.png){kind=icon}
返回
Lithium-Sulfur Cell Equivalent Circuit Network Model Parameterization and Sensitivity Analysis
.png){kind=icon}
PRE
AI
DOI：10.1109/TVT.2017.2678278.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Compared to lithium-ion batteries, lithium-sulfur (Li-S) batteries potentially offer greater specific energy density, a wider temperature range of operation, and safety benefits, making them a promising technology for energy storage systems especially in automotive and aerospace applications. Unlike lithium-ion batteries, there is not a mature discipline of equivalent circuit network (ECN) modelling for Li-S. In this study, ECN modelling is addressed using formal 'system identification' techniques. A Li-S cell's performance is studied in the presence of different charge/discharge rates and temperature levels using precise experimental test equipment. Various ECN model structures are explored, considering the tradeoffs between accuracy and speed. It was concluded that a '2RC' model is generally a good compromise, giving good accuracy and speed. Model parameterization is repeated at various state-of-charge (SOC) and temperature levels, and the effects of these variables on Li-S cell's ohmic resistance and total capacity are demonstrated. The results demonstrate that Li-S cell's ohmic resistance has a highly nonlinear relationship with SOC with a break-point around 75% SOC that distinguishes it from other types of battery. Finally, an ECN model is proposed which uses SOC and temperature as inputs. A sensitivity analysis is performed to investigate the effect of SOC estimation error on the model's accuracy. In this analysis, the battery model's accuracy is evaluated at various SOC and temperature levels. The results demonstrate that the Li-S cell model has the most sensitivity to SOC estimation error around the break-point (around 75% SOC) whereas in the middle SOC range, from 20% to 70%, it has the least sensitivity.
Keyword:
Battery modelling
identification
lithium-sulfur cell
state-of-charge estimation
sensitivity analysis
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
机构
引用论文
.png){kind=icon}
Systematical electrochemical study on the parasitic shuttle-effect in lithium-sulfur-cells at different temperatures and different rates不同温度和不同速率下锂硫电池寄生穿梭效应的系统电化学研究
PREAI
分享收藏
分享收藏A review on electric vehicle battery modelling: From Lithium-ion toward Lithium-Sulphur电动汽车电池建模综述: 从锂离子到锂硫
PREAI
分享收藏
查看更多内容