上海交通大学学报

上海交通大学学报 >

2022 , Vol. 56 >Issue 3: 342 - 352

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2021.027

新型电力系统与综合能源

基于双充电状态的锂离子电池健康状态估计

展开
  • 高新船舶与深海开发装备协同创新中心,上海 200240
卢地华(1995-),男,江西省九江市人,硕士生,主要从事锂离子电池健康状态估计研究.

收稿日期: 2021-01-25

  网络出版日期: 2022-04-01

基金资助

国家自然科学基金资助项目(51677119)

收起

摘要

针对锂离子电池实际应用中存在不完全充放电而导致的充电起始点及截止点不确定问题,提出一种基于双充电状态因子的电池健康状态估计方法.搭建电池老化实验台架,采用8块镍钴锰锂离子电池进行老化实验;区别于传统单状态因子估计,选取不同老化阶段下恒压充电状态前端等时间差的电流平均值,以及恒流充电状态末端等幅值电压的充电时间构造健康因子;分析不同老化阶段实验电池的荷电状态-开路电压对应关系,通过理论推导及实验结果证明健康因子的正确性;建立具备强泛化能力的改进支持向量回归模型,并通过粒子群算法优化模型超参数.实验结果表明:所提双充电状态健康因子与电池老化衰减密切相关,所建立的改进支持向量回归模型可实时估计不同老化状态下的电池健康状态,具备容量局部回弹变化的表征能力,可作为一种有效的嵌入式电池管理系统健康状态估计方法.

关键词： 锂离子电池; 健康状态估计; 支持向量回归; 双充电状态; 老化实验

本文引用格式

卢地华, 陈自强 . 基于双充电状态的锂离子电池健康状态估计[J]. 上海交通大学学报, 2022 , 56(3) : 342 -352 . DOI: 10.16183/j.cnki.jsjtu.2021.027

Abstract

Aimed at the uncertainty of charging starting and ending point caused by incomplete charging and discharging in practical applications of lithium-ion battery, an estimation method of battery health based on dual charging state factors is proposed. A battery aging experiment bench is built, and eight nickel-cobalt-manganese lithium-ion batteries are subjected to aging test. Different from the traditional single state factor estimation, the average value of equal time difference current at the front end of constant voltage charging curve and the equal amplitude voltage charging time at the end of constant current charging curve are selected under different aging conditions to construct health factors. The corresponding relationship between state of charge (SOC) and open circuit voltage (OCV) of the experimental battery in different aging states is analyzed and the correctness of health factor is proved by theoretical deduction and experimental results. An improved support vector regression model with a strong generalization ability is established, and the hyperparameters of the model are optimized through the particle swarm optimization algorithm. The results show that the proposed dual-charging health factor is closely related to battery capacity aging and attenuation. The improved support vector regression model can estimate the health status in different aging states in real time, and has the ability to characterize local capacity rebound change, which can be used as an effective method for estimating the state of health of an embedded battery management system.

Key words： lithium-ion battery; state of health estimation; support vector regression; dual charging state; aging experiment

