Collaborative passivity-based control method for hybrid energy storage systems in urban rail transit

doi:10.19799/j.cnki.2095-4239.2024.0516

Abstract

Abstract:

To regulate voltage fluctuations in urban rail transit traction systems caused by the frequent acceleration and deceleration of trains, this study proposes a passivity-based collaborative control method utilizing ensemble empirical mode decomposition for a hybrid energy storage system (HESS) composed of supercapacitors and batteries. This method enables the recovery of regenerative braking energy and reduces the overall energy consumption of urban rail transit systems. The ensemble empirical mode decomposition technique is employed to extract multiple intrinsic mode functions of the HESS, allowing precise reconstruction of high-frequency and low-frequency components through the instantaneous frequency curve of each intrinsic mode function processed by the Hilbert transform, thereby enhancing the power trajectory accuracy for both supercapacitors and batteries. To address the multi-variable, strongly coupled, and nonlinear nature of the HESS, a bilinear model is developed in dq coordinates, facilitating synchronous linear transformation of state and control variables. A globally asymptotically stable passivity-based controller is then proposed to ensure synchronized and rapid tracking of desired power trajectories, achieving collaborative control even under external uncertainties. Simulation results using MATLAB demonstrate that the proposed method ensures long-term cooperative operation of supercapacitors and batteries, effectively meeting the demands for regenerative braking energy recovery and utilization in urban rail transit. The proposed approach offers advantages such as rapid response and robust stability.

Key words: urban rail transit, hybrid energy storage system, ensemble empirical mode decomposition, passivity-based control

CLC Number:

TM 912

Shanshan SHI, Kai WANG, Yu ZHANG, Kaiyu ZHANG, Kening ZHANG, Yufei WANG, Yani WANG. Collaborative passivity-based control method for hybrid energy storage systems in urban rail transit[J]. Energy Storage Science and Technology, 2024, 13(11): 4040-4052.

Figures/Tables 17

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Table 1

Simulation parameters of traction system and train"

参数	数值
交流牵引网额定电压/kV	35
直流牵引网额定电压/V	1500
直流牵引网电压允许波动范围/V	1000~1800
列车最高运行速度v/(km/h)	80
列车平均加速度/(m/s²)	$≥ 1.0, v ∈ 0,40$ $≥ 0.6, v ∈ 0,80$
列车单位阻力/(N/t)	$1.1064 + 0.0295 v + 0.000248 v 2$
列车运行阻力/(N/t)	$2340.9 + 62.42 v + 0.525 v 2$

Table 1

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参数	数值	参数	数值
锂电池单体额定电压/V	2.7	超级电容器单体额定电压/V	2.7
锂电池单体额定容量/Ah	20	超级电容器单体额定容量/F	3000
锂电池串联数量	185	超级电容器串联数量	223
锂电池并联数量	6	超级电容器并联数量	5
锂电池初始荷电状态/%	85	超级电容器初始荷电状态/%	100
锂电池荷电状态安全范围/%	20~85	超级电容器荷电状态安全范围/%	15~100
锂电池额定电压/V	499.5	超级电容器额定电压/V	594

控制方式	波动电压谷值/V	波动电压峰值/V	电压波动幅度/V
无混合储能系统	1055	1898	843
制动电阻控制	1055	1731	676
传统矢量PI控制	1422	1552	130
基于低通滤波的深度强化学习控制	1460	1515	55
基于集合经验模态分解的无源性控制	1464	1509	45

控制方式	稳压效果/%		节能效果/%
控制方式	加速牵引过程	减速制动过程	加速牵引过程	减速制动过程
传统矢量PI控制	72.3	88.3	71.1	40.3
基于低通滤波的深度强化学习控制	86.2	92.5	83.2	41.6
基于集合经验模态分解的无源性控制	93.5	95.7	86.1	42.2

控制方式	P_sc/kW	P_bat/kW	SOC_sc/%	SOC_bat/%
传统矢量PI控制	-195.1~154.5	-144.8~130.0	50.7~87.1	82.5~83.7
基于低通滤波的深度强化学习控制	-298.3~195.2	-95.3~80.5	19.6~52.5	83.1~84.4
基于集合经验模态分解的无源性控制	-315.2~263.8	-93.5~69.3	15.2~50.1	83.4~84.6

控制方式	V_sc/V	V_bat/V
传统矢量PI控制	410.1~563.1	432.5~453.5
基于低通滤波的深度强化学习控制	261.6~461.2	438.5~457.3
基于集合经验模态分解的无源性控制	242.3~445.5	440.6~456.2