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A Modified Model-Based Resistance Estimation of Lithium-Ion Batteries Using Unscented Kalman Filter

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Wireless and Satellite Systems (WiSATS 2019)

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Abstract

Lithium-ion batteries are critical components for satellite, and it is necessary to monitor their state of health (SOH). At present, the most common Ah-count method in satellite has errors in long-term health monitoring. Therefore, in this work, resistance is adopted to describe SOH and a resistance estimation method is developed based on unscented Kalman filtering (UKF). To reduce the impact of unstable work condition and battery aging, a simplified electrochemistry model of lithium-ion batteries is built to replace equivalent circuit model (ECM) in UKF. In consideration of battery aging, a linear lithium ions loss model is used in this model. Then, the linear relationship between resistance and capacity is analyzed to demonstrate the ability for SOH description by resistance. Experimental data suggests that this model can effectively track the resistance in discharge process and yield satisfactory results with battery aging. Besides, this method is applicable to estimating battery SOH, as suggested by the linear relationship between estimation of resistance and actual measurements of capacity.

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Authors and Affiliations

  1. Deep Space Exploration Research Center, Harbin Institute of Technology, Harbin, China

    Jing-Long Chen & Ri-Xin Wang

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  1. Jing-Long Chen
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  2. Ri-Xin Wang
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Correspondence to Ri-Xin Wang .

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Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, China

    Min Jia

  2. Harbin Institute of Technology, Harbin, China

    Qing Guo

  3. Harbin Institute of Technology, Harbin, China

    Weixiao Meng

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Chen, JL., Wang, RX. (2019). A Modified Model-Based Resistance Estimation of Lithium-Ion Batteries Using Unscented Kalman Filter. In: Jia, M., Guo, Q., Meng, W. (eds) Wireless and Satellite Systems. WiSATS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 280. Springer, Cham. https://doi.org/10.1007/978-3-030-19153-5_3

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  • DOI: https://doi.org/10.1007/978-3-030-19153-5_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19152-8

  • Online ISBN: 978-3-030-19153-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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