Premium
The equivalent circuit battery model parameter sensitivity analysis for lithium‐ion batteries by Monte Carlo simulation
Author(s) -
Guo Feng,
Hu Guangdi,
Zhou Pengkai,
Huang Tiexiong,
Chen Xu,
Ye Mengqi,
He Jiajin
Publication year - 2019
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.4863
Subject(s) - sensitivity (control systems) , battery (electricity) , monte carlo method , estimation theory , equivalent circuit , state of charge , kalman filter , control theory (sociology) , model parameter , voltage , computer science , engineering , electronic engineering , algorithm , electrical engineering , mathematics , power (physics) , statistics , physics , control (management) , quantum mechanics , artificial intelligence
Summary To enhance the estimation accuracy of battery's state of charge, it is imperative to estimate the battery model parameter. To reduce the calculation efforts, the number of the battery model parameter to be estimated should be less while ensuring the state of charge estimation accuracy. Especially in engineering applications, the calculating ability is usually limited. So, it needs to choose the critical battery model parameter to be estimated. This paper's contributions are as follows: The global sensitivity analysis of the battery model parameter is achieved by the Monte Carlo simulation method. The results show that the open circuit voltage and the ohmic resistance are the high sensitivity parameters. Guided by the results of parameter sensitivity analysis, a dual extended Kalman filters method is utilized to achieve online battery model parameter estimation. The experiments prove that the state of charge estimation accuracy is improved by the online parameter estimation. Estimating high sensitivity parameters can reduce running time. And the SOC estimation accuracy can be guaranteed.