fuel consumption of the battery system.

Distributionally Robust Optimal Scheduling of Hybrid Ship Microgrids Considering Uncertain Wind and Wave Conditions

A hybrid ship uses integrated generators, an energy storage system (ESS), and photovoltaics (PV) to match its propulsion and service loads, and together with optimal power and voyage scheduling, this can lead to a substantial improvement in ship operation cost, ensuring compliance with the environmental constraints and enhancing ship sustainability. During the operation, significant uncertainties such as waves, wind, and PV result in considerable speed loss, which may lead to voyage delays and operation cost increases. To address this issue, a distributionally robust optimization (DRO) model is proposed to schedule power generation and voyage. The problem is decoupled into a bi-level optimization model, the slave level can be solved directly by commercial solvers, the master level is further formulated as a two-stage DRO model, and linear decision rules and column and constraint generation algorithms are adopted to solve the model. The algorithm aims at minimizing the operation cost, limiting greenhouse gas (GHG) emissions, and satisfying the technical and operational constraints considering the uncertainty. Extensive simulations demonstrate that the expected total cost under the worst-case distribution is minimized, and compared with the conventional robust optimization methods, some distribution information can be incorporated into the ambiguity sets to generate fewer conservative results. This method can fully ensure the on-time arrival of hybrid ships in various uncertain scenarios while achieving expected operation cost minimization and limiting greenhouse gas (GHG) emissions.

Dynamic Response Control Strategy for Parallel Hybrid Ships Based on PMP-HMPC

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%.

Physics-Informed Data-Driven Approaches to State of Health Prediction of Maritime Battery Systems

Battery systems are increasingly being used for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion and maneuvering is growing. In order to ensure the safety of such electric ships, it is of paramount importance to monitor the available energy that can be stored in the batteries, and classification societies typically require that the state of health (SOH) of the batteries can be verified by independent tests – annual capacity tests. However, this paper discusses physics-informed data-driven approaches to online diagnostics for state of health monitoring of maritime battery systems based on a combination of physical knowledge, physic-based models, insights from extensive characterization tests and operational sensor data collected from the batteries during actual operation. This represents an alternative approach to the annual capacity tests for electric ships that is found to be sufficiently robust and accurate under certain conditions. Previous attempts with purely data-driven models, including both cumulative and snapshot models, semi-supervised learning and simple models based on the state of charge did not achieve the required reliability and accuracy for them to be utilized in a ship classification perspective, as presented at previous PHM conferences. However, preliminary results from the physics-informed data-driven method presented in this paper indicate that it can be relied on for independent verification of state of health as an alternative to physical tests. It has been tested on battery cells cycled in laboratory degradation tests as well as on field returns from batteries onboard ships in service. Notwithstanding, further validation and verification of the method is recommended to further build confidence in the model predictions.

Optimization of marine hybrid power energy consumption

Abstract The energy and power system of the ship is the core of the ship, and the composite energy storage plays a great role in reducing the load power fluctuation of the ship, which effectively optimizes the energy efficiency index and reliability of the ship. [Objective] To better optimize the ship energy management system, a control model is established according to the characteristics of the energy system of hybrid electric ships. [Method] Energy consumption optimization is carried out by using a rule-based energy control strategy and energy control strategy based on equivalent fuel consumption minimum (ECMS). The dynamic control and strategy of the hybrid ship were optimized, and the data simulation and real ship application were carried out on the hybrid experiment platform. [Results] According to the data and results of the real ship application, the following results were shown: The daily fuel saving rate of the ECMS strategy is 14.6% in normal operating conditions and 13.3% in cruise operating conditions. [Conclusion] It has a great effect on the improvement of ship energy efficiency.

Piecewise-linear MILP optimization for energy system design onboard hybrid ships

Abstract In recent decades the share of GHG (Green House Gases) such as carbon dioxide (CO2), and local pollutants as carbon monoxide (CO), nitrogen oxides (NOx), and sulphur oxides (SOx) produced by shipping has significantly increased. In the case of CO2, the IMO (International Maritime Organization) has estimated that emissions will increase by a range of 90-130% in 2050 as compared to 2008. In this scope, penetration of new technologies such as Battery Energy Storage Systems (BES) can reduce the share of emissions, giving more flexibility to the ship’s system. In this work, an optimization algorithm based on Mixed-Integer-Linear-Programming (MILP) approach has been developed to define the best environmentally sustainable technology mix to be installed on board. The model is based on Demand-Driven optimization, where the main goal is to satisfy electrical and thermal energy demand, which is achieved by using linear constraints. The objective function is defined to minimize on-board energy production costs considering the CAPEX and OPEX of each technology, choosing the best technology mix and giving the schedule of working conditions for each installed technology. The non linearity of the components’ characteristic curves is tackled through piecewise-linearization approach. In this way, the algorithm can optimize the real operating conditions for technologies with higher accuracy of partial load conditions. The case study is based on real energy demand profiles of a cruise ship sailing between Stockholm and the island of Åland, in the Baltic Sea.

