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Mechanics-based state of charge estimation for lithium-ion pouch battery using deep learning technique

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  • Jiang, Bo
  • Tao, Siyi
  • Wang, Xueyuan
  • Zhu, Jiangong
  • Wei, Xuezhe
  • Dai, Haifeng

Abstract

Accurate state of charge (SOC) estimation helps achieve efficient battery management, which is essential for transportation electrification. Significantly different from existing data-driven estimation methods only considering battery electrical information, this study proposes a mechanics-based battery SOC estimation using deep learning techniques. First, an experimental setup for measuring pouch-type battery stress is designed, followed by constructing four typical operating conditions to establish a sophisticated battery dataset and investigate the primary relationship between battery stress and SOC. Then, a data-driven estimation model composed of long short-term memory neural network is investigated to achieve the interlink between battery external measurements and internal states, in which the battery voltage, current, and stress sequences within a sliding window length are fed into the deep learning model. Quantitative experimental results demonstrate that the proposed mechanics-based battery SOC estimation approach can achieve acceptable accuracy and is adaptable to different training and operating conditions, as well as ensure the estimation performance under limited data length. Moreover, the proposed method has also been proven robust to the interference of battery measurement noise compared to the traditional estimation method.

Suggested Citation

  • Jiang, Bo & Tao, Siyi & Wang, Xueyuan & Zhu, Jiangong & Wei, Xuezhe & Dai, Haifeng, 2023. "Mechanics-based state of charge estimation for lithium-ion pouch battery using deep learning technique," Energy, Elsevier, vol. 278(PA).
    • Handle: RePEc:eee:energy:v:278:y:2023:i:pa:s0360544223012847
    DOI: 10.1016/j.energy.2023.127890
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    References listed on IDEAS

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    Cited by:

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    2. Wang, Fengfei & Tang, Shengjin & Han, Xuebing & Yu, Chuanqiang & Sun, Xiaoyan & Lu, Languang & Ouyang, Minggao, 2024. "Capacity prediction of lithium-ion batteries with fusing aging information," Energy, Elsevier, vol. 293(C).
    3. Guo, Yongfang & Yu, Xiangyuan & Wang, Yashuang & Huang, Kai, 2024. "Health prognostics of lithium-ion batteries based on universal voltage range features mining and adaptive multi-Gaussian process regression with Harris Hawks optimization algorithm," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    4. Yi, Yahui & Xia, Chengyu & Shi, Lei & Meng, Leifeng & Chi, Qifu & Qian, Liqin & Ma, Tiancai & Chen, Siqi, 2024. "Lithium-ion battery expansion mechanism and Gaussian process regression based state of charge estimation with expansion characteristics," Energy, Elsevier, vol. 292(C).

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