Journal of Terahertz Science and Electronic Information Technology , Volume. 19, Issue 4, 739(2021)

On-line prediction of lithium battery SOC and SOH based on joint algorithms

LIU Xi1,2,3、*, LI Lin1,2, CAO Ju3, and LIU Hailong1,2
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  • 1[in Chinese]
  • 2[in Chinese]
  • 3[in Chinese]
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    References(5)

    [3] [3] ZOU Y,HU X,MA H,et al. Combined state of charge and state of health estimation over lithium-ion battery cell cycle lifespan for electric vehicles[J]. Journal of Power Sources, 2015(273):793-803.

    [4] [4] CHARKHGARD M,FARROKHI M. State of charge estimation for lithium-ion batteries using neural networks and EKF[J]. IEEE Transactions on Industrial Electronics, 2011,57(12):4178-4187.

    [5] [5] WEI K,CHEN Q. States estimation of Li-ion power batteries based on adaptive unscented Kalman filters[J]. Proceedings of the Chinese Society of Electrical Engineering, 2014,34(3):445-452.

    [6] [6] ZHANG C,LI K,PEI L,et al. An integrated approach for real-time model-based state-of-charge estimation of lithium-ion batteries[J]. Journal of Power Sources, 2015(283):24-36.

    [9] [9] GUO Lin,LI Junqiu,FU Zijian. Lithium-ion battery SOC estimation and hardware-in-the-loop simulation based on EKF[J]. Energy Procedia, 2019(158):2599-2604.

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    LIU Xi, LI Lin, CAO Ju, LIU Hailong. On-line prediction of lithium battery SOC and SOH based on joint algorithms[J]. Journal of Terahertz Science and Electronic Information Technology , 2021, 19(4): 739

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    Paper Information

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    Received: Sep. 18, 2019

    Accepted: --

    Published Online: Sep. 17, 2021

    The Author Email: Xi LIU (421684367@qq.com)

    DOI:10.11805/tkyda2019349

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