Energy Management Strategies for Hybrid Ship Propulsion: A Systematic Review of MPC, ECMS, and Predictive Control

Abstract

Effective energy management is critical for maximizing the efficiency and environmental benefits of hybrid propulsion systems (HPS) in ships. This systematic review evaluates various advanced energy management strategies (EMS) applied to marine HPS, focusing on model predictive control (MPC), equivalent consumption minimization strategy (ECMS), sequential quadratic programming (SQP), predictive power-split, and rule-based approaches. Based on an analysis of recent international literature, findings indicate that MPC-based EMS improves fuel efficiency by 10-20% and extends battery life through optimal power distribution under dynamic load conditions. Meanwhile, ECMS reduces equivalent fuel consumption by up to 25% under varying operational cycles. Predictive power-split algorithms achieve 15-20% energy savings by anticipating propulsion load changes up to 30 minutes in advance. Compared to conventional rule-based systems, advanced EMS reduces CO₂ emissions by 20-45% and NOx emissions by 20-60%, especially during harbor maneuvering and low-speed cruising. However, challenges remain, including high computational demands for real-time MPC, dependence on accurate load prediction models, and the lack of standardized interoperability protocols. This review concludes that integrating artificial intelligence and digital twins into EMS represents the most promising research direction. For ferries, patrol boats, and research vessels, implementing MPC or ECMS with adaptive tuning can significantly enhance operational performance while complying with IMO EEDI and CII regulations.