Hybrid fuel cell and battery propulsion system modelling and multi-objective optimisation for a coastal ferry

Wu, Peng; Bucknall, Rwg

doi:10.1016/j.ijhydene.2019.11.152

Cited by 94 publications

(45 citation statements)

References 44 publications

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“…The traditional CPP includes the main engine (ME), as which a diesel engine is usually used, and an electric propulsion motor (EPM), which are connected to the propeller (P). ME and EPM can be located on the same propeller shaft or connected to the propeller through a reduction gear [9,10].…”

Section: Methodsmentioning

confidence: 99%

Computer modeling of ship combined propulsion plants

Grigoryev

Ulitovsky

2021

E3S Web Conf.

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Combined propulsion plants (CPP) are increasingly being used on modern ships of foreign and domestic construction. A feature of such plants is that the energy for the movement of the vessel is generated in them in two (or more) different types of ship engines - heat and electric ones, working on a common propulsor. Combined plants are complex electromechanical systems designed to provide propulsion in various modes of ship operation and generate electricity in a cruising mode or during a lay-up. CPP combine the advantages of traditional propulsion plant with heat main engines and electric propulsion plants. The study of the physical properties and the principle of operation of CPP without a comprehensive study of the object using the model is impossible. Computer models and computer modeling are widely used to study the properties of complex objects. Nowadays, personal digital computers are widely used for computer modeling. Standard packages and programs are used as programs and packages for computer modeling.

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Section: Methodsmentioning

confidence: 99%

Computer modeling of ship combined propulsion plants

Grigoryev

Ulitovsky

2021

E3S Web Conf.

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“…The main classifications for solving the SDO problems are shown in Table 4.2 with some representative references, i.e., (1) mixed-integer linear programming, and (2) constrained non-linear programming, and (3) dynamic programming, and (4) evolutionary algorithm. Mixed-integer non-linear programming [22,[32][33][34][35][36] Dynamic programming Sequential direct method [3,37] Deterministic dynamic programming [38] Evolutionary algorithm Genetic algorithm [39] Particle swarm optimization [40,41] Ant colony algorithm [42] Whale optimization [43]…”

Section: Classification Of the Solution Methodsmentioning

confidence: 99%

Formulation and Solution of Maritime Grids Optimization

Fang

Wang

2021

Optimization-Based Energy Management for Multi-Energy Maritime Grids

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As a special energy system, the optimization of maritime grids can be considered as three levels similar to conventional land-based energy systems [1][2][3].(1) Synthesis optimization. Synthesis is defined as the components used in the maritime grids and their connections. Via synthesis optimization, the optimal configuration of the maritime grids can be determined. For example, the ship hull design, electrical layout, and whether to integrate a component or not. Since the synthesis optimization answers the "Yes-or-No" questions and therefore involves certain binary decision variables.(2) Design optimization or planning optimization. Design optimization is to determine the technical characteristics of components which are determined in synthesis optimization, such as the capacity and rated power. The difference between synthesis optimization and design optimization can be given by the well-known "siting and sizing" problems. The "siting problem" determines which type of components can be used and where to install them, which belongs to the synthesis optimization. Then the "sizing problem" determines the capacities of the installed components, which belongs to the design optimization.In power system research, design optimization is often named as planning optimization.(3) Operation optimization. After the synthesis optimization and design optimization, the operation optimization determines the optimal operating states of each component under specified conditions. Taken the navigation speed as an example. The synthesis optimization determines the type of main engine and the design optimization determines the capacity of the main engine, then operation optimization determines the optimal loading levels to address different navigation scenarios, such as different wave and wind scenarios.

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“…The work in this article is a continuation of the research detailed in [19]. In that work, the hybrid plug-in PEMFC and battery propulsion system sizing has been optimised based upon a proposed hybrid propulsion system model for a specific vessel operating on a particular route.…”

Section: System Overviewmentioning

confidence: 99%

“…In that work, the hybrid plug-in PEMFC and battery propulsion system sizing has been optimised based upon a proposed hybrid propulsion system model for a specific vessel operating on a particular route. Figure 1 provides the quasi-steady-state model which has been developed and validated in [19] for system design optimisation and intelligent EMS development. The system comprises a fuel cell, battery and converters.…”

Section: System Overviewmentioning

confidence: 99%

Cost-effective reinforcement learning energy management for plug-in hybrid fuel cell and battery ships

Partridge

Bucknall

2020

Applied Energy

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Hybrid fuel cell and battery propulsion systems have the potential to offer improved emission performance for coastal ships with access to H 2 replenishment and battery charging infrastructures in ports. However, such systems could be constrained by high power source degradation and energy costs. Cost-effective energy management strategies are essential for such hybrid systems to mitigate the high costs. This article presents a Double Q reinforcement learning based energy management system for such systems to achieve near-optimal average voyage cost. The Double Q agent is trained using stochastic power profiles collected from continuous monitoring of a passenger ferry, using a plug-in hybrid fuel cell and battery propulsion system model. The energy management strategies generated by the agent were validated using another test dataset collected over a different period. The proposed methodology provides a novel approach to optimal use hybrid fuel cell and battery propulsion systems for ships. The results show that without prior knowledge of future power demands, the strategies can achieve near-optimal cost performance (96.9%) compared to those derived from using dynamic programming with the equivalent state space resolution.

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Hybrid fuel cell and battery propulsion system modelling and multi-objective optimisation for a coastal ferry

Cited by 94 publications

References 44 publications

Computer modeling of ship combined propulsion plants

Computer modeling of ship combined propulsion plants

Formulation and Solution of Maritime Grids Optimization

Cost-effective reinforcement learning energy management for plug-in hybrid fuel cell and battery ships

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