The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Data-Driven Battery State of Health Estimation Based on Random Partial Charging Data
Abstract
The rapid development of battery technology has promoted the deployment of electric vehicles (EVs). To ensure the healthy and sustainable development of EVs, it is urgent to solve the problems of battery safety monitoring, residual value assessment, and predictive maintenance, which heavily depends on the accurate state-of-health (SOH) estimation of batteries. However, many published methods are unsuitable for actual vehicle conditions. To this end, a data-driven method based on the random partial charging process and sparse Gaussian process regression (GPR) is proposed in this article. First, the random capacity increment sequences (△Q) at different voltage segments are extracted from the partial charging process. The average value and standard deviation of△Qare used as features to indicate battery health. Second, correlation analysis is conducted for three types of batteries, and high correlations between the features and battery SOH are verified at different temperatures and discharging current rates. Third, by using the proposed features as inputs, sparse GPR models are constructed to estimate the SOH. Compared with other data-driven methods, the sparse GPR has the highest estimation accuracy, and its average maximum absolute errors are only 2.88%, 2.52%, and 1.51% for three different types of batteries, respectively.
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Cited by
An overview of data-driven battery health estimation technology for battery management system
Chen, M., Ma, G., Liu, W., Zeng, N., & Luo, X. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.02.031 · Cited by 172
State of health estimation for lithium battery random charging process based on CNN-GRU method
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Metadata
- Authors
- Deng, Z., Hu, X., Li, P., Lin, X., & Bian, X.
- Venue
- IEEE Transactions on Power Electronics
- Published
- 2022-05
- Volume
- 37
- Issue
- 5
- Pages
- 5021-5031
- ISSN
- 0885-8993 (ISSN-L)
- 1941-0107
- OpenAlex
- W4205976837
- Cited by
- 359
- References
- 46
- FWCI
- 15.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Funders
- National Natural Science Foundation of China (52102420)
- China Postdoctoral Science Foundation (2021M693725)
- Chongqing Natural Science Foundation for Postdoctor (cstc2020jcyjbsh0040)
- Distinguished Young Scholars (cstc2021jcyj-jqX0001)
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- Deng, Z., Hu, X., Li, P., Lin, X., & Bian, X. (2022). Data-Driven Battery State of Health Estimation Based on Random Partial Charging Data. IEEE Transactions on Power Electronics. https://doi.org/10.1109/tpel.2021.3134701
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