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1.国网江苏省电力有限公司 盐城供电分公司,江苏 盐城 224005
2.盐城工学院 电气工程学院,江苏 盐城;224051
彭思敏(1980—),男,博士,副教授,主要研究方向为电池储能控制与管理技术;E-mail: psmsteven@163.com
纸质出版日期:2022-05-10,
收稿日期:2022-04-30,
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胡卫丰, 彭思敏, 胥峥, 等. 考虑电池不一致的串联型电池系统多约束动态功率预测[J]. 机车电传动, 2022,(3):82-88.
HU Weifeng, PENG Simin, XU Zheng, et al. Multi-constrained dynamic power estimation of battery system in series connection considering cell inconsistency[J]. Electric drive for locomotives, 2022,(3):82-88.
胡卫丰, 彭思敏, 胥峥, 等. 考虑电池不一致的串联型电池系统多约束动态功率预测[J]. 机车电传动, 2022,(3):82-88. DOI: 10.13890/j.issn.1000-128X.2022.03.011.
HU Weifeng, PENG Simin, XU Zheng, et al. Multi-constrained dynamic power estimation of battery system in series connection considering cell inconsistency[J]. Electric drive for locomotives, 2022,(3):82-88. DOI: 10.13890/j.issn.1000-128X.2022.03.011.
为满足轨道交通装备及电动汽车在能量和电压级方面的需求,常将多个锂离子电池单体通过串联形成串联型电池系统。针对串联型电池系统中因电池单体不一致导致其功率状态(
SOP
)预测不准的问题,提出一种基于
SOP
差值预测器的串联型电池系统多约束动态功率预测方法。一方面,根据串联型电池系统工作特性及运行参数约束条件,采用基于多约束动态的功率预测法获得电池系统
SOP
预测基值;另一方面,根据串联型电池系统中各电池单体端电压的不一致性,提出一种基于神经网络法的电池系统
SOP
差值预测器,产生因电压不一致引起的电池系统
SOP
差值,并与基于多约束动态法的电池系统
SOP
预测基值叠加,得到电池系统
SOP
预测值。为验证所提方法的准确性,在MATLAB/Simulink环境下建立了系统仿真模型。分别在30 s、2 min及5 min持续时间下,采用所提方法进行串联型电池系统
SOP
预测,其预测值始终跟随电池系统
SOP
实测值,预测误差约为额定持续峰值功率的2%。同时,通过与不同算法的预测结果对比,得出文章所提算法的电池系统
SOP
预测值更接近其
SOP
实测值。由此表明,对于含电池单体不一致的串联型电池系统,文章所提算法能准确预测其
SOP
,为提升大容量电池系统全生命周期管理能力提供一种思路。
Multiple lithium-ion cells are usually connected in series into a battery system in series connection
to meet the requirements of energy and v
oltage level for the rail transit equipment and electric vehicles. To solve the problem of inaccuracy in estimating state of power (
SOP
) due to cell inconsistency
this paper proposes a multi-constrained dynamic power estimation method based on a
SOP
errors predictor for the battery system in series connection was proposed. On the one hand
according to the operating characteristics and constraints on the operating parameters of the battery system in series connection
a
SOP
basic value was forecasted by a multi-constrained dynamic power estimation method. On the other hand
a neural network-based
SOP
errors predictor was proposed on the basis of terminal voltage inconsistency among cells in the battery system. The battery system
SOP
error was caused by voltage inconsistency. The estimated battery system
SOP
was finally attained by the sum of the
SOP
error and the
SOP
basic value. A system simulation model was created in the MATLAB/Simulink environment to verify the accuracy of the proposed method. The battery system
SOP
was estimated by the presented method for a duration of 30 seconds
2 minutes and 5 minutes respectively. The results show that the estimated
SOP
is always consistent with the measured
SOP
with a forecast error approximating to 2% of the rated continuous peak power. Moreover
by comparison with the estimation results by other methods
the estimated
SOP
using the proposed method is the closest to the measured one. Therefore
for a battery system in series connection with inconsistent cells
its
SOP
can be estimated accurately by the proposed method
which provides a way to improve the life cycle management ability of the large capacity battery system.
电池系统电池不一致功率状态荷电状态多约束动态法仿真
battery systemcell inconsistencestate of powerstate of chargemulti-constrained dynamic methodsimulation
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