Journal
IEEE TRANSACTIONS ON POWER ELECTRONICS
Volume 37, Issue 6, Pages 7432-7442Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPEL.2022.3144504
Keywords
Integrated circuits; Batteries; Estimation; Feature extraction; Data mining; Aging; Power electronics; Health indicators (HIs); lithium-ion battery (LIB); partial charging; state of health (SOH)
Categories
Funding
- National Natural Science Foundation of China [52072038]
Ask authors/readers for more resources
This study proposes a multistage SOH estimation method that can accurately estimate the health status of LIBs even under partial charging conditions. By extracting different sets of health indicators and using artificial neural networks for fusion, the accuracy and robustness of the estimates are improved.
State of health (SOH) is critical to the management of lithium-ion batteries (LIBs) due to its deep insight into health diagnostic and protection. However, the lack of complete charging data is common in practice, which poses a challenge for the charging-based SOH estimators. This article proposes a multistage SOH estimation method with a broad scope of applications, including the unfavorable but practical scenarios of heavily partial charging. In particular, different sets of health indicators (HIs), covering both the morphological incremental capacity features and the voltage entropy information, are extracted from the partial constant-current charging data with different initial charging voltages to characterize the aging status. Following this endeavor, artificial neural network based HI fusion is proposed to estimate the SOH of LIB precisely in real time. The proposed method is evaluated with long-term aging experiments performed on different types of LIBs. Results validate several superior merits of the proposed method, including high estimation accuracy, high tolerance to partial charging, strong robustness to cell inconsistency, and wide generality to different battery types.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available