Day-ahead Energy Management for Hybrid Electric Vessel with Different PEM Fuel Cell Modular Configurations

The increasing demand for decarbonization of marine transportation motivates the utilization of low-carbon resources. Among different options, fuel cells are drawing attention. The selection of fuel cell (FC) and the design of energy management strategy would have a great impact on the vessel’s operational efficiency, and thereby needs to be considered carefully. The objective of this paper is to develop energy management system (EMS) to reduce the fuel consumption of a hybrid fuel cell/battery ship. To this end, a day-ahead EMS scheme is proposed that takes full use of information including ship cruising routines and the degradation status of the fuel cell modules. The developed EMS is optimization-based and conducted off-line to provide guideline for the next-day power generation plan. In addition, three power allocating strategies across the multiple fuel cell modules are considered and compared (equal, independent, and sequential). A sequential rotation procedure is proposed to reduce the degradation rates of the fuel cell modules. Simulation results show that the proposed EMS can effectively improve the fuel economy of the hybrid ship while enhancing sufficient energy backup throughout the full voyage. In addition, comparisons between different FC configurations implies that the independent distribution has the highest fuel efficiency, and with the proposed rotation procedure, the sequential distribution can effectively improve the fuel efficiency by up to 23.2%

Paths forward for sustainable maritime transport : A techno-economic optimization framework for next generation vessels

Climate change is omnipresent in our society. It is known that climate change is occurring, and that additional warming is unavoidable. Therefore, the decarbonization of industrial sectors has gained increased importance in the last years. The maritime transport sector is one of the most targeted industries as it contributes to approximately 3% of global GHG emissions. Nevertheless, maritime transport accounts for up to 80% of the global trade volume, underlying its importance for the world economy. A technical feasible and reliable solution is, thus, essential for the shipping industry to reach the ambitious climate goals established by the Paris Agreement. In the past, the maritim sector has been highly reliant on fossil fuels, using heavy fuel oil as the major energy input. Heavy fuel oil has been the most dominant fuel in the industry due to its cost advantage and high energy density. Recent developments in the maritime industry promote the emergence of dual fuel engines (e.g. LNG and HFO). Even though increased efficiencies and low carbon fuels can reduce maritime pollution, they cannot achieve carbon neutrality. In the long-term, it will be necessary to implement zero emission fuels including green hydrogen, ammonia, methanol, and LNG. The implementation of new sustainable technologies and fuels in the maritime sector will however depend on their economic competitiveness compared to alternative solutions. Therefore, the following research question arises: When can sustainable maritime transport achieve cost parity compared to conventional technologies? The master thesis investigates the break-even point of sustainable shipping technologies in order to achieve climate targets. Thereby, the focus is set on the life cycle costs of different maritime technologies. A techno-economic framework is necessary to decide on the most suitable options for the industry in prospective years. The framework should be able to analyze current as well as prospective technologies, and guide during the technological decision-making process. Therefore, the definition of key performance indicators (KPI) is essential to set a standard for further assessments. The KPIs will be the main value to compare technologies from an economic perspective. In order to answer the research question a case study is developed. The case study is formed by an extensive literature review on current and next-generation sustainable energy systems for vessels. A priority lies on potential carbon neutral technologies and engines such as fuel cells and battery systems based on a predetermined shipping route and shipping class. In a first step, a simulation model for the developed case is established. The output of the simulation model will then be used in the techno-economic framework, connecting components of the system through thermodynamic and physical properties. In a last step, cost functions translate the systems behavior into economic behavior. Once the case study is analyzed, a statistical model is applied on the results in order to evaluate the system under varying boundary conditions. This sensitivity approach is further necessary to underline the impact of the aforementioned KPIs. By that, the robustness of the framework is tested and secured. Finally, the results of the analysis are explained and interpreted with regard to the research question. A conclusion is drawn regarding the potential economic benefits of sustainable maritime transport technologies within the light of potential market access.The results of the thesis are to be documented in a scientifically appropriate manner and discussed within the context of existing literature and regulatory targets for the industry

Potentials and challenges of the fuel cell technology for ship applications. A comprehensive techno-economic and environmental assessment of maritime power system configurations

