Energy management strategy for hybrid tugboats based on operating condition recognition and event-triggered mechanism

An energy management strategy integrating operating-condition recognition and event-triggered control (ETC) is proposed for harbor tugboats with low power-system efficiency, battery-power fluctuations, and frequent control updates. A diesel engine and battery–supercapacitor hybrid storage topology is established, reducing battery-power peaks from about 1800 kW to 1100 kW during rapid load fluctuations. A weighted K-means method uses load mean and deviation, engine imbalance and correlation, and spectral entropy to identify four conditions: standby, sailing, maneuvering, and working. Based on these conditions, a hierarchical ETC architecture updates control parameters only when needed, reducing energy loss, low-load engine operation, and unnecessary adjustments. Simulations using representative tugboat load data compare ETC with battery-only filtering, rule-based energy management (REMS), genetic-algorithm (GA) optimization, and event-triggered MPC. ETC raises the average diesel-engine load rate from 49.9% to 53.7% and reduces equivalent fuel consumption from 447.2 kg with REMS to 415.1 kg, outperforming GA. Compared with GA, it lowers battery-power standard deviation by 40.1% to 464.9 kW while maintaining terminal battery SOC near 45%. ETC uses only 225 updates, 97.0% fewer than REMS and GA, with a 0.0372 s per-step computation time.