Battery State-of-Health Estimation: A Step towards Battery Digital Twins

被引:11
作者
Safavi, Vahid [1 ]
Bazmohammadi, Najmeh [1 ]
Vasquez, Juan C. [1 ]
Guerrero, Josep M. [1 ,2 ,3 ]
机构
[1] Aalborg Univ, Ctr Res Microgrids CROM, AAU Energy, DK-9220 Aalborg, Denmark
[2] Tech Univ Catalonia, Ctr Res Microgrids CROM, Dept Elect Engn, Barcelona 08034, Spain
[3] Catalan Inst Res & Adv Studies ICREA, Pg Lluis Co 23, Barcelona 08010, Spain
关键词
lithium-ion batteries; state of health; data pre-processing; discharging characteristics; digital twin; deep learning; CNN-LSTM;
D O I
10.3390/electronics13030587
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
For a lithium-ion (Li-ion) battery to operate safely and reliably, an accurate state of health (SOH) estimation is crucial. Data-driven models with manual feature extraction are commonly used for battery SOH estimation, requiring extensive expert knowledge to extract features. In this regard, a novel data pre-processing model is proposed in this paper to extract health-related features automatically from battery-discharging data for SOH estimation. In the proposed method, one-dimensional (1D) voltage data are converted to two-dimensional (2D) data, and a new data set is created using a 2D sliding window. Then, features are automatically extracted in the machine learning (ML) training process. Finally, the estimation of the SOH is achieved by forecasting the battery voltage in the subsequent cycle. The performance of the proposed technique is evaluated on the NASA public data set for a Li-ion battery degradation analysis in four different scenarios. The simulation results show a considerable reduction in the RMSE of battery SOH estimation. The proposed method eliminates the need for the manual extraction and evaluation of features, which is an important step toward automating the SOH estimation process and developing battery digital twins.
引用
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页数:22
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共 60 条
  • [1] Ahooyi S.S., 2022, P 2022 8 INT C CONTR, P1
  • [2] Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
    Alzubaidi, Laith
    Zhang, Jinglan
    Humaidi, Amjad J.
    Al-Dujaili, Ayad
    Duan, Ye
    Al-Shamma, Omran
    Santamaria, J.
    Fadhel, Mohammed A.
    Al-Amidie, Muthana
    Farhan, Laith
    [J]. JOURNAL OF BIG DATA, 2021, 8 (01)
  • [3] A comparison of methodologies for the non-invasive characterisation of commercial Li-ion cells
    Barai, Anup
    Uddin, Kotub
    Dubarry, Matthieu
    Somerville, Limhi
    McGordon, Andrew
    Jennings, Paul
    Bloom, Ira
    [J]. PROGRESS IN ENERGY AND COMBUSTION SCIENCE, 2019, 72 : 1 - 31
  • [4] Microgrid Digital Twins: Concepts, Applications, and Future Trends
    Bazmohammadi, Najmeh
    Madary, Ahmad
    Vasquez, Juan C.
    Mohammadi, Hamid Baz
    Khan, Baseem
    Wu, Ying
    Guerrero, Josep M.
    [J]. IEEE ACCESS, 2022, 10 : 2284 - 2302
  • [5] A Dynamic Spatial-Temporal Attention-Based GRU Model With Healthy Features for State-of-Health Estimation of Lithium-Ion Batteries
    Cui, Shengmin
    Joe, Inwhee
    [J]. IEEE ACCESS, 2021, 9 (09): : 27374 - 27388
  • [6] Feature-based lithium-ion battery state of health estimation with artificial neural networks
    Driscoll, Lewis
    de la Torre, Sebastian
    Antonio Gomez-Ruiz, Jose
    [J]. JOURNAL OF ENERGY STORAGE, 2022, 50
  • [7] Digital Twin for maintenance: A literature review
    Errandonea I.
    Beltrán S.
    Arrizabalaga S.
    [J]. Computers in Industry, 2020, 123
  • [8] An encoder-decoder model based on deep learning for state of health estimation of lithium-ion battery
    Gong, Qingrui
    Wang, Ping
    Cheng, Ze
    [J]. JOURNAL OF ENERGY STORAGE, 2022, 46
  • [9] Automated Feature Extraction and Selection for Data-Driven Models of Rapid Battery Capacity Fade and End of Life
    Greenbank, Samuel
    Howey, David
    [J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2022, 18 (05) : 2965 - 2973
  • [10] A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model
    Gu, Xinyu
    See, K. W.
    Li, Penghua
    Shan, Kangheng
    Wang, Yunpeng
    Zhao, Liang
    Lim, Kai Chin
    Zhang, Neng
    [J]. ENERGY, 2023, 262