• •
收稿日期:
2025-08-22
修回日期:
2025-10-11
通讯作者:
崔秀芳
E-mail:m230801479@st.shou.edu.cn;xfcui@shou.edu.cn
作者简介:
安楠楠(2001—),男,硕士研究生,船舶与海洋结构物设计制造,E-mail:m230801479@st.shou.edu.cn;
基金资助:
Nannan AN(), Xiufang CUI(
), Jiexi ZHENG, Kejia CHEN
Received:
2025-08-22
Revised:
2025-10-11
Contact:
Xiufang CUI
E-mail:m230801479@st.shou.edu.cn;xfcui@shou.edu.cn
摘要:
燃料电池混合动力系统因其清洁高效的特性,已成为新能源船舶领域的重要研究方向。然而,较高的运行维护成本制约了其进一步的推广应用。为降低船舶燃料电池混合动力系统的运行成本,提出一种基于动态规则优化的船舶燃料电池混合动力系统能量管理方法,并引入包含燃料消耗成本、燃料电池和锂电池性能衰退成本、等效电能消耗成本的系统综合运行成本模型。该方法将规则参数作为可调变量嵌入优化框架,结合壮丽细尾鹩莺算法实现对规则参数的迭代更新与优化,通过成本模型,实现系统综合运行成本最小化的多源协同控制,并在经典船舶工况下,将所提方法与传统能量管理方法进行对比研究。实验结果表明,所提方法的系统综合运行成本与状态机、等效能耗最小化能量管理方法相比,分别降低11.47%和17.76%。进一步分析发现,该方法能够在燃料电池功率输出、锂电池荷电状态波动及系统能量分配方面实现更优平衡,从而减缓锂电池与燃料电池的性能衰退,延长使用寿命。本研究为燃料电池混合动力船舶的经济高效运行提供了一种可行的能量管理方法,并对新能源船舶的推广应用具有积极的参考意义。
中图分类号:
安楠楠, 崔秀芳, 曾杰熙, 陈科佳. 基于动态规则优化的船舶燃料电池混合动力系统能量管理方法[J]. 储能科学与技术, doi: 10.19799/j.cnki.2095-4239.2025.0756.
Nannan AN, Xiufang CUI, Jiexi ZHENG, Kejia CHEN. A dynamic rule-based optimization method for energy management of ship fuel cell hybrid power systems[J]. Energy Storage Science and Technology, doi: 10.19799/j.cnki.2095-4239.2025.0756.
[1] | MIDILLI A, AY M, DINCER I, et al. On hydrogen and hydrogen energy strategiesⅠ: current status and needs[J]. Renewable sustainable energy reviews, 2005, 9(3): 255-271. |
[2] | 王新. 氢能燃料电池的成本分析与效益研究[J]. 储能科学与技术, 2023, 12(06): 2040-2041. |
WANG X. Cost Analysis and Benefit Research of Hydrogen Fuel Cells[J]. Energy Storage Science and Technology, 2023, 12(06): 2040-2041. | |
[3] | CAO M, WANG Z, TANG H, et al. Electric-thermal-gas synergistic dynamics in PEMFC-LIB hybrid systems for hydrogen ships: A multi-scale evaluation framework[J]. Etransportation, 2025, 25: 100430. |
[4] | 严新平. 新能源在船舶上的应用进展及展望[J]. 船海工程, 2010, 39(6): 111-115, 120. |
YAN X P. Progress review of new energy application in ship[J]. Ship Ocean Engineering, 2010, 39(6): 111-115, 120. | |
[5] | ZHANG Q, BAO C, HAN P. Towards intelligent energy management in hybrid ships: Predicting and optimizing solar, wind, and diesel energy[J]. Ocean Engineering, 2025, 339(P1): 122092. |
[6] | ZHAO Z H. Improved fuzzy logic control-based energy management strategy for hybrid power system of FC/PV/battery/SC on tourist ship[J]. International journal of hydrogen energy, 2022, 47(16): 9719-9734. |
[7] | 苗东晓, 陈俐, 王欣然. 基于NSGA-Ⅱ优化的船舶串联式混合动力系统能量管理策略[J]. 舰船科学技术, 2022, 44(14): 113-118. |
MIAO D X, CHEN L, WANG X R. Energy management strategy of marine series hybrid system based on NSGA-Ⅱ optimization[J]. Ship science and technology, 2022, 44(14): 113-118. | |
[8] | 王瑞昌, 陈志华, 明新国. 基于改进模糊逻辑控制的并联式船舶动力系统能量管理[J]. 上海交通大学学报, 2021, 55(10): 1188-1196. |
