灵敏度(控制系统)
可识别性
费希尔信息
电池(电)
数学优化
估计理论
计算机科学
控制理论(社会学)
算法
应用数学
数学
电子工程
统计
物理
热力学
工程类
人工智能
功率(物理)
控制(管理)
作者
Saehong Park,Dylan Kato,Zach Gima,Reinhardt Klein,Scott J. Moura
出处
期刊:Journal of The Electrochemical Society
[The Electrochemical Society]
日期:2018-01-01
卷期号:165 (7): A1309-A1323
被引量:84
摘要
We consider the problem of optimally designing an excitation input for parameter identification of an electrochemical Li-ion battery model. The conventional approach to performing parameter identification uses standard test cycles. In contrast, we optimally design the input trajectory to maximize parameter identifiability in the sense of Fisher information. Specifically, we derive sensitivity equations for the electrochemical model. This approach enables parameter sensitivity analysis and optimal parameter fitting via gradient-based algorithms. This paper presents a general systematic approach to identify the electrochemical parameters in a non-invasive way. First, we group parameters into two sets: (i) equilibrium parameters, and (ii) dynamical parameters. We also divide the dynamical parameters into subsets by calculating orthogonalized sensitivity, which mitigates linear dependence between parameters. A large number of input profiles have been devised to constitute an input library. Then, the optimal inputs are selected from the input library to maximize the Fisher information, via convex programming. Using this framework a number of relevant experiments are obtained to parameterize. To validate our approach experimentally, we consider a 18650 Lithium nickel cobalt aluminum oxide battery. Compared to the conventional approach, our proposal achieves lower voltage RMSE across all experimental testing cycles.
科研通智能强力驱动
Strongly Powered by AbleSci AI