Performance and energy optimization of small-scale electric vessel : field measurement-based simulation and analysis

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Ma’arif, Samsul and Budiyanto, Muhammad Arif and Sunaryo and Mahyuddin, Muhammad Haris and Patil, Chaitanya (2026) Performance and energy optimization of small-scale electric vessel : field measurement-based simulation and analysis. Results in Engineering, 30. 110463. ISSN 2590-1230 (https://doi.org/10.1016/j.rineng.2026.110463)

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Abstract

The electrification of small-scale vessels is an essential pathway for maritime decarbonization, especially in fisheries and inland water transport. This study presents a performance and energy optimization framework for a small-scale electric vessel by integrating field measurements, hydrodynamic simulation, and predictive energy management using machine learning. The propulsion system consists of a 20 kW BLDC motor, a 19.2 kWh battery, and a 2.18 kWp solar photovoltaic array as a renewable ocean energy input. One year of real-time operational data, including motor power, speed, torque, voltage, current, and PV output, was analyzed to capture daily load patterns. Four-based algorithms Random Forest, Linear Regression, Gradient Boosting, and XGBoost were evaluated, with Random Forest achieving the highest accuracy (R² = 0.98, MAE = 0.087 kW, RMSE = 0.145 kW). The predictive results were translated into an adaptive energy management strategy that categorizes daily operation into three modes: (i) full-day operation under low demand (<2.5 kW), (ii) half-day operation with PV support for moderate demand (2.5–3.5 kW), and (iii) recharge or standby mode for high demand (>3.5 kW). The recommended operational profile corresponds to an average power range of 2–3 kW at speeds of 2.5–3.5 knots, enabling up to 50% longer endurance when PV contribution is maximized. This study demonstrates how combining empirical data and predictive models can deliver actionable control strategies, ensuring more sustainable and reliable energy use in small-scale electric vessels.

ORCID iDs

Ma’arif, Samsul, Budiyanto, Muhammad Arif, Sunaryo, Mahyuddin, Muhammad Haris and Patil, Chaitanya ORCID logoORCID: https://orcid.org/0000-0001-8139-1514;
CORE (COnnecting REpositories)