参考文献

[1] 周诗尧, 陈自强, 黄德扬, 等. 轻度混合动力船舶储能系统研究[J]. 装备环境工程, 2018, 15(12):55-59.
[1] ZHOU Shiyao, CHEN Ziqiang, HUANG Deyang, et al. Energy storage system of mild hybrid ship[J]. Equipment Environmental Engineering, 2018, 15(12):55-59.
[2] CHENG Y J, LU C, LI T Y, et al. Residual lifetime prediction for lithium-ion battery based on functional principal component analysis and Bayesian approach[J]. Energy, 2015, 90:1983-1993.
[3] REMMLINGER J, BUCHHOLZ M, MEILER M, et al. State-of-health monitoring of lithium-ion batteries in electric vehicles by on-board internal resistance estimation[J]. Journal of Power Sources, 2011, 196(12):5357-5363.
[4] 刘江波. 基于内阻检测的锂电池健康状态估计研究[D]. 武汉: 武汉理工大学, 2015.
[4] LIU Jiangbo. Research on SOH estimation of Li-ion battery based on internal resistance[D]. Wuhan: Wuhan University of Technology, 2015.
[5] LIU D T, ZHOU J B, LIAO H T, et al. A health indicator extraction and optimization framework for lithium-ion battery degradation modeling and prognostics[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2015, 45(6):915-928.
[6] ZHOU Y P, HUANG M H, CHEN Y P, et al. A novel health indicator for on-line lithium-ion batteries remaining useful life prediction[J]. Journal of Power Sources, 2016, 321:1-10.
[7] HU X S, JIANG J C, CAO D P, et al. Battery health prognosis for electric vehicles using sample entropy and sparse Bayesian predictive modeling[J]. IEEE Transactions on Industrial Electronics, 2016, 63(4):2645-2656.
[8] LI J F, WANG L X, LYU C, et al. A method of remaining capacity estimation for lithium-ion battery[J]. Advances in Mechanical Engineering, 2013, 5:154831.
[9] 孙培坤. 电动汽车动力电池健康状态估计方法研究[D]. 北京: 北京理工大学, 2016.
[9] SUN Peikun. Research of state of health estimation method for electric vehicle lithium-ion power battery[D]. Beijing: Beijing Institute of Technology, 2016.
[10] 郭永芳, 黄凯, 李志刚. 基于短时搁置端电压压降的快速锂离子电池健康状态预测[J]. 电工技术学报, 2019, 34(19):3968-3978.
[10] GUO Yongfang, HUANG Kai, LI Zhigang. Fast state of health prediction of lithium-ion battery based on terminal voltage drop during rest for short time[J]. Transactions of China Electrotechnical Society, 2019, 34(19):3968-3978.
[11] 潘海鸿, 吕治强, 付兵, 等. 采用极限学习机实现锂离子电池健康状态在线估算[J]. 汽车工程, 2017, 39(12):1375-1381.
[11] PAN Haihong, LÜ Zhiqiang, FU Bing, et al. Online estimation of lithium-ion battery’s state of health using extreme learning machine[J]. Automotive Engineering, 2017, 39(12):1375-1381.
[12] MENG J H, CAI L, STROE D I, et al. Lithium-ion battery state-of-health estimation in electric vehicle using optimized partial charging voltage profiles[J]. Energy, 2019, 185:1054-1062.
[13] BIAN X L, LIU L C, YAN J Y. A model for state-of-health estimation of lithium ion batteries based on charging profiles[J]. Energy, 2019, 177:57-65.
[14] WENG C H, CUI Y J, SUN J, et al. On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression[J]. Journal of Power Sources, 2013, 235:36-44.
[15] 刘健, 陈自强, 黄德扬, 等. 基于等压差充电时间的锂离子电池寿命预测[J]. 上海交通大学学报, 2019, 53(9):1058-1065.
[15] LIU Jian, CHEN Ziqiang, HUANG Deyang, et al. Remaining useful life prediction for lithium-ion batteries based on time interval of equal charging voltage difference[J]. Journal of Shanghai Jiao Tong University, 2019, 53(9):1058-1065.
[16] YANG J F, XIA B, HUANG W X, et al. Online state-of-health estimation for lithium-ion batteries using constant-voltage charging current analysis[J]. Applied Energy, 2018, 212:1589-1600.
[17] HUANG D Y, CHEN Z Q, ZHENG C W, et al. A model-based state-of-charge estimation method for series-connected lithium-ion battery pack considering fast-varying cell temperature[J]. Energy, 2019, 185:847-861.
[18] 刘轶鑫, 张頔, 李雪, 等. 基于SOC-OCV曲线特征的SOH估计方法研究[J]. 汽车工程, 2019, 41(10):1158-1163.
[18] LIU Yixin, ZHANG Di, LI Xue, et al. A research on SOH estimation method based on SOC-OCV curve characteristics[J]. Automotive Engineering, 2019, 41(10):1158-1163.
[19] WANG Z K, ZENG S K, GUO J B, et al. State of health estimation of lithium-ion batteries based on the constant voltage charging curve[J]. Energy, 2019, 167:661-669.
[20] LI X Y, WANG Z P, ZHANG L, et al. State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis[J]. Journal of Power Sources, 2019, 410/411:106-114.
Options
文章导航

/

〈 〉