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

Author

Listed:
  • 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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    1. M. Armand & J.-M. Tarascon, 2008. "Building better batteries," Nature, Nature, vol. 451(7179), pages 652-657, February.
    2. Hongwen He & Rui Xiong & Jinxin Fan, 2011. "Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach," Energies, MDPI, vol. 4(4), pages 1-17, March.
    3. Waag, Wladislaw & Käbitz, Stefan & Sauer, Dirk Uwe, 2013. "Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application," Applied Energy, Elsevier, vol. 102(C), pages 885-897.
    4. Saeed Sepasi & Leon R. Roose & Marc M. Matsuura, 2015. "Extended Kalman Filter with a Fuzzy Method for Accurate Battery Pack State of Charge Estimation," Energies, MDPI, vol. 8(6), pages 1-17, June.
    5. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
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    Cited by:

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    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.
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    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).

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