Journal of Terahertz Science and Electronic Information Technology , Volume. 23, Issue 2, 182(2025)

Lithium battery life prediction model for electric vehicles based on hybrid deep learning

FAN Jinheng1, LIU Qiying1, MA Li1, and LIU Lihao2
Author Affiliations
  • 1Guangzhou Power Supply Bureau, Guangdong Power Grid Corporation, Guangzhou Guangdong 510630, China
  • 2Yantai Haiyi Software Co., Ltd., Yantai Shandong 264000, China
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    References(3)

    [9] [9] GUO Runxia, WANG Yu, ZHANG Haochi, et al. Remaining useful life prediction for rolling bearings using EMD-RISI-LSTM[J]. IEEE Transactions on Instrumentation and Measurement, 2021(70): 1-12. doi: 10.1109/TIM.2021.3051717.

    [10] [10] ZHOU Li, FAN Qinwei, HUANG Xiaodi, et al. Weak and strong convergence analysis of Elman neural networks via weight decay regularization[J]. Optimization, 2023, 72(9): 2287-2309.

    [11] [11] SUN Hanlei, SUN Jianrui, ZHAO Kun, et al. Data-driven ICA-Bi-LSTM-combined lithium battery SOH estimation[J]. Mathematical Problems in Engineering, 2022, 2022(1): 9645892.

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    FAN Jinheng, LIU Qiying, MA Li, LIU Lihao. Lithium battery life prediction model for electric vehicles based on hybrid deep learning[J]. Journal of Terahertz Science and Electronic Information Technology , 2025, 23(2): 182

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    Paper Information

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    Received: Jul. 25, 2023

    Accepted: Mar. 13, 2025

    Published Online: Mar. 13, 2025

    The Author Email:

    DOI:10.11805/tkyda2023204

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