[1]于坤杰,王思雨,杨 朵,等.基于多目标优化的燃料电池汽车实时能量管理策略[J].郑州大学学报(工学版),2024,45(02):80-88.[doi:10.13705/j.issn.1671-6833.2024.02.005]
　YU Kunjie,WANG Siyu,YANG Duo,et al.Real-time Energy Management Strategy of Fuel Cell Vehicles Based on Multi-objective Optimization[J].Journal of Zhengzhou University (Engineering Science),2024,45(02):80-88.[doi:10.13705/j.issn.1671-6833.2024.02.005]
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基于多目标优化的燃料电池汽车实时能量管理策略()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
45
期数:
2024年02期
页码:
80-88
栏目:
出版日期:
2024-03-06

文章信息/Info

Title:
Real-time Energy Management Strategy of Fuel Cell Vehicles Based on Multi-objective Optimization
作者:
于坤杰;  王思雨;  杨 朵;  符汉文;  廖粤峰
郑州大学 电气与信息工程学院,河南 郑州 450001
Author(s):
YU Kunjie;  WANG Siyu;  YANG Duo;  FU Hanwen;  LIAO Yuefeng
School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
关键词:
燃料电池;  锂电池;  混合动力系统;  能量管理策略;  多目标白鲸优化;  LSTM 神经网络;  路况分类
Keywords:
fuel cell;  lithium battery;  hybrid power system;  energy management strategy;  multi-objective beluga optimization;  LSTM neural network;  road condition classification
分类号:
TP391
DOI:
10.13705/j.issn.1671-6833.2024.02.005
文献标志码:
A
摘要:
为了降低混合动力系统的燃料消耗并延缓动力元件的老化,提出了一种基于多目标优化和路况分类的能 量管理策略(EMS) 。 首先,构建了燃料电池与锂电池的电气模型,并引入了等效氢耗模型和燃料电池老化模型。 其次,设计了基于规则的多模式 EMS,在此基础上,为了进一步降低系统的等效氢耗,并延长其使用寿命,基于多目 标白鲸算法(MOBWO)对 EMS 参数进行优化。 再次,为了使所设计的 EMS 适用于不同的路况,提出了基于长短期 记忆网络( LSTM)的驾驶路况实时分类方法,旨在根据分类结果切换 EMS 的控制参数以达到最优效果。 最后,在 仿真平台上对所提算法进行分析。 结果表明:与基于规则的方法相比,所提方法氢耗量降低了 2. 3%,燃料电池的 老化程度降低了 1. 02%,验证了所提 EMS 能够有效降低混合系统的燃料消耗,并且能够延缓燃料电池老化,从而 提升了系统的经济性和耐久性。
Abstract:
In order to reduce the equivalent hydrogen consumption of the hybrid system and delay the aging of the fuel cell, an Energy management strategy (EMS) was proposed based on multi-objective optimization and road condition classification. Firstly, the electrical model of the fuel cell and lithium battery hybrid system power was constructed, and the equivalent hydrogen consumption model and fuel cell aging model were introduced. Then, a rulebased multi-mode EMS was designed; on this basis, in order to further reduce the equivalent hydrogen consumption of the system and prolong its service life, the multi-objective beluga whale optimization algorithm (MOBWO) was proposed to optimize the control parameters. Furthermore, in order to make the designed EMS suitable for different road conditions, a real-time classification method of driving road conditions based on long short-term memory ( LSTM) network was proposed, aiming to switch the control parameters of EMS according to the classification results to achieve the optimal effect. Finally, the proposed algorithm was analyzed on the simulation platform. The results showed that the hydrogen consumption of the hybrid system with the proposed method was reduced by 2. 3% and the aging degree of the fuel cell was reduced by 1. 02% compared with the rule-based method,The proposed EMS could effectively reduce the equivalent hydrogen consumption of the hybrid system and delay the aging of the fuel cell.

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更新日期/Last Update: 2024-03-08