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Online Reliable Peak Charge/Discharge Power Estimation of Series-Connected Lithium-Ion Battery Packs

Author

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  • Bo Jiang

    (National Fuel Cell Vehicle & Powertrain System Research & Engineering Center, No. 4800, Caoan Road, Shanghai 201804, China
    School of Automotive Studies, Tongji University, No. 4800, Caoan Road, Shanghai 201804, China)

  • Haifeng Dai

    (National Fuel Cell Vehicle & Powertrain System Research & Engineering Center, No. 4800, Caoan Road, Shanghai 201804, China
    School of Automotive Studies, Tongji University, No. 4800, Caoan Road, Shanghai 201804, China)

  • Xuezhe Wei

    (National Fuel Cell Vehicle & Powertrain System Research & Engineering Center, No. 4800, Caoan Road, Shanghai 201804, China
    School of Automotive Studies, Tongji University, No. 4800, Caoan Road, Shanghai 201804, China)

  • Letao Zhu

    (National Fuel Cell Vehicle & Powertrain System Research & Engineering Center, No. 4800, Caoan Road, Shanghai 201804, China
    School of Automotive Studies, Tongji University, No. 4800, Caoan Road, Shanghai 201804, China)

  • Zechang Sun

    (National Fuel Cell Vehicle & Powertrain System Research & Engineering Center, No. 4800, Caoan Road, Shanghai 201804, China
    School of Automotive Studies, Tongji University, No. 4800, Caoan Road, Shanghai 201804, China)

Abstract

The accurate peak power estimation of a battery pack is essential to the power-train control of electric vehicles (EVs). It helps to evaluate the maximum charge and discharge capability of the battery system, and thus to optimally control the power-train system to meet the requirement of acceleration, gradient climbing and regenerative braking while achieving a high energy efficiency. A novel online peak power estimation method for series-connected lithium-ion battery packs is proposed, which considers the influence of cell difference on the peak power of the battery packs. A new parameter identification algorithm based on adaptive ratio vectors is designed to online identify the parameters of each individual cell in a series-connected battery pack. The ratio vectors reflecting cell difference are deduced strictly based on the analysis of battery characteristics. Based on the online parameter identification, the peak power estimation considering cell difference is further developed. Some validation experiments in different battery aging conditions and with different current profiles have been implemented to verify the proposed method. The results indicate that the ratio vector-based identification algorithm can achieve the same accuracy as the repetitive RLS (recursive least squares) based identification while evidently reducing the computation cost, and the proposed peak power estimation method is more effective and reliable for series-connected battery packs due to the consideration of cell difference.

Suggested Citation

  • Bo Jiang & Haifeng Dai & Xuezhe Wei & Letao Zhu & Zechang Sun, 2017. "Online Reliable Peak Charge/Discharge Power Estimation of Series-Connected Lithium-Ion Battery Packs," Energies, MDPI, vol. 10(3), pages 1-22, March.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:3:p:390-:d:93505
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    References listed on IDEAS

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    2. Tae-Won Noh & Junghoon Ahn & Byoung Kuk Lee, 2022. "Online Cell Screening Algorithm for Maximum Peak Current Estimation of a Lithium-Ion Battery Pack for Electric Vehicles," Energies, MDPI, vol. 15(4), pages 1-14, February.
    3. Rui Xiong & Hailong Li & Xuan Zhou, 2017. "Advanced Energy Storage Technologies and Their Applications (AESA2017)," Energies, MDPI, vol. 10(9), pages 1-3, September.
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