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State of Charge Estimation for Lithium-Bismuth Liquid Metal Batteries

Author

Listed:
  • Xian Wang

    (State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, China)

  • Zhengxiang Song

    (State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, China)

  • Kun Yang

    (State Grid Jiangsu Electric Power Company Research Institute, No.1 Paweier Road, Nanjing 211100, China)

  • Xuyang Yin

    (State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, China)

  • Yingsan Geng

    (State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, China)

  • Jianhua Wang

    (State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

Lithium-bismuth liquid metal batteries have much potential for stationary energy storage applications, with characteristics such as a large capacity, high energy density, low cost, long life-span and an ability for high current charge and discharge. However, there are no publications on battery management systems or state-of-charge (SoC) estimation methods, designed specifically for these devices. In this paper, we introduce the properties of lithium-bismuth liquid metal batteries. In analyzing the difficulties of traditional SoC estimation techniques for these devices, we establish an equivalent circuit network model of a battery and evaluate three SoC estimation algorithms (the extended Kalman filter, the unscented Kalman filter and the particle filter), using constant current discharge, pulse discharge and hybrid pulse (containing charging and discharging processes) profiles. The results of experiments performed using the equivalent circuit battery model show that the unscented Kalman filter gives the most robust and accurate performance, with the least convergence time and an acceptable computation time, especially in hybrid pulse current tests. The time spent on one estimation with the three algorithms are 0.26 ms, 0.5 ms and 1.5 ms.

Suggested Citation

  • Xian Wang & Zhengxiang Song & Kun Yang & Xuyang Yin & Yingsan Geng & Jianhua Wang, 2019. "State of Charge Estimation for Lithium-Bismuth Liquid Metal Batteries," Energies, MDPI, vol. 12(1), pages 1-22, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:1:p:183-:d:195457
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    References listed on IDEAS

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    Cited by:

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    2. Jing Hou & He He & Yan Yang & Tian Gao & Yifan Zhang, 2019. "A Variational Bayesian and Huber-Based Robust Square Root Cubature Kalman Filter for Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(9), pages 1-23, May.

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