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Adaptive Unscented Kalman Filter with Correntropy Loss for Robust State of Charge Estimation of Lithium-Ion Battery
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Energies 2019, 12(1), 183; https://doi.org/10.3390/en12010183

State of Charge Estimation for Lithium-Bismuth Liquid Metal Batteries

1
State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, China
2
State Grid Jiangsu Electric Power Company Research Institute, No.1 Paweier Road, Nanjing 211100, China
*
Authors to whom correspondence should be addressed.
Received: 13 December 2018 / Revised: 1 January 2019 / Accepted: 2 January 2019 / Published: 7 January 2019
(This article belongs to the Special Issue Battery Storage Technology for a Sustainable Future)
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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. View Full-Text
Keywords: lithium-bismuth liquid metal battery; state of charge; extended Kalman filter; unscented Kalman filter; particle filter lithium-bismuth liquid metal battery; state of charge; extended Kalman filter; unscented Kalman filter; particle filter
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Wang, X.; Song, Z.; Yang, K.; Yin, X.; Geng, Y.; Wang, J. State of Charge Estimation for Lithium-Bismuth Liquid Metal Batteries. Energies 2019, 12, 183.

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