上海交通大学学报

上海交通大学学报 >

2024 , Vol. 58 >Issue 5: 747 - 759

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2022.221

新型电力系统与综合能源

基于ECM和SGPR的高鲁棒性锂离子电池健康状态估计方法

展开
  • 上海交通大学 海洋工程国家重点实验室,上海 200240
崔 显(1997-),硕士生,从事锂离子电池健康状态监测研究.
陈自强,研究员,博士生导师;E-mail:chenziqiang@sjtu.edu.cn.

收稿日期: 2022-06-13

  修回日期: 2022-08-25

  录用日期: 2022-09-15

  网络出版日期: 2023-03-10

基金资助

国家自然科学基金(51677119)

收起

A Highly Robust State of Health Estimation Method for Lithium-Ion Batteries Based on ECM and SGPR

Expand
  • State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received date: 2022-06-13

  Revised date: 2022-08-25

  Accepted date: 2022-09-15

  Online published: 2023-03-10

Fold

摘要

锂离子电池健康状态(SOH)的准确估计对于保障电池系统安全运行具有重要意义.针对传统SOH估计方法在可变工况下失效的问题,提出了一种基于等效电路模型和稀疏高斯过程回归的锂离子电池SOH在线估计方法.通过两个在线滤波器,在恒流充电过程中动态地辨识了锂离子电池等效电路模型的各项参数,构建了工况不敏感的健康因子,结合稀疏高斯过程回归实现SOH的间接估计.该方法在多种工况下使用统一的信号处理方法和特征映射模型,兼具鲁棒性强和冗余度低的优点.实验结果表明,该方法在多种工况下的平均绝对误差不超过0.94%,均方根误差不超过1.12%,与现有方法相比,该方法在综合性能上具有显著优势.

关键词： 锂离子电池; 健康状态; 健康因子; 粒子滤波; 稀疏高斯过程回归

本文引用格式

崔显, 陈自强 . 基于ECM和SGPR的高鲁棒性锂离子电池健康状态估计方法[J]. 上海交通大学学报, 2024 , 58(5) : 747 -759 . DOI: 10.16183/j.cnki.jsjtu.2022.221

Abstract

Accurately estimating the state of health (SOH) of lithium-ion batteries is of great significance in ensuring the safe operation of the battery system. Addressing the issue where traditional SOH estimation methods fail under variable working conditions, an online SOH estimation method for lithium-ion batteries based on equivalent circuit model (ECM) and sparse Gaussian process regression (SGPR) is proposed. During the constant current charging process, the parameters of the ECM of lithium-ion battery are dynamically identified by two online filters, based on which, a condition-insensitive health indicator is constructed. In combination with the SGPR, the indirect SOH estimation is achieved. This method uses the unified signal processing method and feature mapping model under various working conditions, and features strong robustness with low redundancy. The experimental results show that the average absolute error of the method proposed under various working conditions does not exceed 0.94%, and the root mean square error stays below 1.12%. When benchmarked against existing methods, this method has significant advantages in comprehensive performance.

Key words： lithium-ion battery; state of health (SOH); health indicator; particle filter; sparse Gaussian process regression (SGPR)

