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[2602.21959] Estimation and Optimization of Ship Fuel Consumption in Maritime: Review, Challenges and Future Directions
Summary
This article reviews methods for estimating and optimizing ship fuel consumption, addressing challenges and proposing future research directions in maritime fuel efficiency.
Why It Matters
Improving fuel efficiency in shipping is vital for reducing carbon emissions and operational costs. This review highlights current methodologies, challenges, and the potential of AI in optimizing fuel consumption, making it relevant for researchers and industry stakeholders focused on sustainable maritime practices.
Key Takeaways
- Categorizes fuel consumption estimation methods into physics-based, machine-learning, and hybrid models.
- Emphasizes the role of data fusion techniques for enhancing accuracy in fuel consumption predictions.
- Identifies key challenges such as data quality and the need for real-time optimization.
- Discusses the emerging role of Explainable AI in improving model transparency.
- Proposes future research directions focusing on hybrid models and standardization of datasets.
Computer Science > Machine Learning arXiv:2602.21959 (cs) [Submitted on 25 Feb 2026] Title:Estimation and Optimization of Ship Fuel Consumption in Maritime: Review, Challenges and Future Directions Authors:Dusica Marijan, Hamza Haruna Mohammed, Bakht Zaman View a PDF of the paper titled Estimation and Optimization of Ship Fuel Consumption in Maritime: Review, Challenges and Future Directions, by Dusica Marijan and 2 other authors View PDF HTML (experimental) 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. Un...
