Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (11): 3135-3147.doi: 10.13229/j.cnki.jdxbgxb.20230056

Previous Articles    

Multi-dimensional aging diagnosis of lithium-ion battery with a long short-term memory neural network

Xian-feng REN1(),Wen-wen YUAN1,Xue-qiang WU1,Yan-ru SHI1,Meng-meng YAO1,Kai-xuan ZHANG2,Rui-xin YANG2(),Yue PAN2   

  1. 1.Weichai Power Co. ，Ltd. ，Weifang 261061，China
    2.School of Mechanical Engineering，Beijing Institute of Technology，Beijing 100081，China
  • Received:2023-01-17 Online:2024-11-01 Published:2025-04-24
  • Contact: Rui-xin YANG E-mail:renxf@weichai.com;yangruixin@bit.edu.cn

RICH HTML

 0   

PDF (PC)

78

Abstract:

Two internal side reactions that have the greatest impact on the battery aging mode are introduced. The negative region equation of the traditional pseudo two-dimensional model is improved， and the electrochemical degradation model of lithium-ion batteries is proposed. The response surface analysis method is applied to establish the aging characteristic parameters that can comprehensively describe the degradation of battery performance. A long short-term memory neural network is established to predict the future capacity. The aging characteristic parameters obtained based on the mechanism model and historical capacity retention rate are as the input of the network. Verification results of capacity forecast show that the prediction error is within 2%.

Key words: lithium-ion battery, aging diagnosis, aging mechanism modeling, response surface methodology, long short-term memory neural network

CLC Number: 

  • TM912

Table 1

Comparison of battery aging diagnosis methods"

老化诊断方法优点缺点
拆解分析法

直接观察电池内部

老化反应

电池不可逆损伤

操作复杂

实验费用高

曲线分析法

无损诊断

通用性高

计算量小

需要交叉验证

需要处理噪声

需要在小电流下获取

曲线

对电池极化敏感

模型分析法

无损诊断

通用性高

准确度高

计算量大

EIS测量复杂，可能会受到接触电阻的干扰

难以排除EM中无关

参数的干扰

Fig. 1

Schematic diagram of battery P2D model"

Fig. 2

Solution flow of battery P2D model"

Table 2

Input variables and their value ranges"

输入变量Level ⅠLevel ⅡLevel Ⅲ

电解液锂离子浓度

cl /（mol·m-3）

1 2001 1201 080
SEI膜厚度δflim/nm1400650
析锂过电位ηLi/V0.450.15-0.2

负极可用锂离子浓度

cs/（mol·m-3）

30 66530 39230 242

负极电化学反应速率

kn/［m2.5·（mol0.5·s）-1］

4.38e-116.7e-111e-10

正极电化学反应速率

kp/［m2.5·（mol0.5·s）-1］

1.63e-112.5e-108e-10

Table 3

Coefficients of multiple quadratic regression equation"

多项式各项各项系数多项式各项各项系数
A-5.79e11A×B0
B-3.075e10A×C-7.4e7
C2.07A×D-4.11e10
D-61.78A×E-6.07e-8
E-0.46A×F2.1e7
F-2.77B×C5.651e7
A20B×D-2.08e10
B20B×E5.23e6
C2-5.06e-4B×F-2.29e-5
D2-21.746C×D0.019
E23.87e-5C×E-1.93e-5
F24.59e-5C×F-2.96e-5

Table 4

Results of variance analysis"

方差来源平方和自由度均方和F值p值
A15.04115.046.720.015 4
B35.04135.0415.670.000 5
C3.37513.3751.510.230 3
D92.04192.0441.18<0.000 1
E360.381360.375161.11<0.000 1
F0.1710.1670.0750.787 0
A×B2.0012.000.900.353 1
A×C0.12510.1250.060.814 9
A×D2.2512.251.0060.325 1
A×E01001
A×F0.12510.1250.0560.814 9
B×C0.12510.1250.0560.814 9
B×D0.4510.500.2240.640 3
B×E0.062 510.062 50.0280.868 5
B×F01001
C×D1.12511.1250.5030.484 5
C×E1.12511.1250.5030.484 5
C×F2.2512.251.0060.325 1
D×E01001
D×F0.12510.1250.0560.815 0
E×F8183.5760.069 8
A23.1713.171.420.244 3
B294.29194.2942.15<0.000 1
C223.57123.5710.540.003 2
D224.89124.8911.130.002 6
E294.29194.2942.15<0.000 1
F257.34157.3425.63<0.000 1
模型58.16262.24
失拟项55.77212.66
纯误差2.3950.48
和1 086.9853

