Modeling of Energy Management System for Fully Autonomous Vessels with Hybrid Renewable Energy Systems Using Nonlinear Model Predictive Control via Grey Wolf Optimization Algorithm
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....
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MDPI AG
2025-06-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/7/1293 |
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| author | Harriet Laryea Andrea Schiffauerova |
| author_facet | Harriet Laryea Andrea Schiffauerova |
| author_sort | Harriet Laryea |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c43e9faf90f245358c4e8e754037b768 |
| institution | DOAJ |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-c43e9faf90f245358c4e8e754037b7682025-08-20T02:45:42ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-06-01137129310.3390/jmse13071293Modeling of Energy Management System for Fully Autonomous Vessels with Hybrid Renewable Energy Systems Using Nonlinear Model Predictive Control via Grey Wolf Optimization AlgorithmHarriet Laryea0Andrea Schiffauerova1Concordia Institute for Information Systems Engineering (CIISE), Concordia University, 1515 Saint Catherine Street West, Montreal, QC H3G 2W1, CanadaConcordia Institute for Information Systems Engineering (CIISE), Concordia University, 1515 Saint Catherine Street West, Montreal, QC H3G 2W1, CanadaThis 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.https://www.mdpi.com/2077-1312/13/7/1293fully autonomoushybrid renewable energy system (HRES)nonlinear model predictive control (NMPC)grey wolf optimization (GWO)extended Kalman filter (EKF)fuel consumption |
| spellingShingle | Harriet Laryea Andrea Schiffauerova 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 fully autonomous hybrid renewable energy system (HRES) nonlinear model predictive control (NMPC) grey wolf optimization (GWO) extended Kalman filter (EKF) fuel consumption |
| title | Modeling of Energy Management System for Fully Autonomous Vessels with Hybrid Renewable Energy Systems Using Nonlinear Model Predictive Control via Grey Wolf Optimization Algorithm |
| title_full | Modeling of Energy Management System for Fully Autonomous Vessels with Hybrid Renewable Energy Systems Using Nonlinear Model Predictive Control via Grey Wolf Optimization Algorithm |
| title_fullStr | Modeling of Energy Management System for Fully Autonomous Vessels with Hybrid Renewable Energy Systems Using Nonlinear Model Predictive Control via Grey Wolf Optimization Algorithm |
| title_full_unstemmed | Modeling of Energy Management System for Fully Autonomous Vessels with Hybrid Renewable Energy Systems Using Nonlinear Model Predictive Control via Grey Wolf Optimization Algorithm |
| title_short | Modeling of Energy Management System for Fully Autonomous Vessels with Hybrid Renewable Energy Systems Using Nonlinear Model Predictive Control via Grey Wolf Optimization Algorithm |
| title_sort | modeling of energy management system for fully autonomous vessels with hybrid renewable energy systems using nonlinear model predictive control via grey wolf optimization algorithm |
| topic | fully autonomous hybrid renewable energy system (HRES) nonlinear model predictive control (NMPC) grey wolf optimization (GWO) extended Kalman filter (EKF) fuel consumption |
| url | https://www.mdpi.com/2077-1312/13/7/1293 |
| work_keys_str_mv | AT harrietlaryea modelingofenergymanagementsystemforfullyautonomousvesselswithhybridrenewableenergysystemsusingnonlinearmodelpredictivecontrolviagreywolfoptimizationalgorithm AT andreaschiffauerova modelingofenergymanagementsystemforfullyautonomousvesselswithhybridrenewableenergysystemsusingnonlinearmodelpredictivecontrolviagreywolfoptimizationalgorithm |