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A Novel Online State-of-Health Estimation Method for Lithium-Ion Batteries with Multi-Input Metabolic Long Short-Term Memory Framework

Author

Listed:
  • Lin Chen

    (School of Mechanical Engineering, Guangxi University, Nanning 530004, China)

  • Deqian Chen

    (School of Mechanical Engineering, Guangxi University, Nanning 530004, China
    School of Intelligent Manufacturing and Automotive Engieering, Chongqing Vocational College of Transportation, Chongqing 402247, China)

  • Manping He

    (School of Mechanical Engineering, Guangxi University, Nanning 530004, China)

  • Haihong Pan

    (School of Mechanical Engineering, Guangxi University, Nanning 530004, China)

  • Bing Ji

    (School of Engineering, University of Leicester, Leicester LE1 7RH, UK)

Abstract

Accurate and effective battery state-of-health (SoH) monitoring is significant to guarantee the security and dependability of electrical equipment. However, adapting SoH estimation methods to diverse battery kinds and operating conditions is a challenge because of the intricate deterioration mechanisms of batteries. To solve the issue, in this article, a novel multi-input metabolic long short-term memory (MM-LSTM) framework is developed. A degradation state model is created with the LSTM network to describe the intricate deterioration mechanisms. To convey more information about battery aging, the capacity degradation, sample entropy of discharge voltage, and ohmic internal resistance increment are extracted as the inputs of the model. To estimate SoH with a few data, the metabolic mechanisms are introduced to update the inputs and reflect the latest developments in aging. The accuracy and robustness of the proposed MM-LSTM framework are verified in different aspects using two kinds of batteries, and the maximum estimation error of SoH is within 1.98%. The findings indicate that the MM-LSTM framework implements the transfer application of SoH estimate successfully, and the framework’s versatility has been proven.

Suggested Citation

  • Lin Chen & Deqian Chen & Manping He & Haihong Pan & Bing Ji, 2025. "A Novel Online State-of-Health Estimation Method for Lithium-Ion Batteries with Multi-Input Metabolic Long Short-Term Memory Framework," Energies, MDPI, vol. 18(5), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1037-:d:1596218
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    References listed on IDEAS

    as
    1. Pan, Haihong & Lü, Zhiqiang & Wang, Huimin & Wei, Haiyan & Chen, Lin, 2018. "Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine," Energy, Elsevier, vol. 160(C), pages 466-477.
    2. Li, Yuanyuan & Sheng, Hanmin & Cheng, Yuhua & Stroe, Daniel-Ioan & Teodorescu, Remus, 2020. "State-of-health estimation of lithium-ion batteries based on semi-supervised transfer component analysis," Applied Energy, Elsevier, vol. 277(C).
    3. Chen, Lin & Wang, Huimin & Liu, Bohao & Wang, Yijue & Ding, Yunhui & Pan, Haihong, 2021. "Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation," Energy, Elsevier, vol. 215(PA).
    4. Tian, Yong & Lai, Rucong & Li, Xiaoyu & Xiang, Lijuan & Tian, Jindong, 2020. "A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter," Applied Energy, Elsevier, vol. 265(C).
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