Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (1)

Search Parameters:
Keywords = lithium-bismuth liquid metal battery

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
22 pages, 8314 KiB  
Article
State of Charge Estimation for Lithium-Bismuth Liquid Metal Batteries
by Xian Wang, Zhengxiang Song, Kun Yang, Xuyang Yin, Yingsan Geng and Jianhua Wang
Energies 2019, 12(1), 183; https://doi.org/10.3390/en12010183 - 7 Jan 2019
Cited by 10 | Viewed by 5363
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Battery Storage Technology for a Sustainable Future)
Show Figures

Figure 1

Back to TopTop