State of Charge Estimation for Lithium-Bismuth Liquid Metal Batteries
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 mana...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-01-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/12/1/183 |
| _version_ | 1817987857743085568 |
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| author | Xian Wang Zhengxiang Song Kun Yang Xuyang Yin Jianhua Wang |
| author_facet | Xian Wang Zhengxiang Song Kun Yang Xuyang Yin Jianhua Wang |
| author_sort | Xian Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T00:27:34Z |
| format | Article |
| id | doaj.art-8af3ca317eb04970b98ec0b16bebd810 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-14T00:27:34Z |
| publishDate | 2019-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-8af3ca317eb04970b98ec0b16bebd8102022-12-22T02:22:41ZengMDPI AGEnergies1996-10732019-01-0112118310.3390/en12010183en12010183State of Charge Estimation for Lithium-Bismuth Liquid Metal BatteriesXian Wang0Zhengxiang Song1Kun Yang2Xuyang Yin3Jianhua Wang4State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, ChinaState Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, ChinaState Grid Jiangsu Electric Power Company Research Institute, No.1 Paweier Road, Nanjing 211100, ChinaState Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, ChinaState Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, ChinaLithium-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.http://www.mdpi.com/1996-1073/12/1/183lithium-bismuth liquid metal batterystate of chargeextended Kalman filterunscented Kalman filterparticle filter |
| spellingShingle | Xian Wang Zhengxiang Song Kun Yang Xuyang Yin Jianhua Wang State of Charge Estimation for Lithium-Bismuth Liquid Metal Batteries Energies lithium-bismuth liquid metal battery state of charge extended Kalman filter unscented Kalman filter particle filter |
| title | State of Charge Estimation for Lithium-Bismuth Liquid Metal Batteries |
| title_full | State of Charge Estimation for Lithium-Bismuth Liquid Metal Batteries |
| title_fullStr | State of Charge Estimation for Lithium-Bismuth Liquid Metal Batteries |
| title_full_unstemmed | State of Charge Estimation for Lithium-Bismuth Liquid Metal Batteries |
| title_short | State of Charge Estimation for Lithium-Bismuth Liquid Metal Batteries |
| title_sort | state of charge estimation for lithium bismuth liquid metal batteries |
| topic | lithium-bismuth liquid metal battery state of charge extended Kalman filter unscented Kalman filter particle filter |
| url | http://www.mdpi.com/1996-1073/12/1/183 |
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