基于电池组模型和聚类算法的锂离子电池组SOC不一致估计

doi:10.3901/JME.2020.18.154

机械工程学报 ›› 2020, Vol. 56 ›› Issue (18): 154-163.doi: 10.3901/JME.2020.18.154

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基于电池组模型和聚类算法的锂离子电池组SOC不一致估计

向兆军^1,2, 胡凤玲³, 罗明华^1,2, 方崇全^1,2, 胡晓松³

1. 中煤科工集团重庆研究院有限公司重庆 400039;
2. 瓦斯灾害监控与应急技术国家重点实验室重庆 400037;
3. 重庆大学汽车工程学院重庆 400044

收稿日期:2020-05-29 修回日期:2020-07-03 出版日期:2020-09-20 发布日期:2020-11-17
通讯作者: 胡晓松(通信作者),男,1983年出生,博士,教授。主要研究方向为电气化车辆动力系统建模与控制。E-mail:xiaosonghu@ieee.org
作者简介:向兆军,男,1987年出生,助理研究员。主要研究方向为检测技术与自动化装置。E-mail:qepyou@126.com
基金资助:
天地科技股份有限公司科技创新创业资金专项（2019-TD-QN036）和国家自然科学基金（51875054）资助项目。

Estimation of SOC Inconsistencies in Lithium-ion Battery Packs Based on Battery Pack Modeling and Clustering Algorithm

XIANG Zhaojun^1,2, HU Fengling³, LUO Minghua^1,2, FANG Chongquan^1,2, HU Xiaosong³

1. China Coal Technology Engineering Group Chongqing Research Institute, Chongqing 400039;
2. State Key Laboratory of Gas Disaster Detecting, Preventing and Emergency Controlling, Chongqing 400037;
3. Department of Automotive Engineering, Chongqing University, Chongqing 400044

Received:2020-05-29 Revised:2020-07-03 Online:2020-09-20 Published:2020-11-17

摘要/Abstract

摘要： 限于单体的功率和能量，电池组必须由成百上千的单体串并联而成，同时依赖于有效的电池管理技术，从而保证车辆行驶的动力性和经济性。然而制造误差和使用环境的不同导致电池单体存在不一致性，这使得准确估计所有单体电池荷电状态（State of charge，SOC）变得困难，从而导致电池发生过充和过放。因此，融合电池组模型和聚类算法，提出一种新的锂离子电池组SOC不一致估计方法，在保证精度的同时极大地降低计算复杂度。基于充电数据特征利用二分k-means算法将众多单体分为不同等级，减小需要考虑的对象；建立考虑参数不一致的电池组模型，用于估计单体SOC；从仿真数据和实车数据两方面验证估计结果的精度和计算复杂度，并与现有模型对比，结果表明所提方法单体SOC估计的误差在0.03以内，计算效率提高3~6倍。

关键词: 锂离子电池, 不一致性, 聚类算法, 电池组模型, SOC估计

Abstract: Limited by the power and energy of a single cell, a battery pack must consist of hundreds or even thousands of cells in series and parallel, and at the same time rely on an effective battery management system to ensure enough driving power and economy of vehicles. However, due to the manufacturing process and operating environment, there exist parameter inconsistencies among battery cells, which brings challenges to accurately estimating the state of charge (SOC) of all the cells in the pack, and results in over-charge and over-discharge. To estimate the SOC inconsistencies in lithium-ion battery packs, a new method is proposed by combing the battery pack modeling and the clustering algorithm, which can greatly reduce the computational complexity while ensuring accuracy. Based on the features of charging data, the bisecting k-means algorithm is adopted to categorize cells into different levels, reducing the objects that need to be considered. Then, a battery pack model considering parameter discrepancies is established to estimate SOC values of cells. The estimation accuracy and computational complexity have been verified by using both simulation and real-test data, and compared with existing models. The results indicate that the SOC estimation error of a single cell is less than 0.03 by using the proposed method, and the computational efficiency can be improved by 3-6 times.

Key words: lithium-ion battery, parameters inconsistency, clustering algorithm, battery pack model, SOC estimation

中图分类号:

TM912

向兆军, 胡凤玲, 罗明华, 方崇全, 胡晓松. 基于电池组模型和聚类算法的锂离子电池组SOC不一致估计[J]. 机械工程学报, 2020, 56(18): 154-163.

XIANG Zhaojun, HU Fengling, LUO Minghua, FANG Chongquan, HU Xiaosong. Estimation of SOC Inconsistencies in Lithium-ion Battery Packs Based on Battery Pack Modeling and Clustering Algorithm[J]. Journal of Mechanical Engineering, 2020, 56(18): 154-163.

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基于电池组模型和聚类算法的锂离子电池组SOC不一致估计

Estimation of SOC Inconsistencies in Lithium-ion Battery Packs Based on Battery Pack Modeling and Clustering Algorithm

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