基于自适应等效能耗最小的燃料电池船舶能量管理策略

许晓彦; 曹伟; 韩冰

doi:10.19912/j.0254-0096.tynxb.2023-1800

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基于自适应等效能耗最小的燃料电池船舶能量管理策略

更新时间：2025-10-18

- 基于自适应等效能耗最小的燃料电池船舶能量管理策略
- ENERGY MANAGEMENT STRATEGY FOR FUEL CELL SHIP BASED ON ADAPTIVE EQUIVALENT ENERGY MINIMIZATION
- 太阳能学报 2025年46卷第3期页码：108-115
- 作者机构：
  
  1. 上海海事大学物流工程学院,上海 201306
  2. 闽江学院物理与电子信息工程学院,福州 350108
  3. 上海船舶运输科学研究所有限公司,上海 200135
- 作者简介：
- 基金信息：
  
  福建省自然科学基金（2022J011128）;上海市优秀学术和技术带头人计划（23XD1431000）
- DOI：10.19912/j.0254-0096.tynxb.2023-1800
  中图分类号： TM619
- 网络出版：2025-04-07，
  
  纸质出版：2025
- 稿件说明：
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许晓彦, 曹伟, 韩冰. 基于自适应等效能耗最小的燃料电池船舶能量管理策略[J]. 太阳能学报, 2025, 46(3): 108-115.

Xu Xiaoyan, Cao Wei, Han Bing. ENERGY MANAGEMENT STRATEGY FOR FUEL CELL SHIP BASED ON ADAPTIVE EQUIVALENT ENERGY MINIMIZATION[J]. 2025, 46(3): 108-115.
许晓彦, 曹伟, 韩冰. 基于自适应等效能耗最小的燃料电池船舶能量管理策略[J]. 太阳能学报, 2025, 46(3): 108-115. DOI： 10.19912/j.0254-0096.tynxb.2023-1800.

Xu Xiaoyan, Cao Wei, Han Bing. ENERGY MANAGEMENT STRATEGY FOR FUEL CELL SHIP BASED ON ADAPTIVE EQUIVALENT ENERGY MINIMIZATION[J]. 2025, 46(3): 108-115. DOI： 10.19912/j.0254-0096.tynxb.2023-1800.

摘要

为实现等效能耗最小策略中等效因子的实时调整

提出一种基于自适应等效能耗最小的能量管理策略。首先

设计一种基于多种群自适应协同粒子群优化算法的最优等效因子提取方法

该方法为双层优化的结构。在上层优化中

以船舶的运行成本、储能系统最终电量和初始电量误差最小为目标函数

求解燃料电池系统和储能系统的最优运行轨迹;在下层优化中

建立等效因子的优化模型

提取最优等效因子的分布。然后

建立以系统状态参数为输入、等效因子为输出的神经网络模型。利用最优的等效因子作为训练样本

对神经网络模型进行训练。最后

将神经网络模型与等效能耗最小策略相结合

可实现等效因子的实时调整。在Matlab/Simulink中搭建船舶混合能源系统的仿真模型

对基于自适应等效能耗最小的能量管理策略进行验证。仿真结果表明

与基于恒定等效因子的等效能耗最小策略相比

储能系统的最终电量更接近初始值

氢气的总消耗量降低1.98%。

Abstract

In order to realize the real-time adjustment of the equivalence factor in the equivalent consumption minimization strategy

an energy management strategy based on adaptive equivalent energy minimization is proposed. Firstly

an optimal equivalent factor extraction method based on multiple swarm adaptive cooperative particle swarm optimization algorithms is designed

which is structured as a two-layer optimization. In the upper-layer optimization

the optimal operation trajectories of the fuel cell system and the energy storage system are solved with the objective functions of minimizing the operating cost of the ship and minimizing the error between the final charge and the initial charge of the energy storage system; In the lower level optimization

the optimization model of the equivalence factor is established to extract the distribution of the optimal equivalence factor. Then

a neural network model with system state parameters as inputs and equivalent factors as outputs is established. The neural network model is trained using the optimal equivalent factors as training samples. Finally

the neural network model is combined with the equivalent energy minimization strategy to realize the real-time adjustment of the equivalence factor. A simulation model of the ship hybrid energy system is built in Matlab/Simulink to verify the energy management strategy based on adaptive equivalent energy minimization. The simulation results show that the final charge of the energy storage system is closer to the initial charge and the total hydrogen consumption is reduced by 1.98% compared with the equivalent energy minimization strategy based on a constant equivalence factor.

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