On state estimation of all solid-state batteries
On state estimation of all solid-state batteries
复制标题
DOI：
10.1016/j.electacta.2019.06.023
复制
发表时间：
2019-09
影响因子：
6.6
通讯作者：
Youngki Kim;Xianke Lin;Armin Abbasalinejad;Sun Ung Kim;S. Chung
中科院分区：
文献类型：
--
作者：
Youngki Kim;Xianke Lin;Armin Abbasalinejad;Sun Ung Kim;S. Chung
本文研究了用偏微分方程系统建模的固态电池的状态估计问题。简化电池模型的三个假设是状态估计研究的基础：（1）忽略固体电解质中锂离子的产生/复合;（2）假设电荷转移数为0.5;以及（3）均匀的电解质浓度。灵敏度研究结果表明，该方法的有效性模型简化的电压预测。特别是，两个简化的模型集中在阴极的扩散动力学，并在阴极和电解质的状态估计，通过应用扩展卡尔曼滤波器（EKF）。仿真结果表明，电池的荷电状态可以合理地估计的EKF。然而，在不包括电解质中的扩散动力学的情况下，由于弱可观测性而导致的在低SOC范围（0.1< SOC< 0.4）中的状态估计的不准确性不能得到解决。这种包含导致充电状态估计误差减少40%，对于所考虑的情况，误差从9%减少到5%。
This paper studies the state estimation of a solid-state battery modeled as a partial differential equation system. Three assumptions simplifying the battery model underlie the study of state estimation:(1) neglecting the generation/recombination of Li-ions in the solid electrolyte;(2) assuming the charge transfer number of 0.5; and (3) the uniform electrolyte concentration. Results of a sensitivity study show the validity of the approaches to model simplification for the voltage prediction. Especially, two simplified models focused on the diffusion dynamics at cathode only and at both cathode and electrolyte are used for state estimation by applying an extended Kalman filter (EKF). Simulation results show that the state-of-charge of the battery can be reasonably well estimated by the EKFs. However, the inaccuracy of the state estimation in low SOC range (0.1< SOC< 0.4) due to weak observability cannot be addressed without including the diffusion dynamics in the electrolyte. This inclusion leads in a 40% reduction in state-of-charge estimation error, from 9% to 5% error for the considered case.