参考文献

[1] ALLAM A, ONORI S. Online capacity estimation for lithium-ion battery cells via an electrochemical model-based adaptive interconnected observer[J]. IEEE Transactions on Control Systems Technology, 2021, 29(4): 1636-1651.
[2] TIAN J Q, XU R L, WANG Y J, et al. Capacity attenuation mechanism modeling and health assessment of lithium-ion batteries[J]. Energy, 2021, 221: 119682.
[3] CHEN L, DING Y H, WANG H M, et al. Online estimating state of health of lithium-ion batteries using hierarchical extreme learning machine[J]. IEEE Transactions on Transportation Electrification, 2022, 8(1): 965-975.
[4] MA Z Y, YANG R X, WANG Z P. A novel data-model fusion state-of-health estimation approach for lithium-ion batteries[J]. Applied Energy, 2019, 237: 836-847.
[5] ZHOU L T, ZHAO Y, LI D, et al. State-of-health estimation for LiFePO4 battery system on real-world electric vehicles considering aging stage[J]. IEEE Transactions on Transportation Electrification, 2022, 8(2): 1724-1733.
[6] 董明, 范文杰, 刘王泽宇, 等. 基于特征频率阻抗的锂离子电池健康状态评估[J]. 中国电机工程学报, 2022, 42(24): 9094-9104.
  DONG Ming, FAN Wenjie, LIU Wangzeyu, et al. Health assessment of lithium-ion batteries based on characteristic frequency impedance[J]. Proceedings of the CSEE, 2022, 42(24): 9094-9104.
[7] 李建林, 李雅欣, 陈光, 等. 退役动力电池健康状态特征提取及评估方法综述[J]. 中国电机工程学报, 2022, 42(4): 1332-1346.
  LI Jianlin, LI Yaxin, CHEN Guang, et al. Research on feature extraction and SOH evaluation methods for retired power battery[J]. Proceedings of the CSEE, 2022, 42(4): 1332-1346.
[8] YANG S J, ZHANG C P, JIANG J C, et al. Review on state-of-health of lithium-ion batteries: Characterizations, estimations and applications[J]. Journal of Cleaner Production, 2021, 314: 128015.
[9] HU X S, CHE Y H, LIN X K, et al. Battery health prediction using fusion-based feature selection and machine learning[J]. IEEE Transactions on Transportation Electrification, 2021, 7(2): 382-398.
[10] 王萍, 弓清瑞, 张吉昂, 等. 一种基于数据驱动与经验模型组合的锂电池在线健康状态预测方法[J]. 电工技术学报, 2021, 36(24): 5201-5212.
  WANG Ping, GONG Qingrui, ZHANG Jiang, et al. An online state of health prediction method for lithium batteries based on combination of data-driven and empirical model[J]. Transactions of China Electrotechnical Society, 2021, 36(24): 5201-5212.
[11] WEI Z B, RUAN H K, LI Y, et al. Multistage state of health estimation of lithium-ion battery with high tolerance to heavily partial charging[J]. IEEE Transactions on Power Electronics, 2022, 37(6): 7432-7442.
[12] 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.
  LU Dihua, CHEN Ziqiang. State of health estimation of lithium-ion batteries based on dual charging state[J]. Journal of Shanghai Jiao Tong University, 2022, 56(3): 342-352.
[13] TAN X J, ZHAN D, LYU P X, et al. Online state-of-health estimation of lithium-ion battery based on dynamic parameter identification at multi timescale and support vector regression[J]. Journal of Power Sources, 2021, 484: 229233.
[14] DENG Z W, HU X S, LI P H, et al. Data-driven battery state of health estimation based on random partial charging data[J]. IEEE Transactions on Power Electronics, 2022, 37(5): 5021-5031.
[15] QIAN C, XU B H, CHANG L, et al. Convolutional neural network based capacity estimation using random segments of the charging curves for lithium-ion batteries[J]. Energy, 2021, 227: 120333.
[16] 李超然, 肖飞, 樊亚翔, 等. 基于卷积神经网络的锂离子电池SOH估算[J]. 电工技术学报, 2020, 35(19): 4106-4119.
  LI Chaoran, XIAO Fei, FAN Yaxiang, et al. An approach to lithium-ion battery SOH estimation based on convolutional neural network[J]. Transactions of China Electrotechnical Society, 2020, 35(19): 4106-4119.
[17] TAN Y D, ZHAO G C. Transfer learning with long short-term memory network for state-of-health prediction of lithium-ion batteries[J]. IEEE Transactions on Industrial Electronics, 2020, 67(10): 8723-8731.
[18] ZHANG C H, KANG Y Z, DUAN B, et al. An adaptive battery capacity estimation method suitable for random charging voltage range in electric vehicles[J]. IEEE Transactions on Industrial Electronics, 2022, 69(9): 9121-9132.
[19] BIRKL C, HOWEY D. Oxford battery degradation dataset 1[DB/OL]. (2017-01-01)[2022-06-10]. https://ora.ox.ac.uk/objects/uuid:03ba4b01-cfed-46d3-9b1a-7d4a7bdf6fac.
[20] 刘海东, 周萍, 周正, 等. 锂离子电池开路电压快速估计研究[J]. 机械工程学报, 2022, 58(8): 227-235.
  LIU Haidong, ZHOU Ping, ZHOU Zheng, et al. Fast estimation of open circuit voltage for lithium-ion batteries[J]. Journal of Mechanical Engineering, 2022, 58(8): 227-235.
[21] BIAN X L, WEI Z B, LI W H, et al. State-of-health estimation of lithium-ion batteries by fusing an open circuit voltage model and incremental capacity analysis[J]. IEEE Transactions on Power Electronics, 2022, 37(2): 2226-2236.
[22] FARMANN A, SAUER D U. A study on the dependency of the open-circuit voltage on temperature and actual aging state of lithium-ion batteries[J]. Journal of Power Sources, 2017, 347: 1-13.
[23] AMINE K, LIU J, BELHAROUAK I. High-temperature storage and cycling of C-LiFePO4/graphite Li-ion cells[J]. Electrochemistry Communications, 2005, 7(7): 669-673.
[24] CANDELA J Q, RASMUSSEN C E. A unifying view of sparse approximate Gaussian process regression[J]. Journal of Machine Learning Research, 2005, 6: 1939-1959.
[25] BIJL H, VAN WINGERDEN J W, SCH?N T B, et al. Online sparse Gaussian process regression using FITC and PITC approximations[J]. IFAC-PapersOnLine, 2015, 48(28): 703-708.
[26] CARL E R, HANNES N. Documentation for GPML Matlab Code version 4.2[CP/OL]. (2020-07-14)[2022-06-10]. http://gaussianprocess.org/gpml/code/matlab/doc/.
[27] TAN X J, ZHAN D, LYU P X, et al. Online state-of-health estimation of lithium-ion battery based on dynamic parameter identification at multi timescale and support vector regression[J]. Journal of Power Sources, 2021, 484: 229233.
[28] XU T T, PENG Z, LIU D G, et al. A hybrid drive method for capacity prediction of lithium-ion batteries[J]. IEEE Transactions on Transportation Electrification, 2022, 8(1): 1000-1012.
[29] JIANG B, DAI H F, WEI X Z. Incremental capacity analysis based adaptive capacity estimation for lithium-ion battery considering charging condition[J]. Applied Energy, 2020, 269: 115074.
Options
文章导航

/

〈 〉