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On-Board State-of-Health Estimation at a Wide Ambient Temperature Range in Lithium-Ion Batteries

Author

Listed:
  • Tiansi Wang

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Lei Pei

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Tingting Wang

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Rengui Lu

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Chunbo Zhu

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

Abstract

A state-of-health (SOH) estimation method for electric vehicles (EVs) is presented with three main advantages: (1) it provides joint estimation of cell’s aging states in terms of power and energy ( i.e. , SOH P and SOH E )—because the determination of SOH P and SOH E can be reduced to the estimation of the ohmic resistance increase and capacity loss, respectively, the ohmic resistance at nominal temperature will be taken as a health indicator, and the capacity loss is estimated based on a mechanistic model that is developed to describe the correlation between resistance increase and capacity loss; (2) it has wide applicability to various ambient temperatures—to eliminate the effects of temperature on the resistance, another mechanistic model about the resistance against temperature is presented, which can normalize the resistance at various temperatures to its standard value at the nominal temperature; and (3) it needs low computational efforts for on-board application—based on a linear equation of cell’s dynamic behaviors, the recursive least-squares (RLS) algorithm is used for the resistance estimation. Based on the designed performance and validation experiments, respectively, the coefficients of the models are determined and the accuracy of the proposed method is verified. The results at different aging states and temperatures show good accuracy and reliability.

Suggested Citation

  • Tiansi Wang & Lei Pei & Tingting Wang & Rengui Lu & Chunbo Zhu, 2015. "On-Board State-of-Health Estimation at a Wide Ambient Temperature Range in Lithium-Ion Batteries," Energies, MDPI, vol. 8(8), pages 1-15, August.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:8:p:8467-8481:d:54018
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    References listed on IDEAS

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    1. 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.
    2. Rui Xiong & Hongwen He & Fengchun Sun & Kai Zhao, 2012. "Online Estimation of Peak Power Capability of Li-Ion Batteries in Electric Vehicles by a Hardware-in-Loop Approach," Energies, MDPI, vol. 5(5), pages 1-15, May.
    3. Pei, Lei & Zhu, Chunbo & Wang, Tiansi & Lu, Rengui & Chan, C.C., 2014. "Online peak power prediction based on a parameter and state estimator for lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 66(C), pages 766-778.
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    Cited by:

    1. 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.
    2. Qiaohua Fang & Xuezhe Wei & Tianyi Lu & Haifeng Dai & Jiangong Zhu, 2019. "A State of Health Estimation Method for Lithium-Ion Batteries Based on Voltage Relaxation Model," Energies, MDPI, vol. 12(7), pages 1-18, April.

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