WANG R C, CHEN Z H, MING X G. Energy management of parallel ship power system based on improved fuzzy logic control[J]. Journal of Shanghai Jiao Tong University, 2021, 55(10): 1188-1196. | |
[9] | WANG C, MOU H, HUANG H. Optimization of power distribution in electric vehicle hybrid energy storage system based on RBF neural network and dynamic programming[J]. Journal of Energy Storage, 2025,130: 117319. |
[10] | 郭晓东, 袁裕鹏, 童亮. 基于庞特里亚金极小值原理的柴电混合动力船舶能量管理策略[J]. 哈尔滨工程大学学报, 2024, 45(11): 2176-2184. |
GUO X D, YUAN Y P, TONG L. Energy Management Strategy for Diesel-Electric Hybrid Ships Based on Pontriagin Minimum Principle[J]. Journal of Harbin Engineering University, 2024, 45(11): 2176-2184. | |
[11] | ZANELLI A, SERVETTO E, DE A, et al. Numerical assessment of auto-adaptive energy management strategies based on SOC feedback, driving pattern recognition and prediction techniques[J]. Energies, 2022, 15(11): 3896. |
[12] | SUN X, CHEN Z, HAN S, et al. Adaptive real-time ECMS with equivalent factor optimization for plug-in hybrid electric buses[J]. Energy, 2024, 304: 132014. |
[13] | YANG C, DU X, WANG W, et al. A Rolling Convergent Equivalent Consumption Minimization Strategy for Plug-in Hybrid Electric Vehicles[J]. IEEE Transactions on Vehicular Technology, 2024, 73(3): 3340-3353. |
[14] | SAHWAL P C, SENGUPTA S, DINH Q T. Advanced Equivalent Consumption Minimization Strategy for Fuel Cell Hybrid Electric Vehicles[J]. Journal of Cleaner Production, 2024, 437: 140366. |
[15] | 姜俊道, 邹亮, 刘星斗, 等. 结合负荷多步预测的氢能船舶双层能量调度策略[J]. 电力系统自动化, 2025, 49(13): 166-176. |
JIANG J D, ZOU L, LIU X D, et al. Double-layer Energy scheduling Strategy for hydrogen-powered ships based on Multi-step Load Prediction [J]. Automation of Electric Power Systems, 2025, 49(13): 166-176. | |
[16] | JIA H, ZHOU X, ZHANG J, et al. Superb Fairy-wren Optimization Algorithm: a novel metaheuristic algorithm for solving feature selection problems[J]. Cluster Computing, 2025, 28(4): 246. |
[17] | HAN J, CHARPENTIER J, TANG T. An Energy Management System of a Fuel Cell/Battery Hybrid Boat[J]. Energies, 2014, 7(5): 2799-2820. |
[18] | 张晗, 杨继斌, 张继业, 等. 燃料电池有轨电车能量管理Pareto多目标优化[J]. 自动化学报, 2019, 45(12): 2378-2392. |
ZHANG H, YANG J B, ZHANG J Y, et al. Pareto-based multi-objective optimization of energy management for fuel cell tramway[J]. Acta Automatica Sinica, 2019, 45(12): 2378-2392. | |
[19] | ZHOU Y, RAVEY A, et al. Real-time cost-minimization power-allocating strategy via model predictive control for fuel cell hybrid electric vehicles[J]. Energy Conversion and Management, 2021, 229: 113721. |
[20] | FLETCHER T, THRING R, WATKINSON M. An Energy Management Strategy to concurrently optimise fuel consumption PEM fuel cell lifetime in a hybrid vehicle[J]. Hydrogen Energy, 2016, 41(46): 21503–21515. |