An energy trade-off management strategy for hybrid ships based on event-triggered model predictive control

This paper addresses the energy management problem of hybrid ships by proposing an event-triggered model predictive control (ET-MPC) method. The novelty in this work lies in the establishment of an event-triggered mechanism and a state prediction model for energy management of hybrid ships. First, torque models of the internal combustion engine (ICE) and electric machine (EM) are developed using a data-driven approach, followed by the construction of fuel consumption and carbon emission models. Second, an event-triggered mechanism, dependent on state prediction error, is introduced and updated at each time step based on the system’s current state. Additionally, a cubature Kalman filter (CKF) is employed to estimate and correct the state prediction error, minimizing inaccuracies. A trade-off coefficient is incorporated to optimize the balance between fuel consumption and carbon emissions. The ET-MPC method results in a 0.68% difference in fuel consumption and 3.43% increase emissions compared to the traditional MPC method. However, ET-MPC significantly reduces computational overhead by 56.66. The ET-MPC method effectively allocates the ship’s energy according to the varying trade-off coefficient, achieving optimal energy management under different constraints.

Approaching zero emissions in ports: implementation of batteries and supercapacitors with smart energy management in hybrid ships

The urgent need to reduce energy consumption and environmental impact in the shipping industry has prompted research and industry to explore new solutions for minimizing fuel usage. This study examines the potential effects and benefits of integrating electrical energy storage systems, such as lithium-ion batteries and supercapacitors, into short sea shipping ships during port stay. Specifically, a novel dynamic simulation tool is developed to conduct suitable analyses that investigate the feasibility of charging electrical storage systems while at navigation and utilizing them as alternatives to traditional diesel generators while in port. The analysis reveals that lithium-ion batteries and supercapacitors are effective tools for reducing pollutant emissions by minimizing diesel generator usage during port stopovers, resulting in a significant reduction in fuel consumption from 1.148 kt/year to 0.511 kt/year. Moreover, under identical conditions, the use of supercapacitors increases the lifespan of batteries from 10.6 years to 11.9 years. Additionally, there is a 55 % reduction in carbon dioxide emissions during port stays, decreasing from 2.98 kt/year to 1.64 kt/year.

Thermal runaway and combustion of lithium-ion batteries in engine room fires on oil/electric-powered ships

The thermal characteristics of traditional fuel fires can be altered by the thermal instability of lithium-ion batteries (LIBs) in–oil-electric hybrid ships. The thermal runaway (TR) and combustion of 18,650 LiFePO4 LIBs with 0 %, 50 %, and 100 % states of charge (SOC) in pool fires were studied with different annular n-heptane pools. The goal is to simulate the thermal safety of LIBs in hybrid ships within the thermal environment of pool fires. The flame morphologies resulting from the combustion of the battery and pool fires, temperatures of the battery surface and flame, exhaust characteristics of the battery, heat radiated from the pool fire to the battery, and mass losses were analyzed. The results showed that the risks of thermal runaway and combustion with LIBs were significantly higher in a thermal fire environment. Lithium batteries produce jet fires that increase the width and height of the flame during pool fires. Moreover, the battery with a 0 % SOC doesn’t experience thermal runaway during the fire, but thermal runaway of the battery with a 100 % SOC is more severe than that of the battery with a 50 % SOC, and the maximum rates for their temperature increases are 12.98 °C/s and 7.12 °C/s, respectively. In addition, the energy balance method showed that the pressure relief by the battery safety valve during a fire was not dependent on the SOC. An equivalent network diagram was constructed for the radiative heat transfer between the lithium battery and the pool fire. It is proposed that the total heats required to induce thermal runaway of batteries with the same SOCs are almost the same in different thermal environments, but the energy required for the 50 % SOC battery is greater than that required for the 100 % SOC battery, 10,161 ± 59 J and 9724 ± 43 J, respectively. Moreover, the combustion rates were proportional to the height of the flame during the mixed combustion of a lithium battery jet fire and pool fire. A dimensionless model was developed for the relationship between flame height and mixed combustion rate.

