A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search
Volume 11 Issue 9
Sep.  2024

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77， TOP 5
Turn off MathJax
Article Contents
Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2024  >  11(9): 1944-1953
K. Liu, Q. Peng, Y. Liu, N. Cui, and  C. Zhang,  “Explainable neural network for sensitivity analysis of lithium-ion battery smart production,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 1944–1953, Sept. 2024. doi: 10.1109/JAS.2024.124539
Citation: K. Liu, Q. Peng, Y. Liu, N. Cui, and  C. Zhang,  “Explainable neural network for sensitivity analysis of lithium-ion battery smart production,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 1944–1953, Sept. 2024. doi: 10.1109/JAS.2024.124539
shu

Explainable Neural Network for Sensitivity Analysis of Lithium-ion Battery Smart Production

doi: 10.1109/JAS.2024.124539
Funds:  This work was supported by the National Natural Science Foundation of China (62373224, 62333013, U23A20327)
More Information
    Abstract

  • Battery production is crucial for determining the quality of electrode, which in turn affects the manufactured battery performance. As battery production is complicated with strongly coupled intermediate and control parameters, an efficient solution that can perform a reliable sensitivity analysis of the production terms of interest and forecast key battery properties in the early production phase is urgently required. This paper performs detailed sensitivity analysis of key production terms on determining the properties of manufactured battery electrode via advanced data-driven modelling. To be specific, an explainable neural network named generalized additive model with structured interaction (GAM-SI) is designed to predict two key battery properties, including electrode mass loading and porosity, while the effects of four early production terms on manufactured batteries are explained and analysed. The experimental results reveal that the proposed method is able to accurately predict battery electrode properties in the mixing and coating stages. In addition, the importance ratio ranking, global interpretation and local interpretation of both the main effects and pairwise interactions can be effectively visualized by the designed neural network. Due to the merits of interpretability, the proposed GAM-SI can help engineers gain important insights for understanding complicated production behavior, further benefitting smart battery production.

     

