Estimation and Optimization of Ship Fuel Consumption in Maritime: Review, Challenges and Future Directions

TL;DR

This review paper discusses methods for estimating and optimizing ship fuel consumption, emphasizing recent advances, challenges, and future research directions including AI, data fusion, and real-time solutions.

Contribution

It categorizes estimation methods into physics-based, machine-learning, and hybrid models, and explores the emerging role of Explainable AI in maritime fuel efficiency.

Findings

Data fusion improves prediction accuracy.

Hybrid models offer promising optimization solutions.

Explainable AI enhances decision-making transparency.

Abstract

To reduce carbon emissions and minimize shipping costs, improving the fuel efficiency of ships is crucial. Various measures are taken to reduce the total fuel consumption of ships, including optimizing vessel parameters and selecting routes with the lowest fuel consumption. Different estimation methods are proposed for predicting fuel consumption, while various optimization methods are proposed to minimize fuel oil consumption. This paper provides a comprehensive review of methods for estimating and optimizing fuel oil consumption in maritime transport. Our novel contributions include categorizing fuel oil consumption \& estimation methods into physics-based, machine-learning, and hybrid models, exploring their strengths and limitations. Furthermore, we highlight the importance of data fusion techniques, which combine AIS, onboard sensors, and meteorological data to enhance accuracy. We make the first attempt to discuss the emerging role of Explainable AI in enhancing model transparency for decision-making. Uniquely, key challenges, including data quality, availability, and the need for real-time optimization, are identified, and future research directions are proposed to address these gaps, with a focus on hybrid models, real-time optimization, and the standardization of datasets.