Intelligent Energy Management and Hybrid Propulsion for Ships: A Review of Configurations, AI-Based Strategies, and Decarbonization Potential - Mastiadi Tamjidillah; Elwas Cahya Wahyu Pribadi; Rachmat Subagyo; Abdul Ghofur; Ma’ruf (Department of Marine Engineering, Institut Teknologi Sepuluh Nopember, 2026)
Stringent IMO regulations targeting a 40% GHG reduction by 2030 and net-zero by 2050 have driven the maritime industry to adopt cleaner technologies, as conventional diesel propulsion often operates inefficiently with high fuel consumption and emissions. This paper presents a descriptive-comparative literature review of hybrid propulsion systems for ships, analyzing peer-reviewed journals published in the last five years. The study examines system configurations (series, parallel, series-parallel), key components (diesel engines, fuel cells, batteries, supercapacitors), and energy management strategies (EMS), with a focus on artificial intelligence-based approaches such as Deep Reinforcement Learning (DRL) and Nonlinear Model Predictive Control (NMPC). The findings indicate that hybrid systems can improve energy efficiency by 15–30% and significantly reduce CO₂, NOₓ, and SOₓ emissions compared to conventional systems, especially during zero-emission port operations. Advanced EMS, particularly DRL algorithms like Proximal Policy Optimization (PPO), enable real-time multi-objective optimization that balances fuel economy, battery health, and component longevity. However, challenges remain, including high initial investment costs, optimal sizing of hybrid energy storage systems, and limited charging infrastructure. This review concludes that despite these barriers, the convergence of intelligent EMS, falling battery prices, and tightening environmental regulations positions hybrid propulsion as a key technology for sustainable maritime transportation, with future potential from waste-heat recovery systems and alternative fuels.