Research on the capacity allocation of composite energy storage systems for hybrid ships

Abstract Aiming at the problem of unstable output power fluctuation of the dredger during operation, a composite energy storage system consisting of lithium iron phosphate battery and supercapacitor is introduced to smooth out the power fluctuation. Considering the configuration cost, operation and maintenance cost of the energy storage system, the capacity optimization model of the composite energy storage system is established with the average daily cost as the objective function, and with the system power balance, the charge state of the energy storage system, and the rated power as the constraints. The optimal capacity is solved by using the modified artificial fish swarm algorithm (MAFSA). The final results show that MAFSA reduces the capacity of the battery and the supercapacitor by 6.9% and 9.9%, respectively, and reduces the average daily cost of the system by 13.5%. Meanwhile, the capacity-optimized energy storage system enables the generator set to work in the optimal operating range, which reduces its output power fluctuation, improves its efficiency, and reduces fuel consumption and pollutant emission.

A novel method of fuel consumption prediction for wing-diesel hybrid ships based on high-dimensional feature selection and improved blending ensemble learning method

Establishing an accurate fuel consumption prediction model is essential to optimize energy efficiency in wing-diesel hybrid ships. This study proposes an improved Blending ensemble learning method for predicting fuel consumption. Initially, the original data is cleaned using artificial experience and Interquartile Range (IQR) method. A high-dimensional feature selection method, variance analysis- Pearson correlation-mutual information-permutation importance (VA-PC-MI-PI), is then proposed to identify 16 key features from the initial 111 features. Furthermore, 9 algorithms are employed for prediction analysis under different temporal resolution data conditions. Based on the results, optimal time step and algorithm models are determined to construct the Blending ensemble model (BEM). Subsequently, the feature data of “sailing speed” and “bow draft”, which account for the highest permutation importance, are imported into the metadata set to alleviate insufficient BEM data utilization. Additionally, Extra Trees (ET) model is chosen as the meta-learner for adaptation and Bayesian method is employed for global optimization. The resulting improved Blending ensemble model (IBEM) exhibits optimal prediction accuracy and generalization ability with an RMSE of 66.04 kg/h, representing a reduction of 16.99% and 13.86% compared to the Support Vector Machine (SVM) model and BEM, respectively. Additionally, the IBEM exhibits significantly smaller residuals and absolute residuals, demonstrating superior predictive stability and consistency. This study provides an important reference and basis for energy efficiency prediction, control, and optimization of wing-diesel hybrid ships.

A novel cooperative optimization method of course and speed for wing-diesel hybrid ship based on improved A* algorithm

The wing-diesel hybrid ship, utilizes a wind propulsion system, which is significantly influenced by weather conditions. Therefore, a cooperative optimization method for course and speed is proposed based on an improved A* algorithm, considering weather changes and the ship characteristics, to achieve further improvement in the ship operational energy efficiency. Firstly, a meteorological and hydrological dataset is utilized to establish a dynamic maritime area model with complex weather conditions. Based on this model, the paper considers the actual navigation circumstances to define prohibited and navigable areas. Secondly, the improved A* algorithm incorporates the impact of weather on wing-sails. The improved A* algorithm considers the ship operating position and enhances the evaluation function by variables normalization of fuel consumption and voyage time. This cooperative optimization ensures the maximization of wind energy utilization, thereby achieving the goal of reducing fuel consumption while meeting the overall voyage time requirement. Experimental validation is conducted with the ship “New Aden” in the Indian Ocean region, and the results show that, compared to the original route, the optimized route reduces total fuel consumption by 5.48%, increases the navigation distance by up to 2.18% and increases the navigation time by 5.74%.

Analysis of Hybrid Ship Machinery System with Proton Exchange Membrane Fuel Cells and Battery Pack

As marine traffic is contributing to pollution, and most vessels have predictable routes with repetitive load profiles, to reduce their impact on environment, hybrid systems with proton exchange membrane fuel cells (PEMFC-s) and battery pack are a promising replacement. For this purpose, the new approach takes into consideration an alternative to diesel propulsion with the additional benefit of carbon neutrality and increase of system efficiency. Additionally, in the developed numerical model, control of the PEMFC–battery hybrid energy system with balance of plant is incorporated with repowering existing vessels that have two diesel engines with 300 kWe. The goal of this paper is to develop a numerical model that analyzes and determines an equivalent hybrid ship propulsion system for a known traveling route. The developed numerical model consists of an interconnected system with the PEMFC stack and a battery pack as power sources. The numerical model was developed and optimized to meet the minimal required power demand for a successful route, which has variable loads and sees ships sail daily six times along the same route—in total 54 nautical miles. The results showed that the equivalent hybrid power system consists of a 300 kWe PEMFC stack and battery pack with 424 kWh battery and state of charge varying between 20 and 87%. To power this new hybrid power system, a hydrogen tank of 7200 L holding 284.7 kg at pressure of 700 bar is required, compared to previous system that consumed 1524 kg of diesel and generated 4886 kg of CO2.