    • FullText(HTML)
  • loading
    • References(32)
  • [1]
    K. Liu, Z. Wei, C. Zhang, Y. Shang, R. Teodorescu, and Q.-L. Han, “Towards long lifetime battery: AI-based manufacturing and management,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1139–1165, Jul. 2022. doi: 10.1109/JAS.2022.105599
    [2]
    F. Duffner, N. Kronemeyer, J. Tübke, J. Leker, M. Winter, and R. Schmuch, “Post-lithium-ion battery cell production and its compatibility with lithium-ion cell production infrastructure,” Nat. Energy, vol. 6, no. 2, pp. 123–134, Jan. 2021. doi: 10.1038/s41560-020-00748-8
    [3]
    F. Degen and O. Krätzig, “Future in battery production: An extensive benchmarking of novel production technologies as guidance for decision making in engineering,” IEEE Trans. Eng. Manage., vol. 71, pp. 1038–1056, Feb. 2022.
    [4]
    Y. Liu, R. Zhang, J. Wang, and Y. Wang, “Current and future lithium-ion battery manufacturing,” iScience, vol. 24, no. 4, p. 102332, Apr. 2021. doi: 10.1016/j.isci.2021.102332
    [5]
    V. Viswanathan, A. H. Epstein, Y.-M. Chiang, E. Takeuchi, M. Bradley, J. Langford, and M. Winter, “The challenges and opportunities of battery-powered flight,” Nature, vol. 601, no. 7894, pp. 519–525, Jan. 2022. doi: 10.1038/s41586-021-04139-1
    [6]
    Z. Wei, K. Liu, X. Liu, Y. Li, L. Du, and F. Gao, “Multilevel data-driven battery management: From internal sensing to big data utilization,” IEEE Trans. Transp. Electrif., vol. 9, no. 4, pp. 4805–4823, Dec. 2023. doi: 10.1109/TTE.2023.3301990
    [7]
    K. Liu, Q. Peng, Y. Che, Y. Zheng, K. Li, R. Teodorescu, D. Widanage, and A. Barai, “Transfer learning for battery smarter state estimation and ageing prognostics: Recent progress, challenges, and prospects,” Adv. Appl. Energy, vol. 9, p. 100117, Feb. 2023. doi: 10.1016/j.adapen.2022.100117
    [8]
    M. Lin, S. Chen, W. Wang, and J. Wu, “Multi-feature fusion-based instantaneous energy consumption estimation for electric buses,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 10, pp. 2035–2037, Oct. 2023. doi: 10.1109/JAS.2022.106010
    [9]
    G. Ma, Z. Wang, W. Liu, J. Fang, Y. Zhang, H. Ding, and Y. Yuan, “Estimating the state of health for lithium-ion batteries: A particle swarm optimization-assisted deep domain adaptation approach,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1530–1543, Jul. 2023. doi: 10.1109/JAS.2023.123531
    [10]
    Y. Wang, K. Li, and Z. Chen, “Battery full life cycle management and health prognosis based on cloud service and broad learning,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 8, pp. 1540–1542, Aug. 2022. doi: 10.1109/JAS.2022.105779
    [11]
    Q. Xu, M. Wu, E. Khoo, Z. Chen, and X. Li, “A hybrid ensemble deep learning approach for early prediction of battery remaining useful life,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 177–187, Jan. 2023. doi: 10.1109/JAS.2023.123024
    [12]
    K. Liu, Q. Peng, R. Teodorescu, and A. M. Foley, “Knowledge-guided data-driven model with transfer concept for battery calendar ageing trajectory prediction,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 272–274, Jan. 2023. doi: 10.1109/JAS.2023.123036
    [13]
    T. Hu, H. Ma, H. Sun, and K. Liu, “Electrochemical-theory-guided modeling of the conditional generative adversarial network for battery calendar aging forecast,” IEEE J. Emerging Sel. Top. Power Electron., vol. 11, no. 1, pp. 67–77, Feb. 2023. doi: 10.1109/JESTPE.2022.3154785
    [14]
    G. Wang, G. Zhao, J. Xie, and K. Liu, “Ensemble learning-based correlation coefficient method for robust diagnosis of voltage sensor and short-circuit faults in series battery packs,” IEEE Trans. Power Electron., vol. 38, no. 7, pp. 9143–9156, Jul. 2023. doi: 10.1109/TPEL.2023.3266945
    [15]
    X. Gu, J. Li, K. Liu, Y. Zhu, X. Tao, and Y. Shang, “A precise minor-fault diagnosis method for lithium-ion batteries based on phase plane sample entropy,” IEEE Trans. Ind. Electron., vol. 71, no. 8, pp. 8853–8861, Aug. 2024. doi: 10.1109/TIE.2023.3319717
    [16]
    T. Zhu, A. Cruden, Q. Peng, and K. Liu, “Enabling extreme fast charging,” Joule, vol. 7, no. 12, pp. 2660–2662, Dec. 2023. doi: 10.1016/j.joule.2023.11.016
    [17]
    Y. Xie, W. Li, Z. Song, B. Chen, K. Liu, R. Yang, and Y. Zhang, “A health-aware AC heating strategy with lithium plating criterion for batteries at low temperatures,” IEEE Trans. Ind. Inf., vol. 20, no. 2, pp. 2295–2306, Feb. 2024. doi: 10.1109/TII.2023.3290186
