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Big data driven lithium-ion battery modeling method based on SDAE-ELM algorithm and data pre-processing technology

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  • Li, Shuangqi
  • He, Hongwen
  • Li, Jianwei

Abstract

As one of the bottleneck technologies of electric vehicles (EVs), the battery hosts complex and hardly observable internal chemical reactions. Therefore, a precise mathematical model is crucial for the battery management system (BMS) to ensure the secure and stable operation of the battery in a multi-variable environment. First, a Cloud-based BMS (C-BMS) is established based on a database containing complete battery status information. Next, a data cleaning method based on machine learning is applied to the big data of batteries. Meanwhile, to improve the model stability under dynamic conditions, an F-divergence-based data distribution quality assessment method and a sampling-based data preprocess method is designed. Then, a lithium-ion battery temperature-dependent model is built based on Stacked Denoising Autoencoders- Extreme Learning Machine (SDAE-ELM) algorithm, and a new training method combined with data preprocessing is also proposed to improve the model accuracy. Finally, to improve reliability, a conjunction working mode between the C-BMS and the BMS in vehicles (V-BMS) is also proposed, providing as an applied case of the model. Using the battery data extracted from electric buses, the effectiveness and accuracy of the model are validated. The error of the estimated battery terminal voltage is within 2%, and the error of the estimated State of Charge (SoC) is within 3%.

Suggested Citation

  • Li, Shuangqi & He, Hongwen & Li, Jianwei, 2019. "Big data driven lithium-ion battery modeling method based on SDAE-ELM algorithm and data pre-processing technology," Applied Energy, Elsevier, vol. 242(C), pages 1259-1273.
    • Handle: RePEc:eee:appene:v:242:y:2019:i:c:p:1259-1273
    DOI: 10.1016/j.apenergy.2019.03.154
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    4. Dai, Haifeng & Jiang, Bo & Hu, Xiaosong & Lin, Xianke & Wei, Xuezhe & Pecht, Michael, 2021. "Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    5. Yong Li & Jue Yang & Wei Long Liu & Cheng Lin Liao, 2020. "Multi-Level Model Reduction and Data-Driven Identification of the Lithium-Ion Battery," Energies, MDPI, vol. 13(15), pages 1-23, July.
    6. Li, Shuangqi & He, Hongwen & Su, Chang & Zhao, Pengfei, 2020. "Data driven battery modeling and management method with aging phenomenon considered," Applied Energy, Elsevier, vol. 275(C).
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    9. Gu, Yuxuan & Wang, Jianxiao & Chen, Yuanbo & Xiao, Wei & Deng, Zhongwei & Chen, Qixin, 2023. "A simplified electro-chemical lithium-ion battery model applicable for in situ monitoring and online control," Energy, Elsevier, vol. 264(C).
    10. Zou, Weitao & Li, Jianwei & Yang, Qingqing & Wan, Xinming & He, Yuntang & Lan, Hao, 2023. "A real-time energy management approach with fuel cell and battery competition-synergy control for the fuel cell vehicle," Applied Energy, Elsevier, vol. 334(C).
    11. Hou, Jie & Liu, Jiawei & Chen, Fengwei & Li, Penghua & Zhang, Tao & Jiang, Jincheng & Chen, Xiaolei, 2023. "Robust lithium-ion state-of-charge and battery parameters joint estimation based on an enhanced adaptive unscented Kalman filter," Energy, Elsevier, vol. 271(C).
    12. Pan, Yongjun & Zhang, Xiaoxi & Liu, Yue & Wang, Huacui & Cao, Yangzheng & Liu, Xin & Liu, Binghe, 2022. "Dynamic behavior prediction of modules in crushing via FEA-DNN technique for durable battery-pack system design," Applied Energy, Elsevier, vol. 322(C).
    13. Khushwant Rai & Farnam Hojatpanah & Firouz Badrkhani Ajaei & Katarina Grolinger, 2021. "Deep Learning for High-Impedance Fault Detection: Convolutional Autoencoders," Energies, MDPI, vol. 14(12), pages 1-25, June.
    14. Yang, Qingqing & Li, Jianwei & Cao, Wanke & Li, Shuangqi & Lin, Jie & Huo, Da & He, Hongwen, 2020. "An improved vehicle to the grid method with battery longevity management in a microgrid application," Energy, Elsevier, vol. 198(C).
    15. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    16. Kim, Seongyoon & Choi, Yun Young & Choi, Jung-Il, 2022. "Impedance-based capacity estimation for lithium-ion batteries using generative adversarial network," Applied Energy, Elsevier, vol. 308(C).
    17. Li, Shuangqi & He, Hongwen & Zhao, Pengfei & Cheng, Shuang, 2022. "Data cleaning and restoring method for vehicle battery big data platform," Applied Energy, Elsevier, vol. 320(C).
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    19. Manzolli, Jônatas Augusto & Trovão, João Pedro & Antunes, Carlos Henggeler, 2022. "A review of electric bus vehicles research topics – Methods and trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    20. Dan Dan & Yihang Zhao & Mingshan Wei & Xuehui Wang, 2023. "Review of Thermal Management Technology for Electric Vehicles," Energies, MDPI, vol. 16(12), pages 1-38, June.

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