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Remaining Useful Life Prediction of Lithium-Ion Battery Using ICC-CNN-LSTM Methodology

Author

Listed:
  • Catherine Rincón-Maya

    (Departamento de Ingeniería Industrial, Universidad de Antioquia, Medellín 050010, Colombia)

  • Fernando Guevara-Carazas

    (Departamento de Ingeniería Mecánica, Universidad Nacional de Colombia, Sede Medellín, Medellín 050034, Colombia)

  • Freddy Hernández-Barajas

    (Escuela de Estadística, Universidad Nacional de Colombia, Sede Medellín, Medellín 050034, Colombia)

  • Carmen Patino-Rodriguez

    (Departamento de Ingeniería Industrial, Universidad de Antioquia, Medellín 050010, Colombia)

  • Olga Usuga-Manco

    (Departamento de Ingeniería Industrial, Universidad de Antioquia, Medellín 050010, Colombia)

Abstract

In recent years, lithium-ion batteries have gained significant attention due to their crucial role in various applications, such as electric vehicles and renewable energy storage. Accurate prediction of the remaining useful life (RUL) of these batteries is essential for optimizing their performance and ensuring reliable operation. In this paper, we propose a novel methodology for RUL prediction using an individual control chart (ICC) to identify and remove degraded data, a convolutional neural network (CNN) to smooth the noise of sensor data and long short-term memory (LSTM) networks to effectively capture both spatial and temporal dependencies within battery data, enabling accurate RUL estimation. We evaluate our proposed model using a comprehensive dataset, and experimental results demonstrate its superior performance compared to existing methods. Our findings highlight the potential of ICC-CNN-LSTM for RUL prediction in lithium-ion batteries and provide valuable insights for future research.

Suggested Citation

  • Catherine Rincón-Maya & Fernando Guevara-Carazas & Freddy Hernández-Barajas & Carmen Patino-Rodriguez & Olga Usuga-Manco, 2023. "Remaining Useful Life Prediction of Lithium-Ion Battery Using ICC-CNN-LSTM Methodology," Energies, MDPI, vol. 16(20), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:20:p:7081-:d:1259238
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    References listed on IDEAS

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    1. Phattara Khumprom & Nita Yodo, 2019. "A Data-Driven Predictive Prognostic Model for Lithium-ion Batteries based on a Deep Learning Algorithm," Energies, MDPI, vol. 12(4), pages 1-21, February.
    2. Gonçalves, Rui & Ribeiro, Vitor Miguel & Pereira, Fernando Lobo & Rocha, Ana Paula, 2019. "Deep learning in exchange markets," Information Economics and Policy, Elsevier, vol. 47(C), pages 38-51.
    3. 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).
    4. Kaijian He & Qian Yang & Lei Ji & Jingcheng Pan & Yingchao Zou, 2023. "Financial Time Series Forecasting with the Deep Learning Ensemble Model," Mathematics, MDPI, vol. 11(4), pages 1-15, February.
    5. Tianfei Sun & Bizhong Xia & Yifan Liu & Yongzhi Lai & Weiwei Zheng & Huawen Wang & Wei Wang & Mingwang Wang, 2019. "A Novel Hybrid Prognostic Approach for Remaining Useful Life Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 12(19), pages 1-22, September.
    6. Deng, Zhongwei & Xu, Le & Liu, Hongao & Hu, Xiaosong & Duan, Zhixuan & Xu, Yu, 2023. "Prognostics of battery capacity based on charging data and data-driven methods for on-road vehicles," Applied Energy, Elsevier, vol. 339(C).
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    1. Guangzai Ye & Li Feng & Jianlan Guo & Yuqiang Chen, 2024. "IIP-Mixer: Intra–Inter-Patch Mixing Architecture for Battery Remaining Useful Life Prediction," Energies, MDPI, vol. 17(14), pages 1-15, July.

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