    [18]
    J. Li K. Liu, Q. Zhou, J. Meng, Y. Ge, and H. Xu, “Electrothermal dynamics-conscious many-objective modular design for power-split plug-in hybrid electric vehicles,” IEEE/ASME Trans. Mechatron., vol. 27, no. 6, pp. 4406–4416, Dec. 2022. doi: 10.1109/TMECH.2022.3156535
    [19]
    E. Ayerbe, M. Berecibar, S. Clark, A. A. Franco, and J. Ruhland, “Digitalization of battery manufacturing: Current status, challenges, and opportunities,” Adv. Energy Mater., vol. 12, no. 17, p. 2102696, May 2022. doi: 10.1002/aenm.202102696
    [20]
    G. Bridge and E. Faigen, “Towards the lithium-ion battery production network: Thinking beyond mineral supply chains,” Energy Res. Soc. Sci., vol. 89, p. 102659, Jul. 2022. doi: 10.1016/j.erss.2022.102659
    [21]
    A. Turetskyy, S. Thiede, M. Thomitzek, N. Von Drachenfels, T. Pape, and C. Herrmann, “Toward data-driven applications in lithium-ion battery cell manufacturing,” Energy Technol., vol. 8, no. 2, p. 1900136, Feb. 2020. doi: 10.1002/ente.201900136
    [22]
    A. Turetskyy, J. Wessel, C. Herrmann, and S. Thiede, “Battery production design using multi-output machine learning models,” Energy Storage Mater., vol. 38, pp. 93–112, Jul. 2021. doi: 10.1016/j.ensm.2021.03.002
    [23]
    M. F. Niri, K. Liu, G. Apachitei, L. R. Ramirez, M. Lain, D. Widanage, and J. Marco, “Machine learning for optimised and clean Li-ion battery manufacturing: Revealing the dependency between electrode and cell characteristics,” J. Cleaner Prod., vol. 324, p. 129272, Nov. 2021. doi: 10.1016/j.jclepro.2021.129272
    [24]
    K. Liu, M. F. Niri, G. Apachitei, M. Lain, D. Greenwood, and J. Marco, “Interpretable machine learning for battery capacities prediction and coating parameters analysis,” Control Eng. Pract., vol. 124, p. 105202, Jul. 2022. doi: 10.1016/j.conengprac.2022.105202
    [25]
    K. Liu, X. Hu, J. Meng, J. M. Guerrero, and R. Teodorescu, “RUBoost-based ensemble machine learning for electrode quality classification in Li-ion battery manufacturing,” IEEE/ASME Trans. Mechatron., vol. 27, no. 5, pp. 2474–2483, Oct. 2022. doi: 10.1109/TMECH.2021.3115997
    [26]
    E. Rohkohl, M. Schönemann, Y. Bodrov, and C. Herrmann, “A data mining approach for continuous battery cell manufacturing processes from development towards production,” Adv. Ind. Manuf. Eng., vol. 4, p. 100078, May 2022.
    [27]
    J. Xiao, F. Shi, T. Glossmann, C. Burnett, and Z. Liu, “From laboratory innovations to materials manufacturing for lithium-based batteries,” Nat. Energy, vol. 8, no. 4, pp. 329–339, Mar. 2023. doi: 10.1038/s41560-023-01221-y
    [28]
    R. P. Cunha, T. Lombardo, E. N. Primo, and A. A. Franco, “Artificial intelligence investigation of NMC cathode manufacturing parameters interdependencies,” Batteries Supercaps, vol. 3, no. 1, pp. 60–67, Jan. 2020. doi: 10.1002/batt.201900135
    [29]
    A. A. Pesaran, “Lithium-ion battery technologies for electric vehicles: Progress and challenges,” IEEE Electrif. Mag., vol. 11, no. 2, pp. 35–43, Jun. 2023. doi: 10.1109/MELE.2023.3264919
    [30]
    T. Daniya, M. Geetha, and K. Suresh Kumar, “Classification and regression trees with GINI index,” Adv. Math. Sci. J., vol. 9, no. 10, pp. 8237–8247, Oct. 2020. doi: 10.37418/amsj.9.10.53
    [31]
    X. Hu, Y. Che, X. Lin, and Z. Deng, “Health prognosis for electric vehicle battery packs: A data-driven approach,” IEEE/ASME Trans. Mechatron., vol. 25, no. 6, pp. 2622–2632, Dec. 2020. doi: 10.1109/TMECH.2020.2986364
    [32]
    L. A. Román-Ramírez and J. Marco, “Design of experiments applied to lithium-ion batteries: A literature review,” Appl. Energy, vol. 320, p. 119305, Aug. 2022. doi: 10.1016/j.apenergy.2022.119305
    • Related Articles
    • Supplements(0)

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(13)  / Tables(1)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (152) PDF downloads(45) Cited by()

    Highlights

    • An explainable neural network is proposed to benefit battery smart production
    • Sensitivity analysis of key parameters in battery manufacturing line is performed
    • The proposed method can accurately predict battery electrode properties
    • The interpretation of both main effects and interactions from production terms is visualized

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return