The decarbonization of the global ship traffic is one of the industry’s greatest challenges for the next decades and will likely only be achieved with new, energy-efficient power technologies. To evaluate the performances of such technologies, a system modeling and optimization approach is introduced and tested, covering three elementary topics: shipboard solid oxide fuel cells (SOFCs), the benefits of decentralizing ship power systems, and the assessment of potential future power technologies and synthetic fuels. In the following, the analyses’ motivations, scopes, and derived conclusions are presented. SOFCs are a much-discussed technology with promising efficiency, fuel versatility, and few operating emissions. However, complex processes and high temperature levels inhibit their stand-alone dynamic operation. Therefore, the operability in a hybrid system is investigated, focusing on component configurations and evaluation approach corrections. It is demonstrated that moderate storage support satisfies the requirements for an uninterrupted ship operation. Depending on the load characteristics, energy-intensive and power-intensive storage applications with diverging challenges are identified. The analysis also emphasizes to treat degradation modeling with particular care, since technically optimal and cost-optimal design solutions differ meaningfully when assessing annual expenses. Decentralizing a power system with modular components in accordance with the load demand reduces both grid size and transmission losses, leading to a decrease of investment and operating costs. A cruise-ship-based case study considering variable installation locations and potential component failures is used to quantify these benefits. Transmission costs in a distributed system are reduced meaningfully with and without component failure consideration when compared to a central configuration. Also, minor modifications ensure the component redundancy requirements, resulting in comparably marginal extra expenses. Nowadays, numerous synthetic fuels are seen as candidates for future ship applications in combination with either combustion engines or fuel cells. To drive an ongoing technology discussion, performance indicators for envisioned system configurations are assessed in dependence on mission characteristics and critical price trends. Even if gaseous hydrogen is often considered not suitable for ship applications due to its low volumetric energy density, resulting little operating costs are accountable for its superior performance on short passages. For extended missions, fuel cells operating on methanol or ammonia surpass hydrogen economically

Optimization-Based Energy Management for Multi-energy Maritime Grids

This open access book discusses the energy management for the multi-energy maritime grid, which is the local energy network installed in harbors, ports, ships, ferries, or vessels. The grid consists of generation, storage, and critical loads. It operates either in grid-connected or in islanding modes, under the constraints of both power system and transportation system. With full electrification, the future maritime grids, such as all-electric ships and seaport microgrids, will become “maritime multi-energy system” with the involvement of multiple energy, i.e., electrical power, fossil fuel, and heating/cooling power. With various practical cases, this book provides a cross-disciplinary view of the green and sustainable shipping via the energy management of maritime grids. In this book, the concepts and definitions of the multi-energy maritime grids are given after a comprehensive literature survey, and then the global and regional energy efficiency policies for the maritime transportation are illustrated. After that, it presents energy management methods under different scenarios for all-electric ships and electrified ports. At last, the future research roadmap are overviewed. The book is intended for graduate students, researchers, and professionals who are interested in the energy management of maritime transportation

On the design of plug-in hybrid fuel cell and lithium battery propulsion systems for coastal ships

A plug-in hybrid propulsion system comprising of a proton exchange membrane fuel cell (PEMFC) and lithium battery capable of being recharged in port offers a promising low carbon propulsion system for small coastal ships, e.g. small container ships, tankers and ferries, which typically sail over short routes at modest speeds. PEMFC operate at high efficiency and emit no harmful emissions, but their poor transient performance necessitates the need for an energy storage system such as a lithium battery. A shore-to-ship electrical connection is needed to recharge the lithium battery from the grid so as to improve the propulsion system performance both environmentally and economically. Production of both H2 and grid electricity have a carbon footprint. In this paper a two-layer optimisation based methodology is used for the design of plug-in hybrid fuel cell and lithium battery propulsion systems for coastal ships. Results from a case study suggest that the design of hybrid PEMFC and battery propulsion systems should be influenced by the ‘well-to-propeller’ carbon footprint

Near-optimal energy management for plug-in hybrid fuel cell and battery propulsion using deep reinforcement learning