Fig. 3

Normal probability distribution of capacity retention rate"

Fig. 4

Influence of interaction between positive electrochemical reaction rate kp and other parameters on capacity retention rate"

Fig. 5

Influence of interaction between negative electrochemical reaction rate kn and other parameters on capacity retention rate"

Fig. 6

The influence of interaction between ηLi and other parameters on capacity retention rate"

Fig. 7

Influence of SEI film thickness and other parameters interaction on capacity retention rate"

"

Fig. 9

Battery capacity decline trajectory prediction algorithm flow based on LSTM RNN"

Fig. 10

LSTM RNN training and testing results"

Fig. 11

Error distribution of LSTM and RNN training and testing"

Table 5

Predicted results of BP and LSTM and RNN"

评价

指标

方法

考虑老化特征

参数体系

不考虑老化特征参数体系
MAELSTM RNN0.006 30.011 4
BP0.008 90.014 2
RMSELSTM RNN0.009 20.014 2
BP0.011 60.016 5
NSELSTM RNN0.999 30.913 1
BP0.981 20.942 7
1 Xiong R, Huang J T, Duan Y Z, et al. Enhanced lithium-ion battery model considering critical surface charge behavior[J]. Applied Energy, 2022, 314: No.118915.
2 Xiong R, Pan Y, Shen W X, et al. Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: recent advances and perspectives[J]. Renewable and Sustainable Energy Reviews, 2020, 131: No.110048.
3 Xiong R, Ma S X, Li H L, et al. Toward a safer battery management system: a critical review on diagnosis and prognosis of battery short circuit[J]. Iscience, 2020, 23(4): No.101010.
4 Waldmann T, Wilka M, Kasper M, et al. Temperature dependent ageing mechanisms in lithium-ion batteries-a post-mortem study[J]. Journal of Power Sources, 2014, 262: 129-135.
5 Waldmann T, Iturrondobeitia A, Kasper M, et al. Post-mortem analysis of aged lithium-ion batteries: disassembly methodology and physico-chemical analysis techniques[J]. Journal of The Electrochemical Society, 2016, 163(10): No.21211609.
6 Waldmann T, Ghanbari N, Kasper M, et al. Correlations between electrochemical data and results from post-mortem analysis of aged lithium-ion batteries[J]. Journal of the Electrochemical Society, 2015, 162(8): No.20411508.
7 Klett M, Eriksson R, Groot J, et al. Non-uniform aging of cycled commercial LiFePO4∥graphite cylindrical cells revealed by post-mortem analysis[J]. Journal of Power Sources, 2014, 257: 126-137.
8 Golla S U, Zeibig D, Prickler L, et al. Characterization of degeneration phenomena in lithium-ion batteries by combined microscopic techniques[J]. Micron, 2018, 113: 10-19.
9 Buqa H, Wursig A, Vetter J, et al. SEI film formation on highly crystalline graphitic materials in lithium-ion batteries[J]. Journal of Power Sources, 2006, 153(2): 385-390.
10 Darma M S D, Lang M, Kleiner K, et al. The influence of cycling temperature and cycling rate on the phase specific degradation of a positive electrode in lithium ion batteries: a post mortem analysis[J]. Journal of Power Sources, 2016, 327: 714-725.
11 Stiasazny B, Ziegler J C, Kraub E E, et al. Electrochemical characterization and post-mortem analysis of aged LiMn2O4–Li (Ni0.5Mn0.3Co0.2) O2/graphite lithium-ion batteries part I: cycle aging[J]. Journal of Power Sources, 2014, 251: 439-450.
12 Badey Q, Cherouvrier G, Reynier Y, et al. Mechanisms and modeling of lithium-ion battery aging for a vehicle usage[J]. ECS Meeting Abstracts IOP Publishing, 2011, 2(15): 742.
