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Article

Modeling of Energy Management System for Fully Autonomous Vessels with Hybrid Renewable Energy Systems Using Nonlinear Model Predictive Control via Grey Wolf Optimization Algorithm

by
Harriet Laryea
* and
Andrea Schiffauerova
Concordia Institute for Information Systems Engineering (CIISE), Concordia University, 1515 Saint Catherine Street West, Montreal, QC H3G 2W1, Canada
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2025, 13(7), 1293; https://doi.org/10.3390/jmse13071293
Submission received: 27 May 2025 / Revised: 26 June 2025 / Accepted: 29 June 2025 / Published: 30 June 2025
(This article belongs to the Special Issue Advancements in Hybrid Power Systems for Marine Applications)

Abstract

This study presents a multi-objective predictive energy management system (EMS) for optimizing hybrid renewable energy systems (HRES) in autonomous marine vessels. The objective is to minimize fuel consumption and emissions while maximizing renewable energy usage and pure-electric sailing durations. The EMS combines nonlinear model predictive control (NMPC) with metaheuristic optimizers—Grey Wolf Optimization (GWO) and Genetic Algorithm (GA)—and is benchmarked against a conventional rule-based (RB) method. The HRES architecture comprises photovoltaic arrays, vertical-axis wind turbines (VAWTs), diesel engines, generators, and a battery storage system. A ship dynamics model was used to represent propulsion power under realistic sea conditions. Simulations were conducted using real-world operational and environmental datasets, with state prediction enhanced by an Extended Kalman Filter (EKF). Performance is evaluated using marine-relevant indicators—fuel consumption; emissions; battery state of charge (SOC); and emission cost—and validated using standard regression metrics. The NMPC-GWO algorithm consistently outperformed both NMPC-GA and RB approaches, achieving high prediction accuracy and greater energy efficiency. These results confirm the reliability and optimization capability of predictive EMS frameworks in reducing emissions and operational costs in autonomous maritime operations.
Keywords: fully autonomous; hybrid renewable energy system (HRES); nonlinear model predictive control (NMPC); grey wolf optimization (GWO); extended Kalman filter (EKF); fuel consumption; emission fully autonomous; hybrid renewable energy system (HRES); nonlinear model predictive control (NMPC); grey wolf optimization (GWO); extended Kalman filter (EKF); fuel consumption; emission

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MDPI and ACS Style

Laryea, H.; Schiffauerova, A. Modeling of Energy Management System for Fully Autonomous Vessels with Hybrid Renewable Energy Systems Using Nonlinear Model Predictive Control via Grey Wolf Optimization Algorithm. J. Mar. Sci. Eng. 2025, 13, 1293. https://doi.org/10.3390/jmse13071293

AMA Style

Laryea H, Schiffauerova A. Modeling of Energy Management System for Fully Autonomous Vessels with Hybrid Renewable Energy Systems Using Nonlinear Model Predictive Control via Grey Wolf Optimization Algorithm. Journal of Marine Science and Engineering. 2025; 13(7):1293. https://doi.org/10.3390/jmse13071293

Chicago/Turabian Style

Laryea, Harriet, and Andrea Schiffauerova. 2025. "Modeling of Energy Management System for Fully Autonomous Vessels with Hybrid Renewable Energy Systems Using Nonlinear Model Predictive Control via Grey Wolf Optimization Algorithm" Journal of Marine Science and Engineering 13, no. 7: 1293. https://doi.org/10.3390/jmse13071293

APA Style

Laryea, H., & Schiffauerova, A. (2025). Modeling of Energy Management System for Fully Autonomous Vessels with Hybrid Renewable Energy Systems Using Nonlinear Model Predictive Control via Grey Wolf Optimization Algorithm. Journal of Marine Science and Engineering, 13(7), 1293. https://doi.org/10.3390/jmse13071293

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