Energy Management Strategy for Diesel–Electric Hybrid Ship Considering Sailing Route Division Based on DDPG

The continuous evolution of the shipping industry has resulted in increasingly severe environmental pollution. Hybrid ships can be a promising solution. However, for certain aspects such as the complex power structure of hybrid ships and the different characteristics of energy sources, it is difficult to control the energy conversion and transmission process and to reasonably allocate the power output of each energy unit. In this study, an improved K-means++ algorithm was newly adopted to divide the ship’s route. Subsequently, an energy management strategy (EMS) based on the Deep Deterministic Policy Gradient (DDPG) algorithm for diesel–electric hybrid ships was established. Based on the current navigation environment, the EMS can allocate the output power of each energy unit, and optimize the working space of the diesel engine in real time. The simulation results show that the DDPG-based EMS can reduce fuel consumption by 2.3%, 5.5%, 18.1%, and 20.1% with respect to the results of the Deep Q Network (DQN), Q Learning, HAAR, and rule-based algorithms, respectively.

Statistical Models for Condition Monitoring and State of Health Estimation of Lithium-Ion Batteries for Ships

Battery systems are increasingly being used for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion is growing. To ensure the safety of such ships, it is important to monitor the available energy that can be stored in the batteries, and classification societies typically require the state of health (SOH) to be verified by independent tests. This paper addresses statistical modelling of SOH for maritime lithium-ion batteries based on operational sensor data. Various methods for sensor-based, data-driven degradation monitoring will be presented, and advantages and challenges with the different approaches will be discussed. The different approaches include cumulative degradation models and snapshot models, models that need to be trained and models that need no prior training, and pure data-driven models and physics-informed models. Some of the methods only rely on measured data, such as current, voltage and temperature, whereas others rely on derived quantities such as state of charge (SOC). Models include simple statistical models and more complicated machine learning techniques. Insight from this exploration will be important in establishing a framework for data-driven diagnostics and prognostics of maritime battery systems within the scope of classification societies. Conflict of Interest Statement The authors declare no conflicts of interest.

Hydrodynamic Simulation of Green Hydrogen Catamaran Operating in Lisbon, Portugal

Similar to other industries, the maritime industry is also facing increasing restrictions on ships regarding pollution control. The research presented in this paper is aimed at studying the pros and cons of alternative fuels followed by a detailed analysis on hydrogen fuel cells (PEMFC) for a particular ship operating in Lisbon, Portugal. Dynamic forces acting on the ship have been studied for a year. Assessing various scenarios based on these results aids ship operators in making informed decisions regarding the future course of action for their existing vessels. These different cases are first: business as usual (diesel engine), second: replacing the diesel engine with a hydrogen hybrid system and, third: replacement of the ship with a new hydrogen hybrid ship. The study is based on the simulation of numerical equations and CFD simulation results. As the result, the second scenario is best suited in both aspects; namely, environmental and economic.

Data-Driven Approaches to Diagnostics and State of Health Monitoring of Maritime Battery Systems

Battery systems are increasingly being used for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion and maneuvering is growing. In order to ensure the safety of such electric ships, it is important to monitor the available energy that can be stored in the batteries, and classification societies typically require that the state of health (SOH) can be verified by independent tests. However, this paper addresses data-driven approaches to state of health monitoring of maritime battery systems based on operational sensor data. Results from various approaches to sensor-based, data-driven degradation monitoring of maritime battery systems will be presented, and advantages and challenges with the different methods will be discussed. The different approaches include cumulative degradation models and snapshot models. Some of the models need to be trained, whereas others need no prior training. Moreover, some of the methods only rely on measured data, such as current, voltage and temperature, whereas others rely on derived quantities such as state of charge (SOC). Models include simple statistical models and more complicated machine learning techniques. Different datasets have been used in order to explore the various methods, including public datasets, data from laboratory tests and operational data from ships in actual operation. Lessons learned from this exploration will be important in establishing a framework for data-driven diagnostics and prognostics of maritime battery systems within the scope of classification societies.