13 Jaumann T, Balach J, Klose M, et al. SEI-component formation on sub 5 nm sized silicon nanoparticles in Li-ion batteries: the role of electrode preparation, FEC addition and binders[J]. Physical Chemistry Chemical Physics, 2015, 17(38): 24956-24967.
14 Liu S, Jiang J, Shi W, et al. State of charge and peak power estimation of NCM/Li4Ti5O12 battery using ic curve for rail tractor application[C]∥ 2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific), Beijing, China, 2014: 1-3.
15 Pan B, Dong D, Wang J G, et al. Aging mechanism diagnosis of lithium ion battery by open circuit voltage analysis[J]. Electrochimica Acta, 2020, 362: No.137101.
16 He J T, Bian X L, Liu L C, et al. Comparative study of curve determination methods for incremental capacity analysis and state of health estimation of lithium-ion battery[J]. Journal of Energy Storage, 2020, 29: No. 101400.
17 Li X Y, Yuan C G, Li X H, et al. State of health estimation for Li-ion battery using incremental capacity analysis and Gaussian process regression[J]. Energy, 2020, 190: No.116467.
18 Pan W J, Luo X S, Zhu M T, et al. A health indicator extraction and optimization for capacity estimation of Li-ion battery using incremental capacity curves[J]. Journal of Energy Storage, 2021, 42: No.103072.
19 Pastor F C, Widanage W D, Marco J, et al. Identification and quantification of ageing mechanisms in lithium-ion batteries using the EIS technique[C]∥ 2016 IEEE Transportation Electrification Conference and Expo (ITEC), Derborn, USA, 2016: 1-6.
20 Han X B, Ouyang M G, Lu L G, et al. A comparative study of commercial lithium ion battery cycle life in electrical vehicle: aging mechanism identification[J]. Journal of Power Sources, 2014, 251: 38-54.
21 Zhang Q, White R E. Capacity fade analysis of a lithium ion cell[J]. Journal of Power Sources, 2008, 179(2): 793-798.
22 Schmidt A P, Bitzer M, Imre Á W, et al. Model-based distinction and quantification of capacity loss and rate capability fade in Li-ion batteries[J]. Journal of Power Sources, 2010, 195(22): 7634-7638.
23 Ramadesigan V, Chen K, Burns N A, et al. Parameter estimation and capacity fade analysis of lithium-ion batteries using reformulated models[J]. Journal of the Electrochemical Society, 2011, 158(9):No. 13609926.
24 Fu R, Choe S Y, Agubra V, et al. Modeling of degradation effects considering side reactions for a pouch type Li-ion polymer battery with carbon anode[J]. Journal of Power Sources, 2014, 261: 120-135.
25 Wu J, Chen J X, Feng X, et al. State of health estimation of lithium-ion batteries using autoencoders and ensemble learning[J]. Journal of Energy Storage, 2022, 55: No.105708.
26 Yang B W, Wang D F, Zhang B, et al. Aging diagnosis-oriented three-scale impedance model of lithium-ion battery inspired by and reflecting morphological evolution[J]. Journal of Energy Storage, 2023, 59: No.106357.
27 Lee Y K. Effect of transition metal ions on solid electrolyte interphase layer on the graphite electrode in lithium ion battery[J]. Journal of Power Sources, 2021, 484: No.229270.
28 Chouchaine M, Arcelus O, Franco A. Heterogeneous solid-electrolyte interphase in graphite electrodes assessed by 4D-resolved computational simulations[J]. Batteries & Supercaps, 2021, 4(9): 1457-1463.
29 Ren D S, Smith K, Guo D X, et al. Investigation of lithium plating-stripping process in Li-ion batteries at low temperature using an electrochemical model[J]. Journal of the Electrochemical Society, 2018, 165(10): No.20661810.
