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Online Lithium-Ion Battery Internal Resistance Measurement Application in State-of-Charge Estimation Using the Extended Kalman Filter

Author

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  • Dian Wang

    (Department of Physics, Donghua University, Shanghai 201620, China)

  • Yun Bao

    (Department of Physics, Donghua University, Shanghai 201620, China)

  • Jianjun Shi

    (Department of Physics, Donghua University, Shanghai 201620, China)

Abstract

The lithium-ion battery is a viable power source for hybrid electric vehicles (HEVs) and, more recently, electric vehicles (EVs). Its performance, especially in terms of state of charge (SOC), plays a significant role in the energy management of these vehicles. The extended Kalman filter (EKF) is widely used to estimate online SOC as an efficient estimation algorithm. However, conventional EKF algorithms cannot accurately estimate the difference between individual batteries, which should not be ignored. However, the internal resistance of a battery can represent this difference. Therefore, this work proposes using an EKF with internal resistance measurement based on the conventional algorithm. Lithium-ion battery real-time resistances can help the Kalman filter overcome defects from simplistic battery models. In addition, experimental results show that it is useful to introduce online internal resistance to the estimation of SOC.

Suggested Citation

  • Dian Wang & Yun Bao & Jianjun Shi, 2017. "Online Lithium-Ion Battery Internal Resistance Measurement Application in State-of-Charge Estimation Using the Extended Kalman Filter," Energies, MDPI, vol. 10(9), pages 1-11, August.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:9:p:1284-:d:110163
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    References listed on IDEAS

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    Cited by:

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    3. In-Ho Cho & Pyeong-Yeon Lee & Jong-Hoon Kim, 2019. "Analysis of the Effect of the Variable Charging Current Control Method on Cycle Life of Li-ion Batteries," Energies, MDPI, vol. 12(15), pages 1-11, August.
    4. Taysa Millena Banik Marques & João Lucas Ferreira dos Santos & Diego Solak Castanho & Mariane Bigarelli Ferreira & Sergio L. Stevan & Carlos Henrique Illa Font & Thiago Antonini Alves & Cassiano Moro , 2023. "An Overview of Methods and Technologies for Estimating Battery State of Charge in Electric Vehicles," Energies, MDPI, vol. 16(13), pages 1-18, June.
    5. Sayfutdinov, Timur & Vorobev, Petr, 2022. "Optimal utilization strategy of the LiFePO4 battery storage," Applied Energy, Elsevier, vol. 316(C).
    6. Yun Bao & Yuansheng Chen, 2021. "Lithium-Ion Battery Real-Time Diagnosis with Direct Current Impedance Spectroscopy," Energies, MDPI, vol. 14(15), pages 1-16, July.
    7. Xu, Nan & Kong, Yan & Zhang, Yuanjian & Yue, Fenglai & Sui, Yan & Li, Xiaohan & Liu, Heng & Xu, Zhe, 2022. "Determination of vehicle working modes for global optimization energy management and evaluation of the economic performance for a certain control strategy," Energy, Elsevier, vol. 251(C).
    8. Youssef NaitMalek & Mehdi Najib & Anas Lahlou & Mohamed Bakhouya & Jaafar Gaber & Mohamed Essaaidi, 2022. "A Hybrid Approach for State-of-Charge Forecasting in Battery-Powered Electric Vehicles," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
    9. Zhu, Tao & Lot, Roberto & Wills, Richard G.A. & Yan, Xingda, 2020. "Sizing a battery-supercapacitor energy storage system with battery degradation consideration for high-performance electric vehicles," Energy, Elsevier, vol. 208(C).
    10. Nicola Campagna & Vincenzo Castiglia & Rosario Miceli & Rosa Anna Mastromauro & Ciro Spataro & Marco Trapanese & Fabio Viola, 2020. "Battery Models for Battery Powered Applications: A Comparative Study," Energies, MDPI, vol. 13(16), pages 1-26, August.
    11. Jong-Hyun Lee & In-Soo Lee, 2021. "Lithium Battery SOH Monitoring and an SOC Estimation Algorithm Based on the SOH Result," Energies, MDPI, vol. 14(15), pages 1-16, July.
    12. Foad H. Gandoman & Emad M. Ahmed & Ziad M. Ali & Maitane Berecibar & Ahmed F. Zobaa & Shady H. E. Abdel Aleem, 2021. "Reliability Evaluation of Lithium-Ion Batteries for E-Mobility Applications from Practical and Technical Perspectives: A Case Study," Sustainability, MDPI, vol. 13(21), pages 1-24, October.
    13. Yonghui Sun & Yi Wang & Linquan Bai & Yinlong Hu & Denis Sidorov & Daniil Panasetsky, 2018. "Parameter Estimation of Electromechanical Oscillation Based on a Constrained EKF with C&I-PSO," Energies, MDPI, vol. 11(8), pages 1-15, August.
    14. Nicolae Tudoroiu & Mohammed Zaheeruddin & Roxana-Elena Tudoroiu, 2020. "Real Time Design and Implementation of State of Charge Estimators for a Rechargeable Lithium-Ion Cobalt Battery with Applicability in HEVs/EVs—A Comparative Study," Energies, MDPI, vol. 13(11), pages 1-45, May.
    15. Wiesław Madej & Andrzej Wojciechowski, 2021. "Analysis of the Charging and Discharging Process of LiFePO 4 Battery Pack," Energies, MDPI, vol. 14(13), pages 1-12, July.
    16. Yun Bao & Wenbin Dong & Dian Wang, 2018. "Online Internal Resistance Measurement Application in Lithium Ion Battery Capacity and State of Charge Estimation," Energies, MDPI, vol. 11(5), pages 1-11, April.
    17. Naga Kavitha Kommuri & Andrew McGordon & Antony Allen & Dinh Quang Truong, 2020. "Evaluation of a Modified Equivalent Fuel-Consumption Minimization Strategy Considering Engine Start Frequency and Battery Parameters for a Plugin Hybrid Two-Wheeler," Energies, MDPI, vol. 13(12), pages 1-26, June.
    18. Seo, Minhwan & Song, Youngbin & Kim, Jake & Paek, Sung Wook & Kim, Gi-Heon & Kim, Sang Woo, 2021. "Innovative lumped-battery model for state of charge estimation of lithium-ion batteries under various ambient temperatures," Energy, Elsevier, vol. 226(C).
    19. Zhang, Jie & Xiao, Bo & Niu, Geng & Xie, Xuanzhi & Wu, Saixiang, 2024. "Joint estimation of state-of-charge and state-of-power for hybrid supercapacitors using fractional-order adaptive unscented Kalman filter," Energy, Elsevier, vol. 294(C).
    20. Choi, Mingi & Cha, Junepyo & Song, Jingeun, 2024. "Analysis of fuel economy reduction factors of hybrid electric vehicles in winter using on-road driving data," Energy, Elsevier, vol. 289(C).

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