基于SOC的锂动力电池组双向主动均衡控制

doi:10.16182/j.issn1004731x.joss.201703019

系统仿真学报 ›› 2017, Vol. 29 ›› Issue (3): 609-617.doi: 10.16182/j.issn1004731x.joss.201703019

基于SOC的锂动力电池组双向主动均衡控制

宋绍剑, 王志浩, 林小峰

广西大学电气工程学院,广西南宁 530004

收稿日期:2015-06-03 修回日期:2015-09-03 出版日期:2017-03-08 发布日期:2020-06-02
作者简介:宋绍剑(1970-),男,广西象州,硕士,教授,硕导,研究方向为新能源转换与控制,复杂系统建模与优化。
基金资助:
国家自然科学基金(61364007),广西科学研究与技术开发计划项目(桂科攻14122007-33),科学研究与技术开发计划项目(20141050)

SOC-Based Bi-Directional Active Equalization Control for Lithium-Ion Power Battery

Song Shaojian, Wang Zhihao, Lin Xiaofeng

School of Electrical Engineering, Guangxi University, Nanning 530004, China

Received:2015-06-03 Revised:2015-09-03 Online:2017-03-08 Published:2020-06-02

摘要/Abstract

摘要： 电动汽车锂动力电池组中单体电池的不一致性会导致电池组的容量和使用寿命的衰减,严重影响了电动汽车的性能。为此设计了一种以双向Buck-Boost拓扑为主电路的主动均衡控制系统,采用极限学习机(Extreme Learning Machine, ELM) 预测电池的荷电状态(State of Charge, SOC),并以SOC作为主要的均衡判据,提出了一种新型的主动均衡控制策略,实现了锂电池组在充电过程和静置状态下的主动均衡。实验结果表明：所提出的双向主动均衡控制方法可以准确高效地实现均衡目标,且能量损耗较少。

关键词: 锂电池组, 电池荷电状态, 极限学习机, 均衡控制策略

Abstract: The inconsistency of cell in the electric vehicles' lithium-ion power battery pack leads to decrease the battery pack's capacity and lifetime, and even seriously affect the electric vehicles' performance. An active equalization system was designed based on the bi-directional Buck-Boost topology. A novel active equalization control strategy was proposed to balance the lithium-ion battery pack in the process of charging and static state, which extreme learning machine (ELM) was used to predict the state of charge(SOC), and SOC was employed as the main balance criterion. The simulation results show that the proposed bi-directional active equalization method can realize the equalization target accurately and efficiently with less energy loss.

Key words: Lithium-Ion battery string, SOC, ELM, equalization control strategy

中图分类号:

TM912

宋绍剑, 王志浩, 林小峰. 基于SOC的锂动力电池组双向主动均衡控制[J]. 系统仿真学报, 2017, 29(3): 609-617.

Song Shaojian, Wang Zhihao, Lin Xiaofeng. SOC-Based Bi-Directional Active Equalization Control for Lithium-Ion Power Battery[J]. Journal of System Simulation, 2017, 29(3): 609-617.

