Multi-constrained dynamic power estimation of battery system in series connection considering cell inconsistency

HU Weifeng; PENG Simin; XU Zheng; XU Lu

doi:10.13890/j.issn.1000-128X.2022.03.011

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Multi-constrained dynamic power estimation of battery system in series connection considering cell inconsistency

Low Carbon Urban Rail Transport | 更新时间：2024-08-02

- Multi-constrained dynamic power estimation of battery system in series connection considering cell inconsistency
- Electric drive for locomotives Issue 3, Pages: 82-88(2022)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.13890/j.issn.1000-128X.2022.03.011
  CLC： TM911
- Published：10 May 2022，
  
  Received：30 April 2022，
- 稿件说明：
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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 (3):82-88(2022)
DOI：

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 (3):82-88(2022) DOI： 10.13890/j.issn.1000-128X.2022.03.011.

摘要

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。

Abstract

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

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.

关键词

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

Keywords

battery systemcell inconsistencestate of powerstate of chargemulti-constrained dynamic methodsimulation

references

吴健, 王占国, 张言茹. 锂离子电池在轨道交通装备上的适应性研究[J]. 铁道机车车辆, 2022, 42(2): 43-49.

WU Jian, WANG Zhanguo, ZHANG Yanru. Research on adaptability of lithium ion battery in rail transit equipment[J]. Railway Locomotive & Car, 2022, 42(2): 43-49.

王亚楠, 韩雪冰, 卢兰光, 等. 电动汽车动力电池研究展望: 智能电池、智能管理与智慧能源[J]. 汽车工程, 2022, 44(4): 617-637.

WANG Yanan, HAN Xuebing, LU Languang, et al. Prospects of research on traction batteries for electric vehicles: intelligent battery, wise management, and smart energy[J]. Automotive Engineering, 2022, 44(4): 617-637.

彭思敏, 施刚, 蔡旭, 等. 基于等效电路法的大容量蓄电池系统建模与仿真[J]. 中国电机工程学报, 2013, 33(7): 11-18.

PENG Simin, SHI Gang, CAI Xu, et al. Modeling and simulation of large capacity battery systems based on the equivalent circuit method[J]. Proceedings of the CSEE, 2013, 33(7): 11-18.

范汝新, 张宵洋, 张振福, 等. 锂电池能量状态与功率状态的联合估计[J]. 电源技术, 2021, 45(10): 1252-1255.

FAN Ruxin, ZHANG Xiaoyang, ZHANG Zhenfu, et al. Joint estimation of state of energy and state of power of lithium battery[J]. Chinese Journal of Power Sources, 2021, 45(10): 1252-1255.

金鑫娜, 顾启蒙, 潘宇巍, 等. 锂离子动力电池SOP在线估计方法研究[J]. 电源技术, 2019, 43(9): 1448-1452.

JIN Xinna, GU Qimeng, PAN Yuwei, et al. Online state of power estimation methods for lithium-ion batteries in EV[J]. Chinese Journal of Power Sources, 2019, 43(9): 1448-1452.

朱浩, 张文博, 邓元望, 等. 基于SA+BP混合算法的动力电池放电峰值功率估算[J]. 江苏大学学报(自然科学版), 2020, 41(2): 192-198.

ZHU Hao, ZHANG Wenbo, DENG Yuanwang, et al. Peak power estimation of power battery discharge based on SA+BP hybrid algorithm[J]. Journal of Jiangsu University(Natural Science Edition), 2020, 41(2): 192-198.

孙丙香, 高科, 姜久春, 等. 基于ANFIS和减法聚类的动力电池放电峰值功率预测[J]. 电工技术学报, 2015, 30(4): 272-280.

SUN Bingxiang, GAO Ke, JIANG Jiuchun, et al. Research on discharge peak power prediction of battery based on ANFIS and subtraction clustering[J]. Transactions of China Electrotechnical Society, 2015, 30(4): 272-280.

WANG Junping, CHEN Quanshi, CAO Binggang. Support vector machine based battery model for electric vehicles[J]. Energy Conversion and Management, 2006, 47(7/8): 858-864.

PENG Simin, ZHU Xuelai, XING Yinjiao, et al. An adaptive state of charge estimation approach for lithium-ion series-connected battery system[J]. Journal of Power Sources, 2018, 392: 48-59.

SONG Xu, LU Yongjun, WANG Fenghui, et al. A coupled electro-chemo-mechanical model for all-solid-state thin film Li-ion batteries: the effects of bending on battery performances[J]. Journal of Power Sources, 2020, 452: 227803.

PLETT G L. High-performance battery-pack power estimation using a dynamic cell model[J]. IEEE Transactions on Vehicular Technology, 2004, 53(5): 1586-1593.

JIN Zhihong, ZHANG Zhenli, ALIYEV T, et al. Estimating the power limit of a lithium battery pack by considering cell variability[C]//SAE International. SAE 2015 World Congress & Exhibition. United States: SAE International, 2015.

WANG Shunli, STROE D I, FERNANDEZ C, et al. A novel power state evaluation method for the lithium battery packs based on the improved external measurable parameter coupling model[J]. Journal of Cleaner Production, 2020, 242: 118506.

ZHOU Zhongkai, KANG Yongzhe, SHANG Yunlong, et al. Peak power prediction for series-connected LiNCM battery pack based on representative cells[J]. Journal of Cleaner Production, 2019, 230: 1061-1073.

PENG Simin, CHEN Chong, SHI Hongbing, et al. State of charge estimation of battery energy storage systems based on adaptive unscented Kalman filter with a noise statistics estimator[J]. IEEE Access, 2017, 5: 13202-13212.

熊瑞. 动力电池管理系统核心算法[M]. 北京: 机械工业出版社, 2018: 157-176.

XIONG Rui. Core algorithm of battery management system for EVs[M]. Beijing: China Machine Press, 2018: 157-176.

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