30 Hein S, Danner T, Latz A. An electrochemical model of lithium plating and stripping in lithium ion batteries[J]. ACS Applied Energy Materials, 2020, 3(9): 8519-8531.
31 Doyle M, Newman J, Fuller T. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell[J]. Journal of the Electrochemical Society, 1993, 140(6): 1526-1533.
32 刘兴涛, 刘晓剑, 武骥, 等. 基于曲线压缩和极限梯度提升算法的锂离子电池健康状态估计[J]. 吉林大学学报: 工学版, 2022, 52(6): 1273-1280.
Liu Xing-tao, Liu Xiao-jian, Wu Ji, et al. State of health estimation method for lithium⁃ion battery based on curve compression and extreme gradient boosting [J]. Journal of Jilin University (Engineering and Technology Edition), 2022, 52(6): 1273-1280.
33 高金武, 贾志桓, 王向阳,等. 基于PSO-LSTM的质子交换膜燃料电池退化趋势预测[J]. 吉林大学学报: 工学版, 2022, 52(9): 2192-2202.
Gao Jin-wu, Jia Zhi-huan, Wang Xiang-yang, et al. Degradation trend prediction of proton exchange membrane fuel cell based on PSO⁃LSTM[J]. Journal of Jilin University (Engineering and Technology Edition), 2022, 52(9): 2192-2202.
34 Sak H, Senior A W, Beaufavs F. Long short-term memory recurrent neural network architectures for large scale acoustic modeling[J]. Interspeech, 2014, 2: 338-342.
[1] Rong-han YAO,Wen-tao XU,Wei-wei GUO. Drivers' takeover behavior and intention recognition based on factor and long short⁃term memory [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 758-771.
[2] Jian-cheng WENG,Rui-cong WEI,Han-mei HE,Hai-hui XU,Jing-jing WANG. Urban road network short-term traffic flow prediction model based on associated road chain group [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(11): 3104-3112.
[3] Xing-tao LIU,Xiao-jian LIU,Ji WU,Yao HE,Xin-tian LIU. State of health estimation method for lithium⁃ion battery based on curve compression and extreme gradient boosting [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(6): 1273-1280.
[4] Ji-cheng HUANG,Cheng SHEN,Ai-min JI,Xian-wang LI,Bin ZHANG,Kun-peng TIAN,Hao-lu LIU. Optimization of cutting⁃conveying key working parameters of hemp harvester [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 772-780.
[5] Feng-wen PAN,Dong-liang GONG,Ying GAO,Ming-wei XU,Bin MA. Fault diagnosis of current sensor based on linearization model of lithium ion battery [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 435-441.
[6] Fei GAO,Yang XIAO,Wen-hua ZHANG,Jin-xuan QI,Zi-qiao LI,Xiao-yuan MA. Influence of coupling of elevated temperature and state of charge on mechanical response of Li⁃ion battery cells [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1574-1583.
[7] Fang-wu MA,Lu HAN,Yang ZHOU,Shi-ying WANG,Yong-feng PU. Multi material optimal design of vehicle product using polylactic acid composites [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(5): 1385-1391.
[8] CHEN Yan-hong, WU Wei-jing, LIU Hong-wei, SHEN Shuai, LI Ce-yuan, GENG Huan-liang. Thermal characteristics of battery for pure electric vehicles [J]. 吉林大学学报(工学版), 2014, 44(4): 925-932.
[9] DING Jian-feng, SUN Yong-hai, Fu Tian-yu, Xie Gao-peng. Response surface methodology optimization of Morchella mycelium selenium enriched fermentation [J]. 吉林大学学报(工学版), 2013, 43(增刊1): 557-563.
[10] ZHANG Cai-ping, JIANG Jiu-chun. Extended Kalman filter algorithm for parameters identification of dynamic battery model based on genetic algorithm optimization [J]. , 2012, (03): 732-737.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd