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考虑燃料电池性能差异的燃料电池船功率分配策略。

A power allocation strategy for fuel cell ship considering fuel cell performance difference.

机构信息

Logistics Engineering College, Shanghai Maritime University, Shanghai, 201306, China.

China Institute of FTZ Supply Chain, Shanghai, China.

出版信息

Sci Rep. 2023 Jun 19;13(1):9905. doi: 10.1038/s41598-023-37076-2.

DOI:10.1038/s41598-023-37076-2
PMID:37337036
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10279748/
Abstract

This paper focuses on designing a power allocation strategy for a fuel cell ship. The performance of the fuel cell varies during operation, so a power allocation strategy considering fuel cell performance differences is proposed, which consists of two layers. In the first layer, the maximum power and maximum efficiency of each fuel cell system (FCS) are updated in real-time with an online parameter identification model, which is composed of the fuel cell semi-empirical model and adaptive Kalman filter. The second layer takes the state of charge of the battery energy storage system, the maximum power, and the maximum efficiency as inputs for power allocation. Compared with the equal allocation strategy and daisy chain strategy, the total hydrogen consumption reduces by 5.3% and 15.1% and the total output power of the FCS with poor performance reduces by 14.1% and 15.7%. The results show that the proposed method can improve the efficiency of the ship power system and reduce the operational burden of the FCS with poor performance.

摘要

本文专注于设计一种用于燃料电池船的功率分配策略。燃料电池在运行过程中的性能会发生变化,因此提出了一种考虑燃料电池性能差异的功率分配策略,该策略由两层组成。在第一层中,使用带有在线参数识别模型的实时更新每个燃料电池系统(FCS)的最大功率和最大效率,该模型由燃料电池半经验模型和自适应卡尔曼滤波器组成。第二层将电池储能系统的荷电状态、最大功率和最大效率作为功率分配的输入。与平均分配策略和菊花链策略相比,总氢气消耗分别减少了 5.3%和 15.1%,性能较差的 FCS 的总输出功率分别减少了 14.1%和 15.7%。结果表明,所提出的方法可以提高船舶动力系统的效率,并降低性能较差的 FCS 的运行负担。

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Modeling and simulation of high energy density lithium-ion battery for multiple fault detection.用于多故障检测的高能量密度锂离子电池建模与仿真
Sci Rep. 2022 Jun 13;12(1):9800. doi: 10.1038/s41598-022-13771-4.
2
Deep learning approach towards accurate state of charge estimation for lithium-ion batteries using self-supervised transformer model.基于自监督变压器模型的锂离子电池荷电状态精确估计的深度学习方法。
Sci Rep. 2021 Oct 1;11(1):19541. doi: 10.1038/s41598-021-98915-8.
3
From gangue to the fuel-cells application.从煤矸石到燃料电池应用。
Sci Rep. 2020 Nov 18;10(1):20022. doi: 10.1038/s41598-020-76503-6.