城市轨道交通混合储能系统无源性协同控制方法

doi:10.19799/j.cnki.2095-4239.2024.0516

摘要/Abstract

摘要：

为解决城市轨道交通列车频繁启停引起的牵引网电压波动问题，针对由超级电容和电池组成的混合储能系统，本文提出了基于集合经验模态分解的无源性协同控制方法，从而平抑网侧电压波动，实现再生制动能量回收，降低城市轨道交通能耗。利用集合经验模态分解方法求取混合储能系统多个本征模态函数，对每个本征模态函数瞬时频率曲线进行Hilbert变换，精确重构高频、低频分量，提升超级电容和电池功率期望轨迹精度。针对混合储能系统多变量、强耦合、非线性特性，在dq坐标下建立双线性模型，实现状态变量与控制变量同步线性变换，设计全局渐近稳定的无源性控制器，使系统存在外部不确定性扰动情形时始终保持超级电容和锂电池功率期望轨迹同步快速跟踪，达成协同控制目标。基于MATLAB的仿真结果表明，所提方法既实现了超级电容和锂电池长寿命协同运行，又能满足城市轨道交通再生制动能量回收利用需求，具有响应快速、稳定性好的特点。

关键词: 城市轨道交通, 混合储能系统, 集合经验模态分解, 无源性控制

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

中图分类号:

TM 912

时珊珊, 王凯, 张宇, 张开宇, 张珂宁, 王育飞, 王雅妮. 城市轨道交通混合储能系统无源性协同控制方法[J]. 储能科学与技术, 2024, 13(11): 4040-4052.

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.

图/表 17

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表1

牵引系统和列车仿真参数"

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)	$2340.9 + 62.42 v + 0.525 v 2$

表1

表2

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表5

图11

表6

参考文献 18

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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