基于混杂模型预测控制的顺序实时决策混合动力能量管理策略研究

doi:10.3969/j.issn.1004-132X.2025.06.026

摘要/Abstract

摘要： 提出了一种用于船舶混合动力推进系统的动力分配优化策略和评估架构。从实验数据和组件特性角度出发，结合系统辨识、聚类算法等方法将连续变量和离散变量有效融合以构建反映船舶混合动力混杂特性的状态空间方程。定义了满足减少燃料消耗和提高动力性能的权衡性能指标函数，并设计了基于混合逻辑动态模型的混杂模型预测控制器（HMPC），使得天然气发动机、永磁同步电机和离合器的协同工作达到最优控制效果。最后，利用测试工况对所提出的HMPC在节能和动力提升方面进行实机测试，结果表明，与兼顾经济性与动力性的HMPC相比，燃料最优HMPC可节约2.72%的燃料，动力最佳HMPC可提高59.72%动力性能。

关键词: 混合动力模型, 能量管理, 船舶混合动力, 系统辨识, 混杂系统

Abstract: An optimization strategy and evaluation framework for power distribution in marine hybrid propulsion systems was developed. Experimental data and component characteristics were analyzed, and system identification and clustering algorithms were employed to integrate continuous and discrete variables effectively. A state-space equation was formulated to represent the hybrid characteristics of the propulsion systems. A trade-off performance index function was defined to balance fuel consumption reduction and power performance enhancement. Based on a mixed logical dynamic model, a hybrid model predictive controller（HMPC） was designed to optimize the collaborative operations of natural gas engines, permanent magnet synchronous motors and clutches. Finally, the proposed HMPC was tested in real machine in terms of energy saving and power improvement using test conditions. The results show that the fuel-optimal HMPC saves 2.72% fuel and the power-optimal HMPC improves power performance by 59.72% compared to the HMPC that combines economy and power.

Key words: hybrid power model, energy management, marine hybrid power, system identification, hybrid system

中图分类号:

U674.925

孙晓军1, 刘丛浩1, 张乔1, 宋恩哲2. 基于混杂模型预测控制的顺序实时决策混合动力能量管理策略研究[J]. 中国机械工程, 2025, 36(06): 1380-1393.

SUN Xiaojun1, LIU Conghao1, ZHANG Qiao1, SONG Enzhe2. Research on Hybrid Power Energy Management Strategy Based on Sequential Real-time Decision Making with Hybrid Model Predictive Control[J]. China Mechanical Engineering, 2025, 36(06): 1380-1393.

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链接本文: https://www.cmemo.org.cn/CN/10.3969/j.issn.1004-132X.2025.06.026

https://www.cmemo.org.cn/CN/Y2025/V36/I06/1380

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