A Fast Model-Free Method for Identifying Internal Short Circuit Resistance in Lithium-ion Battery
Internal short circuit fault is one of the prerequisite causes of thermal runaway in lithium-ion batteries. Early identification of internal short circuit resistance is of great significance to ensure their safety. This article proposes a novel accurate and fast model-free internal short circuit resistance identification method. Specifically, the Cramer–Rao lower bound analysis first indicates a method to improve the accuracy of internal short circuit resistance identification, which is by increasing the number of data points for leakage charge. Then, through contrasting the equivalent circuit models of normal cell and internal short circuit cell, a way to calculate a series of leakage charges is introduced, which is used to identify the leakage current. Fault diagnosis is achieved by comparing the identified leakage current with the general leakage current of the normal cell, after which the identified leakage current can be converted to the internal short circuit resistance. Various substitute internal short circuit experiments show that the proposed method offers high identification accuracy and fast diagnostic speed. It achieves consistent identified relative errors of 3.15% in 1 cycle. The experimental results also support the conclusion of the Cramer–Rao lower bound analysis.
Authors
- Bin Liu (ORCID: https://orcid.org/0000-0002-0363-0863)
- Peiyu Chen (ORCID: https://orcid.org/0009-0008-8819-4511)
- Guanzheng Li (ORCID: https://orcid.org/0000-0003-4801-3938)
- Chao Li (ORCID: https://orcid.org/0000-0002-3292-0253)
- Shuai Wang (ORCID: https://orcid.org/0000-0002-5117-7856)
- Bin Li (ORCID: https://orcid.org/0000-0002-7088-8274)
Institutions
- Tianjin University (CN)
- Tianjin Research Institute of Electric Science (China) (CN)
- Ministry of Education (PT)
- Beijing Automotive Group (China) (CN)
Publication Details
- Journal
- IEEE Transactions on Power Electronics
- Published
- 2024-11-19
- DOI
- https://doi.org/10.1109/tpel.2024.3502673
- Citations
- 5
- Primary Topic
- Advanced Battery Technologies Research
- Type
- article
- Field-Weighted Citation Impact
- 1.02