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Long-range battery state-of-health and end-of-life prediction with neural networks and feature engineering

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  • Pepe, Simona
  • Ciucci, Francesco

Abstract

Determining the state of health (SOH) and end of life (EOL) represents a critical challenge in battery management. This study introduces an innovative neural network-based methodology that forecasts both the SOH and EOL, utilizing features engineered from charge-discharge voltage profiles. Specifically, long-short-term memory (LSTM) and gated-recurrent unit (GRU) neural networks are trained against fast-charging datasets with novel loss function that emphasizes SOH regression while penalizing its decay. The devised models yield low average errors in SOH and EOL predictions (5.49% and − 1.27%, respectively, for LSTM), over extended horizons encompassing 80% of the forecast battery lifespan. From a combined evaluation using Pearson's correlation and saliency analysis, it is found that voltages most strongly associated with aging occur after the initial constant current rate step. In short, this study offers a new perspective on the precise prediction of SOH and EOL by integrating feature engineering with neural networks.

Suggested Citation

  • Pepe, Simona & Ciucci, Francesco, 2023. "Long-range battery state-of-health and end-of-life prediction with neural networks and feature engineering," Applied Energy, Elsevier, vol. 350(C).
  • Handle: RePEc:eee:appene:v:350:y:2023:i:c:s030626192301125x
    DOI: 10.1016/j.apenergy.2023.121761
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    References listed on IDEAS

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    1. Hong, Joonki & Lee, Dongheon & Jeong, Eui-Rim & Yi, Yung, 2020. "Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning," Applied Energy, Elsevier, vol. 278(C).
    2. Kristen A. Severson & Peter M. Attia & Norman Jin & Nicholas Perkins & Benben Jiang & Zi Yang & Michael H. Chen & Muratahan Aykol & Patrick K. Herring & Dimitrios Fraggedakis & Martin Z. Bazant & Step, 2019. "Data-driven prediction of battery cycle life before capacity degradation," Nature Energy, Nature, vol. 4(5), pages 383-391, May.
    3. Yayuan Liu & Yangying Zhu & Yi Cui, 2019. "Challenges and opportunities towards fast-charging battery materials," Nature Energy, Nature, vol. 4(7), pages 540-550, July.
    4. Yang, Yixin, 2021. "A machine-learning prediction method of lithium-ion battery life based on charge process for different applications," Applied Energy, Elsevier, vol. 292(C).
    5. Zhang, Qisong & Yang, Lin & Guo, Wenchao & Qiang, Jiaxi & Peng, Cheng & Li, Qinyi & Deng, Zhongwei, 2022. "A deep learning method for lithium-ion battery remaining useful life prediction based on sparse segment data via cloud computing system," Energy, Elsevier, vol. 241(C).
    6. Li, Xiaoyu & Yuan, Changgui & Li, Xiaohui & Wang, Zhenpo, 2020. "State of health estimation for Li-Ion battery using incremental capacity analysis and Gaussian process regression," Energy, Elsevier, vol. 190(C).
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    Cited by:

    1. Wang, Cong & Chen, Yunxia, 2024. "Unsupervised dynamic prognostics for abnormal degradation of lithium-ion battery," Applied Energy, Elsevier, vol. 365(C).
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