参考文献

[1] 安志胜, 孙志毅, 何秋生. 车用锂离子电池管理系统综述[J]. 电源技术, 2013, 37(6): 1069-1071.
(An Zhisheng, Sun Zhiyi, He Qiusheng.Summarization of automotive Li-ion battery management system[J]. Chinese Journal of Power Sources, 2013, 37(6): 1069-1071.)
[2] 宋永华, 阳岳希,胡泽春. 电动汽车电池的现状及发展趋势[J]. 电网技术, 2011, 35(4): 1-7.
(Song Yonghua, Yang Yuexi, Hu Zechun.Present Status and Development Trend of Batteries for Electric Vehicles[J]. Power System Technology, 2011, 35(4): 1-7.)
[3] 符晓玲, 商云龙, 崔纳新. 电动汽车电池管理系统研究现状及发展趋势[J]. 电力电子技术, 2011, 45(12): 27-30.
(Fu Xiaoling, Shang Yunlong, Cui Naxin.Research and Development Trend on Battery Management System for EV[J]. Power Electronics, 2011, 45(12): 27-30.)
[4] Javier Gallardo-Lozano, Enrique Romero-Cadaval, M Isabel Milanes-Montero, et al. Guerrero-Martinez. Battery Equalization Active Methods[J]. Journal of Power Sources (S0378-7753), 2014, 246(8): 934-949.
[5] Moon-Young Kim, Chol-Ho Kim, Jun-Ho Kim, et al.A Chain Structure of Switched Capacitor for Improved Cell Balancing Speed of Lithium-ion Batteries[J]. IEEE Transactions on Industrial Electronics (S0278-0046), 2014, 61(8): 3989-3999.
[6] Robert Stala.Application of Balancing Circuit for DC-link Voltages Balance in A Single-phase Diode- clamped Inverter with Two Three-level Legs[J]. IEEE Transactions on Industrial Electronics (S0278-0046), 2011, 58(9): 4185-4195.
[7] Chang-Soon Lim, Kui-Jun Lee, Nam-Joon Ku, et al.A Modularized Equalization Method Based on a Magnetizing Energy for Series-Connected Lithium-Ion Battery String[J]. IEEE Transactions On Power Electronics (S0885-8993), 2014, 29(4): 1791-1799.
[8] Federico Baronti, Roberto Roncella, Roberto Saletti.Performance Comparison of Active Balancing Techniques for Lithium-ion Batteries[J]. Journal of Power Sources (S0378-7753), 2014, 267(6): 603-609.
[9] 熊永华, 杨艳. 基于SOC的锂动力电池多层双向自均衡方法[J]. 电子学报, 2014, 42(4): 766-773.
(Xiong Yonghua, Yang Yan, Li Hao, et al.Multi-Level Bi-Directional Active Equalization Method in Lithium-Ion Power Battery Based on State-of-Charge[J]. Acta Electronica Sinica, 2014, 42(4): 766-773.)
[10] 孙金磊, 逯仁贵, 魏国. 一种电动汽车串联电池组主动均衡器的设计和实现[J]. 电机与控制学报, 2013, 17(10): 33-38.
(Sun Jinlei, Lu Rengui, Wei Guo, et al.Design and realization of active equalizer for lithium battery string[J]. Electric Machines and Control, 2013, 17(10): 33-38.)
[11] 胡志坤, 刘斌, 林勇, 等. 电池SOC 的自适应平方根无极卡尔曼滤波估计算法[J]. 电机与控制学报, 2014, 18(4): 111-116.
(Hu Zhikun, Liu Bin, Lin Yong, et al.Adaptive square root unscented Kalman filter for SOC estimation of battery[J]. Electric Machines and Control, 2014, 18(4): 111-116.)
[12] Huang Guangbin, Zhu Qinyu, SIEW C K.Extreme Learning Machine: Theory and Applications[J]. Neurocomputing (S0925-2312), 2006, 70(1): 489-501.
[13] Huang Guangbin, Zhou Hongming, Ding Xiaojian.Extreme Learning Machine for Regression and Multiclass Classification[J]. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on (S1083-4419), 2012, 42(2): 513-529.
[14] Huang Guangbin, Zhu Qinyu, SIEW C K.Real-time Learning Capability of Neural Networks[J]. Neural Networks, IEEE Transactions on (S1045-9227), 2006, 17(4): 863-878.
[15] Thanh Phung, Alexandre Collet, Jean-Christophe Crebier.An Optimized Topology for Next-to-Next Balancing of Series-Connected Lithium-ion Cells[J]. IEEE Transactions on Industrial Electronics (S0278-0046), 2014, 29(9): 4603-4613.

基于SOC的锂动力电池组双向主动均衡控制

SOC-Based Bi-Directional Active Equalization Control for Lithium-Ion Power Battery

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