Predicting Route-Specific Energy Demand Under Realistic Environmental Behaviour

Abstract To meet current and forthcoming international regulations, it is essential to reduce emissions and increase the efficiency of ships. A prevailing approach is to install hybrid drive systems to optimize the engine load. Evaluating the actual efficiency gains of a battery-supported ship compared to a conventional system requires detailed knowledge of its operational profile. Battery support proves advantageous primarily in applications with highly variable power demands. However, the data basis is often limited to statistical power distributions rather than continuous data. Since the state of charge of the battery is time-dependent, a continuous time series of the power demand is crucial for accurate sizing of system components. Additionally, the operational profile is influenced by environmental factors that vary statistically and regionally. To capture realistic power demand patterns, the environmental forces must also be incorporated in time domain. Therefore, this paper presents a manoeuvering simulation for predicting power demand along a ship route under realistically changing environmental conditions. The calculations utilize a fast, in-house, force-based manoeuvering approach that accounts for environmental factors such as seaway, wind, currents, and shallow water effects. Arbitrary ship routes are defined by way-points on a map, which are automatically navigated using the line-of-sight concept. Environmental data is sourced from the ERA5 database, providing information at specific geographical grid points. A spatial and temporal interpolation process generates a realistic continuous profile of these ambient parameters based on the ship’s current position along its route. The simulated results align strongly with measured data, confirming the model’s validity.

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Publication Details

Published
2025-06-22
DOI
https://doi.org/10.1115/omae2025-155994
Primary Topic
Transportation Planning and Optimization
Type
article
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article

Predicting Route-Specific Energy Demand Under Realistic Environmental Behaviour

Christian Emmersberger, Stefan Krüger, Tom Philipson
Transportation Planning and Optimization
article

Predicting Route-Specific Energy Demand Under Realistic Environmental Behaviour

Christian Emmersberger, Stefan Krüger, Tom Philipson
article en

Abstract

Abstract To meet current and forthcoming international regulations, it is essential to reduce emissions and increase the efficiency of ships. A prevailing approach is to install hybrid drive systems to optimize the engine load. Evaluating the actual efficiency gains of a battery-supported ship compared to a conventional system requires detailed knowledge of its operational profile. Battery support proves advantageous primarily in applications with highly variable power demands. However, the data basis is often limited to statistical power distributions rather than continuous data. Since the state of charge of the battery is time-dependent, a continuous time series of the power demand is crucial for accurate sizing of system components. Additionally, the operational profile is influenced by environmental factors that vary statistically and regionally. To capture realistic power demand patterns, the environmental forces must also be incorporated in time domain. Therefore, this paper presents a manoeuvering simulation for predicting power demand along a ship route under realistically changing environmental conditions. The calculations utilize a fast, in-house, force-based manoeuvering approach that accounts for environmental factors such as seaway, wind, currents, and shallow water effects. Arbitrary ship routes are defined by way-points on a map, which are automatically navigated using the line-of-sight concept. Environmental data is sourced from the ERA5 database, providing information at specific geographical grid points. A spatial and temporal interpolation process generates a realistic continuous profile of these ambient parameters based on the ship’s current position along its route. The simulated results align strongly with measured data, confirming the model’s validity.

Universität Hamburg (DE), Hamburg University of Technology (DE)
Affordable and clean energy
Openalex Percentile: Top 78%
Transportation Planning and Optimization
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