Evaluation of energy management strategies for fuel cell/battery-powered underwater vehicles against field trial data

Deutsch, Clemens; Chiche, Ariel; Bhat, Sriharsha; Lagergren, Carina; Kuttenkeuler, Jakob

doi:10.1016/j.ecmx.2022.100193

Cited by 6 publications

(2 citation statements)

References 57 publications

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“…The FC stack in our simulation is represented as a hydrogen PEM‐FC. For this purpose, a generic FC model was utilized, which comes pre‐loaded with parameters for a 1.26 kW (24 Vdc) PEM‐FC 36 . The Wind and Tidal Turbine blocks were employed to model the steady‐state power characteristics of both wind and tidal turbines.…”

Section: Numerical Testing Resultsmentioning

confidence: 99%

“…For this purpose, a generic FC model was utilized, which comes pre-loaded with parameters for a 1.26 kW (24 Vdc) PEM-FC. 36 The Wind and Tidal Turbine blocks were employed to model the steady-state power characteristics of both wind and tidal turbines. It is important to note that the drive train's stiffness is considered infinite, and to accurately represent the system, the friction factor and inertia of the turbine are combined with those of the permanent magnet synchronous generator (PMSG) coupled to the turbine.…”

Section: Numerical Testing Resultsmentioning

confidence: 99%

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Novel design and adaptive coordinated energy management of hybrid fuel‐cells/tidal/wind/PV array energy systems with battery storage for microgrids

Belkhier,

Oubelaid

2024

Energy Storage

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This paper proposes a comprehensive solution to the challenges of managing a hybrid microgrid that generates electricity from multiple sustainable energy sources by proposing a coordinated energy management strategy and storage system. As renewable energy generation becomes increasingly popular, it introduces greater intermittency and stochasticity in energy management. To address this issue, a coordinated energy management strategy and storage system is proposed, which includes a fuzzy logic modified super twisting algorithm (MSTA). The objectives are to optimize the design and operation of microgrid including electrical based energy conversion systems such as photovoltaic and wind turbines, fuel cells, tidal energy, electric vehicle charging stations, and main grid. The second objective is to develop an energy management system for hybrid energy storage systems (HESS) and renewable energy sources (RESs) to maximize power production and ensure service continuity and smooth output energy of the microgrid, while also providing optimal benefits. To maintain cost‐effectiveness, an On/Off maximum power point tracking (MPPT) algorithm is also proposed. The contribution of this paper is to provide a solution to the intermittent and stochastic nature of renewable energy management and to improve the efficiency and durability of the energy conversion systems. The proposed management unit provides consistent output power and long‐term service.

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Section: Numerical Testing Resultsmentioning

confidence: 99%

Section: Numerical Testing Resultsmentioning

confidence: 99%

Novel design and adaptive coordinated energy management of hybrid fuel‐cells/tidal/wind/PV array energy systems with battery storage for microgrids

Belkhier,

Oubelaid

2024

Energy Storage

View full text Add to dashboard Cite

show abstract

Modelling and simulation hybrid electric vehicle with hydrogen fuel cells

Shchurov,

Dedov,

Shtang

2024

International Journal of Hydrogen Energy

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Motion states identification of underwater glider based on complex networks and graph convolutional networks

Guo,

Sun,

et al. 2024

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Underwater glider (UG) plays an important role in ocean observation and exploration for a more efficient and deeper understanding of complex ocean environment. Timely identifying the motion states of UG is conducive for timely attitude adjustment and detection of potential anomalies, thereby improving the working reliability of UG. Combining limited penetrable visibility graph (LPVG) and graph convolutional networks (GCN) with self-attention mechanisms, we propose a novel method for motion states identification of UG, which is called as visibility graph and self-attention mechanism-based graph convolutional network (VGSA-GCN). Based on the actual sea trial data of UG, we chose the attitude angle signals of motion states related sensors collected by the control system of UG as the research object and constructed complex networks based on the LPVG method from pitch angle, roll angle, and heading angle data in diving and climbing states. Then, we build a self-attention mechanism-based GCN framework and classify the graphs under different motion states constructed by a complex network. Compared with support vector machines, convolutional neural network, and GCN without self-attention pooling layer, the proposed VGSA-GCN method can more accurately distinguish the diving and climbing states of UG. Subsequently, we analyze the variation of the transitivity coefficient corresponding to these two motion states. The results suggest that the coordination of the various sensors in the attitude adjustment unit during diving becomes closer and more efficient, which corresponds to the higher network measure of the diving state compared to the climbing state.

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Evaluation of energy management strategies for fuel cell/battery-powered underwater vehicles against field trial data

Cited by 6 publications

References 57 publications

Novel design and adaptive coordinated energy management of hybrid fuel‐cells/tidal/wind/PV array energy systems with battery storage for microgrids

Novel design and adaptive coordinated energy management of hybrid fuel‐cells/tidal/wind/PV array energy systems with battery storage for microgrids

Modelling and simulation hybrid electric vehicle with hydrogen fuel cells

Motion states identification of underwater glider based on complex networks and graph convolutional networks

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