Turn off MathJax
Article Contents
MA Chenghao, ZHUANG Ziao, SHIN Jonghyeon, XING Bobin, XIA Yong, ZHOU Qing. Data-driven safety prediction of battery pack under side pole collision[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2024-0318
Citation: MA Chenghao, ZHUANG Ziao, SHIN Jonghyeon, XING Bobin, XIA Yong, ZHOU Qing. Data-driven safety prediction of battery pack under side pole collision[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2024-0318

Data-driven safety prediction of battery pack under side pole collision

doi: 10.11883/bzycj-2024-0318
  • Received Date: 2024-08-31
  • Rev Recd Date: 2024-10-16
  • Available Online: 2024-10-18
  • The battery pack of electric vehicles is highly susceptible to failure under side pole collision. To accurately and quickly evaluate the safety of battery packs under such conditions, this paper introduces a local region refined battery pack model that can effectively characterize the deformation and mechanical response of the jellyroll of battery. Simulation analyses were conducted under varying impact velocity, angles, positions, and vehicle loading configuration, with the latter achieved by uniformly applying mass compensation to the side wall of the battery pack. A simulation matrix was designed using an optimized Latin hypercube sampling (LHS) strategy, and a dataset was generated through image recognition methods. This dataset includes parameters such as the maximum intrusion depth, intrusion location, intrusion width of the battery pack side wall, and the deformation of the jellyroll of battery. New features, including collision energy and velocity components in the x and y directions, were derived and selected as input features for model training through correlation analysis. Support vector machine (SVM), random forest (RF), and back propagation neural networks (BPNN) were employed to build a data-driven predictive model. The SVM model demonstrated superior performance, achieving an average R2 of 0.96 across prediction parameters. The prediction of the maximum intrusion depth of the battery pack side wall was particularly accurate, with an R2 exceeding 0.95 for all three models. Additionally, the robustness of the models was tested by introducing Gaussian noise, where the BP neural network exhibited better robustness. Even with the addition of Gaussian noise with a standard deviation of 0.5, the BP model maintained an average R2 of 0.91 for the prediction parameters. The established data-driven model can effectively predict mechanical response of battery packs under side pole collisions and provide a reliable tool for evaluating battery pack safety.
  • loading
  • [1]
    CHEN P W, XIA Y, ZHOU Q. Inclined battery cells for mitigating damage in undercarriage collision [J]. International Journal of Crashworthiness, 2024, 29(3): 508–520. DOI: 10.1080/13588265.2023.2258645.
    [2]
    LI W, XIA Y, CHEN G H, et al. Comparative study of mechanical-electrical-thermal responses of pouch, cylindrical, and prismatic lithium-ion cells under mechanical abuse [J]. Science China Technological Sciences, 2018, 61(10): 1472–1482. DOI: 10.1007/s11431-017-9296-0.
    [3]
    LAI W J, ALI M Y, PAN J. Mechanical behavior of representative volume elements of lithium-ion battery modules under various loading conditions [J]. Journal of Power Sources, 2014, 248: 789–808. DOI: 10.1016/j.jpowsour.2013.09.128.
    [4]
    SAHRAEI E, BOSCO E, DIXON B, et al. Microscale failure mechanisms leading to internal short circuit in Li-ion batteries under complex loading scenarios [J]. Journal of Power Sources, 2016, 319: 56–65. DOI: 10.1016/j.jpowsour.2016.04.005.
    [5]
    SAHRAEI E, CAMPBELL J, WIERZBICKI T. Modeling and short circuit detection of 18650 Li-ion cells under mechanical abuse conditions [J]. Journal of Power Sources, 2012, 220: 360–372. DOI: 10.1016/j.jpowsour.2012.07.057.
    [6]
    XIAO F Y, XING B B, XIA Y. Mechanical response of laterally-constrained prismatic battery cells under local loading: SAE Technical Paper 2020-01-0200 [R]. SAE, 2020. DOI: 10.4271/2020-01-0200.
    [7]
    XIA Y, WIERZBICKI T, SAHRAEI E, et al. Damage of cells and battery packs due to ground impact [J]. Journal of Power Sources, 2014, 267: 78–97. DOI: 10.1016/j.jpowsour.2014.05.078.
    [8]
    KUKREJA J, NGUYEN T, SIEGMUND T, et al. Crash analysis of a conceptual electric vehicle with a damage tolerant battery pack [J]. Extreme Mechanics Letters, 2016, 9: 371–378. DOI: 10.1016/j.eml.2016.05.004.
    [9]
    ZHANG J Y, NING L N, HAO Y M, et al. Topology optimization for crashworthiness and structural design of a battery electric vehicle [J]. International Journal of Crashworthiness, 2021, 26(6): 651–660. DOI: 10.1080/13588265.2020.1766644.
    [10]
    CHEN P W, XIA Y, ZHOU Q, et al. Staggered layout of battery cells for mitigating damage in side pole collisions of electric vehicles [J]. eTransportation, 2023, 16: 100238. DOI: 10.1016/j.etran.2023.100238.
    [11]
    LI W, ZHU J E, XIA Y, et al. Data-driven safety envelope of lithium-ion batteries for electric vehicles [J]. Joule, 2019, 3(11): 2703–2715. DOI: 10.1016/j.joule.2019.07.026.
    [12]
    ZHANG Z, ZHOU H P, MA J Y, et al. Space deployable bistable composite structures with C-cross section based on machine learning and multi-objective optimization [J]. Composite Structures, 2022, 297: 115983. DOI: 10.1016/j.compstruct.2022.115983.
    [13]
    PAN Y J, ZHANG X X, LIU Y, et al. Dynamic behavior prediction of modules in crushing via FEA-DNN technique for durable battery-pack system design [J]. Applied Energy, 2022, 322: 119527. DOI: 10.1016/j.apenergy.2022.119527.
    [14]
    XU D X, PAN Y J, ZHANG X X, et al. Data-driven modelling and evaluation of a battery-pack system’s mechanical safety against bottom cone impact [J]. Energy, 2024, 290: 130145. DOI: 10.1016/J.ENERGY.2023.130145.
    [15]
    中国汽车技术研究中心有限公司. C-NCAP管理规则(2024版) [R/OL]. 天津, 2024. https://www.c-ncap.org.cn/article-detail/1747900203303780353?type=2.

