【摘要】 目的：船舶在实际运行中往往处于动态变化的通航环境，可能导致能量分配在实际航行中的滞后或不匹配，从而影响燃油经济性和电量状态（SOC）的维持性。本文旨在研究一种能够适应动态工况变化的等效因子动态调整机制，以更高效地实现瞬时优化，并提高复杂通航环境下的能量分配适应性。创新点：1.利用径向基函数（RBF）网络结合通航环境预测船舶推进电机转速，并提出模型预测控制（MPC）方法优化船舶航速；2.根据转速变化动态调整等效因子，形成工况自适应能量管理策略。方法：1.利用RBF网络结合船舶通航环境预测船舶推进电机转速，通过多组实验得出合理的预测时域（图9），并以最小化能效运行指标（EEOI）为目标利用MPC方法优化船舶推进电机转速（图10）;2.改进等效油耗策略（ECMS）中等效因子的计算公式，通过PI控制模块动态调整等效因子，并利用超级电容的SOC反馈消除系统稳态误差，以增强控制系统的适应性；3.自适应ECMS控制模块通过动态等效因子的调节，在柴油发电机和超级电容之间优化能量分配，实现船舶推进功率的精确控制和燃油经济性的提升（公式(28)）。结论：1.基于MPC的航速优化方法在提高航行效率和节能方面表现优越，可缩短航行时间约2.59%，降低EEOI约11.61%，同时可平缓推进电机转速变化；2.提出的工况自适应ECMS策略相较传统方法在降低燃油消耗（最多约9.47%）和减小柴油发电机负载波动方面更具优势，提高了能量管理系统的适应性与稳定性。更多还原

【Abstract】 The concept of hybrid ships has gained significant attention in recent years, as they offer an effective means of enhancing energy utilization and reducing environmental pollution. However, the navigational environments of ships are often subject to changes, which in turn affect their energy efficiency in a complex manner. It is therefore evident that enhancing the energy efficiency of hybrid ships is a worthwhile goal. In this study, we take a diesel-electric hybrid ship navigating in inland waterways as the research object, and propose a hierarchical optimization method for ship energy efficiency. The upper-layer control establishes a predictive model for propulsion motor speed and fuel consumption through multivariate time series predictions, and employs the model predictive control(MPC) method to optimize the propulsion motor speed. The lower-layer control utilizes an equivalent fuel consumption minimization method, which is based on improving the equivalence factor. This involves combining the variation of the supercapacitor’s state of charge(SOC) with the propulsion motor speed obtained from the MPC optimization in the upper-layer control. Furthermore, a proportional integral(PI) controller is used to adjust the equivalence factor, in order to adapt the equivalent fuel consumption minimization method to the working conditions. Our results demonstrate that the proposed hierarchical optimization method can reduce the energy efficiency operating indicator(EEOI) by approximately 11.54% and the fuel consumption by approximately 9.47% in comparison to the pre-optimization scenario.更多还原