[21] | CHEN H, PEI P, SONG M. Lifetime prediction and the economic lifetime of Proton Exchange Membrane fuel cells[J]. Applied Energy, 2015, 142: 154-163. |
[22] | ZHOU B, BURL J B, REZAEI A. Equivalent Consumption Minimization Strategy With Consideration of Battery Aging for Parallel Hybrid Electric Vehicles[J]. IEEE Access, 2020, 8: 204770-204781. |
[23] | WANG J, LIU P, HICKS-GARNER J, et al. Cycle-life model for graphite-LiFePO4 cells[J]. Journal of Power Sources, 2010, 196(8): 3942-3948. |
[24] | KITTNER N, LILL F, et al. Energy storage deployment and innovation for the clean energy transition[J]. Nature Energy, 2017, 2(9): 649-653. |
[25] | ABDELHALIM T, KOUIDER L, REZK H, et al. Optimal energy management based equivalent hydrogen consumption minimization strategy of DC microgrid[J]. International Journal of Hydrogen Energy, 2024, 83: 355-366. |
[26] | R D, REVATHI V. Enhancing Whale Optimization Algorithm with Levy Flight for coverage optimization in wireless sensor networks[J]. Computers and Electrical Engineering, 2021, 94: 107359. |
[27] | BASSAM M A, PHILLIPS B A, TURNOCK R S, et al. Development of a multi-scheme energy management strategy for a hybrid fuel cell driven passenger ship[J]. International Journal of Hydrogen Energy, 2016, 42(1): 623-635. |
[1] | 王亚丽, 李晓艳, 孙航宇, 付云枫, 刘召波, 杜国山, 刘君, 陈宋璇, 胡蒙蒙. 固体氧化物燃料电池支撑体研究进展[J]. 储能科学与技术, 2025, 14(7): 2590-2601. |
[2] | 许晓茹, 欧建臻, 刘佳伟, 陈智聪, 叶豪, 刘颖隆, 刘英丽, 林泽宇, 刘晶晶, 简俊辉, 罗栩, 范竞敏, 王超, 雷励斌, 梁波. 带嵌入式微通道陶瓷裂解反应器的管式氨燃料电池[J]. 储能科学与技术, 2025, 14(5): 1818-1828. |
[3] | 王阳峰, 任博, 王红涛, 侯栓弟. 质子交换膜燃料电池用碳纤维纸技术的关键及产业化研究进展[J]. 储能科学与技术, 2025, 14(3): 984-996. |
[4] | 李薛茹, 马哲杰, 李平. 质子交换膜燃料电池阴极催化层微观结构表征研究进展[J]. 储能科学与技术, 2025, 14(2): 812-821. |
[5] | 曹艳刚, 高翔, 张军, 张明震, 张艳蕊. 日本燃料电池汽车与加氢站的发展历程对中国的启示[J]. 储能科学与技术, 2025, 14(1): 456-463. |
[6] | 李从心, 岳美玲, 李昕彤, 熊庆辉, 刘孝艳. 基于条件神经网络的质子交换膜燃料电池的老化性能预测[J]. 储能科学与技术, 2024, 13(9): 3094-3102. |
[7] | 李畅, 郑伟波, 朱帅, 姜云文, 明平文. 基于模型的燃料电池空气子系统动态过程研究[J]. 储能科学与技术, 2024, 13(8): 2580-2588. |
[8] | 白颖. 低温燃料电池在物流运输系统中的供能作用研究[J]. 储能科学与技术, 2024, 13(7): 2459-2461. |
[9] | 李淼, 盖克荣, 周凤颖, 黄欢, 杨永强. 低温燃料电池在汽车工程中的供储能特性分析[J]. 储能科学与技术, 2024, 13(7): 2483-2485. |
[10] | 刘偲艳, 钟根香, 葛庆. 基于FDC氢燃料电池堆在线智能监测系统[J]. 储能科学与技术, 2024, 13(6): 2030-2038. |
[11] | 张忠豪, 邱殿凯, 彭林法, 易培云. 一体式再生燃料电池双功能氧电极高分散工艺研究[J]. 储能科学与技术, 2024, 13(5): 1417-1426. |
[12] | 刘鑫宇, 张安安, 廖长江. 不同支撑结构的固体氧化物燃料电池数值模拟分析[J]. 储能科学与技术, 2024, 13(5): 1710-1720. |
[13] | 孙航宇, 李卓华, 王亚丽, 李晓艳, 付云枫, 杜国山, 陈宋璇. 固体氧化物燃料电池直接内重整的模型研究进展[J]. 储能科学与技术, 2024, 13(4): 1277-1292. |
[14] | 黄沙, 李亚新. 基于计算机软件的燃料电池混合储能系统分析[J]. 储能科学与技术, 2024, 13(4): 1350-1352. |
[15] | 乔雨田, 刘永峰, 禹永帅, 张璐, 姚圣卓, 裴普成. 温湿度变化对车用燃料电池输出性能的影响[J]. 储能科学与技术, 2024, 13(3): 870-878. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||