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Regression Models Using Fully Discharged Voltage and Internal Resistance for State of Health Estimation of Lithium-Ion Batteries

Author

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  • Kuo-Hsin Tseng

    (Department of Mechanical Engineering, Ming Chi University of Technology, 84 Gungjuan Road, Taishan, New Taipei City 24301, Taiwan)

  • Jin-Wei Liang

    (Department of Mechanical Engineering, Ming Chi University of Technology, 84 Gungjuan Road, Taishan, New Taipei City 24301, Taiwan)

  • Wunching Chang

    (Department of Mechanical Engineering, Ming Chi University of Technology, 84 Gungjuan Road, Taishan, New Taipei City 24301, Taiwan)

  • Shyh-Chin Huang

    (Department of Mechanical Engineering, Ming Chi University of Technology, 84 Gungjuan Road, Taishan, New Taipei City 24301, Taiwan)

Abstract

Accurate estimation of lithium-ion battery life is essential to assure the reliable operation of the energy supply system. This study develops regression models for battery prognostics using statistical methods. The resultant regression models can not only monitor a battery’s degradation trend but also accurately predict its remaining useful life (RUL) at an early stage. Three sets of test data are employed in the training stage for regression models. Another set of data is then applied to the regression models for validation. The fully discharged voltage (V dis ) and internal resistance (R) are adopted as aging parameters in two different mathematical models, with polynomial and exponential functions. A particle swarm optimization (PSO) process is applied to search for optimal coefficients of the regression models. Simulations indicate that the regression models using V dis and R as aging parameters can build a real state of health profile more accurately than those using cycle number, N. The Monte Carlo method is further employed to make the models adaptive. The subsequent results, however, show that this results in an insignificant improvement of the battery life prediction. A reasonable speculation is that the PSO process already yields the major model coefficients.

Suggested Citation

  • Kuo-Hsin Tseng & Jin-Wei Liang & Wunching Chang & Shyh-Chin Huang, 2015. "Regression Models Using Fully Discharged Voltage and Internal Resistance for State of Health Estimation of Lithium-Ion Batteries," Energies, MDPI, vol. 8(4), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:4:p:2889-2907:d:48190
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    References listed on IDEAS

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    2. Nickolay I. Shchurov & Sergey I. Dedov & Boris V. Malozyomov & Alexander A. Shtang & Nikita V. Martyushev & Roman V. Klyuev & Sergey N. Andriashin, 2021. "Degradation of Lithium-Ion Batteries in an Electric Transport Complex," Energies, MDPI, vol. 14(23), pages 1-33, December.
    3. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Modeling the Performance of an Electric Vehicle Considering Various Driving Cycles," Mathematics, MDPI, vol. 11(11), pages 1-26, June.
    4. Calum Strange & Shawn Li & Richard Gilchrist & Gonçalo dos Reis, 2021. "Elbows of Internal Resistance Rise Curves in Li-Ion Cells," Energies, MDPI, vol. 14(4), pages 1-15, February.
    5. Prakash Venugopal & Vigneswaran T., 2019. "State-of-Health Estimation of Li-ion Batteries in Electric Vehicle Using IndRNN under Variable Load Condition," Energies, MDPI, vol. 12(22), pages 1-29, November.
    6. Shi, Man & He, Hongwen & Li, Jianwei & Han, Mo & Jia, Chunchun, 2021. "Multi-objective tradeoff optimization of predictive adaptive cruising control for autonomous electric buses: A cyber-physical-energy system approach," Applied Energy, Elsevier, vol. 300(C).
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