Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (10): 3064-3076.doi: 10.13229/j.cnki.jdxbgxb.20221593

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Energy management strategy for fuel cell trams combining online and offline control

Feng-yang GAO(),Ya-xin QIANG,Zhi-shan GAO,Hao XU,Zhi-long SHI   

  1. School of Automation and Electrical Engineering，Lanzhou Jiaotong University，Lanzhou 730070，China
  • Received:2022-12-13 Online:2024-10-01 Published:2024-11-22

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Abstract:

The dynamic programming in the global energy management strategy of fuel cell/lithium battery/supercapacitor hybrid trams is effective and can improve the fuel economy， but it requires a priori operating conditions， is computationally intensive， time-consuming， and cannot be controlled online， and an improvement strategy is proposed to address the above problems. Firstly， the Markov power state transfer matrix is established based on the historical tram operation data to predict the tram operation conditions； secondly， the fuel cell power data in the tram is updated by using the sliding window with a large number of continuous and infinitely fast observation data to speed up the iteration of the dynamic planning algorithm， and the minimum hydrogen consumption is used as the objective function to obtain the optimal control by solving the forward search in the inverse direction. Finally， the proposed strategy is compared with the state machine strategy and the traditional dynamic planning strategy. The results show that the proposed strategy significantly reduces the operation time compared with the traditional dynamic planning， effectively reduces the number and amplitude of fuel cell high-current discharges， and improves the durability of the fuel cell； the reasonable power distribution of the main and auxiliary power sources during the real-time operation of the tram improves the fuel economy and average efficiency of the hybrid system.

Key words: hybrid tram, global optimization, working condition prediction, energy management strategy

CLC Number: 

  • TM91

Fig.1

Topological structure of fuel cell hybrid tram"

Table 1

Main technical parameters of tram"

参 数数 值
驱动电机（8个）/kW8×110
变流器/VDC750（500~900）
轴重/t12
列车自重/t47
列车车体长度/mm31 075
续驶里程/km>40
最高运行速度/（km·h-1）60

Fig.2

Fuel cell model"

Fig.3

Polarization test curve of fuel cell monomer"

Fig.4

Fuel cell model accuracy test"

Fig.5

Lithium battery RINT equivalent circuit"

Fig.6

Simulation curve of lithium battery dischargecharacteristics"

Fig.7

Lithium battery model accuracy test"

Fig.8

Supercapacitor model"

Fig.9

Supercapacitor model accuracy test"

Fig.10

Speed-demand power probability distribution"

Fig.11

Next moment demand power state transfer matrix"

Fig.12

Work condition prediction result"

Fig.13

Slide window update graph"

Fig.14

Double-loop control block diagram"

Table 2

Simulation parameters of on-board composite power supply"

参数数 值
燃料电池单体数量/个735
燃料电池堆电压/V540
燃料电池堆最大储氢量/kg14
锂电池组串并联数目114串2并
锂电池组额定电压/V331
锂电池组最大放电电流/A120
超级电容组串并联数量/个11串3并
超级电容组额定电压/V750
超级电容组最大电流/A98（持续）；1 900（1 s）

Fig.15

Comparison of power allocation under three strategies"

Fig.16

Comparison of SOC of lithium battery and super capacitor under three strategies"

Fig.17

Comparison of hydrogen consumption and fuel cell current under three strategies"

Table 3

Simulation results comparison"

对比指标状态机传统动态规划本文策略
系统氢耗量/kg2.872.652.29
系统效率/%78.2583.6189.72
锂电池SOC始末状态差值/%-4.83.50
超级电容SOC最大偏移率/%31.610.59.4
燃料电池峰值电流/A100.580.740.3
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