Parameter identification and identifiability analysis of lithium-ion batteries
- Authors
- Choi Y.Y.; Kim, Seongyoon; Kim K.; Kim S.; JUNG-IL CHOI
- Issue Date
- Feb-2022
- Publisher
- Wiley-Blackwell
- Keywords
- Fisher information matrix; genetic algorithm; identifiability analysis; lithium-ion battery; parameter identification
- Citation
- Energy Science & Engineering, v.10, no.2, pp 488 - 506
- Pages
- 19
- Journal Title
- Energy Science & Engineering
- Volume
- 10
- Number
- 2
- Start Page
- 488
- End Page
- 506
- URI
- https://yscholarhub.yonsei.ac.kr/handle/2021.sw.yonsei/6394
- DOI
- 10.1002/ese3.1039
- ISSN
- 2050-0505
- Abstract
- Parameter identification (PI) is a cost-effective approach for estimating the parameters of an electrochemical model for lithium-ion batteries (LIBs). However, it requires identifiability analysis (IA) of model parameters because identifiable parameters vary with reference data and electrochemical models. Therefore, we propose a PI and IA (PIIA) framework for a robust PI that can adapt to discharge data. The IA results show that the best subset with 15 parameters is determined by the Fisher information matrix and the sample-averaged RDE criterion under various operating conditions. The identification process based on a genetic algorithm determines the optimal parameters. The identified-parameter model predicts voltage curves with uncertainty bounds, considering the confidence intervals of identified parameters. Further, we demonstrate that the proposed PIIA framework robustly identifies the parameters of the electrochemical model from experimental data.
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Collections - 일반대학원 > 일반대학원 계산과학공학과 > 1. Journal Articles
- College of Science > 이과대학 수학 > 1. Journal Articles
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