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Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features

Author

Listed:
  • Muhammad Umair Ali

    (School of Electrical Engineering, Pusan National University, Pusan 46241, Korea)

  • Amad Zafar

    (Department of Electrical Engineering, Wah Engineering College, University of Wah, Wah Cantt 47040, Pakistan)

  • Sarvar Hussain Nengroo

    (School of Electrical Engineering, Pusan National University, Pusan 46241, Korea)

  • Sadam Hussain

    (School of Electrical Engineering, Pusan National University, Pusan 46241, Korea)

  • Gwan-Soo Park

    (School of Electrical Engineering, Pusan National University, Pusan 46241, Korea)

  • Hee-Je Kim

    (School of Electrical Engineering, Pusan National University, Pusan 46241, Korea)

Abstract

Online accurate estimation of remaining useful life (RUL) of lithium-ion batteries is a necessary feature of any smart battery management system (BMS). In this paper, a novel partial discharge data (PDD)-based support vector machine (SVM) model is proposed for RUL prediction. The proposed algorithm extracts the critical features from the voltage and temperature of PDD to train the SVM models. The classification and regression attributes of SVM are utilized to classify and predict accurate RUL. The different ranges of PDD were analyzed to find the optimal range for training the SVM model. The SVM model trained with optimal PDD features classifies the RUL into six different classes for gross estimation, and the support vector regression is used to estimate the accurate value of the last class. The classification and predictive performance of SVM model trained using the full discharge data and PDD are compared for publicly available data. Results show that the SVM classification and regression model trained with PDD features can accurately predict the RUL with low storage pressure on BMS. The PDD-based SVM model can be utilized for online RUL estimation in electric vehicles.

Suggested Citation

  • Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Gwan-Soo Park & Hee-Je Kim, 2019. "Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features," Energies, MDPI, vol. 12(22), pages 1-14, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:22:p:4366-:d:287601
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    References listed on IDEAS

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    Cited by:

    1. Wang, Fu-Kwun & Amogne, Zemenu Endalamaw & Chou, Jia-Hong & Tseng, Cheng, 2022. "Online remaining useful life prediction of lithium-ion batteries using bidirectional long short-term memory with attention mechanism," Energy, Elsevier, vol. 254(PB).
    2. Dongxu Guo & Geng Yang & Guangjin Zhao & Mengchao Yi & Xuning Feng & Xuebing Han & Languang Lu & Minggao Ouyang, 2020. "Determination of the Differential Capacity of Lithium-Ion Batteries by the Deconvolution of Electrochemical Impedance Spectra," Energies, MDPI, vol. 13(4), pages 1-14, February.
    3. Dominik Dvorak & Daniele Basciotti & Imre Gellai, 2020. "Demand-Based Control Design for Efficient Heat Pump Operation of Electric Vehicles," Energies, MDPI, vol. 13(20), pages 1-18, October.
    4. Ma’d El-Dalahmeh & Maher Al-Greer & Mo’ath El-Dalahmeh & Michael Short, 2020. "Time-Frequency Image Analysis and Transfer Learning for Capacity Prediction of Lithium-Ion Batteries," Energies, MDPI, vol. 13(20), pages 1-19, October.
    5. Guishuang Tian & Shaoping Wang & Jian Shi & Yajing Qiao, 2022. "State Estimation and Remaining Useful Life Prediction of PMSTM Based on a Combination of SIR and HSMM," Sustainability, MDPI, vol. 14(24), pages 1-21, December.
    6. Yue Zhou & Hussein Obeid & Salah Laghrouche & Mickael Hilairet & Abdesslem Djerdir, 2020. "A Disturbance Rejection Control Strategy of a Single Converter Hybrid Electrical System Integrating Battery Degradation," Energies, MDPI, vol. 13(11), pages 1-19, June.
    7. Shaheer Ansari & Afida Ayob & Molla Shahadat Hossain Lipu & Aini Hussain & Mohamad Hanif Md Saad, 2021. "Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries," Energies, MDPI, vol. 14(22), pages 1-22, November.
    8. Sadam Hussain & Muhammad Umair Ali & Gwan-Soo Park & Sarvar Hussain Nengroo & Muhammad Adil Khan & Hee-Je Kim, 2019. "A Real-Time Bi-Adaptive Controller-Based Energy Management System for Battery–Supercapacitor Hybrid Electric Vehicles," Energies, MDPI, vol. 12(24), pages 1-24, December.

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