Plug-in hybrid fuel cell and battery propulsion systems appear promising for decarbonising transportation applications such as road vehicles and coastal ships. However, it is challenging to develop optimal or near-optimal energy management for these systems without exact knowledge of future load profiles. Although efforts have been made to develop strategies in a stochastic environment with discrete state space using Q-learning and Double Q-learning, such tabular reinforcement learning agents’ effectiveness is limited due to the state space resolution. This article aims to develop an improved energy management system using deep reinforcement learning to achieve enhanced cost-saving by extending discrete state parameters to be continuous. The improved energy management system is based upon the Double Deep Q-Network. Real-world collected stochastic load profiles are applied to train the Double Deep Q-Network for a coastal ferry. The results suggest that the Double Deep Q-Network acquired energy management strategy has achieved a further 5.5% cost reduction with a 93.8% decrease in training time, compared to that produced by the Double Q-learning agent in discrete state space without function approximations. In addition, this article also proposes an adaptive deep reinforcement learning energy management scheme for practical hybrid-electric propulsion systems operating in changing environments

Prognostics and Health Management for the Optimization of Marine Hybrid Energy Systems

Decarbonization of marine transport is a key global issue, with the carbon emissions of international shipping projected to increase 23% to 1090 million tonnes by 2035 in comparison to 2015 levels. Optimization of the energy system (especially propulsion system) in these vessels is a complex multi-objective challenge involving economical maintenance, environmental metrics, and energy demand requirements. In this paper, data from instrumented vessels on the River Thames in London, which includes environmental emissions, power demands, journey patterns, and variance in operational patterns from the captain(s) and loading (passenger numbers), is integrated and analyzed through automatic, multi-objective global optimization to create an optimal hybrid propulsion configuration for a hybrid vessel. We propose and analyze a number of computational techniques, both for monitoring and remaining useful lifetime (RUL) estimation of individual energy assets, as well as modeling and optimization of energy use scenarios of a hybrid-powered vessel. Our multi-objective optimization relates to emissions, asset health, and power performance. We show that, irrespective of the battery packs used, our Relevance Vector Machine (RVM) algorithm is able to achieve over 92% accuracy in remaining useful life (RUL) predictions. A k-nearest neighbors algorithm (KNN) is proposed for prognostics of state of charge (SOC) of back-up lead-acid batteries. The classifier achieved an average of 95.5% accuracy in a three-fold cross validation. Utilizing operational data from the vessel, optimal autonomous propulsion strategies are modeled combining the use of battery and diesel engines. The experiment results show that 70% to 80% of fuel saving can be achieved when the diesel engine is operated up to 350 kW. Our methodology has demonstrated the feasibility of combination of artificial intelligence (AI) methods and real world data in decarbonization and optimization of green technologies for maritime propulsion

Particle swarm optimization for a hybrid freight train powered by hydrogen or ammonia solid oxide fuel cells

All diesel-only trains in the UK will be phased out by 2040. Hydrogen and ammonia emerge as alternative zero-carbon fuel for greener railway. Solid Oxide Fuel Cells (SOFCs) provide an alternative prime mover option, which efficiently convert zero-carbon fuels into electricity without emitting nitrogen oxides (NOx), unlike traditional engines. Superior to Proton Exchange Membrane Fuel Cells (PEMFCs) in efficiency, SOFCs fulfil MW-scale power needs and can use ammonia directly. This study investigates innovative strategies for integrating SOFCs into hybrid rail powertrains using hydrogen or ammonia. Utilizing an optimization framework incorporating Particle Swarm Optimization (PSO), the study aims to minimize operational costs while considering capital and replacement expenditures, powertrain performance, and component sizing. The findings suggest that hybrid powertrains based on ammonia-fueled SOFCs may potentially reduce costs by 30% compared to their hydrogen counterparts, albeit requiring additional space for engine compartments. Ammonia-fueled SOFCs trains also exhibit a 5% higher efficiency at End-of-Life (EoL), showing less performance degradation than those powered by hydrogen. The State of Charge (SoC) of the batteries in range of 30–70% for both cases is identified as most cost-effective