    China Automotive Technology and Research Center Co. , Ltd. C-NCAP management rules (2024 Edition) [R/OL]. Tianjin, 2024. https://www.c-ncap.org.cn/article-detail/1747900203303780353?type=2.
    [16]
    国家市场监督管理总局, 国家标准化管理委员会. GB/T 37337-2019 汽车侧面柱碰撞的乘员保护 [S]. 北京: 中国标准出版社, 2019.

    State Administration of Market Supervision and Administration of the People's Republic of China, Standardization Administration of the People's Republic of China. GB/T 37337-2019 Protection of the occupants in the event of a lateral pole collision [S]. Beijing: Standards Press of China, 2019.
    [17]
    马骋浩, 申宗玹, 汪俊, 等. 侧面柱碰撞工况电池包碰撞安全性快速预测 [J]. 汽车工程, 2024.

    MA C H, SHEN Z X, WANG J, et al. Fast prediction of battery pack safety under side pole collision [J]. Automotive Engineering, 2024.
    [18]
    QU Y L, GE Y L, XING B B, et al. Development of detailed model and simplified model of lithium-ion battery module under mechanical abuse: SAE Technical Paper 2022-01-7120 [R]. SAE, 2022. DOI: 10.4271/2022-01-7120.
    [19]
    陈涛, 李宁宁, 李卓, 等. 侧面柱碰撞条件下电动汽车电池系统结构优化 [J]. 中国机械工程, 2020, 31(9): 1021–1030. DOI: 10.3969/j.issn.1004-132X.2020.09.002.

    CHEN T, LI N N, LI Z, et al. Structural optimization of electric vehicle battery systems under pole side impacts [J]. China Mechanical Engineering, 2020, 31(9): 1021–1030. DOI: 10.3969/j.issn.1004-132X.2020.09.002.
    [20]
    YI J, LI X Y, XIAO M, et al. Construction of nested maximin designs based on successive local enumeration and modified novel global harmony search algorithm [J]. Engineering Optimization, 2017, 49(1): 161–180. DOI: 10.1080/0305215X.2016.1170825.
    [21]
    RAUTELA M, GOPALAKRISHNAN S. Ultrasonic guided wave based structural damage detection and localization using model assisted convolutional and recurrent neural networks [J]. Expert Systems with Applications, 2021, 167: 114189. DOI: 10.1016/j.eswa.2020.114189.
  • 加载中

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(11)  / Tables(3)

    Article Metrics

    Article views (84) PDF downloads(19) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return