Energy Management Strategies and Capacity Sizing for Hybrid Ship Systems

Engineering notes

Key topics: autonomous driving. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

This comprehensive review investigates hybrid propulsion technologies as a pathway to decarbonization and improved energy efficiency in the maritime sector. Through a review of the recent literature, this study synthesizes current knowledge on energy management strategies and capacity sizing approaches for hybrid ship propulsion systems. Reported results indicate that optimized energy management can reduce fuel consumption and greenhouse gas emissions while minimizing total operational costs. Among real-time strategies, the Equivalent Consumption Minimization Strategy emerges as particularly suitable for maritime use due to its low computational demand and independence from full voyage profile knowledge, yet its maritime application remains far less developed than in the automotive domain. Capacity sizing and energy management are usually treated as separate optimization problems, limiting the achievability of truly optimal solutions. Only a few studies adopt integrated co-optimization frameworks, and these are typically built around simplified or fixed operational profiles. Moreover, the coupling between energy management parameters, such as the ECMS equivalence factor, and hardware sizing remains insufficiently explored. To address this, the review contributes a ship-specific classification of energy management strategies, a consolidated treatment of battery sizing methods with explicit attention to degradation, and a generalized two-loop framework that couples component sizing with ECMS-based energy management. The findings suggest that future research should prioritize adaptive energy management formulations calibrated for stochastic maritime duty cycles, the incorporation of battery degradation models into co-optimization, and validation against stochastic, real-world operating conditions.

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