首页期刊在线 2022年（第25卷）第10期

基于速度优化与粒子群优化算法的插电式燃料电池有轨电车能量管理策略

张爱军1,2方陆文1,2胡后征3

Energy Management Strategy of Plug-in Fuel Cell Tram Based on Speed Optimization and PSO Algorithm

ZHANG AijunFANG LuwenHU Houzheng

作者信息:

1.1. 中铁建云南投资有限公司， 650220，昆明；
2.2. 中国铁建昆仑投资集团有限公司， 610040，成都；
3.3. 昆明理工大学， 650500，昆明
Affiliation:

China Railway Construction Yunnan Investment Co., Ltd., 650220, Kunming, China
关键词:

有轨电车;插电式燃料电池;能量管理策略;速度优化;粒子群优化算法
Key words:

tram;plug-in fuel cell;energy management strategy;speed optimization;PSO (particle swarm optimization) algorithm
DOI:

10.16037/j.1007-869x.2022.10.049
中图分类号/CLCN:

TM911.4; U482.19
栏目/Col:

其他

摘要:

以燃料电池有轨电车为研究对象，采用分层控制的方法，以能耗经济性最优为总体目标，开展基于速度优化的能量管理策略研究。该策略中的上层基于车与交通设施之间的通信(V2I)，以驱动能耗最小为目标，考虑交通信号灯的影响，设计基于伪谱法的速度优化策略；其下层基于上层得到的最优速度，以及基于粒子群优化(PSO)算法对燃料电池有轨电车能量进行优化分配。仿真结果表明，基于速度优化的有轨电车能量管理策略能够避免列车的急变速和红灯怠速行为；基于速度优化与PSO算法的插电式燃料电池有轨电车能量管理策略能对动力电池和燃料电池的功率进行优化分配，相比基于速度优化与规则的有轨电车能量管理策略下的燃油经济性提高了3.72%。

Abstracts:

Taking fuel cell tram as research object, and taking the optimal energy consumption economy as the overall goal by adopting hierarchical control method, research on energy management strategy based on speed optimization is carried out. In the strategy, the upper layer is based on the communication between vehicles and traffic facilities (V2I), aiming at the minimum driving energy consumption, but considering the influence of traffic lights, a speed optimization strategy based on Pseudo-spectral method is designed. Based on the optinal speed obtained by the upper layer and the PSO (Particle Swarm Optimization) algorithm, the lower layer optimizes the distribution of fuel cell tram energy. Simulation results show that the rapid speed change and red-light idling behavior of the train can be avoided by tram energy management strategy based on speed optimization. The plug-in fuel cell tram energy management strategy based on speed optimization and PSO algorithm carries out optimized power distribution of power cell and fuel cell. Compared with the tram energy management strategy based on speed optimization and rule, its fuel cell economy is improved by 3.72%.